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8 Spectroscopy 27(4) April 2012
CONTENTS
Spectroscopy (ISSN 0887-6703 [print], ISSN 1939-1900 [digital]) is published monthly by Advanstar Communications, Inc., 131 West First Street, Duluth, MN 55802-2065. Spectroscopy is distributed free of charge to users and specifiers of spectroscopic equipment in the United States. Spectroscopy is available on a paid subscription basis to nonqualified readers at the rate of: U.S. and possessions: 1 year (12 issues), $74.95; 2 years (24 issues), $134.50. Canada/Mexico: 1 year, $95; 2 years, $150. International: 1 year (12 issues), $140; 2 years (24 issues), $250. Periodicals postage paid at Duluth, MN 55806 and at additional mailing of fices. POSTMASTER: Send address changes to Spectroscopy, P.O. Box 6196, Duluth, MN 55806-6196. PUBLICATIONS MAIL AGREEMENT NO. 40612608, Return Undeliverable Canadian Addresses to: Pitney Bowes, P. O. Box 25542, London, ON N6C 6B2, CANADA. Canadian GST number: R-124213133RT001. Printed in the U.S.A .
www.spec t roscopyonl ine .com
®®
April 2012 Volume 27 Number 4 www.spectroscopyonline.com
FT-IR and Raman Analysis of Automotive Coatings Following Hit-
Results are generalized from the last installment and considerations are made for how it
applies to one of Maxwell’s equations.
David W. Ball
22
Is Cloud Computing Right for Your Laboratory . . . or Are You
Living in Cloud Cuckoo Land?
A discussion of the various options for cloud computing, avoiding the marketing hype and
focusing on the potential advantages and disadvantages for your laboratory
R.D. McDowall
30
Advantages of High-Brightness Lasers in Confocal Raman Spectroscopy
The theoretical and experimental differences between high-brightness and low-brightness
lasers used in a dispersive confocal Raman microscope system are compared.
Dick Wieboldt
Articles
Discriminating Paints with Different Clay Additives in 36Forensic Analysis of Automotive Coatings by FT-IR and Raman Spectroscopy An investigation of the different kinds of clay used as paint additives with the goal of
Improving Accuracy in Inductively Coupled Plasma–Quadrupole 44Mass Spectrometry: The Interference Standard MethodAn approach for dealing with one of the main limitations of ICP-MS, low sensitivity and
accuracy caused by spectral interferences
George L. Donati, Renata S. Amais, and Joaquim A. Nóbrega
Cover image courtesy of
Getty Images.
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Editorial Advisory Board
Ramon M. Barnes University of Massachusetts
Paul N. Bourassa Blue Moon Inc.
Deborah Bradshaw Consultant
Kenneth L. Busch Wyvern Associates
Ashok L. Cholli Polnox Corporation
David M. Coleman Wayne State University
Bruce Hudson Syracuse University
David Lankin University of Illinois at Chicago, College of Pharmacy
Barbara S. Larsen DuPont Central Research and Development
Ian R. Lewis Kaiser Optical Systems
Jeffrey Hirsch Thermo Fisher Scientific
Howard Mark Mark Electronics
R.D. McDowall McDowall Consulting
Gary McGeorge Bristol-Myers Squibb
Linda Baine McGown Rensselaer Polytechnic Institute
Robert G. Messerschmidt Rare Light, Inc.
Francis M. Mirabella Jr. Mirabella Practical Consulting Solutions, Inc.
John Monti Montgomery College
Michael L. Myrick University of South Carolina
John W. Olesik The Ohio State University
Jim Rydzak GlaxoSmithKline
Jerome Workman Jr. Unity Scientific
Contributing Editors:
Fran Adar Horiba Jobin Yvon
David W. Ball Cleveland State University
Kenneth L. Busch Wyvern Associates
Howard Mark Mark Electronics
Volker Thomsen Consultant
Jerome Workman Jr. Unity Scientific
Spectroscopy ’s Editorial Advisory Board is a group of distinguished individuals
assembled to help the publication fulfill its editorial mission to promote the effective
use of spectroscopic technology as a practical research and measurement tool.
With recognized expertise in a wide range of technique and application areas, board
members perform a range of functions, such as reviewing manuscripts, suggesting
authors and topics for coverage, and providing the editor with general direction and
feedback. We are indebted to these scientists for their contributions to the publication
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News Spectrum
People most often think of gargantuan systems that cost millions of dollars when someone mentions nuclear magnetic resonance (NMR) spectroscopy. However, benchtop NMR systems have grown to become a major market segment due in large part to demand from the agriculture and food industry. There is also significantly more competition in the benchtop NMR market than the traditional high-field NMR market. Benchtop NMR systems make use of a fixed magnet, as opposed to a superconducting magnet for high-field systems. This design has allowed manufacturers to develop very compact benchtop NMR systems that cost a fraction of their bigger brothers. Benchtop NMR eliminates the need for solvents and sample preparation associated with Soxhlet extraction. In contrast to near-infrared (NIR) spectroscopy, NMR analysis probes throughout the bulk of a sample, whereas NIR only analyzes the surface, making NMR better for inhomogeneous samples that are common in the agriculture and food industry. These advantages
have helped benchtop NMR become a widely accepted technique in the industry over the past decade. Similar to the high-field NMR market, a select few vendors, namely Bruker and Oxford Instruments, dominate the benchtop NMR market. Unlike the high-field market, however, there are a handful of small companies that compete in the benchtop NMR system market as well. The agriculture and
food portion of the benchtop NMR market was worth close to $40 million in 2011, and should continue to see high single-digit growth going forward. The foregoing data were extracted from SDi’s market analysis and perspectives report
entitled The Global Assessment Report, 11th Edition: The Laboratory Life Science and Analytical Instrument Industry, October 2010. For more information, contact Stuart Press, Vice President, Strategic Directions International, Inc., 6242 Westchester Parkway, Suite 100, Los Angeles, CA 90045, (310) 641-4982, fax: (310) 641-8851, www.strategic-directions.com.
Bruker-47%
Oxford Instruments-40%
Other-13%
47%
40%
13%
Agriculture and food benchtop NMR vendor share for 2011.
Market Profile: Benchtop NMR for Agriculture and Food Applications
European Chemicals Agency Launches Chemical Classification and Labeling InventoryThe European Chemicals Agency (ECHA) in Helsinki,
Finland, recently launched a new inventory of chemicals
called the Public Classification and Labeling (C&L)
Inventory. Information for the inventory came from
the European Community regulation on chemicals
and their safe use, known as “REACH” (Registration,
Evaluation, Authorization, and restriction of Chemical
substances), and classification, labeling, and packaging
of substances and mixtures (CLP) notifications received
by the agency. The aim of the CLP regulation is to
ensure that the hazards presented by chemicals are
clearly communicated to workers and consumers in the
European Union.
The publication of the inventory, which was set out
as a milestone in the CLP regulation, represents a
significant step forward toward transparency on the
physical, health, or environmental hazards of chemical
substances. The inventory provides a wealth of
information from industry on how it has self-classified
chemicals, indicating how some companies have
classified the same substance differently.
The Public C&L Inventory represents the largest
database of self-classified substances available globally.
A number of options are available for searching the
inventory, based on both the substance identity and
its classification. Future updates of the inventory will
continuously improve the search functions in order to
enhance access to the information.
The inventory is maintained by the agency and the
data will be refreshed on a regular basis with incoming
and updated C&L information.
Geert Dancet, executive director of ECHA said in a
statement, “With this increased transparency, we are
contributing to a more effective communication on
the hazardous chemicals to workers and ultimately to
consumers.” He also encouraged industry to use the
inventory data as a common ground for discussions
between companies to reach agreement on the self-
classification and labeling of hazardous substances. To
provide support for the hazard communication process,
ECHA is planning to develop an IT platform to facilitate
contacts among notifiers of chemicals to give them
the opportunity to discuss reasons for differences and,
where appropriate, agree on a uniform classification.
According to the ECHA, more than 3 million
submission records that cover more than 90,000
chemicals are freely accessible from their website:
http:echa.europa.eu/information-on-chemicals/
cl-inventory.
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www.spec t roscopyonl ine .com14 Spectroscopy 27(4) April 2012
Coblentz Society News Francis Esmonde-White and Karen Esmonde-White
The Coblentz Society, a nonprofit organization founded
in 1954, is dedicated to fostering the understanding
and application of vibrational spectroscopy. In addition
to holding a comprehensive infrared spectral library,
the society is robustly active at conferences where
vibrational spectroscopy is featured. Coblentz is active
at Pittcon, FACSS (now SciX), the Eastern Analytical
Symposium (EAS), the International Conference on
Advanced Vibrational Spectroscopy (ICAVS), and
the Ohio State University Symposium on Molecular
Spectroscopy, where members organize technical
sessions, present awards, and have a booth on the
exhibit hall. A biannual newsletter to members is
published twice a year in Applied Spectroscopy, and
here we present highlights from the Spring 2012
newsletter.
Coblentz Award Granted to Greg Engel
Assistant Professor Greg Engel from the University
of Chicago, department of chemistry is the recipient
of the 2012 Coblentz Award. The Coblentz Award is
presented annually to an outstanding young molecular
spectroscopist under the age of 40. The prize consists
of an honorarium and a plaque with a prism from the
periscope of a World War II Navy submarine. Engel was
a graduate student at Harvard with James Anderson and
a postdoctoral researcher at the University of California
at Berkeley with Graham Fleming. His research focuses
on new approaches to observe, measure, and control
excited state reactivity. His group specifically focuses
on excitonic transport, open quantum dynamics, and
photochemical reaction dynamics.
Joel Harris Wins Bomem-Michelson Award
Professor Joel Harris of the University of Utah will
receive the 2012 Bomem–Michelson Award (sponsored
by ABB) from the Coblentz Society. Harris’s research
has focused on analytical chemistry and spectroscopic
studies of low concentrations of molecules in liquids
and at liquid–solid interfaces. He and his students have
developed new measurement concepts in photothermal
spectroscopy, methods to analyze multidimensional
spectroscopic data, Raman spectroscopy and
microscopy techniques, and quantitative analysis at the
single-molecule level.
They have applied these methods to investigate
chemistry of liquid–solid interfaces and dispersed
particles and kinetics of molecular transport,
adsorption, and binding that govern chemical
separations and chemical analysis at interfaces.
Coblentz Activities at FACSS 2011
The Coblentz Society was very active at the 2011 FACSS
conference in Reno, Nevada: Coblentz organized nine
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invited technical sessions, two awards sessions, and
one contributed technical session, and manned a
staffed booth in the Exhibit Hall.
Craver Award
FACSS attendees were treated to a plenary lecture by
Michael George of the University of Nottingham (UK),
the 2011 recipient of the Clara Craver Award. George
was a graduate student at the University of Nottingham
and remained at Nottingham for a further 18 months
as a postdoctoral fellow. Later, he was awarded a Royal
Society/STA of Japan postdoctoral fellowship to work on
organic photochemistry with Professor Hiro-o Hamaguchi
at the Kanagawa Academy of Science and Technology
(KAST). George returned to Nottingham in 1993, and was
later promoted to chair in 2002. His research focuses on
the development and application of fast time-resolved
infrared spectroscopy for the characterization of excited
states and the detection of short-lived intermediates and
the elucidation of reaction mechanisms.
Lippincott Award
The Ellis R. Lippincott Award is presented annually to an
outstanding vibrational spectroscopist. It is cosponsored
by the Coblentz Society, the Society for Applied
Spectroscopy, and the Optical Society of America.
Isao Noda was awarded the 2011 Lippincott Award
in recognition of his pioneering contributions to 2D
infrared analysis of polymeric materials. Mitsuo Tasumi
and Jaan Laane presided over the Lippincott award
session at FACSS 2011, where Noda gave a plenary
lecture and was presented with the award during a
special technical session honoring his work.
Coblentz Society Student Awards
The 2011 Coblentz Student Awards were presented by
the society’s president at FACSS 2011 during the Sunday
evening poster session. The winners were Rohith Reddy
from Professor Rohit Bhargava’s group at the University of
Illinois at Urbana-Champaign; Esther Ocola from Professor
Jaan Laane’s group at Texas A&M University; Nathaniel
Gomer from Professor Michael Angel’s group at the
University of South Carolina; Megan Pearl (Baranowski)
from Professor Micky Myrick’s group at the University of
South Carolina, and Savitha Panikar from Professor James
Durig’s group at the University of Missouri-Kansas City.
Coblentz at the Eastern Analytical Symposium
At the 2011 Eastern Analytical Symposium, the society
organized two technical sessions: “SERS for real-world
trace analysis and spectroscopy in the pharmaceutical
field,” chaired by Stuart Farquharson of Real-Time
Analyzers, and ‘‘Advances in Spectroscopic Techniques
for the Pharmaceutical Industry’’ presided over by
Gary McGeorge of Bristol-Myers Squibb. Additionally,
Coblentz hosted a member reception and staffed a
booth in the exhibit hall. ◾
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The Baseline
This is the sixth installment in a series devoted to explaining Maxwell’s equations, the four mathematical statements upon which the classi-
cal theory of electromagnetic fields — and light — is based. Previous installments can be found on Spec-troscopy’s website (www.spectroscopyonline.com/The+Baseline+Column). Maxwell’s equations are expressed in the language of vector calculus, so a sig-nificant part of some previous columns have been de-voted to explaining vector calculus, not spectroscopy. In the last installment, we were developing the vector calculus tools to express Maxwell’s equations. An un-derstanding of the math makes it easier to understand the equations — that’s why we’re spending so much time on math, rather than spectroscopy. Apologies if this frightens or dismays you, but that’s the way it goes. Once again, the numbering of the figures is in-tentional; it’s meant to provide continuity throughout this entire series.
Where We Left OffIn our last installment, we left off with an expression for the infinitesimal work per unit area in a rectangu-lar field in the (x,y) plane. The overall vector field is given by the equation
F = iFx + jFy + kFz [1]
and we were assuming a closed path in just the afore-mentioned plane. We found that the infinitesimal work was
− [2]
Thus, the closed loop in the (x,y) plane actually related to a vector in the z direction. The integral over the closed path is also referred to as the circulation of the vector field.
Now we are ready to generalize our result and con-sider how it applies to an equation — specifically, one of Maxwell’s equations.
Introducing the CurlFor a vector function F = Fxi + Fyj + Fzk, I will hereby define the function
− [3]
as the one-dimensional curl of F. I designate it “one dimensional”, possibly improperly, because the result is a vector in one dimension, in this case the z dimen-sion. The analysis we performed in the earlier section — defining a closed path in a single plane and taking the limit of the path integral — can be performed for the (x,z) and (y,z) planes. When we do that, we get the following analogous results:
(x, z) plane: − [4a]
(y, z) plane: − [4b]
The combination of all three expressions gives us a general expression for the curl of F:
= − + − + − curl [5]
Maxwell’s Equations, Part VIMaxwell’s equations are expressed in the language of vector calculus, so a significant part of some previous installments in this series have been devoted to explaining vector calculus, not spectroscopy. In this installment, we will generalize our results from last time and consider how it applies to one of Maxwell’s equations.
David W. Ball
www.spec t roscopyonl ine .com April 2012 Spectroscopy 27(4) 17
This expression allows us to determine
lim · ∮ [6]
for any vector function F in any plane.But what does the curl of a vector function mean?
One way of thinking about it is that it is a variation in the vector function F that causes a rotational effect in a perpendicular axis. (Indeed, “curl F” is sometimes still designated “rot F,” and a vector function whose curl equals zero [see Figure 38] is termed “irrota-tional.”) Furthermore, a vector function with a non-zero curl can be thought of as curving around a par-ticular axis, with that axis being normal to the plane of the curve. Thus, the rotating water in Figure 37 of our previous installment has a nonzero curl, while the linearly f lowing water in Figure 38 has a zero curl. You may want to refer to the previous installment of this column to refresh your memory of what they look like (1).
A mnemonic (that is, a memory aid) for the general expression for curl F takes advantage of the structure of a 3 × 3 determinant:
=curl [7]
Understand that curl F is not a determinant; a deter-minant is a number that is a characteristic of a square matrix of numerical values. However, the expression for curl F can be constructed by performing the same operations on the expressions as one would do with
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i j k
Fx Fy Fz
i
Fx
j
Fy
+ + +
Figure 39: To determine the expression using a determinant, multiply
the three terms on each arrow and apply the positive or negative sign
to that product, as indicated. Combining all terms yields the proper
expression for the curl of a vector function F having components Fx,
Fy, and Fz.
www.spec t roscopyonl ine .com18 Spectroscopy 27(4) April 2012
numbers to determine the value of a 3 × 3 determi-nant: constructing the diagonals and adding the right-downward diagonals and subtracting the left-upwards diagonals. If you have forgotten how, Figure 39 shows how to determine the expression for the curl.
The determinantal form of the curl can be expressed in terms of the del operator, ∇. Recall from part III of this series (2) that the del operator is
+ +
∆
≡ [8]
Also recall from vector calculus that the cross prod-uct of two vectors A ≡ iAx + jAy + kAz and B defined analogously is written A × B and is given by the ex-pression
× = [9]
By comparing this expression to the determinantal form of the curl, it should be easy to see that the curl of a vector function F can be written as
F Fcurl
∆
≡ × [10]
Like the fact that curl is not technically a determinant, it is technically not a cross product, as del is an opera-
tor, not a vector. The parallels, however, make it easy to gloss over this technicality and use the “del cross F” symbolism to represent the curl of a vector function.
Because the work integral over a closed path through an electro-static field E is zero, it is a short, logical step to state that therefore
E = 0
∆
×
[11]
This is one more property of an electrostatic field: The field is not rotating about any point in space. Rather, an electrostatic field is purely radial, with all field “lines” going from the point in space straight to the electric charge.
Faraday’s LawAn electrostatic field caused by a charged particle is thought of as beginning at a positive charge and ending at a negative charge. Be-cause overall matter is electrically neutral, then every electric field emanating from a positive charge
Figure 40: Top: In an electrostatic field, the field lines go from the
positive charge to the negative charge. Bottom: A moving magnetic field
induces an electric field, but in this case the electric field is in a circle,
following the axial nature of the magnetic field lines.
www.spec t roscopyonl ine .com April 2012 Spectroscopy 27(4) 19
eventually ends at a negative charge. This is illustrated in Figure 40, top diagram.
However, when a changing mag-netic field creates a current in a conductor, this current is the prod-uct of an induced electric field. In the case of a bar-type magnet, the magnetic field is axially symmet-ric about the length of the bar, so the induced electric field is axially symmetric as well: that is, it is cir-cular. This is illustrated in Figure 40, bottom diagram.
The induced, circular electric field caused by a moving magnet causes charges to move in that circle. The circulation of the in-duced electric field vector can be constructed from our definition of “circulation” above; it is
= · circulation ∮ [12]
where E is the induced electric
field, t is the tangent vector along the path, and s is the infinitesi-mal amount of path. In this case, the “circulation” is defined as the “electromotive force,” or emf. What is this force doing? Why, caus-ing charges to move, of course! As such, it is doing work, and our ar-guments using work in the sections above are all valid here.
What Faraday found experimen-tally is that a changing magnetic field induced an electric field (which then forced a current). If you imagine that a magnetic field is composed of discrete field lines, what is happening is that as the number of magnetic field lines in a given area changes with time, an electric field is induced. Figure 41 illustrates this. Consider the loop of area outlined by the black line. As the magnet is moved to the right, the number of magnetic field lines intersecting the loop changes. It is this change that induces the
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Figure 41: As the magnet is moved farther from the loop, the number of imaginary magnetic field
lines intersecting the loop changes (here, from seven lines to three lines). It is this change that
induces an electric field in the loop.
www.spec t roscopyonl ine .com20 Spectroscopy 27(4) April 2012
electric field.The number of field lines per
area is called the magnetic f lux. In part III of this series (2), we pre-sented how we determine the f lux of a vector field. For a changing vector field F having a unit vector perpendicular (or normal) to its direction of motion n over some surface S, the f lux is defined as
= ∫ · S
flux
[13]
For our magnetic field B, this be-comes
= ∫ · S
magnetic flux[14]
But the induced electric field is related to the change in magnetic f lux with time. Thus, we are actu-ally interested in the time deriva-tive of the magnetic f lux:
∫ · S
[15]
At this stage, we bring everything together by citing the experimental facts as determined by Faraday and others: The electromotive force is equal to the change in the magnetic flux over
time. That is,
· = ∫ · S ∮
[16]
Let us divide each side of this equa-tion by the area A of the circular path of the induced current. This area also corresponds to the sur-face S that the magnetic field f lux is measured over, so we divide one side by A and one side by S. We get
∮ · = ∫ · S [17]
Suppose we want to consider the limit of this expression as the area of the paths shrink to zero size; that is, as A → 0. We would have
∮lim · = [18]
lim ∫ · S
The left side is, by definition, the curl of E. What about the right side? Rather than prove it math-ematically, let’s consider the fol-lowing argument. As the surface S goes to zero, the limit of the magnetic f lux ultimately becomes one magnetic f lux line. This single line will be perpendicular to the infinitesimal surface — look at the rendering of the magnetic field
lines in Figure 41 if you need to convince yourself of this. Thus, the dot product B • n is simply B, and the infinite sum of infinitesimal pieces (which is what an integral is) degenerates to a single value of B. I, therefore, argue that
lim ∫ · S = [19]
So what we now have is
× =
∆
[20]
We are almost done. The law of conservation of energy must be satisfied. Although it appears that we are getting an induced current from nowhere, understand that this induced current also gener-ates a magnetic field. For the law of conservation of energy to be satis-fied, the new magnetic f lux must oppose the original magnetic f lux (this concept is known as Lenz’s law after Henrich Lenz, the Rus-sian physicist who discovered it). To represent this mathematically, a negative sign must be included in the last equation. By convention, the minus sign is put on the right side, so our final equation is
∆
× = − [21]
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This expression is known as Fara-day’s law of induction, given that Michael Faraday discovered (or rather, first announced) magnetic induction of current. It is consid-ered the third of Maxwell’s equa-tions: a changing magnetic field induces an electromotive force, which in a conductor will promote a current.
Not meaning to minimize the importance of Maxwell’s other equations, but the impact of what this equation embodies is huge. Electric motors, electrical gen-erators, and transformers are all direct applications of a changing magnetic field being related to an electromotive force. Given the elec-trified nature of modern society, and the machines that make it that way, we realize that there is a huge impact of Maxwell’s equations in our everyday lives.
www.spec t roscopyonl ine .com22 Spectroscopy 27(4) April 2012
Focus on Quality
R.D. McDowall
Various options for cloud computing are available, but not all are appropriate for analytical labora-tories operating under GXP regulations. Is cloud computing right for your laboratory? If so, which options should you choose?
Is Cloud Computing Right for Your Laboratory . . . or Are You Living in Cloud Cuckoo Land?
There is a lot of talk about cloud computing and its benefits. Surprisingly, there is little discussion about the downside of this technology. And, what exactly is
cloud computing? How is it delivered? Are there any ben-efits for an analytical laboratory? What is the impact of any ISO requirements or GXP (that is, good laboratory practice [GLP], good clinical practice [GCP], or good manufacturing practice [GMP]) regulations for a laboratory that is using or considering using the cloud? In this installment, I dis-cuss the various options for cloud computing, avoiding the marketing hype and focusing on the potential advantages and disadvantages for your laboratory. This column is not intended to be a definitive discussion of the cloud, but rather an introduction to the topic and what some of the implica-tions could be for analytical data. This topic also allows me to write some dubious subheadings.
Not a Cloud in the Sky . . .In the beginning, the information technology (IT) depart-ment of any company bought and managed its own IT infrastructure (servers, cables, workstations, and switches). Then the company purchased, installed, and managed the operating systems, such as general office applications like word processing and spreadsheet programs, plus laboratory applications such as laboratory information management systems (LIMS), statistical software (like SAS or Minitab), and the spectrometry software applications used to drive
the instruments. This is how most laboratories still oper-ate today. For application software, however, this approach often is costly because there is the upfront cost of purchas-ing the application plus the purchase of an adequate num-ber of user licenses, and often an ongoing annual main-tenance contract, which can be up to 20% of the purchase price of the application. Because the majority of IT depart-ments report to the finance department, it is difficult for these costs to be “massaged” because they are directly vis-ible. Hence the trend, beginning in the mid-1990s, to move to outsourced or offshore IT operations, a trend that has accelerated in recent years as companies attempt to reduce overall IT costs.
Cloudy Skies AheadAs the Internet became more widely accepted and greater bandwidth was coupled with greater global coverage and reliability, the options offered by the Internet started to be exploited by service providers and software vendors. Hosting services for some business applications, such as enterprise resource planning and scientific software like LIMS, became available as a service provided by some software vendors. In such offerings, hosting would be at a single location, and thus this approach is not considered to be cloud computing.
Cloud computing is large-scale distributed computing that can use virtual servers, hosted or leased applications, and computer services to offer a different way for companies
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to set up and run IT resources on a sliding scale from small to large. E-mail is a simple example of cloud com-puting: The e-mail service provider has the application software and the storage space for the mail; users only need to type in a URL into a browser and off they go. The best example of cloud computing taken to its ultimate extreme is the Chromebook device, which only contains the operating system, a web browser, and a small amount of solid-state memory to run the two; all user applications and data storage are located in the cloud.
Cloud Computing Concepts Just as in weather, cloud computing can be classified into various types. Let’s start with some definitions and terms to get a better idea of what’s going on. First, let’s see what is avail-able from authoritative sources to make sense of the marketing hype around cloud computing. There is a white paper from the Software Engi-neering Institute at Carnegie Mellon University that looks at the basics of cloud computing (1). There also is a special publication from the National Institute of Science and Technology (NIST) that focuses on the definitions of the services and modes of delivery (2). Additionally, Peter Boogaard has provided a review of cloud computing in the pharmaceutical research and development environment that readers will also find interesting (3). Further-
more, there is a draft NIST publication that goes into detailed discussions of the various cloud options and dis-cusses their pros and cons (4); this is an invaluable source of information for anybody considering the use of cloud computing for laboratory and business data.
In essence, cloud computing is the distribution of computing infrastruc-ture, applications, or services from within an organization to outside service providers on the Internet. As such, it aims to move the costs of com-puting (both hardware and applica-tion software) from an upfront capital cost to one that is based on leasing the services and facilities from a service or application provider. NIST defines cloud computing as follows (2):
Cloud computing is a model for en-abling ubiquitous, convenient, on-de-mand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, ap-plications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential character-istics, three service models, and four deployment models.
This is described in Figure 1 and below in more detail.
Cloudy Customer Requirements NIST SP800-145 (2) is a very short document, about seven pages long, that describes the main elements and ser-
vices available with cloud computing. From this document I derived Figure 1 to show all of the elements that are pos-sible with the cloud. Moving from left to right across the top of Figure 1, we can look at the elements of cloud com-puting in detail to make more sense of the technology and approaches used. Essentially, there are three elements that comprise the cloud:• customer requirements• service model• delivery model.All three elements need to be consid-ered, but in my opinion, for the ana-lytical laboratory, we can narrow down many of the options to consider just one or two.
The process begins with defin-ing your requirements: Why do you want to move to cloud computing? Is it really what you need, or are you being seduced by technology? Just as with the acquisition of any spec-trometry or laboratory software, you must get a good understanding of the business reasons for using the cloud. According to NIST (2) and Figure 1, one or more of the fol-lowing can be the requirements for starting this journey:• On-demand self-service: The abil-
ity to expand computing capa-bilities (such as server time or data storage) automatically without re-quiring discussion with the service provider.
• Broad network access: Services and capabilities are available over the network and are accessed mainly via a browser.
• Resource pooling: A provider’s computing resources are pooled to serve multiple consumers using a multitenant model, with different physical and virtual resources dy-namically assigned and reassigned according to consumer demand.
• Rapid elasticity: IT capabilities can be agreed to and released, in some cases automatically, to accommo-date customer demand.
• Measured service: Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and user of the utilized service.
Customer
Requirements
On demandservice
Privatecloud
Communitycloud
Publiccloud
Hybridcloud
Software as aservice
SaaS
Platform as aservice
PaaS
Infrastructure asa service
IaaS
Broad networkaccess
Resourcepooling
Rapid elasticity
Measuredservice
The
Cloud
Service
Model
Delivery
Model
Figure 1: The main elements of cloud computing derived from NIST SP800-145 (adapted from reference 2).
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Cloudy Services Just as weather clouds are classified into three main types as stratus, cumu-lus, and cirrus, cloud computing has three types of service models, as shown in Figure 1. If you want more informa-tion on any of these models, NIST has a draft special publication available (4), as I mentioned earlier, that goes into much more detail than I can here.
Software as a Service
Perhaps the most common cloud com-puting service model is software as a service (SaaS), which is the provision of one or more applications to a cus-tomer or laboratory to meet its busi-ness needs. This can vary from e-mail or office applications such as word processing to GXP (GLP, GCP, and GMP) applications. Typically, the ap-plications will be delivered through a web browser (thin client architecture) to reduce additional software installa-tion costs for the laboratory, although some programs may be accessed via a program interface (thick client archi-tecture) installed on each workstation or via terminal emulation at a user facility. The important point to note with the SaaS service model is that overall management of the application and environment remains with the service provider and not the labora-tory. This fact will raise concerns with some quality and regulatory require-ments, as we will discuss later in this column. However, the laboratory will be responsible for user account man-agement and possibly application con-figuration, depending on the delivery model used, which we will discuss in the next section.
Infrastructure as a Service
The infrastructure as a service (IaaS) model provides computer infra-structure via the Internet allowing a laboratory or its parent organization to expand computer infrastructure on demand. Typically, the provider has large servers on which virtual machines are created on which each customer or user installs its own op-erating systems and applications. If required, data storage facilities can be added. IaaS includes providing infra-
structure elements such as desktop as a service (DaaS) as noted by Boogaard (3). In this service model the con-sumer has control over the operating systems, applications, and data storage but does not control the underlying cloud infrastructure.
Platform as a Service
The third cloud computing service model, platform as a service (PaaS), is really for software developers. PaaS is the provision of infrastructure and a development environment to cre-ate, test, and deploy applications. The provider’s development environment can include programming languages, libraries, services, and utilities that are all supported by the provider. The customer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, or storage, but has control over its own developed and deployed applications. One type of PaaS cus-tomer could be somebody developing and running a web site. I mention this only for completeness; PaaS will not be discussed further here in relation to the analytical laboratory.
Delivering the Cloud Having discussed the service mod-els that can be found under the ban-ner of cloud computing, we need to consider how it will be delivered. According to NIST (2) there are four models: • Private cloud: Here, the cloud in-
frastructure is provided solely for a single organization. The private cloud can be owned, managed, and operated by either the organization
or a third-party provider and can be located on-site or off-site. Typically, however, a private cloud is owned by the provider and located off-site.
• Community cloud: This delivery model is the provision of a cloud specifically for a defined commu-nity of companies with shared aims, such as regulatory compliance. Similar to the private cloud, it can be owned by one or more members of the community or a third party and can be located on- or off-site.
• Public cloud: Access to the cloud in-frastructure is for anybody. Because of the open access, this delivery model cannot be controlled and therefore is not suitable for labora-tory use; thus we will not consider it further here.
• Hybrid cloud: This is a combina-tion of two separate cloud infra-structures (private, community, or public) that are linked to allow data and application portability (such as cloud bursting for load balancing between clouds).
Combination of Services and
Delivery Options: Because the cloud is very f lexible, you can have com-binations of services and delivery options. For example, you could have a multitenant option using a com-munity cloud incorporating PaaS with tens or even hundreds of com-panies sharing the same software and architecture. The client companies would have no ability to control the application configuration with the sole exceptions of look and feel and user views of their data. The chal-lenge with such a model is that the
Laboratory
Customers
Laboratory 1 Application Application Application
Laboratory3
database
Laboratory2
database
Laboratory1
databaseLaboratory 2
Laboratory 3
The
Cloud
Cloud
Provider
Physical computer and operating system
Figure 2: A SaaS cloud architecture illustrating isolated installations of an application.
REVOLUTIONIZE YOUR PROTEIN ANALYSIS
Researching protein stability,
structure or formulations?
Protein structural, stability and
aggregation data in a single
automated experiment
Automation revolutionizes the way circular
dichroism can be used in protein stability and bio-
►Optimize protein higher-order structural stability
►Compare innovator and generic product characteristics
►Minimal hands-on setup time and walk-away operation
►50-fold increase in productivity
►As little as 10μg of protein per sample for far-UV work
REVOLUTIONIZE YOUR PROTEIN ANALYSIS
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service provider can force all tenants to be upgraded en masse on software, hardware, and architecture, leaving clients with no ability to control the environment. In a normal business environment, this could be accept-able, but in a quality and especially in a regulated environment with GXP data, such a situation would be totally unacceptable, because valida-tion would be impossible.
Floating on a Cloud? For the purposes of this column, we will limit our discussions to the SaaS service model with delivery through a private cloud. The rationale is twofold. If your laboratory is a research labora-tory, you will want to protect your in-tellectual property. If your focus is de-velopment or manufacturing, the data and information generated from your activities must be protected, so you do not want to run the risk of compromis-ing your data with data from another company (as could happen through a community, public, or hybrid cloud), regardless of which industry it is in.
Because of the nature of the cloud,
the users are physically and logically separated from the application and the data center where the computing re-sources are located. The problem with this separation is that when real-time control is required, such as for a spec-trometer system, then the SaaS model is really not applicable, because the delay in controlling the instrument over the Internet is not acceptable. In addition, the size of some spectrometry files will be large — for example, many high resolution spectra from nuclear mag-netic resonance (NMR) or mass spec-trometry (MS) instruments can exceed 1 gigabyte — and there will be problems transmitting a file this large to the cloud resources as well as retrieving it.
On the other hand, business applica-tions such as enterprise resource plan-ning, a quality management system, or a LIMS could be operated using cloud computing, because as the latency of the Internet (delay between entering data and receiving a response from the software) usually is acceptable for those applications. However, because the Internet is a public service and out of the laboratory’s control, there can
be no guarantee of acceptable levels of service unless the company has a dedi-cated line to the cloud computing site.
SaaS Service Cloud Options The great advantage of SaaS from the perspective of the customer is that the cost moves from a capital cost model (purchase of the software and associ-ated licences) to a revenue cost model (hire of user accounts to use the soft-ware). However, we need to go into more detail about how a SaaS service can be delivered. We have two main options to consider.
The first is shown in Figure 2. Here, the cloud service provider has the com-puting resources and each customer has its own version of the application with a separate database in separate virtual machines or even on separate physical computers (not shown in Figure 2). Separate running instances of an application have a number of ad-vantages from my perspective:• It may be possible to configure the
application to meet your business needs and processes.
• Your data are in a separate database. • You set up and control the user ac-
count management for the whole of your application instance.
• It is easier to convince an inspec-tor or auditor that you are in con-trol and that data integrity is not compromised. If required, your application can sit on a separate virtual server.
• Change control is easier and can be phased, because each instance of the application is separated.The potential cost savings may
not be a great as you think, however, because this approach is similar to running the system in-house on your own servers and therefore a supplier may require that you purchase the software rather than lease it.
In contrast, in the second version of SaaS (shown in Figure 3), the service provider offers a single instance of the application with a single database. Here, each laboratory’s operations and data are separated logically within a single database (company-specific user groups are set up) and there is the logi-cal separation of each company’s data.
Table I: Some potential advantages and disadvantages of cloud computing
Advantages Disadvantages
Collaboration: ability to share data and information between authorized people
Potential lack of information security
Lower infrastructure costs: move from a purchase to a lease model
Lack of control over performance and reliability
Scalability: the ability to add additional computational power on demand
Potential problems with Internet reliability and availability
Mobility and elasticity Latency or time delays
Where are my data located?
Lack of regulatory compliance
Laboratory
Customers
Laboratory 1
Application
Lab 1 data
Lab 2 data
Lab 3 data
Commondatabase
Laboratory 2
Laboratory 3
The
Cloud
Cloud
Provider
Physical computer and operating system
Figure 3: An alternative SaaS cloud architecture with a single application with database instance.
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With this approach, costs should be lower than with single-company instances of the application. However, the application will usually be “one-size-fits-all.” Because there is only a single instance, it will be difficult to configure the software to an indi-vidual laboratory’s business processes. Therefore, it will be a take-it-or-leave-it option: There is no configuration other than user account management. This means that your business process will have to conform to the applica-tion’s mode of operation.
Validation of some elements of data integrity (such as a shared application and database) also can be difficult, because accessing another user’s por-tion of the system will not be allowed. However, there could be a good case for having a basic validation that is then confirmed by each regulated company. However, the basic valida-tion may not meet every company’s computer system validation policies and procedures, so there would be
some additional work needed.Each laboratory’s data are separated
logically in the database. But how will you convince an inspector or auditor that one laboratory cannot change the data in a second laboratory’s portion of the database? Also, change control will be difficult from my perspective. Does each company delegate change control to the cloud company? This would be very unlikely, because the service provider could issue service packs and application software updates with no consultation with the client companies. There could be a situation in which simple patching of the operat-ing system with security patches could be delegated to the service provider who had an appropriate procedure. However, no changes could be made to an application without agreement of allparties involved.
So from my perspective, and for the reasons given above, the SaaS approach shown in Figure 2 is much more pref-erable to that illustrated in Figure 3.
Cloudy Vision, 1 So far, we have looked at the archi-tecture of the cloud and the options. But we also need to look at technical feasibility. Running software applica-tions over the web is not new and has been done for a number of years. I know of LIMS that operate on a global basis where there is a central server and services are made available to any labo-ratory within an organization. Those are not part of a cloud architecture, but there are indeed options now where LIMS is available via SaaS.
But what about spectroscopic in-struments and their accompanying software applications? Can these be made to operate via the cloud? Not in the short term, at least in my opinion. Have a look around your laboratory and observe the nature of the vari-ous spectrometers. Typically, they are standalone instruments with data held locally on the workstation hard drive; or sometimes there is an option to store data on a network drive. In the
Table II: EU GMP Annex 11 regulations applicable to cloud computing (9)
Annex 11 Clause Regulatory Requirement
1. Principle IT infrastructure should be qualified.
2. PersonnelAll personnel should have appropriate qualifications, level of access, and defined responsibilities to carry out their assigned duties.
3. Suppliers and service providers
3.1 When third parties (e.g., suppliers, service providers) are used e.g., to provide, install, configure, integrate, validate, maintain (e.g., via remote access), modify or retain a computerized system or related service or for data processing, for-mal agreements must exist between the manufacturer and any third parties, and these agreements should include clear statements of the responsibilities of the third party. IT departments should be considered analogous.
3.2 The competence and reliability of a supplier are key factors when selecting a product or service provider. The need for an audit should be based on a risk assessment.
4. Data storage
7.1 Data should be secured by both physical and electronic means against damage.
7.2 Regular backups of all relevant data should be done. Integrity and accuracy of backup data and the ability to restore the data should be checked during vali-dation and monitored periodically.
10. Change and configuration management
10. Any changes to a computerized system including system configurations should only be made in a controlled manner in accordance with a defined procedure.
12. Security
12.1 Physical and/or logical controls should be in place to restrict access to computerized system to authorized persons. Suitable methods of preventing unauthorized entry to the system may include the use of keys, pass cards, personal codes with passwords, biometrics, restricted access to computer equipment, and data storage areas.
13. Incident management 13. All incidents, not only system failures and data errors, should be reported and
assessed. The root cause of a critical incident should be identified and should form the basis of corrective and preventive actions.
16. Business continuity
16. For the availability of computerized systems supporting critical processes, provisions should be made to ensure continuity of support for those processes in the event of a system breakdown (e.g. a manual or alternative system). The time required to bring the alternative arrangements into use should be based on risk and appropriate for a particular system and the business process it supports. These arrangements should be adequately documented and tested.
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latter case, either the data are acquired locally and then transferred to the network or the files can be transferred to the network directly. Regardless of the approach, the application software needs to be next to the instrument to enable real-time control. If there is a time delay via the cloud, what will this do to your data acquisition?
Consider also the size of files gener-ated by each system. Low resolution instruments may have relatively small file sizes; for example, bioanalytical data files from liquid chromatography (LC)–MS-MS analysis are likely to be about 1 megabyte in size. But high-resolution NMR data may be up to 1 gigabyte in size. The latter is enough to cause heart failure among local IT staff if a few of these are moving around an internal network. However if you are storing files this large in the cloud, there is the time to store, but more importantly, the time to retrieve the files from wherever they may be stored. You could be overdoing it on caffeine with all those cups of coffee you’ll be drinking waiting for files to be retrieved from the cloud.
Cloudy Vision, 2One aspect of cloud computing that is usually ignored is the contract. With the cloud, we are dealing with a service that you will be accessing remotely, so there will be a number of potential con-cerns that you need to consider before deciding if it is suitable for you (4): • Performance and availability: If a
service provider offers you 99.5% availability, what does this mean in practice and how is it calcu-lated? Look at the nuances and examine the potential impact on your laboratory.
• Compensation for failure to per-form: What are the financial penal-ties or credits if the service fails to perform?
• Data preservation: How will your data be backed up and preserved? And what happens if you terminate the service or the service provider terminates your contract?
• Scheduled outages: Providers must give sufficient notification of sched-uled outages; putting a notice on
their web site is insufficient. The key users in the laboratory must be noti-fied by e-mail to ensure that work can be planned accordingly.
• A shark ate the Internet cable: What happens if there are events outside the service provider’s ability to con-trol (or force majeure, as the lawyers would call it). Earthquakes, tsuna-mis, weather, and human screwups all fall under this category.
• Security: Who is responsible for which aspect of security? This must be clearly stated in the contract or service level agreement.The points named above are just an
overview of some of the issues you need to consider about a cloud computing contract; if you want more information, please read the NIST draft SP800-146 report (4). Also, do not think that be-cause you have a contract that it will be acceptable to sue the service provider if things go wrong. The key to success is to spend time reviewing the contract and asking questions before you sign on the dotted line and you have moved all your data to the cloud. We will return to the contract later in this column when we consider the regulatory com-pliance aspects of the cloud.
Sunny or Stormy Outlook? So having discussed the cloud, where are we in the technology cycle? Ac-cording to a Gartner Group estimate, by the end of 2012, up to 20% of companies will not own their own IT assets. However, there is a lot of hype about cloud computing that makes the approach appear more mature; indeed, Gartner estimates that it is two to five years from being adopted as a mainstream technology (1). On the Gartner Group’s hype cycle, cloud computing is currently at the peak of the “inflated expecta-tion” stage and still needs to migrate through the “trough of disillusion-ment” and the “slope of enlighten-ment” before reaching nirvana or the “plateau of productivity” (1).
Any technology has advantages and disadvantages. The pros and cons of SaaS cloud computing are sum-marized in Table I. I will not go into these in detail with the exception of
the potential disadvantage of regula-tory compliance. We will discuss this in some detail in the next section.
Thunder Clouds Forming If you are in a regulated laboratory working to one of the “good practice” disciplines, you need to know what impact GXP regulations will have on the cloud and vice versa. You need to know this up front rather than wait for an inspector to start writing citations because you could not be bothered to read regulations or guidance. You know this makes sense!
So, do you want the good news or the bad news?
The good news (perhaps this is bad news if you work in quality assurance) is that the GLP and GCP regulations make no mention of IT systems. Where the GLP regulations mention equip-ment (5) or apparatus (6), it could be interpreted as including IT infrastruc-ture. Similarly, US GMP regulations refer to equipment being of adequate size, properly installed, and fit for intended use (7). However, there is an ongoing program from the US Food and Drug Administration that puts increased emphasis on data integrity under Compliance Program Guidance (CPG) manual 7346.832 (8) for pre-approval inspections.
The bad news is that we have new regulations that should serve as a benchmark for all laboratories gov-erned by GXP regulations and who are also considering cloud computing. It is when we turn to Europe and the new version of Annex 11 for comput-erized systems (9) that I reviewed in an earlier column (10) that we find the most modern and explicit regula-tory requirements that can be applied to cloud computing. The first point to make is that in the glossary to Annex 11, IT infrastructure is defined as: follows:
The hardware and software such as net-working software and operation systems, which makes it possible for the applica-tion to function.
So the infrastructure is the building blocks on which the regulated applica-tions will be installed and validated.
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For your reading pleasure, Table II summarizes the Annex 11 regula-tions that are most applicable to IT infrastructure and, in my view, also ap-plicable to cloud computing. The most important of these is from the “Princi-ple” section of Annex 11, which simply states that IT infrastructure should be qualified. Therefore, in a regulated en-vironment, there must be documented evidence that the server and operating system (such as IP address services) on which an application is running has been installed and configured cor-rectly. In addition, if the version of the application is installed as a virtual ma-chine, this too needs to have been in-stalled and qualified. Evidence of this work needs to be available to you as well as inspectors and auditors. Some cloud service providers are specializing in this area and will provide qualified infrastructure and will provide the copies of the work performed for their customers.
However, we also need to look at the requirements of clause 3 of Annex 11, which considers service providers. There are two key requirements here. First, we need an agreement between the laboratory and the service pro-vider, and second, we must consider whether or not we need to perform an audit. Putting the horse before the cart, section 3.3 in Table II notes that an audit of a service provider should be based on a documented risk assess-ment. So how critical is the system and the data held in it? If the applica-tion and data are critical; you must conduct an audit to ensure that the center where the computer is housed is acceptable to host your system. If you don’t know where the data center is, the process stops here; just find an-other service provider.
In the audit, you should find out about the systems the provider has in place to protect your data, such as se-curity (access to the site and the com-puter room), antivirus and intruder protection, alternative power sup-plies, standby electricity generation, fire suppression, and data backup (if part of your service). These elements also form some of the requirements of an effective business continuity
plan to meet the requirements of clause 16.
Two elements in Table II that are closely related are clauses 10 (change control and configuration manage-ment) and 2 (personnel). After a sys-tem is up and running, it is the change control processes and personnel at the service provider that will make or break any compliant operation. The service provider’s staff need to be aware of the GXP regulations appli-cable to their role and the impact they can have on the laboratory’s data. This is not a “nice to have” element. It is the law. Furthermore, any uncontrolled changes to a system by the staff will destroy the validation status; there-fore this is a critical area to consider. Before committing to either SaaS op-tion, it is vital to know how patches and service packs for the operating system, database, and changes to the application are controlled, installed, documented and, where necessary, validated.
I am not trying to put you off using the cloud — there are benefits to be obtained from it. But if you are work-ing under GXP regulations, you need to know the requirements before you zoom off into the sky.
Summary The most common variant of cloud computing that could be used in a spectroscopy laboratory is SaaS. It will not be used for controlling spec-trometers or other applications that need real-time control, but it offers advantages for laboratories using applications where response time is not critical. The technology is still maturing, however, and therefore one should exercise care in evaluat-ing and deploying it, especially in a regulated environment. The con-tract between the laboratory and the service provider must be carefully analyzed to ensure the laboratory is protected and gets the service it is paying for. If working under GXP regulations, further requirements are necessary and the relevant clauses from EU GMP Annex 11 must be used to guide laboratories consider-ing cloud services.
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Lasers and Optics Interface
Dick Wieboldt
We compare the theoretical and experimental differences between high-brightness and low-bright-ness lasers as used in a dispersive confocal Raman microscope system. Spectral maps of a 1-µm diameter polystyrene sphere are measured using both types of lasers.
Advantages of High-Brightness Lasers in Confocal Raman Spectroscopy
Confocal Raman microscopy makes it possible to sample a precisely defined area at the focus of an instrument while excluding signals from the sur-
rounding area. This is achieved by placing an aperture at a secondary focus in the optical path between the sample and the detector. One can think of this as projecting the confocal aperture onto the sample plane. Only Raman signals originating within this projected aperture can pass through the confocal aperture; all out-of-focus Raman sig-nals are rejected.
Ideally, the Raman excitation laser is focused on the same confocal area in the sample plane. Under these con-ditions, the laser creates the maximum Raman scattering exactly in the desired sampling location.
Not all lasers, however, produce the best results for confocal scattering. At high magnification, such as with a 50× or 100× microscope objective, the diameter of a confocal aperture projected on the sample is on the order of 1 μm. To focus a laser into such a small spot size, the laser must be what is termed high-brightness. The bright-ness in this context refers to the laser beam quality — its ability to achieve a high power density by focusing all of its energy into the smallest possible spot size.
Diode lasers can also be “low brightness” because of their design parameters. Such lasers cannot be focused to a small
spot size regardless of the optics used. The result is that areas of the sample outside of the confocal aperture are illu-minated by the laser. Spatial resolution may be reduced, but the immediate effect is much lower Raman signal strength than might otherwise be expected. High-brightness lasers are readily available with output power levels of 30–80 mW. Low-brightness lasers have much higher power levels, from 250 mW to several watts. However, at high magnifications and with small confocal apertures, high-brightness lasers can produce equal or higher signal-to-noise ratios (S/N). This improved S/N can make the difference between success or failure in energy-starved applications such as Raman mi-croscopy where every photon of Raman signal counts.
TheoryThe focusing quality of a laser beam is characterized by its diameter and divergence. These parameters are often com-bined in a laser beam figure of merit known as the M2 factor.
Beam diameter, also known as beam waist, is the mini-mum diameter of a laser beam along its axis of propaga-tion. The beam waist is sometimes reported as the beam waist radius, which is half the beam diameter.
Beam divergence is a measure of how much the laser beam diameter spreads along its propagation axis. It is usually re-ported as a half-angle in mrad (where mrad is milliradians).
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M2 factor is the product of beam diameter times divergence relative to that of an ideal Gaussian (TEM00) beam. For this reason, this factor can be thought of as “times diffrac-tion limit” of the laser beam. A laser beam that is diffraction-limited has ideal beam quality and can be focused into the smallest possible spot size. This ideal beam quality is designated by an M2 value of 1.0. For example, a laser with an M2 of 2 can
only focus to a spot diameter that is twice the theoretical minimum. The best lasers for focusing to a small spot size will have an M2 value close to 1.0.
Beam diameter, D, and divergence, Θ, are locked in an inverse relation-ship, as shown in equation 1 (1,2):
θ ≈π
2
DM
4 [1]
where θ is the beam divergence full-an-gle (in milliradians), λ is the laser wave-length (in nanometers), and D is the beam waist diameter (in millimeters). The factor of 4 in the numerator is due to the use of full angle (2× half-angle) and diameter (2× radius) in this equa-tion. You may see this equation in some texts simplified for an ideal Gaussian beam (M2 = 1) as in equation 2 (3):
θ ≈λ
D1.27 [2]
Equations 1 and 2 show that you can decrease either the beam divergence or the beam diameter, but you cannot decrease both at the same time. This is the fundamental key to understand-ing laser brightness.
The other key to achieving high brightness is the transverse electro-magnetic mode structure, or TEM, of a laser beam. In an ideal laser, the beam energy distribution across the beam is a Gaussian function. This is called a TEM00 mode structure. A laser may have multiple transverse modes with nodes in between. These multimode lasers have designations such as TEM10, TEM11, and so forth and have poor spatial coherence. Examples of these additional modes can be seen in Figure 1 or in other sources (5). Only a TEM00 can be focused to the smallest possible spot size and achieve the highest power density (2,4).
Power density, milliwatts per unit area, is the key factor for achieving good signal strength in a Raman microscope system. We can directly set or measure the laser power (mil-liwatts). To calculate the area term, we need to calculate the beam diameter, d, at the focus of a microscope objec-tive. This is given by equation 3:
=π
f2
Dd M
4[3]
where f is the focal length and D is the diameter of the beam entering the ob-jective. This is shown in Figure 2.
It may not be obvious whether a laser in a Raman microscope system is high or low brightness. This can be determined from specifications pro-vided by the laser manufacturer: TEM,
D
fd
Figure 2: Focusing of a Gaussian laser beam by a lens (DXR Raman microscope, Thermo Fisher Scientific).
Table I: Expected power densities for two types of 780-nm lasers: a 150-mW low-brightness laser and a 24-mW high-brightness laser
Low-Brightness 780 nm High-Brightness 780 nm
Beam size 5 × 5 mm 1 mm
Spot size (100× objective) 8 μm 1.7 μm
Laser power 150 mW 24 mW
Power density at sample 3 mW/μm2 11 mW/μm2
00 10 20 30
01 11 21 31
02 12 22 33
Figure 1: Transverse laser beam mode patterns.
www.spec t roscopyonl ine .com April 2012 Spectroscopy 27(4) 33
beam divergence, beam size, and M2
factor. For example, a high-power 780-nm laser might have a beam size of 5 × 5 mm and a beam divergence of <5 mrad. The M2 factor can be calculated by rearranging equation 1 to solve for M2, as shown in equation 4:
≈λ
π D θ2
4M [4]
Using the numbers given, we can see the product of beam diameter times beam divergence, Dθ, would be 25 times diffraction limited.
Equation 3 gives us the spot size for this low-brightness laser. A 100×microscope objective having a focal length f = 1.6 mm will produce a spot diameter of d = 4λM2f/πd or 8 µm.
Following the same approach for a high-brightness 780-nm laser with an M2 of 1.1 and a beam diameter of 1.0 mm gives a spot size of 1.7 µm.
Table I compares the power densi-ties achievable with the high- and low-brightness lasers used for this example. Note that the lower-power
laser produces nearly 3.5× the power density at the sample com-pared to the high-powered laser be-cause it is high brightness.
ExperimentalA 1-μm diameter polystyrene bead was used as a test sample to demon-strate the performance difference be-
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Figure 3: Image of 1-µm polystyrene spheres taken with a 100× microscope objective.
www.spec t roscopyonl ine .com34 Spectroscopy 27(4) April 2012
tween low- and high-brightness lasers. The instrument used was the Thermo Scientific DXR Raman microscope equipped with a 780-nm, 150-mW low-brightness laser and a 780-nm, 24-mW high-brightness laser.
The polystyrene beads were placed on a calcium f luoride sub-strate to reduce any background f luorescence.
Spectral maps were acquired of a region surrounding an isolated poly-
styrene sphere using both lasers on the same Raman microscope system. A 100× objective and a 25-μm aperture were used for both maps. The perfor-mance was optimized in each case by focusing for maximum Raman signal before beginning the measurement.
Each map is a 3.0 μm × 3.0 μm area around the same isolated poly-styrene bead. Data collection param-eters for both maps were the same: step size, 200 nm for both x and yaxes; microscope objective, 100×(0.96 N.A.); spectrograph aperture, 25 μm; length of acquisition per pixel, 5 s; number of acquisitions per pixel, 2. Both lasers were set to their maxi-mum power output: 150 mW for the low-brightness laser and 24 mW for the high-brightness laser.
Results and Discussion
Figure 4 shows spectral maps acquired with each laser.
The maps both show nearly the same size for the polystyrene sphere, which is because of the limitation imposed by the small confocal aperture. When using a 100× mi-croscope objective, the conjugate of the confocal aperture in the sample plane of the microscope objective is 1 μm diameter. Keep in mind that this is the area the spectrograph is seeing, which is smaller than the laser spot size discussed in the “theory” section above. Higher sig-nal may be obtained with the low-brightness laser by using a larger spectrograph aperture, but the system would no longer have a tight confocal aperture, resulting in a loss of spatial resolution.
Theory predicts we will observe strongly different peak signals from the two lasers, with higher peak signal from the laser with lower power but high brightness. This prediction is well met by the experimental data. If you look at the vertical intensity axis in the 3D plots in Figure 4, you will notice the Raman signal strength for the high-brightness map is much lower.
Figure 5 compares spectra taken from the peak in each map on the same Raman intensity axis. The high-brightness laser produces ap-
High brightness laser, 370 cps
380
360
340
320
300
280
260
240
220
200
180
160
140
120
100
80
60
40
20
1600 1400 1200 1000 800 600
Low brightness laser, 39 cps
Ram
an
in
ten
sity
(cp
s)
Raman shift (cm-1)
Figure 5: Peak height comparison for central spectra taken from maps shown in Figure 4.
Position (µm) Position (µm) Position (µm)Position (µm)
Position (µm)
Po
siti
on
(µ
m)
Po
siti
on
(µ
m)
Figure 4: Spectral maps of a 1-µm polystyrene sphere with two different lasers. (Left) Map
acquired with a 150-mW low-brightness laser. (Right) Map acquired with a 24-mW high-
brightness laser.
www.spec t roscopyonl ine .com April 2012 Spectroscopy 27(4) 35
proximately 10 times more signal than the low-brightness laser even though the power output is only 24 mW com-pared to 150 mW.
ConclusionHigher-power lasers that do not have high brightness (low M2) do not have an advantage for signal strength and may sacrifice spatial resolution when used with a small confocal aperture in Raman spectroscopy. Low-power, high-brightness lasers produce sig-nificantly higher signal strength compared to high-power, low-bright-ness lasers in this application.
Similar spatial resolution was seen with the two lasers in the case of an isolated polystyrene sphere on a nonfluorescing background. It is important to note that the majority of the laser power for the high-power, low-brightness laser is wasted. Not only does it not contribute to Raman signal within the confocal aperture in the sampling plane, it may produce spurious signals by scattering from
the surrounding area (f luorescence or Raman signal from different com-pounds). In addition, the excess laser power will certainly contribute to sample heating in the area surround-ing the region of interest.
AcknowledgmentsThanks to colleagues at Thermo Fisher Scientific: Kelly Cox who car-ried out the original work for this paper, Francis Deck who provided assistance with the optical param-eters, and Ali Mirabedini who mea-sured laser quality parameters.
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Pigments and resins, as important ingredients in paints, are widely used in the classification and identification of paints and in the investigation of
hit-and-run accidents (1–12). However, few studies focus on the classification of paints with additives. Clays are now widely used as additives in paints, which can en-hance the coating’s mechanical strength and resistance against degradation. They exist in almost all kinds of paints including amino resin paints, alkyd resin paints, polyurethane paints, and acrylic resin paints. The ability to discriminate these kinds of clay will be of great help in paint discrimination.
Infrared (IR) spectroscopy, scanning electron micros-copy (SEM), X-ray f luorescence (XRF), and pyrolysis gas chromatography–mass spectrometry (GC–MS) are use-
ful techniques frequently used in paint identification (13–15). Among all of these techniques, IR spectroscopy has been proven to be effective and accurate for paint analysis since the 1970s, requiring only a small quan-tity of sample to achieve rapid analysis and high-quality spectra (1–12). The Raman spectrum usually has differ-ent peak positions compared to the Fourier-transform infrared (FT-IR) spectrum for the same substance, so Raman spectroscopy can yield extra peak information. Raman spectroscopy covers some shortcomings of FT-IR spectroscopy and has proven to be a powerful tool in polymer characterization. Optical observation for dif-ferent clays with these two methods was performed in this study to investigate their spectral characteristics. The spectroscopic properties of clay minerals have been
Jungang Lv, Jimin Feng, Yong Liu, Zhaohong Wang, Meng Zhao, Yanming Cai, and Rongguang Shi
The analysis of automotive coatings is important for forensic scientists in the investigation of hit-and-run accidents. However, many kinds of paint are similar in structure and cannot be discriminated easily. In this study, Fourier-transform infrared (FT-IR) and Raman microscopy were employed to investigate different kinds of clay as the additives in paints to help discriminate the paints. The IR and Raman spectra were measured and tentatively interpreted. The indicative peaks distinguishing kaolin and bentonite were summarized in the IR spectrum. For some other kinds of clay that could not be discriminated from kaolin in the IR spectrum, several peaks in the Raman spectrum of kaolin were found to be weak but characteristic as indicators of kaolin. The method was applied and veri-fied in a complex paint analysis case successfully. The data in this study can help forensic scientists identify paints accurately, particularly in cases with interference in the spectrum (<3000 cm-1).
Discriminating Paints with Different Clay Additives in Forensic Analysis of Automotive Coatings by FT-IR and Raman Spectroscopy
www.spec t roscopyonl ine .com38 Spectroscopy 27(4) April 2012
investigated in many previous stud-ies (16,17). However, to the best of our knowledge, few studies have ex-amined the spectroscopic properties (particularly in Raman) of clays in paints. In addition, how to use the weak but indicative peaks in spec-
tra to discriminate the paints is not clear and needs to be solved.
ExperimentsKaolin, bentonite, and some other clay samples were obtained from the National Technical Committee on
Plastic Products of Standardization Administration of China. Calcium carbonate (CaCO3), titanium diox-ide (TiO2), polystyrene, and acrylic resin paint samples were collected in cases. A Nicolet iN10 at tenu-ated total ref lectance (ATR)-FT-IR system (Thermo Fisher Scientif ic) with a mercur y–cadmium–tel lu-r ide (MCT) detector and Omnic Picta software (Thermo Fisher Sci-enti f ic) was employed for IR ob-servation. Spectra were col lected in high-resolution (8-cm-1) mode. A Spectrum GX 2000 system from PerkinElmer with a diamond anvil cell, a deuterated triglycine sulfate (DTGS) detector, and Spectra 5.01 software was employed to verify the IR results from the iN10 system. The background was subtracted for every measurement. Triplicate tests were performed at different sites for each sample.
A Renishaw inVia confocal Raman microscopy system with two lasers emitting at 532 nm and 633 nm, re-spectively, and a charge-coupled de-vice (CCD) detector was employed to collect the Raman spectrum. The 532-nm laser was chosen for emis-sion in 50 –100% power. Cr ysta l silicon with a fixed peak position at 520 cm-1 was used to calibrate the Raman shifts before measurement. The spectra (100–4000 cm-1) were collected in WIRE 3 software (Re-nishaw) in extensive mode. Spectra were obtained by three accumula-tions to enhance the signal-to-noise ratio if necessary. Multiple measure-ments (n = 3) were made at different regions of the same sample to avoid variation.
Results and DiscussionIR Spectroscopy
Figure 1 shows the IR spectra of ka-olin and bentonite obtained using the PerkinElmer Spectrum GX 2000 system. Peaks of kaolin and benton-ite appeared at similar positions. The main IR peaks of kaolin were at 3696 cm-1, 3670 cm-1, 3655 cm-1, 3622 cm-1, 1117 cm-1, 1038 cm-1, 1013 cm-1, 938 cm-1, 915 -1, 797 cm-1, 756 cm-1, 694 cm-1, 542 cm-1, 472 cm-1, and 432 cm-1.
Wavenumber (cm-1)
4000.0
36963670
3655
3622
3625
1636
1117
1038
1013
916
1034
799 695
530
469
472
542
436
694
797
938
915
756
3000.0 2000.0 1500.0 1000.0 400.0
Tra
nsm
itta
nce
(%
)
Figure 1: Comparison of kaolin (top) and bentonite (bottom).
Figure 2: IR spectrum of acrylic resin paint containing TiO2, bentonite, and polystyrene.
4000.0 3000.0 2000.0 1500.0 1000.0 400.0
3625
30272923
2850
3060
2934
2877 (a)
(b)
(c)
(d)
3380
1731 1454
1386 1240
1160
1072
702
758
1583
1601
1640
1493
1452
1372
1329
1181
1155
1069
1028
965
907
841
818
757 702
540
469530
799
916
1034
Tra
nsm
itta
nce
(%
)
b ( 1)
Figure 3: IR spectra of (a) acrylic resin paint, (b) TiO2, (c) polystyrene, and (d) bentonite.
www.spec t roscopyonl ine .com April 2012 Spectroscopy 27(4) 39
The main peaks of bentonite were at 3625 cm-1, 1638 cm-1, 1034 cm-1, 916 cm-1, 799 cm-1, 695 cm-1, 530 cm-1, and 469 cm-1. Four peaks in the region greater than 3000 cm-1 (3696 cm-1, 3670 cm-1, 3655 cm-1, and 3622 cm-1) and the peak at 938 cm-1 help to iden-tify kaolin, while the peaks at 3625 cm-1 and 1638 cm-1 help to identify bentonite. In addition, peaks at 542 cm-1 and 472 cm-1 in kaolin shifted slightly to 530 cm-1 and 469 cm-1 for
bentonite. These changes in peak position also will help discriminate these two clays.
Kaolin (Al4[Si4O10][OH]6) and ben-tonite ([Na,Ca]0.33[Al,Mg]2[Si4O10][OH]2-nH 2O), two k inds of clay minerals with mass production, were used in paint to extend titanium di-oxide and thus modify gloss levels of coatings. Kaolin is a layered silicate mineral, with one tetrahedral sheet linked through oxygen atoms to one
octahedral sheet of alumina octahe-dral. There are various types of ben-tonite, and they are named after the respective dominant element, such as potassium, sodium, calcium, and aluminum. Feng revealed that differ-ent types of bentonite had similar IR spectra (18). According to the compo-nent and the structure, IR spectra of kaolin and bentonite were tentatively interpreted as follows.
For kaolin, 3696 cm-1, 3670 cm-1, 3 655 c m -1, a nd 3 622 c m -1, OH stretching; 1117 cm-1 and 1038 cm-1, Si-O stretching; 938 cm-1 and 915 cm-1, OH bending; 797 cm-1, Si-O-Mg; 542 cm-1, Si-O-Al; 472 cm-1, Si-O-Mg. The intensity of peaks at 3696 cm-1, 3670 cm-1, 3655 cm-1, and 3622 cm-1 indicated the crystallinity of kaolin. For bentonite, 3625 cm-1, OH stretching; 1638 cm-1, OH bend-ing; 530 cm-1, Si-O-Al; 469 cm-1, Si-O-Mg. Comparison of IR spectra showed that peaks of kaolin at 3696 cm-1, 3670 cm-1, 3655 cm-1, 3622 cm-1, 938 cm-1, 915 cm-1, and those
Figure 4: IR spectrum of acrylic resin paint containing TiO2, polystyrene, kaolin, and CaCO3.
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of bentonite at 3625 cm-1, 1638 cm-1, and 916 cm-1 were characterist ic. These peaks can be used to discrimi-nate the two clay minerals.
Case Studies
Figure 2 shows the IR spectrum of acrylic resin paint containing TiO2, clay, and polystyrene in a hit-and-run
case. Figure 3 shows the IR spectra of acrylic resin paint, TiO2, clay, and polystyrene used to interpret the spectrum of Figure 2. Figure 4 shows
Table I: Assignment of IR peaks
Acrylic Resin Paint Polystyrene CaCO3
Wavenumber (cm-1)
VibrationWavenumber
(cm-1)Vibration
Wavenumber (cm-1)
Vibration
3385 OH stretching >3000 =C-H 2517Combination of 1440
and 1080
2934 CH3 stretching 2800–3000 CH3, CH2 stretching 1796Combination of 1070
and 712
2877 CH2 stretching1601, 1583 1493, 1450
Benzene ring skeleton 1444 C-O stretching
1731 C=O stretching 1069, 1028=C-H in ortho
substituents of benzene in plane bending
874 C-O in-plane bending
1599 1580
Benzene ring skeleton 750, 702=C-H in ortho
substituents of benzene out plane bending
712 C-O out-plane bending
1454CH3 symmetric
bending in O-CH3967, 907 =CH2 out-plane bending
1386CH3 symmetric
bending
1240 1160
C-O stretching
1070 760700
=C-H bending in ortho substituents of
benzene bending
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the IR spectra of acrylic resin paint containing TiO2, CaCO3, bentonite, and polystyrene in a case. Figure 5 shows the IR spectra of acrylic resin paint, TiO2, CaCO3, clay, and polystyrene to interpret the spectrum of Figure 4.
The spectra in both Figure 2 and Figure 4 are acrylic resin paint containing TiO2, clay mineral, and polystyrene, so the two spectra were similar and hard to discriminate. The peak posit ions of the above substances were as follows: acrylic resin paint: 2875 cm-1, 1732 cm-1, 1453 cm-1, 1394 cm-1, 1162 cm-1, 759 cm-1, and 701 cm-1; TiO2: 800 cm-1
and ~500 cm-1; polystyrene: 3027 cm-1, 1493 cm-1, 1453 cm-1, 1330 cm-1, 1028 cm-1, 843 cm-1, 759 cm-1, and 701 cm-1; clay minerals: 3625 cm-1, 1120 cm-1, 1028 cm-1, 535 cm-1, and 473 cm-1. The assignments of the functional groups in the spectrum are listed in Table I. Without the information from clay min-erals and CaCO3 in Figure 4, it would not be easy to discriminate between the two samples and avoid a wrong certificate of authenticity.
In Figure 2, only one peak (3625 cm-1) was found above 3000 cm-1. Weak absorbance appeared at
4000.0 3000.0 2000.0 1500.0 1000.0 400.0
36223696
3060
3027 2923
2850
2875
29583385
1731 1554
1471
1230
1167
1073
815
760
702 536
818
841
757 702
540
907
965
10281069
118113721452
1149160
1434
1117 1032
915
798694
538473
713
87617962513
1357
Tra
nsm
itta
nce
(%
)
Wavenumber (cm-1)
(a)
(b)
(c)
(d)
(e)
Figure 5: IR spectra of (a) acrylic resin paint, (b) TiO2,
(c) polystyrene, (d) kaolin, and (e) CaCO3.
4000 3500 3000 2500 2000 1500 1000 500
10
20
30
40
50
60
70
80
90
100
65
70
75
80
85
90
95
100
Tra
nsm
itta
nce
(%
)Tra
nsm
itta
nce
(%
)
Wavenumber (cm-1)
Figure 6: IR spectra of GBW03101a (top, clay) and GBW 03121
(bottom, kaolin).
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1648 cm-1 and only one peak (916 cm-1) appeared in t he 900 –1000 cm-1 region. Si-O-Al and Si-O-Mg vibration appeared at 530 cm-1 and 469 cm-1, respectively. In Figure 4, there were four peaks in the region greater than 3000 cm-1 (3696 cm-1, 3670 cm-1, 3655 cm-1, and 3622 cm-1) and two peaks in the 900–1000 cm-1
reg ion (938 cm-1 a nd 915 cm-1), which is not the same as in Figure 2. Si-O-Al and Si-O-Mg vibration appeared at 546 cm-1 and 473 cm-1, respectively. Bentonite and kaolin were identified from the two spec-tra separately; therefore, the two samples were discriminated.
Raman Spectroscopy
There are many kinds of clay miner-als with different components and crystal linity. Some minerals can-not be easi ly discriminated by IR
spectroscopy. Furthermore, peaks of many inorganic compounds (in-cluding clay minerals in this study) of ten appeared in the region less than 500 cm-1 and cannot be ana-lyzed with IR spectroscopy. Raman spectroscopy offers a new and pow-erful tool in solving this problem. In this study, Raman spectroscopy was employed for future determination of clay minerals and paints.
Figure 6 shows the IR spectra of two China National Standard Mate-rials: GBW03101a (one kind of clay minerals) and GBW 03121 (kaolin). Indicative peaks above 3000 cm-1, 900–1000 cm-1, and 1640 cm-1 were too weak to employ as qualitative peaks even with an MCT detector. For Raman analysis, both samples were found to have serious f luores-cence interference. Sample bleaching for 60 min and 3–9 accumulations
were used in sample preparat ion to obta in bet ter spectra . Signa ls in the 1000–3000 cm-1 region of Raman shifts still were affected by f luorescence inter ference, which will not be discussed further in this study. Figure 7 shows the Raman spectra of GBW03101a (clay) and GBW 03121 (kaol in) in the 130–1000 cm-1 region. Figure 8 shows the Raman spectra of GBW03101a (clay) and GBW 03121 (kaolin) in the 3000–4000 cm-1 region. Signifi-cant differences in Raman spectra of the two minerals were observed in Figures 7 and 8. Peaks of kaolin appeared at 144 cm-1, 196 cm-1, 240 cm-1, 274 cm-1, 334 cm-1, 393 cm-1, 428 cm-1, 464 cm-1, 508 cm-1, 636 cm-1, 706 cm-1, 749 cm-1, 790 cm-1, 912 cm-1, 934 cm-1, 3240 cm-1, 3623 cm-1, 3654 cm-1, 3667 cm-1, and 3698 cm-1. Peaks of GBW03101a appeared at 140 cm-1, 198 cm-1, 278 cm-1, 459 cm-1, 603 cm-1, 616 cm-1, 687 cm-1, 708 cm-1, 779 cm-1, 930 cm-1, 3240 cm-1, and 3800 cm-1. The two sam-ples were discriminated clearly in Raman spectra.
Conclusion
In this study, FT-IR and Raman mi-croscopy were employed to investi-gate different kinds of clays to dis-criminate the paints in hit-and-run cases. The IR and Raman spectra were tentatively interpreted. The in-dicative peaks distinguishing kaolin (3696 cm-1, 3670 cm-1, 3655 cm-1, 3622 cm-1, 938 cm-1, and 915 cm-1) and bentonite (3625 cm-1, 1638 cm-1, and 916 cm-1) were summarized in IR spectra . Raman spectroscopy was used for some kinds of clay that could not be discriminated from ka-olin in IR spectrum. Kaolin peaks in the region greater than 3000 cm-1
and at 934 cm-1, 636 cm-1, and 274 cm-1 in its Raman spectrum were found to be characteristic and can be used as indicators. The method was applied and successfully veri-fied in a complex case. Three kinds of clay minerals were investigated in this study to set up a database with adequate spectra. Spectra of more kinds of clay minerals and the com-
200 300 400 500 600 700 800 900 1000
Raman shift (cm-1)
-10,000
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
110,000
120,000
130,000
0
Co
un
ts
Figure 7: Raman spectra of GBW03101a (top, clay) and GBW 03121 (bottom, kaolin) (130–1000 cm-1).
3100 3200 3300 3400 3500 3600 3700 3800 3900 4000
Raman shift (cm-1)
0
10,000
20,000
30,000
40,000
Co
un
ts
Figure 8: Raman spectra of GBW03101a (top, clay) and GBW 03121(bottom, kaolin) (3000–4000 cm-1).
www.spec t roscopyonl ine .com April 2012 Spectroscopy 27(4) 43
bined spectra of clays and paints will be involved in future studies.
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Inductively coupled plasma–mass spectrometry (ICP-MS) is a powerful instrumental technique that has become in-creasingly popular in recent years, especially because of
a reduction in costs and the growing demand for fast, sen-sitive analytical methods from developing economies (1). Despite their high sensitivity and multielement capabilities, quadrupole-based instruments present an important limi-tation related to their intrinsic inability to solve spectral in-terferences in the 1 amu range. Thus, polyatomic ions with
mass-to-charge ratios (m/z) close to the analytes can severely compromise ICP-quadrupole mass spectrometry (QMS) sen-sitivity and accuracy. Such interfering ions can be formed by interactions among species originated in the plasma, the solvent, the sample matrix, and atmosphere gases diffusing into the plasma. The problem is even more critical for spe-cies present in high concentrations in one of the interference sources — for example, 40Ar+, 40Ar35Cl+, 12C16O+, 14N2
+, and 16O2
+ — and can compromise the determination of important
George L. Donati, Renata S. Amais, and Joaquim A. Nóbrega
One of the main limitations with inductively coupled plasma–mass spectrometry (ICP-MS) is related to the occurrence of spectral interferences. Considering the relatively low resolution presented by quadrupole-based mass spectrometry (ICP-QMS), sensitivity and accuracy can be compromised by interfering ions formed in the plasma. Several strategies have been proposed to overcome the prob-lem, and the most successful ones are based on high-resolution instruments, mixed-gas plasmas, or collision-reaction cells and interfaces. A different approach was recently proposed based on the idea that interfering polyatomic ions and some plasma naturally occurring Ar species present similar behaviors. The so-called interference standard method (IFS) uses the analytical to IFS signal ratio associated with external calibration to minimize the interfering species contribution to the analytical signal, thereby improving ICP-QMS accuracy. In this work, the efficiency of the IFS method in the determination of Si is evaluated. A 130% recovery was found for 28Si determined in a standard refer-ence material (Typical Diet, NIST 1848a) using the conventional external calibration method with-out any interference correction. Such high recovery is a result of spectral interferences caused by molecular ions such as N2
+ and 12C16O+. On the other hand, no statistically significant difference at a 95% confidence level was found between reference- and IFS-determined values using the 36ArH+ or 38Ar+ probes. Limits of detection of 6.0, 5.0, and 8.0 μg/L were calculated for determinations at m/z 28 or using the 28/37 and 28/38 signal ratios, respectively. Possible mechanisms responsible for the IFS method efficiency also are discussed here.
Improving Accuracy in Inductively Coupled Plasma–Quadrupole Mass Spectrometry: The Interference Standard Method
April 2012 Spectroscopy 27(4) 45www.spec t roscopyonl ine .com
elements such as Ca, As, Si, and S. Con-sidering all of these variables, it is clear that correcting spectral interferences in ICP-QMS is not trivial. Several strate-gies have been proposed to overcome this problem, and the most common are based on collision-reaction cells and interfaces (2,3), mixed-gas plasmas (4,5) and mathematical correction (6,7).
A different approach to reducing spectral interferences in ICP-QMS was recently proposed by Donati and col-leagues (8). The interference standard method (IFS) uses Ar species naturally present in the plasma to reduce the con-tribution of interfering polyatomic ions to the analytical signal. This method uses the same reasoning behind a con-ventional internal standard method, but targets the interfering species rather than the analytes. The method assumes that Ar ions and some polyatomic spe-cies present similar behaviors in the plasma and that by using the analytical to IFS signal ratio associated with an ex-ternal calibration method, it is possible to improve accuracy in ICP-QMS deter-minations. In fact, significant improve-ments have been reported by using this strategy even for some severely affected elements, such as S and Fe (9).
In this article, the IFS method capa-bilities are demonstrated by determining Si in a standard reference material and comparing the results with values ob-tained with a conventional external cali-bration method without any interference correction. Determining this element by ICP-QMS is not a trivial task, especially for nitric acid–digested samples that contain high concentrations of elements that are precursors of Si main interfer-ences. Ions such as 14N2
+ and 12C16O+, which present m/z close to the most abundant isotope of Si (28Si, 91.23%), can significantly compromise accuracy and prevent determinations in complex ma-trices. The IFS probes 36ArH+ and 38Ar+
were used to improve accuracy. Possible mechanisms contributing to the method efficiency also are discussed.
Experimental Instrumentation
An inductively coupled plasma–quad-rupole mass spectrometer (model 820-MS, Varian) was used in all determina-
tions. The sample introduction system comprised an automatic sampler (SPS3, Varian), a concentric nebulizer, and a double-pass, Scott-type spray chamber. The spray chamber temperature was controlled by a Peltier device and was kept at 2 °C to minimize the formation of oxides. The analyte was monitored at m/z = 28, and the IFS probes were moni-tored at m/z = 37 and 38. Table I presents the operational conditions used in this work.
A cavity microwave oven (Ethos 1600, Milestone-MLS) was used for sample digestion.
Reagents, Standard
Reference Solutions, and Samples
Ultrapure HNO3 was produced using
a sub-boiling distillation system (Mile-stone) and was used in the sample di-gestion and preparation of all standard reference solutions. Hydrogen peroxide (30% m/m, Labsynth) also was used for sample preparation. All standard refer-ence solutions were prepared by dilution of a 1000-mg/L Si stock solution (Que-mis) with distilled–deionized water (18.2 MΩ·cm, Milli-Q, Millipore) in 1% (v/v) HNO3 medium. The external calibra-tion method was used in all determina-tions. All glass, propylene, or PTFE–PFA materials were kept in 10% (v/v) HNO3
overnight and rinsed with distilled–de-ionized water before use. The Ar plasma source was a 99.999% liquid argon Dewar (White Martins). A standard reference material (Typical Diet, SRM 1848a) from
Table I: ICP-QMS operating conditions
Instrumental Parameter Operating Condition
Plasma gas flow rate (L/min) 18.0
Auxiliary gas flow rate (L/min) 1.8
Nebulizer gas flow rate (L/min) 0.93
Sheath gas flow rate (L/min) 0.19
Peristaltic pump rate (rpm) 6
Sampling depth (mm) 5.5
RF power (kW) 1.4
Points per peak 3
Scans per replicate 3
Replicate per sample 5
Dwell time (ms) 10
First extraction lens (V) -1
Second extraction lens (V) -172
Third extraction lens (V) -227
Corner lens (V) -307
Mirror lens right (V) 40
Mirror lens left (V) 35
Mirror lens bottom (V) 27
Entrance lens (V) 0
Fringe bias (V) -2.5
Entrance plate (V) -29
46 Spectroscopy 27(4) April 2012 www.spec t roscopyonl ine .com
the National Institute of Standards and Technology (NIST) was used to check the method accuracy.
Sample Preparation
Approximately 250 mg of Typical Diet was accurately weighted in PTFE–PFA digestion f lasks. Aliquots of 2.5 mL of concentrated ultrapure HNO3
(14 mol/L) were added to each sample replicate (n = 3) and a predigestion pe-riod of 30 min at room temperature was observed. Then, a volume of 2.5 mL of distilled–deionized water was added to each f lask and an additional period of 30 min without heating was observed. Finally, 3.0 mL of H2O2
30% m/m was added to the digestion f lasks, which were then submitted to microwave-assisted digestion in a cav-
ity oven. Analytical blanks were pre-pared in the same manner without any sample. The digestion heating program used consisted of five steps: 250 W for 2 min at 80 °C; 0 W for 3 min at 70 °C; 550 W for 4 min at 120 °C; 650 W for 5 min at 180 °C; and 750 W for 3 min at 200 °C. The solutions were allowed to cool down, transferred to 15-mL poly-ethylene tubes, and diluted to a final volume of 10 mL with distilled–deion-ized water. Further dilution was then carried out just before analysis to en-sure the analyte concentration compat-ibility with the analytical calibration curve linear range.
Results and Discussion
Improving ICP-QMS Accuracy
As previously discussed, the relatively
Table II: Determination of Si in typical diet (NIST SRM 1848a) by ICP-QMS using the external calibration method with or without the IFS correction
Mass-to-Charge Ratio (m/z) Si Content (mg/kg)
Reference Found*
28 78.7 102 ± 14
28/37 79 ± 8
28/38 88 ± 15
*Values are the mean ± 1 standard deviation (n = 3)
1.02
1
0.98
0.96
0.94
0.92
0.9
0.88
Replicate
Rela
tive in
ten
sity
0 5 10 15 20
m/z = 28
m/z = 37
m/z = 38
Figure 1: Signal profiles for a 1% (v/v) solution of HNO3 determined by ICP-QMS in 20 consecutive
April 2012 Spectroscopy 27(4) 47www.spec t roscopyonl ine .com
low resolution of ICP-QMS may be an important limitation for complex ma-trix applications. This aspect is even more critical for elements presenting major isotopes that are prone to severe spectral interferences (10). Silicon’s most abundant isotope at m/z 28 was used to determine this element in a standard reference material and the results for a conventional external calibration with or without applying the IFS method are compared in Table II. For the IFS method, the ratio between signal inten-sities at m/z 28 (analytical signal) and m/z
37 or 38 (IFS) were used for all reference solutions, blanks, and sample replicates.
As we expected, for an organic com-plex matrix submitted to acid digestion with HNO3, overestimated values were obtained for Si determined at m/z 28, because of spectral interferences caused by 14N2
+ and 12C16O+ (11). Using the ex-ternal calibration method without IFS correction, a recovery of 130% was ob-tained for Typical Diet (NIST 1848a). On the other hand, significant accu-racy improvements were observed for determinations using either the 36ArH+ or the 38Ar+ IFS probes. Results for the 28/37 and 28/38 ratios presented no statistically significant difference from the reference value at a 95% confidence level (Table II).
IFS Correction
The IFS method is based on the hypoth-esis that interfering ions and IFS probes present similar behaviors in the plasma (8). A piece of evidence to support this assumption is presented in Figure 1. In this case, a 1% (v/v) HNO3 solution was analyzed by ICP-QMS, and the signals at m/z 28, 37, and 38 were monitored during 20 consecutive measurements. In general, it can be observed that there is a similar signal profile for all m/z monitored, which may indicate similar behaviors for 14N2
+, 36ArH+, and 38Ar+ in the plasma. The correlation coefficients calculated from the data presented in Figure 1 were equal to 0.85 and 0.54 for the 28/37 and 28/38 pairs. The values and results presented in Table II suggest that the closer the rela-tionship between the interfering ion and the IFS probe, the better the accuracy while using the IFS strategy. It is impor-tant to note in this case that variations in
interfering and IFS signals must not only present a relatively linear relationship (that is, a correlation coefficient close to 1), but also have similar magnitudes, which may be the case considering the results in Table II. Another important observation is that an eventual signal overlap at the IFS probe m/z can compromise its efficiency. The IFS probe at m/z 37, for example, will probably have a negative effect on accu-racy while analyzing samples with high concentrations of Cl because of the signal overlap from the 37Cl isotope. In this con-text, an interesting advantage of using the 38Ar+ IFS probe is that it presents no signal
overlap from other species.The mechanism responsible for the
IFS method efficiency is not yet under-stood, but it might be related to thermo-dynamic equilibria in the plasma and to direct and indirect interactions between interfering ions and IFS probes (8). Con-sidering 28Si+ spectral interferences, the possibility of direct interactions between the 36ArH+ IFS probe and interfering precursor species such as N2 and CO may be demonstrated by the reactions represented in equations 1 and 2 (12). In this case, a decrease in the IFS signal would be indirectly related to the same
Table III: Instrumental limits of detection and quantification for the determination of 28Si+ by ICP-QMS using the external calibration method with or without the IFS correction
Mass-to-Charge Ratio (m/z) LOD (μg/L) LOQ (μg/L) BEC (μg/L)
28 6.0 20 102
28/37 5.0 20 107
28/38 8.0 30 114
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behavior for the interfering species, since IFS and interfering ion precursors would be consumed. These reactions are both relatively fast and thermodynami-cally favorable, with ΔH equal to -1026 and -581 kJ/mol, respectively (13).
ArH+ + N2 → N2H+ + Ar k = 8.00 × 10-10 cm3/s [1]
ArH+ + CO → HCO+ + Ar k = 1.25 × 10-9 cm3/s [2]
where k is a rate constant. In addition to these direct reactions, it is possible that both interfering species and IFS probes interact in a similar manner with the same reactant, which would also result in similar signal profiles. This fact is exemplified by equations 3–5 (14–16). It can be observed that the interfering ion N2
+ and the IFS probes 36ArH+ and 38Ar+ present similar rate constant values (k) for reactions with H2O. They also present similar energy variations, that is, -288, -303, and -315 kJ/mol, respectively (13).
N2+ + H2O → H2O+ + N2
k = 2.80 × 10-9 cm3/s [3]
Ar+ + H2O → H2O+ + Ar k = 1.80 × 10-9 cm3/s [4]
ArH+ + H2O → H3O+ + Ar k = 4.50 × 10-9 cm3/s [5]
Analytical Figures of Merit
As it was observed in previous works (8,9), the IFS method has limited impact on sensitivity (Table III). This fact is ex-pected considering that the interfering species are not physically destroyed. On the other hand, a precision depreciation would also be expected by applying the IFS method because another source of noise is being added (the IFS signal). However, depending on the source of noise, this precision depreciation could be negligible, as can be seen in Table II. The precision varies from 14% without the IFS method to 10% and 17% with the 28/37 and 28/38 ratios, respectively. Table III presents the instrumental limits of de-
tection (LOD) and quantification (LOQ) for determinations with or without the IFS method. The LOD was calculated as three times the background equivalent concentration (BEC) and multiplied by the blank relative standard deviation (RSD, n = 20) (17). The BEC was obtained by dividing the Si concentration in one of the calibration curve reference solutions by its respective signal-to-background ratio (SBR). The SBR is the net analytical signal divided by the blank signal. The LOQ was calculated as 10 times BEC, multiplied by the blank RSD (n = 10).
Conclusions
The interference standard method is an interesting alternative to correct spectral interferences in ICP-QMS determinations. No instrumental modification, reagent ad-dition, or introduction of reactive gases is required, which contributes to easy imple-mentation in routine procedures.
It is important to note that the accu-racy improvements observed in this and previous works using the IFS method may be a result of complex mechanisms.
April 2012 Spectroscopy 27(4) 49www.spec t roscopyonl ine .com
This work’s goal is not to elucidate such mechanisms, but to present some pieces of evidence that may contribute to a bet-ter understanding of the method. More fundamental studies and application to different analytical contexts are nec-essary to access the full potential and shortcomings of the IFS strategy.
Acknowledgments The authors would like to thank the Fundação de Amparo à Pesquisa do Es-tado de São Paulo (FAPESP) for grants and fellowships provided (2006/59083-9, 2010/50238-5, and 2010/17387-7). The support from the Instituto Nacional de Ciências e Tecnologias Analíticas Avançadas, Conselho Nacional de De-senvolvimento Científico e Tecnológico (INCTAA, CNPq, and FAPESP) also is greatly appreciated.
For more information on this topic, please visit our homepage at: www.spectroscopyonline.com
www.spec t roscopyonl ine .com50 Spectroscopy 27(4) April 2012
Product resourcesPurity test application noteAn application note from Analytik Jena AG describes a purity test of chemical reagents using the example of As determination in sulfuric acid with the HydrEA hydride–graphite furnace technique. According to the publication, the method is suited for analysis of hydride-forming elements. Analytik Jena AG, Jena, Germany;www.analytik-jena.de
Fiber-optic Raman probesFiberTech Optica’s compact fiber-optic Raman probes are designed to provide enhanced throughput and collected Raman signal quality. According to the company, design features include high collection efficiency (f/2) optics, internal filtering, about 4 mm working distance, and assembly with no moving parts. FiberTech Optica, Kitchener, Ontario, Canada; www.fibertech-optica.com
PolarizerMoxtek’s 3D enabling pixilated polarizer is designed for use with the visible, UV, and IR light spectrum. According to the company, the polarizer allows the user to obtain 3D information and gather Stokes information, overlaid real-time into an image. Moxtek, Orem, UT; www.moxtek.com
Hyperspectral imaging cameraHoriba Scientific’s Verde hyperspectral imaging camera is designed to measure complete image and spectral information simultaneously. According to the company, the camera can capture the complete spectrum of every point in an image in a single measurement in 3 ms. The camera reportedly uses a 2-D dispersion element to capture all spatial and spectral information, and no averaging or repeated experiments are required. The camera has no moving parts. Possible applications are field work and industrial quality control, including plasma monitoring in semiconductor foundries, color quality control for fabrics, paints, foods, computer monitors, and televisions. Horiba Scientific, Edison, NJ; www.horiba.com
SpectrometerThe AvaSpec-ULS2048XL spectrometer from Avantes is designed to combine the quantum efficiency of a back-thinned CCD detector with the electronic control required for high-speed applications such as LIBS and pulsed light source measurements. According to the company, the instruments’ detector has 2048 pixels, each measuring 14 × 500 µm.Avantes, Broomfield, CO; www.avantes.com
Diode sourceHoriba’s DeltaDiodes diode source is designed to use laser diode and LED technology to generate short optical pulses down to 50 ps with repetition rates up to 100 MHz over a range of wavelengths. According to the company, the system has a USB interface and hot-swappable, plug-and-play heads. Horiba Scientific, Edison, NJ; www.horiba.com
IR gas cellThe 2.4 m IR gas cell from PIKE is designed with a metal body (nickel-coated aluminum), diamond-turned optics, and tool-less assembly and disassembly. According to the company, features include small volume and a heated 200 °C option for analysis of samples containing low-boiling-point components and water vapor. PIKE Technologies, Madison, WI; www.piketech.com
EDXRF elemental analyzerRigaku’s benchtop energy dispersive X-ray fluorescence spectrometer is designed as a compact elemental analyzer that can deliver quantitative determination of sodium to uranium in solids, liquids, powders, and thin films. According to the company, the analyzer has a single-position sample stage with three analysis spot size options — 3 mm, 8 mm, and 14 mm — that are changeable. Rigaku Corporation, The Woodlands, TX; www.rigaku.com
www.spec t roscopyonl ine .com April 2012 Spectroscopy 27(4) 51
EDXRF spectrometerShimadzu’s EDX-LE energy dispersive X-ray fluorescence spectrometer is designed for screening elements regulated by RoHS/ELV directives. According to the company, the spectrometer is equipped with automated analysis functions and a detector that does not require liquid nitrogen. Shimadzu Scientific Instruments, Columbia, MD; www.ssi.shimadzu.com
Polymer identification and quantificationB&W Tek’s PolymerIQ system for identification and quantification of polymers combines the company’s iRaman high-resolution portable Raman system with Gnosys’ chemometric software. According to the company, the software enables users to perform multivariate statistical analysis to relate spectral information to the chemistry, properties, and metrics of interest. B&W Tek, Newark, DE; www.bwtek.com
ICP-MS systemPerkinElmer’s NexION 300 ICP-MS system is designed to provide the benefits of a collision cell and the detection limits of a true reaction cell. According to the company, the instrument can be run in three different modes: standard, collision, and reaction. A scanning quadrupole reportedly removes targeted interferences and reaction products in the universal cell. PerkinElmer, Waltham, MA; www.perkinelmer.com
Raman analyzerThe ASSUR handheld Raman analyzer for raw material verification from Enwave Optronics is designed to be fully 21 CFR Part 11 compliant for GMP requirements. According to the company, it is suitable for the analysis of pharmaceutical compounds and industrial chemicals and for applications requiring high-speed Raman analysis. Enwave Optronics, Inc., Irvine, CA; www.enwaveopt.com
Variable-pathlength FT-IR cellInternational Crystal’s heated variable-pathlength cell is designed with the ability to be heated to 200 °C and comes with a programmable temperature controller with a RS-232 serial cable computer interface. According to the company, the cell back plate is water-cooled, and the body and vernier scale adjustment on the front of the cell are insulated to enable in situ variation of the pathlength while the cell is situated in the sample compartment of an FT-IR spectrophotometer. International Crystal Laboratories, Garfield, NJ; www.internationalcrystal.net
Hyperspectral imaging systemThe HI 90 hyperspectral imaging system from Bruker Optics is designed as a remote chemical sensing system that permits detection, identification, and quantification of hazardous gas clouds. According to the company, the system allows remote operation several kilometers from the area of interest. Bruker Optics, Billerica, MA; www.brukeroptics.com
ICP-MS systemThermo Fisher Scientific’s iCAP Q ICP-MS system is designed to provide increased throughput to enable laboratories to cut analysis times by up to 50%. According to the company, the system features an interface that enables one-click setup and allows users to go from standby to performance-qualified analysis with the push of a button. Thermo Fisher Scientific, Inc., San Jose, CA; www.thermoscientific.com
Wine standardsCertified reference materials for trace metals analysis in a natural wine matrix are available from Spex Certiprep. The company is reportedly accredited by UL DQS for ISO 9001 and accredited by A2LA for ISO 17025 and ISO Guide 34. Spex Certiprep, Metuchen, NJ; www.spexcertiprep.com
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XRF kitAmptekÕs XRF Kit is designed to help users begin performing elemental analysis via X-ray fluorescence. According to the company, the kit includes the companyÕs X-123 complete spectrometer with an SDD or Si-PIN detector; a mini-X USB controlled X-ray tube; XRF-FP QA software; a mounting plate; and a test sample. Amptek Inc., Bedford, MA; www.amptek.com
Mercury analyzersThe Hydra II mercury analyzers from Teledyne Leeman Labs are designed to provide configuration flexibility. According to the company, the analyzers can be configured to conduct the analysis of liquids by sample digestion followed by cold vapor atomic absorption or cold vapor atomic fluorescence, and the direct analysis of solid or semisolid sample matrices through thermal decomposition followed by cold vapor atomic absorption. Teledyne Leeman Labs, Hudson, NH; www.LeemanLabs.com
ICP-MS systemThe Agilent 8800 triple-quadrupole ICP-MS system is designed to provide improved performance compared with single-quadrupole ICP-MS and to provide MS-MS operation for interference removal in reaction mode. According to the company, the system can be used to analyze elements in life-science, soil, rock, and plant materials. The system reportedly also can be set up to operate like a single-quadrupole ICP-MS system. Agilent Technologies, Santa Clara, CA; www.agilent.com
FT-NIR analyzersPrecalibrated FT-NIR analyzers from Thermo Fisher Scientific feature the Antaris II FT-NIR analyzer and are designed for feed and ingredient analysis in agriculture and for flour and milling analysis in the food processing industry. According to the company, the analyzers can analyze multiple components simultaneously without consumables, chemicals, or disposables. Thermo Fisher Scientific, Inc., Madison, WI; www.thermofisher.com
Automated circular dichroism spectrometerThe Chirascan-plus ACD spectrometer from Applied Photophysics is designed to provide flexible, robust automation and simultaneous measurement of circular dichroism and absorbance. According to the company, the spectrometerÕs software provides automation control, application development, and integrated spectroscopic analysis tools. Applied Photophysics Ltd., Leatherhead, UK; www.photophysics.com
Diffraction gratingsZeiss mechanically ruled or holographically recorded diffraction gratings from Hellma are designed using holographic exposure systems and ultrahigh-precision ruling engines. According to the company, the gratings are used with monochromators, spectographs, spectrophotometers, dye lasers, and other laser types. Hellma, Plainview, NY; www.hellmausa.com
Microvolume UV spectrophotometerShimadzuÕs BioSpec-nano UV spectrophotometer is designed to be a dedicated instrument for reproducible concentration determination of nucleic acids and proteins. According to the company, the instrument requires 1 µL (pathlength: 0.2 mm) or 2 µL (pathlength: 0.7 mm) of sample, which is pipetted onto the measurement plate, and for ultrasmall sample volumes, no standard rectangular cell is needed. Shimadzu Scientific Instruments, Columbia, MD; www.ssi.shimadzu.com
XRD analyzerRigakuÕs MiniFlex general-purpose X-ray diffractometer is designed to perform qualitative and quantitative analysis of polycrystalline materials. According to the company, the benchtop instrument is available in two variations Ñ a 600-W model and a 300-W model that does not require an external heat exchanger. Rigaku Corporation, The Woodlands, TX; www.rigaku.com
www.spec t roscopyonl ine .com April 2012 Spectroscopy 27(4) 53
Vis–NIR spectrometerThe Maya2000 Pro-Vis–NIR system from Ocean Optics is designed as a back-thinned 2D FFT-CCD spectrometer with 80% peak quantum efficiency. According to the company, the device has a low-etalon, scientific-grade detector that provides high quantum efficiency from ~400 nm to 1100 nm. Ocean Optics, Dunedin, FL; www.oceanoptics.com
Infrared filtersInfrared filters from CVI Melles Griot are manufactured using a sputter coating process and are designed for durability and environmental resistance. According to the company, the filters are optimized for peak transmission and blocking. The filters can be used for applications such as gas analyzers, nondispersive, or Fourier-transform instruments, spectrometers, and biomedical devices. CVI Melles Griot, Albuquerque, NM; www.cvimellesgriot.com
Photovoltaic measurement systemNewport Corporation’s Oriel IQE-200 photovoltaic cell measurement system is designed for simultane-ous measurement of the external and internal quantum efficiency of solar cells, detectors, and other pho-ton-to-charge converting devices. The system reportedly splits the beam to allow for concurrent measurements. The system includes a light source, a monochroma-tor, and related electronics and software. According to the company, the system can be used for the measurement of silicon-based cells, amorphous and mono/poly crystalline, thin-film cells, copper indium gallium diselenide, and cadmium telluride. Newport Corporation, Irvine, CA; www.newport.com
Dual laser source Raman modulePD-LD’s LS-2 LabSource benchtop Raman module has two laser sources. According to the company, the laser sources are VBG-stabilized, and are available in standard and custom wavelengths. When the two laser sources’ wavelengths are closely spaced together, the module reportedly is capable of performing surface enhanced Raman differential spectroscopy. PD-LD, Inc., Pennington, NJ; www.pd-ld.com
Mircowave plasma–atomic emission spectrometerThe model 4100 microwave plasma–atomic emission spectrometer from Agilent is designed to run on air. According to the company, the device uses a nitrogen-based plasma that runs on air and does not require external cylinder connections or gases. Agilent Technologies, Santa Clara, CA;www.agilent.com
Dichroic shortpass filtersEdmund Optics’ Techspec dichroic shortpass filters are designed for a 45° angle of incidence. According to the company, light rejected by the filter is reflected at 90°. The filters reportedly feature low polarization dependence, broad spectral ranges, and a precision fused-silica substrate. The filters can be used in fluorescence applications and as spectral beamsplitters. Edmund Optics, Barrington, NJ; www.edmundoptics.com
Chemical reaction monitorThe MB-Rx in-situ chemical reaction monitor from ABB Analytical Measurements is designed for use in laboratories and pilot plants. According to the company, the monitor is a plug-and-play system that provides real-time information about chemical or biochemical reaction kinetics and parameters. Data reportedly are collected with an insertion probe and can be analyzed via a software interface. ABB Analytical Measurements, Quebec, Canada;www.abb.com/analytical
Ruling enginesOptometrics reportedly has upgraded its three ruling engines. According to the company, its premier ruling engine has been outfitted with interferometric, thermal, and electronic control systems and is driven through a computer-controlled operation, allowing for ghost-free UV–vis rulings and custom rulings at wavelengths ranging from UV to far IR. Optometrics, Ayer, MA; www.optometrics.com
www.spec t roscopyonl ine .com54 Spectroscopy 27(4) April 2012
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n How to measure spectral reflectance and how to transform
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n How to handle traceability and uncertainty issues
PRESENTERS
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Avian Rochester, LLC
Art Springsteen, PhD
Avian Technologies, LLC
MODERATOR
Laura Bush
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Who Should Attend:
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www.picoquant.com/_events.htm
August
13–15 Mass Spectral Interpretation
Atlantic City, NJ
pacslabs.com
21–23 Infrared Spectral Interpretation
Atlantic City, NJ
pacslabs.com
30 Fluorescence Spectroscopy
Easton, MD
www.jascoinc.com/Training/Fluores-
cence-Spectroscopy.aspx
September
5–7 Single Molecule Spectroscopy and
Ultra Sensitive Analysis in the Life Sciences
Berlin, Germany
www.picoquant.com/_events.htm
October
2 Analytical Raman Spectroscopy
Kansas City, MO
facss.org/contentmgr/showdetails.php/id/458
2 Forensic Science: Microscopy in Trace
Analysis
Kansas City, MO
facss.org/contentmgr/showdetails.php/
id/39176
2 Raman Chemical Imaging Technologies
and Methods
Kansas City, MO
facss.org/contentmgr/showdetails.php/
id/38658
3 Infrared Spectral Interpretation:
A Strategic Approach
Kansas City, MO
facss.org/contentmgr/showdetails.php/
id/38233
3 Professional Analytical Chemists
in Industry: A Short Course
for Undergraduate Students
Kansas City, MO
facss.org/contentmgr/showdetails.php/
id/38659
4 Hands-on Chemometric Analysis
with the Unscrambler
Kansas City, MO
facss.org/contentmgr/showdetails.php/
id/38663
29–1 November Principles and
Applications of Time-Resolved Fluo-
rescence Spectroscopy
Baltimore, MD
www.picoquant.com/_events.htm
Showcase
April 2012 Spectroscopy 27(4) 57www.spec t roscopyonl ine .com®
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www.spec t roscopyonl ine .com58 Spectroscopy 27(4) April 2012
What types of photovoltaic materials do you analyze with X-ray fluorescence? And do you use other X-ray techniques as well?Brubaker: For years, my colleagues and I within DuPont’s Corporate Center for Analytical Sciences have provided analytical problem solving to support the development and manufacture of materials that are widely used in today’s pho-tovoltaic modules. Examples of such products include fluo-ropolymer and polyvinyl fluoride materials for frontsheet and backsheet applications, respectively, as well as pastes used for metallization within modules.
The intensification of research and development (R&D) efforts across the photovoltaics industry has presented new opportuni-ties to showcase the capabilities of X-ray analysis. For example, in the development of new active-layer materials, X-ray fluorescence (XRF) spectroscopy is used to determine elemental stoichiometry, while X-ray absorption near edge structure (XANES) fingerprint-ing provides for quantitative phase speciation in this system. X-ray reflectivity (XRR), like XRF, can measure the thickness of thin films but can also go one step further than XRF by providing direct measurements of a film’s density. The complementary information from these various X-ray techniques allows our re-searchers valuable insights into their new materials.
Are there challenges in using XRF for this work?Brubaker: XRF is often selected for photovoltaic-related mea-surements because of its well-known attributes: It can be fast, highly quantitative, and often performed with little to no sam-ple preparation. Also, compared to other elemental analysis techniques, such as inductively coupled plasma (ICP) spectros-copy or neutron activation analysis (NAA), XRF has a much greater potential to be used in at-line or online testing after materials advance beyond the R&D phase and into the realm of manufacturing and commercialization. It is much easier to transfer a method from the laboratory to the frontlines when the basic underlying technology remains the same.
I regularly tell colleagues that the most frequent challenge with developing quantitative XRF methods (perhaps 9 times out of 10) is acquiring acceptable standards for calibration, and this is particularly true with photovoltaic-related materials.
The most accurate methods require well-characterized, matrix-matched standards because of XRF’s susceptibility to inter- element and matrix effects. Samples related to our photovoltaic research tend to be unique and “first of a kind,” meaning that standards are seldom available. We often have to characterize some samples by other techniques, such as ICP and NAA, so that those samples may ultimately serve as our standards. Other times, we have to be a bit more creative in coming up with stan-dards. In one case, for example, we developed a fusion prepara-tion that enabled us to make our own standards.
Could you explain how you use XRF for the characterization and quality control of metallization pastes?Brubaker: Our metallization pastes represent a wide range of compositions, each highly specialized for an intended applica-tion. Pastes for frontside metallizations, for example, consist primarily of silver, whereas aluminum tends to be the major component of backside pastes. Various glass frit materials are also introduced into compositions to provide adhesion prop-erties when an end user fires a paste onto a substrate.
These pastes are largely inorganic in nature; therefore, XRF provides an excellent means for their elemental characterization. By XRF we can measure a paste nondestructively —“as received” on just a thin polypropylene film support — allowing the sample to be retained for additional testing and thus conserving as much of the high valued material as possible. (Remember that silver is not cheap these days.) High-concentration elements such as Ag and Al are best determined by XRF’s fundamental parameters methodology; at the same time, a minimal amount of sample also has to be analyzed by ICP to determine any trace elements and contaminants, given the lack of XRF standards for such a variety of paste compositions. We have found this complemen-tary approach of using both XRF and ICP gives our teams the most complete and efficient “elemental picture” of the pastes.
This interview was edited for length and clarity.
For the full interview with Brubaker, please visit:
www.spectroscopyonline.com/Brubaker ◾
As interest increases in expanding the use of renewable energy, compa-nies like DuPont are developing new photovoltaic materials for use in solar panels. Spectroscopy recently spoke to Dr. Wayne Brubaker, senior research chemist at Dupont’s Corporate Center for Analytical Sciences, about the use of X-ray fluorescence spectroscopy in the analysis of those materials, during both development and manufacturing.
X-ray Fluorescence Analysis Advances the Development and Manufacture of Photovoltaic Materials
Elemental Speciation Made Easy and Robust
with Separations (HPLC and GC) Systems
Interfaced to an Agilent ICP-MS
ON-DEMAND WEBCAST
Register free at www.spectroscopyonline.com/elemental
EVENT OVERVIEW:
Elemental speciation has come into greater demand in environmen-
tal, food, clinical, and life science applications, and rapid, robust instru-
mental methods that are user-friendly have been needed to meet
this demand. New hyphenated systems such as the Agilent Bio-inert
1260 HPLC system interfaced to a 7700 ICPMS have been introduced
to meet these needs.
User-friendly innovations such as fully integrated chromatographic
control in the ICP-MS software, High Matrix Introduction, and the
Octapole Reaction system to remove eluant interferences increase
productivity and confidence in results. The ease with which an Agilent
HPLC or GC is interfaced to and controlled by the 7700 ICP-MS along
with data processing will be discussed.
Several hardware and software tools will be discussed such as micro-
bore LC columns, which improve chromatographic resolution and pro-
duce more robust plasmas as a result of lower solvent loading. Soft-
ware tools such as Compound Independent Calibration (CIC) allow
quantitation of difficult-to-find species without even having them in
calibration standards.
Straightforward speciation experimental setup and applications for
As, Se, Cr, and Hg species in a number of matrices will be presented,
including solutions for the recently proposed USP regulations.
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