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Ryan Hatch, Tanner Hunt, Steven Rupp, Hyrum Wendel · Introduction This project aims to utilize the...

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Introduction This project aims to utilize the advantages of a microfluidic system to generate a testing apparatus that can be used in drug trials to observe the effects of different concentrations of nutrients or drugs on cell growth. With this proposed device drug research companies can save time, materials and money during the research and development phase. Critical aspects of the project include three areas: simulation, fabrication, and validation. This project is supported by GE Healthcare and is a continuation of a senior design project completed in the 2015 school year. By utilizing the COMSOL modeling software to simulate the microfluidic flow, accurate alterations can be made to the previous design to enhance the accuracy of the concentration gradient generator (CGG). The CGG will then be combined with a cell culturing well array to test and analyze cell growth in various concentrations. Background/Literature Review It was determined that microfluidic devices will be made using both photolithography and 3D printing. Photolithography Widely used in microfluidics Highly accurate Reusable 3D Printing Widely available Inexpensive Rapid prototyping Reusable Varying depths of channels and cell wells are easily made Acknowledgments Ryan Hatch, Tanner Hunt, Steven Rupp, Hyrum Wendel Utah State University, Logan UT, 84322 Literature Cited Objectives Simulation: Develop complete, multi-physics models in COMSOL to predict channel mixing and appropriate channel length. Fabrication: Following the procedures outlined and dimensions within COMSOL and CAD, create a working system. Validation: Ensure the concentration levels are within a range of 5% and repeat until it fits the criteria. Ensure that cell array is capable of viable cell growth. Criteria Simulation: Using COMSOL, achieve a theoretical concentrations of 0%, 25%, 50%, 75%, and 100% in channel outputs. Fabrication: Replicate the models using photolithographyand 3D printing without leaks and complete glass adhesion. Validation: Use a spectrophotometer to verify less than 5% error in the CGG. Have cell count within 10% of the control flasks. AV Plastics. 3D Printing History. Avplastics. Dec. 5 2016. Barentine, Charles, Nathan Hebert, Ryan Putman, Donald Wooley. Microfluidic Device for Cell Culture Optimization. December 16, 2015. Utah State University. Beebe D. J., Mensing GA, Walker GM. Physics and applications of microfluidics in biology. Annu Rev Biomed Eng. 2002;4:261-86., De jong J, Lammertink RG, Wessling M. Membranes aGravevesin, P. "Microfluidic a Review." N.p., n.d. Web.nd microfluidics: a review. Lab Chip. 2006;6(9):1125-39. Beebe, David J., Glennys A. Mensing, and Glenn M. Walker. "Physics and applications of microfluidics in biology." Annual review of biomedical engineering 4.1 (2002): 261-286. Chung, B. G., Manbachi, A., & Khademhosseini, A. (2007). A Microfluidic Device with Groove Patterns for Studying Cellular Behavior. Journal of Visualized Experiments : JoVE, (7), 270. Advance online publication. Ferry, M. S., Razinkov, I. A., & Hasty, J. (2011). Microfluidics for Synthetic Biology: From Design to Execution. Methods in Enzymology, 497, 295–372. Foote, Robert S., Khandurina, Julia, Ramsey, J. Michael. (2005). Preconcentration of proteins on microfluidic devices using porous silica membranes. Analytical Chemistry, 77, 57-63. Goldberg, Dana. History of 3D Printing: It’s Older Than You Are (That Is, If You’re Under 30). Redshift. September 5, 2014. Gravevesin, P. "Microfluidic a Review." N.p., n.d. Web. Huang, H., Jiang, L., Li, S., Deng, J., Li, Y., Yao, J., … Zheng, J. (2014). Using microfluidic chip to form brain-derived neurotrophic factor concentration gradient for studying neuron axon guidance. Biomicrofluidics, 8(1), 014108. Kim, P., Kwon, K., Park, M., Lee, S., Kim, S., & Suh, K. (2008). Soft lithography for microfluidics: A review. Biochip Journal, 2(1), 1-11. Kim, Samuel C., Stefano Cestellos-Blanco, Keisuke Inoue, Richard N. Zare. Miniaturized Antimicrobial Susceptibility Test by Combining Concentration Gradient Generation and Rapid Cell Culturing. Antibiotics 2015, 4, 455-456. October 29, 2015. Long T, Ford RM (2009) Enhanced transverse migration of bacteria by chemotaxis in a porous T-Sensor. Environ Sci Technol 43(5):1546–1552 Park JY, Hwang CM et al (2007) Gradient generation by an osmotic pump and the behavior of human mesenchymal stem cells under the fetal bovine serum concentration gradient. Lab Chip 7(12):1673–1680 Reusch, William. "UV-Visible Spectroscopy." MSU Chemisty. Msu, 5 May 2013. Web. 01 June 2016. Rexius, M. L., Mauleon, G., Malik, A. B., Rehman, J., & Eddington, D. T. (2014). Microfluidic Platform Generates Oxygen Landscapes for Localized Hypoxic Activation. Lab on a Chip, 14(24), 4688–4695. S.C.Terry, J.H.Jerman and J.B.Angell:A Gas Chromatographic Air Analyzer Fabricated on a Silicon Wafer,IEEE Trans.Electron Devices,ED-26,12(1979)1880–1886. Tehranirokh, Masoomeh et al. “Microfluidic Devices for Cell Cultivation and Proliferation.” Biomicrofluidics 7.5 (2013): 051502. PMC. Web. 9 Dec. 2016. Toh, Alicia G. G. Wang, Z.P, Yang. Chun and Nguyen, Nam-Trung (2013) Engineering microfluidic concentration gradient generators for biological applications. Microfluid Nanofluid 16:1–18 Volpatti, Lisa R., Ali K. Yetisen, Commercialization of microfluidic devices. Trends in Biotechnology. Volume 32, Issue 7, July 2014, Pages 347–350. Walsh CL, Babin BM et al (2009) A multipurpose microfluidic device designed to mimic microenvironment gradients and develop targeted cancer therapeutics. Lab Chip 9(4):545– 554 Wag, Y., Mukherjee, T., & Lin, Q. (2006). Systematic modeling of microfluidic concentration gradient generators. Journal of Micromechanics and Microengineering, 2128-2137. Web Whitesides, G. (2006). The origins and the future of microfluidics. Nature, 442, 368-373. Web Young, Edmond WK, and David J. Beebe. "Fundamentals of microfluidic cell culture in controlled microenvironments." Chemical Society Reviews 39.3 (2010): 1036-1048. Design Process Simulation AutoCAD was used to design the devices and was exported to COMSOL Multiphysics. Laminar flow and diluted species physics were used to calculate the concentration gradient across the device. Validation CGG Fabrication Validation Cell Culture Cells were grown in stock solutions of 0%, 25%, 50%, 75%, and 100%. (right) as a control, and compared to final results. Confluence was observed and recorded after 24 hours, and confluence in the microfluidic devices was compared to controls. As seen in the table on the left, most of the cell wells did not show the amount of confluent cells expected in each cell well and cell growth in the devices were not comparable to control growth. The device did not meet the evaluation criteria for any of the CGG channels, besides the 0% and 25% which, from observing the control flasks, were not expected to have any substantial growth. It was determined that a higher concentration of cells is needed in loading to provide an adequate amount of cell confluence to compare to control cell growth. Final Design The production of a CGG microfluidic device was successful. The concentration was within 5% of the theoretical values in channels 1, 3, and 5. Most cells died after 2 days and were not comparable to control stock. Conclusions The outputs from the final device were 0.1%, 17.3%, 51.9%, 80.7%, and 97.6%. Though these were close to the desired values, they did not meet the evaluation criteria in channels 2 and 4. The CGG design did show improvements when compared to the design of the previous group. The cell well array was a major source of error for this final design. The final confluent cells were not comparable to the control batches of cells created. It was determined that the error resulted from an insufficient cell concentration that was originally injected into the device. One major accomplishment of this project was the proven validity of utilizing a 3D printer to create a mold for a microfluidic device, providing that the 3D printer has adequate accuracy. This finding enables the creation of custom microfluidic devices for any company or institution with access to a 3D printer. More benefits from 3D printing includes rapid prototype and iterative testing, as well as drastically reduced costs. Dr. Anhong Zhou providing funding and being the group mentor. GE Healthcare for materials and time spent advising on this project. Dr. T. C. Shen for help exposing the photolithography masters. Han Zhang for explaining proper procedure for the CGG production and on cell culturing. Dr. Timothy Taylor for consulting and advising us on all aspects of the project. Photolithography Master: Photolithography uses photoresist to make a design on a wafer. Photoresist is a liquid that solidifies when exposed to UV light. Using a photomask to direct the exposure of the photoresist allows for controlled production of a master. An example of a photomask and its resulting master are shown below. 3D Master: 3D printing is an emerging application that was experimented with in this project. A CAD file is designed to the desired specifications and then a 3D model can be printed this is a cheep and time saving alternative to the photolithographical production method. Examples of a 3D printed master is shown below. PDMS and Plasma Bonding: After the master is produced PDMS is allowed to cure on the master copy to create a negative imprint of the design. The PDMS is then bonded to a glass slide using a Plasma etcher to ionize the surface and form a bond. The hollow areas are then used as the desired channels. A completed microfluidic device is shown below. Microfluidic chip are made by producing a master, and making a PDMS imprint of the master to form the channels. The absorbance values were obtained, and the concentrations were calculated, and the values were compared (shown below). Upon the completion of the microfluidic devices, dye and distilled water were ran through the device and the output concentrations were collected and tested for accuracy using spectroscopy. In the figure above, the concentrations generated are shown. The shown concentrations are from the 3D printed device (left), the photolithographic device (middle), and the controls used to calculate concentrations (right). The absorbance values were obtained, and the concentrations were calculated, and the values were compared (shown below). Statistical t-tests were performed using percent error of each trial, and the two designs were proven to be statistically comparable. Above is shown the COMSOL concentration gradient profile with the channel outlet concentration percentage values. To the left is shown the AutoCAD design. The AutoCAD design was sent to make a photomask for photolithography methods and extruded to be 3D printed.
Transcript
Page 1: Ryan Hatch, Tanner Hunt, Steven Rupp, Hyrum Wendel · Introduction This project aims to utilize the advantages of a microfluidic system to generate a testing apparatus that can be

IntroductionThis project aims to utilize the advantages of a microfluidic

system to generate a testing apparatus that can be used in drug

trials to observe the effects of different concentrations of

nutrients or drugs on cell growth. With this proposed device drug

research companies can save time, materials and money during

the research and development phase. Critical aspects of the

project include three areas: simulation, fabrication, and

validation. This project is supported by GE Healthcare and is a

continuation of a senior design project completed in the 2015

school year. By utilizing the COMSOL modeling software to

simulate the microfluidic flow, accurate alterations can be made

to the previous design to enhance the accuracy of the

concentration gradient generator (CGG). The CGG will then be

combined with a cell culturing well array to test and analyze cell

growth in various concentrations.

Background/Literature Review

It was determined that microfluidic devices will be made using

both photolithography and 3D printing.

Photolithography• Widely used in

microfluidics

• Highly accurate

• Reusable

3D Printing• Widely available

• Inexpensive

• Rapid prototyping

• Reusable

• Varying depths of channels

and cell wells are easily

made

Acknowledgments

Ryan Hatch, Tanner Hunt, Steven Rupp, Hyrum WendelUtah State University, Logan UT, 84322

Literature Cited

ObjectivesSimulation:

Develop complete, multi-physics models in COMSOL

to predict channel mixing and appropriate channel length.

Fabrication:

Following the procedures outlined and dimensions within

COMSOL and CAD, create a working system.

Validation:

Ensure the concentration levels are within a range of 5%

and repeat until it fits the criteria. Ensure that cell array is

capable of viable cell growth.

CriteriaSimulation:

Using COMSOL, achieve a theoretical concentrations of

0%, 25%, 50%, 75%, and 100% in channel outputs.

Fabrication:

Replicate the models using photolithographyand 3D

printing without leaks and complete glass adhesion.

Validation:

Use a spectrophotometer to verify less than 5% error in

the CGG.

Have cell count within 10% of the control flasks.AV Plastics. 3D Printing History. Avplastics. Dec. 5 2016.

Barentine, Charles, Nathan Hebert, Ryan Putman, Donald Wooley. Microfluidic Device for Cell Culture

Optimization. December 16, 2015. Utah State University.

Beebe D. J., Mensing GA, Walker GM. Physics and applications of microfluidics in biology. Annu Rev

Biomed Eng. 2002;4:261-86., De jong J, Lammertink RG, Wessling M. Membranes

aGravevesin, P. "Microfluidic a Review." N.p., n.d. Web.nd microfluidics: a review. Lab Chip.

2006;6(9):1125-39.

Beebe, David J., Glennys A. Mensing, and Glenn M. Walker. "Physics and applications of microfluidics

in biology." Annual review of biomedical engineering 4.1 (2002): 261-286.

Chung, B. G., Manbachi, A., & Khademhosseini, A. (2007). A Microfluidic Device with Groove Patterns

for Studying Cellular Behavior. Journal of Visualized Experiments : JoVE, (7), 270. Advance

online publication.

Ferry, M. S., Razinkov, I. A., & Hasty, J. (2011). Microfluidics for Synthetic Biology: From Design to

Execution. Methods in Enzymology, 497, 295–372.

Foote, Robert S., Khandurina, Julia, Ramsey, J. Michael. (2005). Preconcentration of proteins on

microfluidic devices using porous silica membranes. Analytical Chemistry, 77, 57-63.

Goldberg, Dana. History of 3D Printing: It’s Older Than You Are (That Is, If You’re Under 30). Redshift.

September 5, 2014.

Gravevesin, P. "Microfluidic a Review." N.p., n.d. Web.

Huang, H., Jiang, L., Li, S., Deng, J., Li, Y., Yao, J., … Zheng, J. (2014). Using microfluidic chip to form

brain-derived neurotrophic factor concentration gradient for studying neuron axon

guidance. Biomicrofluidics, 8(1), 014108.

Kim, P., Kwon, K., Park, M., Lee, S., Kim, S., & Suh, K. (2008). Soft lithography for microfluidics: A

review. Biochip Journal, 2(1), 1-11.

Kim, Samuel C., Stefano Cestellos-Blanco, Keisuke Inoue, Richard N. Zare. Miniaturized Antimicrobial

Susceptibility Test by Combining Concentration Gradient Generation and Rapid Cell

Culturing. Antibiotics 2015, 4, 455-456. October 29, 2015.

Long T, Ford RM (2009) Enhanced transverse migration of bacteria by chemotaxis in a porous T-Sensor.

Environ Sci Technol 43(5):1546–1552

Park JY, Hwang CM et al (2007) Gradient generation by an osmotic pump and the behavior of human

mesenchymal stem cells under the fetal bovine serum concentration gradient. Lab Chip

7(12):1673–1680

Reusch, William. "UV-Visible Spectroscopy." MSU Chemisty. Msu, 5 May 2013. Web. 01 June 2016.

Rexius, M. L., Mauleon, G., Malik, A. B., Rehman, J., & Eddington, D. T. (2014). Microfluidic Platform

Generates Oxygen Landscapes for Localized Hypoxic Activation. Lab on a Chip, 14(24),

4688–4695.

S.C.Terry, J.H.Jerman and J.B.Angell:A Gas Chromatographic Air Analyzer Fabricated on a Silicon

Wafer,IEEE Trans.Electron Devices,ED-26,12(1979)1880–1886.

Tehranirokh, Masoomeh et al. “Microfluidic Devices for Cell Cultivation and

Proliferation.” Biomicrofluidics 7.5 (2013): 051502. PMC. Web. 9 Dec. 2016.

Toh, Alicia G. G. Wang, Z.P, Yang. Chun and Nguyen, Nam-Trung (2013) Engineering microfluidic

concentration gradient generators for biological applications. Microfluid Nanofluid 16:1–18

Volpatti, Lisa R., Ali K. Yetisen, Commercialization of microfluidic devices. Trends in Biotechnology.

Volume 32, Issue 7, July 2014, Pages 347–350.

Walsh CL, Babin BM et al (2009) A multipurpose microfluidic device designed to mimic

microenvironment gradients and develop targeted cancer therapeutics. Lab Chip 9(4):545–

554

Wag, Y., Mukherjee, T., & Lin, Q. (2006). Systematic modeling of microfluidic concentration gradient

generators. Journal of Micromechanics and Microengineering, 2128-2137. Web

Whitesides, G. (2006). The origins and the future of microfluidics. Nature, 442, 368-373. Web

Young, Edmond WK, and David J. Beebe. "Fundamentals of microfluidic cell culture in controlled

microenvironments." Chemical Society Reviews 39.3 (2010): 1036-1048.

Design Process

SimulationAutoCAD was used to design the devices and was exported

to COMSOL Multiphysics. Laminar flow and diluted species

physics were used to calculate the concentration gradient

across the device.

Validation CGG

Fabrication Validation Cell CultureCells were grown in stock solutions of 0%, 25%,

50%, 75%, and 100%. (right) as a control, and

compared to final results.

Confluence was observed and recorded after 24 hours, and confluence in the

microfluidic devices was compared to controls. As seen in the table on the left,

most of the cell wells did not show the amount of confluent cells expected in each

cell well and cell growth in the devices were not comparable to control growth.

The device did not meet the evaluation criteria for any of the CGG channels,

besides the 0% and 25% which, from observing the control flasks, were not

expected to have any substantial growth. It was determined that a higher

concentration of cells is needed in loading to provide an adequate amount of cell

confluence to compare to control cell growth.

Final Design

The production of a CGG microfluidic device was successful. The

concentration was within 5% of the theoretical values in channels 1, 3, and 5.

Most cells died after 2 days and were not comparable to control stock.

ConclusionsThe outputs from the final device were 0.1%, 17.3%, 51.9%, 80.7%,

and 97.6%. Though these were close to the desired values, they did

not meet the evaluation criteria in channels 2 and 4. The CGG design

did show improvements when compared to the design of the

previous group.

The cell well array was a major source of error for this final design.

The final confluent cells were not comparable to the control batches

of cells created. It was determined that the error resulted from an

insufficient cell concentration that was originally injected into the

device.

One major accomplishment of this project was the proven validity of

utilizing a 3D printer to create a mold for a microfluidic device,

providing that the 3D printer has adequate accuracy. This finding

enables the creation of custom microfluidic devices for any company

or institution with access to a 3D printer. More benefits from 3D

printing includes rapid prototype and iterative testing, as well as

drastically reduced costs.

• Dr. Anhong Zhou providing funding and being the group mentor.

• GE Healthcare for materials and time spent advising on this project.

• Dr. T. C. Shen for help exposing the photolithography masters.

• Han Zhang for explaining proper procedure for the CGG production

and on cell culturing.

• Dr. Timothy Taylor for consulting and advising us on all aspects of the

project.

Photolithography Master:

Photolithography uses photoresist to

make a design on a wafer. Photoresist

is a liquid that solidifies when

exposed to UV light. Using a

photomask to direct the exposure of

the photoresist allows for controlled

production of a master. An example of

a photomask and its resulting master

are shown below.

3D Master:

3D printing is an emerging application

that was experimented with in this

project. A CAD file is designed to the

desired specifications and then a 3D

model can be printed this is a cheep

and time saving alternative to the

photolithographical production

method. Examples of a 3D printed

master is shown below.

PDMS and Plasma Bonding:

After the master is produced PDMS is

allowed to cure on the master copy to

create a negative imprint of the design.

The PDMS is then bonded to a glass slide

using a Plasma etcher to ionize the

surface and form a bond. The hollow

areas are then used as the desired

channels. A completed microfluidic

device is shown below.

Microfluidic chip are made by producing a master, and making a PDMS imprint of the master to form the channels.

The absorbance values were obtained, and the

concentrations were calculated, and the values were

compared (shown below).Upon the completion of the microfluidic devices, dye and

distilled water were ran through the device and the output

concentrations were collected and tested for accuracy

using spectroscopy.

In the figure above, the concentrations generated are

shown. The shown concentrations are from the 3D printed

device (left), the photolithographic device (middle), and

the controls used to calculate concentrations (right).

The absorbance values were obtained, and the

concentrations were calculated, and the values were

compared (shown below). Statistical t-tests were

performed using percent error of each trial, and the two

designs were proven to be statistically comparable.

Above is shown the COMSOL concentration

gradient profile with the channel outlet

concentration percentage values. To the left is

shown the AutoCAD design.

The AutoCAD design was sent to make a

photomask for photolithography methods and

extruded to be 3D printed.

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