Comments on the wetting behavior of non-porous substratesfor ceramic coated-conductor applications

Pieter Vermeir • Frank Deruyck • Jonas Feys •

Petra Lommens • Joseph Schaubroeck •

Isabel Van Driessche

Received: 18 July 2011 / Accepted: 5 March 2012 / Published online: 27 March 2012

� Springer Science+Business Media, LLC 2012

Abstract This work gives an overview of the possibilities

to improve the wetting behavior of precursors for coated

conductors on non-porous substrates. Within this work, all

coatings were performed on a metallic Ni–W/La2Zr2O7/

CeO2 substrate using water-based Y, Ba, Cu containing

precursors. The results described in this paper can be used

for different technologies of chemical solution deposition,

as there are ink jet printing, dip coating, spin coating etc.

Starting from the forces involved during wetting, a sepa-

ration between solid and liquid modifications was made.

This study revealed that if a good cleaning procedure of the

substrate, whether or not combined with a targeted modi-

fication of the precursor is applied, water-based solutions

can be used without restriction towards their wetting

behaviour leading to a sustainable technology within the

coating industry. Within this work, special attention is

given to (1) fast determination of the substrate cleaning

procedure quality by the creation of wetting envelopes and

(2) the use of a screening design of experiment to study the

effects of intrinsic solution factors, such as precursor for-

mulation, influencing the coating behavior. All modifica-

tion discussed are expandable to all kinds of precursors and

substrates.

Keywords Wetting � Chemical solution deposition �Coating � Water-based

1 Introduction

The achievement of low cost deposition techniques for high

critical current YBa2Cu3O7-d coated conductors is one of the

major objectives to achieve a widespread use of supercon-

ductivity in power applications. The main advantages of

using chemical solution deposition (CSD) for the develop-

ment of textured oxide coatings are the lower investment

cost, an energy efficient operation mode, the faster deposi-

tion with higher yield and the processing under ambient

pressure enabling a complete continuous process [1–8].

Deposition of thin layers for coated conductors is today

mainly based on physical deposition processes. The most

common techniques are laser ablation, thermal and electron

beam evaporation and sputtering [9, 10]. Various products

with such functional layers have been commercialized in

the past, especially in the field of micro-electronics

[11, 12]. Nevertheless, the established physical deposition

processes are hampered by their complex process tech-

nology. The necessity of the deposition in vacuum cham-

bers, the use of energy consuming evaporation systems and

the low yield and deposition speed are the main factors

restricting the further economic use of these processes.

In this regard, taking into account that, in solution chem-

istry, highly organized materials can be prepared by gelation

followed by heating at moderate temperatures, soft processing

for advanced ceramics has been developed opening the way

for a new generation of low expenditure of energy in chemical

engineering. The possibility to fabricate a variety of materials

from solutions has been demonstrated already, but for the

design of innovative materials processing, particularly when

P. Vermeir � J. Schaubroeck

Faculty of Applied Engineering Sciences, University College

Ghent, Schoonmeersstraat 52 (C), 9000 Ghent, Belgium

P. Vermeir � J. Feys � P. Lommens � I. Van Driessche (&)

Department of Inorganic and Physical Chemistry, Ghent

University, Krijgslaan 281 (S3), 9000 Ghent, Belgium

e-mail: Isabel.vandriessche@ugent.be

F. Deruyck

Faculty of Science and Technology, University College Ghent,

Valentin Vaerwyckweg 1B, 9000 Ghent, Belgium

123

J Sol-Gel Sci Technol (2012) 62:378–388

DOI 10.1007/s10971-012-2737-3

rapid and environmentally friendly routes are preferred, sig-

nificant industrial progress is still needed.

Therefore, within the CSD-methods, there is an

increasing tendency to prepare films starting from envi-

ronmental friendly water-based precursor solutions,

resulting in a sustainable process for film deposition

[13–17]. The main challenge of this approach is the poor

coating behavior of these water-based precursors on a

variety of substrates. Polarity and cohesive hydrogen

bonding between water molecules lead to a very high

surface tension causing this poor coating performance. In

addition, the low surface tension of several substrates such

as metals, polyesters etc. makes things even more

complicated.

In this work, a summary is given of several possibilities to

improve wettability. Starting from Young’s equation, a

subdivision of two categories is made: (1) substrate or solid

and (2) solution or liquid modifications. Within the last

category, this paper gives new insights in the coating

behavior of water-based CSD precursors, more specifically

on the influence of precursor composition. This study was

performed using a design of experiment (DOE). In this work,

CSD precursors containing Y, Ba and Cu are used to evaluate

the coating behavior on Ni–W/La2Zr2O7/CeO2-substrates

applicable in coated-conductor applications. The results are

expandable to different precursor systems and substrates.

2 Experimental

2.1 Precursor solutions

Preparing clear and stable metal (M) containing precursor

solutions is a primary step in producing good quality

coatings via CSD. In order to realize this, a general strategy

was followed. To obtain a suitable layer thickness, all

precursor solutions were set to obtain a total M-concen-

tration of 0.6 M. Therefore, as all metals are introduced via

M-salts into the solvent, the most common precipitation is

in the form of the M-salt itself as the solubility product is

mostly exceeded. When water is used as a solvent, one

has to take into account the formation of insoluble

M-hydroxides [18]. The latter implies the importance of pH

control in every step of the precursor preparation. In order

to avoid precipitation, complexing agents are added to

form complexes with the free metals in the solution. Again

pH-control is a very important issue as complex formation

is pH dependent [19–22]. In this work, nitrates and acetates

are used as M-sources, ethylenediaminetetraacetic acid

(EDTA) and citric acid (CA) are used as complexing

agents, ammonia and ethanolamine are used as a base

while formic acid is used as an acid. As every precursor is

based on different M-sources, complexing agents and

bases, specific details for the preparation of different pre-

cursor solutions are given in Table 3.

2.1.1 Preparation of the precursor solutions containing

M-acetates, EDTA and NH4OH as base

EDTA (Sigma Aldrich [ 99 %) is mixed with 40vol% of

H2O. NH4OH (Sigma Aldrich 28 % in H2O) is added

until a stable pH of 8.5 is reached. After dissolution

of all components, Y(CH3COO)3�xH2O (Sigma Aldrich

99.9 %) and Ba(CH3COO)2 (Sigma Aldrich 99 %) were

added successively. Before Cu(CH3COO)2�H2O (Sigma

Aldrich C 98 %) was added, the pH was set neutral using

NH4OH. Afterwards, NH4OH or HCOOH (Sigma

Aldrich C 96 %) was used until the exact pH, conform

Table 1 or 2, was reached. Finally, H2O was used to dilute.

2.1.2 Preparation of the precursor solutions containing

M-acetates, EDTA and ethanolamine as base

EDTA is mixed with 40 vol% of H2O and ethanolamine

(Sigma Aldrich C 98 %) is added until a stable pH of 8.5

is reached. After dissolution of all components, Ba(CH3-

COO)2 was added. Another 20 vol% of H2O was added to

dissolve the Ba(CH3COO)2. Then, Y(CH3COO)3.xH2O

and Cu(CH3COO)2�H2O are added followed by 10 vol% of

additional H2O. Finally, the pH was reached using etha-

nolamine or HCOOH until the exact pH, conform Table 1

or 2, was reached. H2O was added to dilute.

2.1.3 Preparation of the precursor solutions containing

M-acetates, CA and NH4OH as base

After CA (Sigma Aldrich 99 %) is mixed with 20 vol% of

H2O, the pH is stabilized at 8 using NH4OH. Once everything

is dissolved Y(CH3COO)3�xH2O, Ba(CH3COO)2 and

Cu(CH3COO)2�H2O are added separately. Finally, NH4OH

or HCOOH was used until the exact pH, conform Table 1 or

2, was reached and H2O was added to dilute.

2.1.4 Preparation of the precursor solutions containing

M-acetates, CA and ethanolamine as base

After CA is mixed with 40vol% of H2O, the pH is stabi-

lized at 4 using ethanolamine. Once everything is dissolved

Y(CH3COO)3�xH2O, Ba(CH3COO)2 and Cu(CH3COO)2�H2O are added separately. Finally, ethanolamine or

HCOOH was used until the exact pH, conform Table 1 or

2, was reached and H2O was added to dilute.

J Sol-Gel Sci Technol (2012) 62:378–388 379

123

2.1.5 Preparation of the precursor solutions containing

M-nitrates, EDTA and NH4OH as base

EDTA is mixed with 40 vol% of H2O and NH4OH is added

until a stable pH of 8.5 is reached. When everything

is dissolved, Y(NO3)3�6H2O (Alfa Aesar 99.9 %) and

Ba(NO3)2 (Alfa Aesar 99 %) were added successively. For

a low M/EDTA-ratio, the pH needs to be adapted to 8.3

using NH4OH before continuing. Then Cu(NO3)2�2.5H2O

(Alfa Aesar 98 %) was added very slowly, adding 5 wt%

each time till dissolved in the mixture. Afterwards,

NH4OH or HCOOH was used until the exact pH,

conform Table 1 or 2, was reached. Finally, H2O was

used to dilute.

2.1.6 Preparation of the precursor solutions containing

M-nitrates, EDTA and ethanolamine as base

EDTA is mixed with 40vol% of H2O and ethanolamine is

added until a stable pH of 8.5 is reached. When everything is

dissolved, Y(NO3)3�6H2O and Ba(NO3)2 were added suc-

cessively. Before Cu(NO3)2 was added, the pH was changed

to 8.5 using ethanolamine. Then Cu(NO3)2�2.5H2O was added

Table 2 Experimental runs

DOE High pH rangeRun M-source Complexant pH high [Complexant] Alkalinity

control

Surface

treatment

Coverage

score

1 Acetate CA 9.5 Low Ammonia Heat 10

2 Acetate EDTA 8.5 High Ethanolamine Heat 10

3 Nitrate EDTA 9.5 Low Ethanolamine Ultrasonic 7

4 Nitrate CA 8.5 High Ammonia Ultrasonic 2

5 Nitrate EDTA 8.5 Low Ammonia Heat 10

6 Acetate EDTA 9.5 High Ammonia Ultrasonic 4

7 Nitrate CA 9.5 High Ethanolamine Heat 10

8 Acetate CA 8.5 Low Ethanolamine Ultrasonic 3

9 Nitrate EDTA 8.5 High Ethanolamine Ultrasonic 2

10 Acetate EDTA 9.5 Low Ethanolamine Heat 7

11 Acetate CA 8.5 High Ammonia Heat 6

12 Nitrate CA 9.5 Low Ammonia Ultrasonic 4

13 Nitrate EDTA 9.5 High Ammonia Heat 10

14 Acetate CA 9.5 High Ethanolamine Ultrasonic 10

15 Acetate EDTA 8.5 Low Ammonia Ultrasonic 1

16 Nitrate CA 8.5 Low Ethanolamine Heat 10

Table 1 Experimental runs

DOE Low pH rangeRun M-source Complexant pH low [Complexant] Alkalinity

control

Surface

treatment

Coverage

score

1 Acetate CA 7.5 Low Ammonia Heat 9

2 Acetate EDTA 6.5 High Ethanolamine Heat 6

3 Nitrate EDTA 7.5 Low Ethanolamine Ultrasonic 2

4 Nitrate CA 6.5 High Ammonia Ultrasonic 3

5 Nitrate EDTA 6.5 Low Ammonia Heat 10

6 Acetate EDTA 7.5 High Ammonia Ultrasonic 4

7 Nitrate CA 7.5 High Ethanolamine Heat 10

8 Acetate CA 6.5 Low Ethanolamine Ultrasonic 1

9 Nitrate EDTA 6.5 High Ethanolamine Ultrasonic 1

10 Acetate EDTA 7.5 Low Ethanolamine Heat 10

11 Acetate CA 6.5 High Ammonia Heat 10

12 Nitrate CA 7.5 Low Ammonia Ultrasonic 2

13 Nitrate EDTA 7.5 High Ammonia Heat 10

14 Acetate CA 7.5 High Ethanolamine Ultrasonic 5

15 Acetate EDTA 6.5 Low Ammonia Ultrasonic 7

16 Nitrate CA 6.5 Low Ethanolamine Heat 10

380 J Sol-Gel Sci Technol (2012) 62:378–388

123

followed by a pH adjustment, conform Table 1 or 2, using

ethanolamine or HCOOH. Finally, H2O was used to dilute.

2.1.7 Preparation of the precursor solutions containing

M-nitrates, CA and NH4OH as base

After CA is mixed with 40 vol% respectively 20 vol% of

H2O for a low respectively high M/CA-ratio, the pH is

stabilized at 4.0 using NH4OH. Once everything is dis-

solved Y(NO3)3�6H2O and Ba(NO3)2 are added separately.

Again, the pH was adjusted using NH4OH to 8.3 respec-

tively 6.0 for respectively a low and high M/CA-ratio.

Then Cu(NO3)2�2.5H2O was added very slowly, adding

5 w% each time till dissolved in the mixture. Afterwards,

NH4OH or HCOOH was used until the exact pH, conform

Table 1 or 2, was reached. Finally, H2O was used to dilute.

2.1.8 Preparation of the precursor solutions containing

M-nitrates, CA and ethanolamine as base

After CA is mixed with 30 vol% of H2O, the pH is stabi-

lized at 4 using ethanolamine. For a high M/CA-ratio no

pH adjustment is needed. After adding Y(NO3)3.6H2O and

Ba(NO3)2 separately, the pH was changed using ethanol-

amine to 6.0 respectively 3.0 for respectively a low and

high M/CA-ratio. Then Cu(NO3)2�2.5H2O was added very

slowly, adding 5 w% each time till dissolved in the mix-

ture. Afterwards, ethanolamine or HCOOH was used until

the exact pH, conform Table 1 or 2, was reached. Finally,

H2O was used to dilute.

2.2 Substrates and coatings

The substrates used are Ni–W/La2Zr2O7/CeO2-substrates.

The Ni–W was performed by Evico using the RABiTS

procedure [23], while the La2Zr2O7 and CeO2 layers were

obtained by Zenergy power GmbH via a CSD method [24].

In order to avoid dust contamination during thin film

deposition, cleaning procedures are performed in a dedi-

cated class 10,000 clean room. Coating procedures are

performed using a computer-controlled precision dip coater

in a laminar flow box at class 10.

Dust particles are removed from all solutions by filtering

the solution with 0.45 lm hydrophilic polypropene (GHP)

membrane filters. All films were dip coated at a withdrawal

speed of 50 mm min-1. Again, this principle is expandable

to other coating technologies such as spin coating and ink

jet printing [25–27].

2.3 Techniques

Contact angle measurements and surface tension calcula-

tions were obtained using the DSA30 module from Kruss.

For screening the possible effect of the precursor compo-

sition parameters the fractional factorial DOEs approach is

used. Using the experimental data, a regression model was

constructed. This model was validated using (1) the cor-

relation coefficient Radjusted2 which is a modified R2 that

adjusts for the number of explanatory terms in the model,

(2) the F-test statistic determining if the regression model is

statistically significant and (3) the t test of every parameter

estimate. The validation of the regression model using the

F-test, lies in the rejection or acceptance of the so-called

‘Null’ hypothesis H0, which states that the linear model

regression is not significant. As F is defined as the ratio of

the mean squares of the regression model to residuals, large

F-values occur with models that explain a significant

fraction of the variation in the response. In that case, the H0

is rejected. Together with the F-ratio, a p value is obtained,

defined as the probability of the actual outcome of the test

statistic, assuming H0 is true. If p \ 0.05 we take \5 %

risk of rejecting H0. The 5 % significance level was set in

advance. The validation of parameter estimates with the

t test shows a similar interpretation as the F-test, where H0

states that the parameter of the regression model is not

significant.

In order to comprehend the analysis of variance

(ANOVA) table, several definitions are given: (1) df stands

for degrees of freedom, specified as the number of obser-

vations minus the number of constraints, (2) mean squares

can be calculated as the sum of squares divided by the

respectively degrees of freedom.

3 Results and discussion

The use of water as primary solvent involves a high surface

tension of the obtained solutions, mostly resulting in poor

wetting behavior when used for coatings. According to the

extended Young’s equation for non-ideal surfaces [28–30],

one can anticipate substrate and/or precursor manipulations

to increase the wetting behaviour. Therefore, a subdivision

between solid and liquid modifications is made.

3.1 Substrate

In order to improve the wettability for a certain precursor

solution on a specific substrate, a good cleaning procedure

will generally be very important [31–33]. Normally, when

the extended Young equation is used, a measurement of

many different parameters, which is time consuming

knowing that maybe not all parameters are included, is

necessary. As an alternative, this work proposes to incor-

porate all extending variables correlating to a non-ideal

surface into a semi interfacial tension of the solid versus

vapour csv’ in order to have a quick overview of the

J Sol-Gel Sci Technol (2012) 62:378–388 381

123

different substrate pre-treatments. To expand this theory

from a static to a dynamic environment, the evaluation

criterion is changed towards the coating quality using a

coverage score going from 0 to 10 which is in turn

reflecting the coverage percentage of respectively 0 to

100 % of the substrate. Figure 1 gives an overview of the

coverage scores that are generally seen in practice.

In this work the wettability of Ni–W/La2Zr2O7/CeO2

substrates using three different cleaning procedures was

evaluated: (1) uncleaned, (2) degreased with isopropanol

followed by an ultrasonic (US) treatment for 10 min in

isopropanol and (3) degreased with isopropanol followed

by a heat-treatment at 400 �C for 5 min on a hot-plate.

Precursor 5 with a high amount of complexant and a final

pH of 9.5 was used to obtain the coverage scores after the

different cleaning procedures. For an uncleaned Ni–W/

La2Zr2O7/CeO2 substrate a coverage score of 2 was

obtained, for the US-cleaned one the coverage score

increased to 4, while for a heat-treated Ni–W/La2Zr2O7/

CeO2 substrate a coverage score of 10 was obtained.

When csv

0was determined using the Owens–Wendt

method [34], a csv

0of 37.19 ± 0.16, 40.24 ± 0.23 and

52.76 ± 0.17 mN/m was obtained for an uncleaned, US

cleaned and respectively heat-treated substrate (Table 3).

These values were calculated based on the contact angle

measurement of water and diiodomethane. Remark that

these results are in good agreement with the experimental

coverage scores, as an increase in csv

0implies an increase in

wettability. With the Owens–Wendt, not only the total csv

0,

but also the polar and dispersive part is calculated

(Table 3). This subdivision is based on different interaction

forces: the dispersive component contains Van der Waals

forces while the polar component contains polar interaction

forces and lewis acid–base interactions.

The introduction of wetting envelopes is an extra tool to

predict wetting of different precursors on a specific sub-

strate. When the plot of the polar and dispersive part of the

surface tension of a liquid is enclosed in the area within the

curve, the liquid meets the requirements of the character-

istics of the specific wetting envelope. The wetting enve-

lope of a solid can be calculated using a reverse Owens–

Wendt method. Moreover, different wetting envelopes

can be obtained for a certain substrate corresponding to a

certain wetting angle: complete wetting for a h = 0�,

complete dewetting when h = 180�.

When combining the introduction of csv

0with wetting

envelopes, we can set up different envelopes (f.e. h = 0�)

for different cleaning procedures of a certain substrate.

This makes it possible to verify each cleaning procedures

rapidly. In Fig. 2, three wetting envelopes for complete

wetting (h = 0�) for Ni–W/La2Zr2O7/CeO2 substrates with

the different cleaning procedures described before are

presented. It clearly shows that the area within the curve is

the smallest for an uncleaned Ni–W/La2Zr2O7/CeO2 sub-

strate, while it is the biggest for a heat-treated substrate. As

a final proof, we plotted the surface tension subdivision of

precursor 5 onto Fig. 2. This subdivision can be made by

measuring the total clv in air and clv, polar in heptane,

Fig. 1 Different coverage scores using a dip-coating technique (gray line is upper coating limit)

Table 3 Contact angle measurements for the determination of csv

0

Treatment Contact angle versus

water (�)

Contact angle versus

diiodomethane (�)

csv

0(mN/m) csv, polar (mN/m) csv, dispersive (mN/m)

Uncleaned 37.19 ± 0.16 1.24 ± 0.05 35.95 ± 0.11

93.3 ± 2.07 44.7 ± 0.41

US cleaned 40.24 ± 0.24 4.22 ± 0.04 36.02 ± 0.18

82.3 ± 0.52 41.0 ± 0.78

Heat treated 52.76 ± 0.17 22.47 ± 0.12 30.29 ± 0.05

41.8 ± 1.07 42.5 ± 0.20

382 J Sol-Gel Sci Technol (2012) 62:378–388

123

respectively 50.0 and 32.5 mN/m, via the pendant drop

method. As clv is the sum of the polar and dispersive part,

clv, dispersive is 17.5 mN/m. It is noticed that only the heat

treated substrate give rise to complete wetting, as obtained

by the experiment. Remark that this method can be used in

an opposite approach as well, f.e. when arrays or patterns

are needed [35].

3.2 Solution

Secondly, an improvement of wettability can be obtained

by adapting the CSD precursor. A common way to lower

the surface tension is the introduction of wetting agents

such as surfactants or alcohols [36, 37]. A less understood

way of improving the wettability is located within the

precursor composition.

3.2.1 Surfactants

The influence of Rokanol IT9, C13H27O(CH2CH2O)9H,

addition to the surface tension of precursor 5, is observed in

Fig. 3. For a 10-4 M surfactant containing precursor, a

coverage score of 9 was obtained on uncleaned Ni–W/

La2Zr2O7/CeO2 and a score of 10 was obtained for both US

cleaned and heat-treated Ni–W/La2Zr2O7/CeO2. A clear

improvement in comparison with the use of precursor 5

without surfactant, where a coverage score of 2, 4

respectively 10 was obtained.

Nevertheless good coatings are observed, the amorphous

layer need to be transformed to a crystalline phase by a

specific heat-treatment profile. It was found that the addi-

tion of surfactant resulted in inhomogeneous and porous

layers. If this inhomogeneity does not affect the coating

application, surfactants can be used [38–40], if not, an

alternative needs to be found. In this work, YBCO

formation at high temperatures is affected by this inho-

mogeneity resulting in bad layers.

3.2.2 Alcohol

Firstly, ethanol, the most simple and sustainable water

soluble alcohol, was used to verify the influence on coating

quality. As can be seen from Fig. 4, the decrease of clv of

precursor 5 is observed in function of an increase in vol%

of ethanol. Nevertheless, the decrease is much slower than

in the case of a surfactant. A coating resulting from pre-

cursor 5 with the addition of 10 vol% of ethanol, resulted

in a coverage score of 6, 8 and 10 for an uncleaned, US

cleaned and respectively heat-treated Ni–W/La2Zr2O7/

CeO2 substrate. Nevertheless, during drying at 80�C for

several minutes, layer shrinkage was observed resulting in

a lower coverage score of respectively 3, 5 and 10, which is

close to the initial coverage score for precursor solutions

without the addition of alcohol. This phenomenon can be

explained by the rapid evaporation of the alcohol during

drying. The disappearance of the alcohol increases the

surface tension of the precursor again resulting in the

contraction of the liquid. It was observed that an increasing

Fig. 2 Wetting envelopes for complete wetting of different cleaning

procedures and (filled circle) surface tension subdivision plot of

precursor 5

Fig. 3 Influence of clv on the concentration of surfactant

Fig. 4 Influence of clv on the vol% ethanol

J Sol-Gel Sci Technol (2012) 62:378–388 383

123

amount of ethanol had a positive effect on the shrinkage.

Nevertheless, an amount higher than 30 vol% of ethanol

was necessary to overcome shrinkage on both uncleaned

and US cleaned Ni–W/La2Zr2O7/CeO2. Higher boiling

alcohols such as isopropanol, showed a similar effect not-

withstanding the fact that less alcohol is needed if com-

pared to lower boiling alcohols to overcome shrinkage.

3.2.3 Intrinsic factors of precursor chemistry

A third and new way to improve wettability is the adaption

of the precursors chemical formulation. Preliminary

experiments have shown that by changing the precursor

formulation a strong variability of the coating behavior

from very poor to excellent was observed. A screening

DOE approach is chosen to identify the influencing factors

out of five potential formulation composition effects: (1)

nature of the M-source, (2) type of complexing agent, (3)

pH of the solution in the range 6.5–9.5, (4) complexant

concentration and (5) base for alkalinity adjustment.

Moreover, the substrate surface treatment (US cleaning and

heat-treatment) was included in the design.

In a screening DOE each factor is tested for a linear

effect at two levels, however in this broad pH range non-

linear effects can be expected. To overcome this, the pH

range is split up in two sub-ranges: a lower pH sub-range

between 6.5 and 7.5 and a higher pH sub-range between 8.5

and 9.5. For each pH sub-range a screening DOE is

launched.

A. Input variables The factor levels in the two screening

DOE’s are the following:

1. M-source: M-nitrates/M-acetates

2. Complexant: EDTA/CA

3. pH: screening DOE 1: Lower pH range : 6.5–7.5;

screening DOE 2: Higher pH range: 8.5–9.5

4. Complexant concentration [complexant]: High/Low

(Dependent on the complexant type)

5. Alkalinity control: Ammonia/Ethanolamine

6. Surface treatment: Heat/Ultrasonic (to verify previous

results)

Note that there is only one continuous variable (pH); the

others are categorical factors.

B. Response variables The influence of the 6 factors will

be tested on the coverage of the liquid after deposition (dip

coating) on the substrate (Score 0–10).

C. Experimental design For each pH sub-range a frac-

tional factorial 26-2 Resolution IV DOE with 16 experi-

mental runs is performed. This screening approach allows

to determine (1) the significant main effects non-con-

founded with possible interactions and (2) second order

interactions which are confounded (aliased). The aliasing

of effects is presented in Table 4. The experimental runs of

each pH-range are presented in Table 1 and 3.

D. Analysis low pH range First, a screening analysis is

performed to obtain a first idea of influencing parameters.

The results are given in Table 5. One can observe a very

significant (p value \ 0.05) effect of the factor ‘‘surface

treatment’’. The results also show two significant interac-

tion effects: ‘‘alkalinity control 9 pH’’ and ‘‘surface

treatment 9 M-source’’, however they are confounded

with other second order interactions.

Using the significant factors from the screening analysis

above a screening DOE (Resolution IV) model for Lower

pH range can be constructed; the model is presented in

Table 6 and visualized in Fig. 5.

These results indicate that (1) a very good screening

regression model is obtained: R2 = 93 %, R2adj = 91 %

and a p value \ 0.0001, (2) a heat treatment cleaning

procedure of the substrate has a strong positive effect on

surface coverage (p value \ 0.0001), as already observed

in paragraph 3.1, (3) the choice of alkalinity controlling

agent should be taken into account as it is borderline sig-

nificant (p value = 0.0490) and (4) two interactions, which

are confounded with other interactions, are significant

(p value \ 0.05). Taking into account the alias structure of

Table 4 Confounding effectsEffects Aliases

M-source 9 complexant =Alkalinity control 9 surface treatment

M-source 9 pH low =[Complexant] 9 surface treatment

M-source 9 [complexant] =pH low 9 surface treatment

M-source 9 alkalinity control =Complexant 9 surface treatment

M-source 9 surface treatment =Complexant 9 alkalinity control = pH low 9 [complexant]

Complexant 9 pH low =[Complexant] 9 alkalinity control

Complexant 9 [complexant] =pH low 9 alkalinity control

384 J Sol-Gel Sci Technol (2012) 62:378–388

123

the interaction effects (Table 4), we can conclude that there

are 6 possible models left:

y¼6:25þ3:125x1þ0:625x2�0:875x1 �x3�1:75x2 �x4 ð1Þy¼6:25þ3:125x1þ0:625x2�0:875x1 �x3�1:75x5 �x6 ð2Þy¼6:25þ3:125x1þ0:625x2�0:875x2 �x5�1:75x2 �x4 ð3Þy¼6:25þ3:125x1þ0:625x2�0:875x2 �x5�1:75x5 �x6 ð4Þy¼6:25þ3:125x1þ0:625x2�0:875x4 �x6�1:75x2 �x4 ð5Þy¼6:25þ3:125x1þ0:625x2�0:875x4 �x6�1:75x5 �x6 ð6Þ

With y = coverage score, x1 = surface treatment, x2 =

alkalinity control, x3 = M-source, x4 = pH, x5 = complexant,

x6 = [complexant].

It is expected that the interaction surface treat-

ment 9 M-source has a significant higher probability than

his two aliases (Table 4) because of the high significance

and effect of the factor ‘‘surface treatment’’. The aliased

interactions of surface treatment 9 M-source do not have a

significant main effect. Therefore, we can state with a very

high certainty that this specific second order interaction

surface treatment 9 M-source can be included in the

model’s equation, leaving out Eqs. 3 till 6. A visual rep-

resentation of this interaction is given in Fig. 6. It is clearly

observed that the nitrate salt precursor compositions (1)

yield a significantly higher coverage consistency and (2)

coverage scores are significantly higher than acetate pre-

cursor compositions for heat treated substrates and vice

Table 5 Screening analysis of

lower pH range

* Significant effect

Table 6 Model lower pH range

* Significant factors

Summary of fit

R2 0.93

R2 Adj 0.91

Root mean square error 1.13

Mean of response 6.25

Observations 16

Source df Sum of squares Mean square F p [ F

Analysis of variance

Model 4 187 46.8 36.732 \0.0001*

Residuals 11 14 1.27

C. Total 15 201

Term Estimate SE t ratio p [ |t|

Parameter estimates

Intercept 6.25 0.28 22.16 \0.0001*

Alkalinity control 0.625 0.28 2.22 0.0490

Surface treatment 3.125 0.28 11.08 \0.0001*

M-source 9 surface treatment -0.875 0.28 -3.10 0.0101*

pH low 9 alkalinity control -1.75 0.56 -3.10 0.0101*

J Sol-Gel Sci Technol (2012) 62:378–388 385

123

versa for US cleaned substrates. This difference in wetting

behavior of M-nitrates versus M-acetates as a function of

cleaning procedure, can be explained by starting from the

subdivided csv’ . From Table 3, it is observed that a heat

treated cleaning procedure shows a much higher csv,polar

than an ultrasonic cleaned substrate, 22.47 versus 4.22 mN/

m, while a slightly lower csv,dispersive is obtained, 30.29

versus 36.02 mN/m.

To explain the coating behavior two interactions are

taking into account: (1) polar interaction forces and (2) Van

der Waals forces as those can be linked to csv,polar and

csv,dispersive respectively. For the polar interactions one can

have the interaction between substrate and (1) salts in the

bulk of the solution as well as (2) M-salt coordination

complexes. Nitrates show a higher polar interaction

because (1) in the bulk, acetates are positioned with the

apolar methyl-group focused towards the substrate instead

of a polar O atom of the nitrate and (2) acetates show a

potential for coordination with Y, Ba and Cu resulting with

a non coordinating methyl-group at the outside of the

complex [41]. For the Van der Waals forces, acetates are

slightly higher than nitrates.

As for the heat treated substrate, a high csv,polar part is

observed so the polar interaction takes the upper hand of

the weak Van der Waals forces. As a conclusion, nitrate-

based precursors show better wetting behavior on cleaned

substrates using heat treatment because (1) the polar

interactions are much larger for nitrates when compared to

acetates and (2) weak Van der Waals forces are negligible

when compared to the polar interactions. Moreover, for an

US cleaned substrate, a very low csv,polar part is observed so

the Van der Waals forces become relevant. As the Van der

Waals forces are higher for acetates than for nitrates,

acetates coat better on ultrasonic cleaned substrates. Both

examples clearly show that precursor composition in

combination with substrate preparation is a very important

issue in obtaining good coatings.

For the two other confounded interactions, alkalinity

control 9 pH and [complexant] 9 complexant, only alka-

linity control is a borderline significant main effect. Conse-

quently, these aliased interaction effect have a nearly equal

probability to occur making further interpretation of these

interaction effects impossible. Therefore a separation of both

aliases is necessary by performing a resolution V DOE.

E. Analysis high pH range A screening analysis (Table 7)

shows, similar as for the lower pH range, a very significant

(p value of 0.0092) effect of the factor ‘‘surface treatment’’.

For the higher pH-range, also pH with a p value of 0.1027

needs to be taken up in the further high pH range modeling.

From the screening there is no indication for significant

interaction effects.

With the factors surface treatment and pH, a screening

model for Higher pH range can be constructed; the model

is presented in Table 8 and visualized in Fig. 7. It is

observed that (1) a significant (p value = 0.0008)

screening regression model with a slightly lower R2 of

67 % and Radj2 of 62 % is obtained when compared to the

lower pH range, (2) as for the lower pH range again heat

treatment has a strong positive effect on the surface

coverage score (p value = 0.0007) and (3) pH has a sig-

nificant effect on the coverage score (p value = 0.0260).

Again, an intrinsic factor of the precursor chemistry, pH

for the higher pH range, shows a significant effect on the

wettability.

As no significant second order interactions are present, a

linear model with only main factors is obtained:

Fig. 5 Predicted lower pH Model plot

Fig. 6 Coverage score versus M-source by surface treatment for the

low pH range

386 J Sol-Gel Sci Technol (2012) 62:378–388

123

y ¼ �17:19þ 2:312x1 þ 2:625x4 ð7Þ

With y = coverage score, x1 = surface treatment, x2 =

alkalinity control, x3 = M-source, x4 = pH, x5 = com-

plexant, x6 = [complexant].

Since the acquisition of a quantitative model was not the

main focus, no further optimization of the design was

performed.

In order to produce high quality films, in this specific

case YBCO as final compound, the realization of a good

coating is a primary step in the optimization process.

Therefore, a good cleaning procedure whether or not

combined with an adjusted precursor composition seems to

be very effective. For YBCO production this can be

achieved by using a heat treatment cleaning procedure

for the Ni–W/La2Zr2O7/CeO2 substrate combined with

preferably metal-nitrate containing precursors. This

approach results in numerous combinations that could give

rise to high quality YBCO films, after optimizing the

subsequent thermal process.

4 Conclusion

This work gives an overview of the opportunities to

improve the coating behavior of CSD precursors. The main

focus was on water-based solutions, but is expandable to all

types of CSD precursors. Starting from the extended

Young’s equation, a separation between solid and liquid

modifications was made. Substrate cleaning procedures

form the base of solid modifications. We showed that

wetting envelopes can be used as a fast determination of

the cleaning procedure quality by the introduction of csv

0.

Liquid modifications include: (1) the addition of wetting

agents like surfactants or alcohols as commonly done and

(2) intrinsic factors, such as precursor formulation. The

Table 7 Screening analysis of

higher pH range

* Significant factors

Table 8 Model higher pH

range

* Significant factors

Summary of fit

R2 0.67

R2 Adj 0.62

Root mean square error 2.09

Mean of response 6.44

Observations 16

Source df Sum of squares Mean square F p [ F

Analysis of variance

Model 2 113 56.6 12.94 0.0008*

Residuals 13 57 4.37

C. Total 15 170

Term Estimate SE t ratio p [ |t|

Parameter estimates

Intercept -17.19 9.42 -1.82 0.0912

Surface treatment 2.312 0.52 4.42 0.0007*

pH 2.625 1.05 2.51 0.0260*

J Sol-Gel Sci Technol (2012) 62:378–388 387

123

latter was performed using a screening DOE. More spe-

cifically, it was found that intrinsic factors of precursor

composition, such as the origin of the M-source in com-

bination with the cleaning procedure of the substrate within

the low pH range and the pH itself within the high pH

range, are very important for the coating behaviour. Since

intrinsic factors show an effect on the coverage score, a

broad range of precursor compositions can give rise to

equivalent good coatings, showing that water-based

designs and CSD in general are promising technologies for

coated conductor development and coating of ceramics

layers in general.

As a final conclusion, this study revealed that the nature

of sustainable water-based CSD precursors does not ham-

per their coating ability, as long as a good cleaning pro-

cedure of the substrate, whether or not combined with a

targeted modification of the precursor is used.

References

1. Shimoda T, Matsuki Y, Furusawa M, Aoki T, Yudasaka I, Tanaka

H, Iwasawa H, Wang DH, Miyasaka M, Takeuchi Y (2006)

Nature 440:783–786

2. Schwartz RW, Schneller T, Waser R (2004) C R Chim 7:433–461

3. Hardy A, Mondelaers D, Vanhoyland G, Van Bael MK, Mullens

J, Van Poucke LC (2003) J Sol Gel Sci Technol 26:1103–1107

4. Vermeir P, Cardinael I, Schaubroeck J, Verbeken K, Baecker M,

Lommens P, Knaepen W, D’haen J, De Buysser K, Van Dries-

sche I (2010) Inorg Chem 49:4471–4477

5. Llordes A, Zalamova K, Ricart S, Palau A, Pomar A, Puig T,

Hardy A, Van Bael MK, Obradors X (2010) Chem Mater 22:

1686–1694

6. Van Driessche I, Penneman G, Bruneel E, Hoste S (2002) Pure

Appl Chem 74:2101–2109

7. Zhong XL, Wang JB, Liao M, Huang GJ, Xie SH, Zhou YC, Qiao

Y, He JP (2007) Appl Phys Lett 90:152903

8. Sakamoto W, Iwata A, Yogo T (2008) J Appl Phys 104:104106

9. Frumar M, Frumarova B, Nemec P, Wagner T, Jedelsky J,

Hrdlicka M (2006) J Non-Cryst Solids 352:544–561

10. Zheng J, Yang R, Xie L, Qu JL, Liu Y, Li XG (2010) Adv Mater

22:1451–1473

11. Yang Z, Ko C, Ramanathan S (2010) J Appl Phys 108:073708

12. Peng XL, Matthews A, Xue S (2011) J Mater Sci 46:1–37

13. Vermeir P, Cardinael I, Baecker M, Schaubroeck J, Schacht E,

Hoste S, Van Driessche I (2009) Supercond Sci Technol 22:

075009

14. Van de Velde N, Van de Vyver D, Brunkahl O, Hoste S, Bruneel

E, Van Driessche I (2010) Eur J Inorg Chem 2:233–241

15. Berber H, Sarac A, Yildirim H (2011) Prog Org Coat 71:225–233

16. Chang YM, Hu WH, Fang WB, Chen SS, Chang CT, Ching HW

(2011) J Air Waste Manage 61:35–45

17. Arin M, Lommens P, Avci N, Hopkins SC, De Buysser K,

Arabatzis IM, Fasaki I, Poelman D, Van Driessche I (2011) J Eur

Ceram Soc 31:1067–1074

18. Schoofs B, Van de Vyver D, Vermeir P, Schaubroeck J, Hoste S,

Herman G, Van Driessche I (2007) J Mater Chem 17:1714–1724

19. Schoofs B, Cloet V, Vermeir P, Schaubroeck J, Hoste S, Van

Driessche I (2006) Supercond Sci Technol 19:1178–1184

20. Van Driessche I, Penneman G, De Meyer C, Stambolova I,

Bruneel E, Hoste S (2002) Key Eng Mater 206–2:479–482

21. De Buysser K, Lommens P, De Meyer C, Bruneel E, Hoste S,

Van Driessche I (2004) Ceram. Silikaty 48:139

22. Penneman G, Van Driessche I, Bruneel E, Hoste S (2004) Key

Eng Mater 264–268:501–504

23. Goyal A, Paranthaman MP, Schoop U (2004) MRS Bull 29:

552–561

24. Cordero-Cabrera MC, Mouganie T, Glowacki BA, Baecker M,

Falter M, Holzapfel B, Engell J (2007) J Mater Sci 42:7129–7134

25. Krebs FC (2009) Sol. Energ. Mat. Sol. C. 93:394–412

26. Le HP (1998) J Imaging Sci Techn 42:49–62

27. Wang YH, Zhou WD (2010) J. Nanosci. Nanotechno. 10:

1563–1583

28. Young T (1805) Phil Trans R Lond 95:65–87

29. Degennes PG (1985) Rev Mod Phys 57:827–863

30. Chow TS (1998) J. Phys.-Condens. Mat. 10:L445–L451

31. Lubna N, Auner G, Patwa R, Herfurth H, Newaz G (2011) Appl

Surf Sci 257:4749–4753

32. Yaghoubi H, Taghavinia N, Alamdari EK (2010) Surf Coat Tech

204:1562–1568

33. Petersson L, Meier P, Kornmann X, Hillborg H (2011) J Phys D

Appl Phys 44:034011

34. Owens DK, Wendt RC (1969) J Appl Polym Sci 13:1741–1747

35. Lejeune M, Chartier T, Dossou-Yovo C, Noguera R (2009) J Eur

Ceram Soc 29:905–911

36. Zdziennicka A, Janczuk B (2010) J Colloid Interf Sci 343:

594–601

37. He G, Cai JH, Ni G (2008) Mater Chem Phys 110:110–114

38. Yang JX, Peterlik H, Lomoschitz M, Schubert U (2010) J Non-

Cryst Solids 356:25–27

39. Ban T, Tanaka Y, Ohya Y (2011) Thin Solid Films 519:

3468–3474

40. Han C, Pelaez M, Likodimos V, Kontos AG, Falaras P, O’Shea

K, Dionysiou DD (2011) Appl Catal B Environ 107:77–87

41. Martell AE, Smith RM (1977) Critical stability constants—vol-

ume 3: other organic ligands. Plenum Press, New York

Fig. 7 Predicted higher pH Model plot

388 J Sol-Gel Sci Technol (2012) 62:378–388

123