A

Na

b

a

ARRAA

KPAF

1

oia

sesvd

edtp

cPfosob

i

0d

Synthetic Metals 159 (2009) 501–507

Contents lists available at ScienceDirect

Synthetic Metals

journa l homepage: www.e lsev ier .com/ locate /synmet

crylic blends based on polyaniline. Factorial design

icoleta Plesu a, Ion Grozavb, Smaranda Iliescua, Gheorghe Iliaa,∗

Institute of Chemistry, Romanian Academy, 24 Mihai Viteazul Bvd., 300223 Timisoara, Romania“POLITEHNICA” University of Timisoara, 1 Mihai Viteazul Bvd., 300222 Timisoara, Romania

r t i c l e i n f o

rticle history:eceived 11 June 2008eceived in revised form 14 October 2008ccepted 17 November 2008vailable online 3 January 2009

a b s t r a c t

Processable conducting materials for large-scale utilization were prepared by mechanical mixing ofpolyaniline (PANI) paste and commercially acrylic resin. Doped PANI with organic phosphorus acid wassynthesized. These blends can be used for the production of semiconductive paints with good corrosionresistance. By mixing doped PANI with commercially acrylic acid (SMP 63 AZUR SA) hard semiconducting

eywords:olyanilinecrylic resinactorial design

and low elastic films are obtain. The effect of four variables was simultaneously studied: PANI (concen-tration), stirring speed (ST), mixing time (MT), dispersing agent (DA). Due to the number of variables, afactorial-design was chosen in order to reduce the number of experiments required in order to obtaincoatings with high hardness and elasticity and semiconductive behavior. The results indicated that theinfluences of control factors decrease in order: PANI (concentration), mixing time (MT), dispersing agent

T). Frand M

(DA) and stirring speed (Stwo control factors PANI

. Introduction

The replacement of classical materials used in industry withther cheaper, environment friendly capable to prevent corrosions one of the goals in the research area. Electrotechnical industrynd engineering needs this special material.

The conductive and semiconductive coatings belong to thesepecial materials and occupy a large application domain in mod-rn industry, used to remove the electrostatic charges, and for thehielding effects (i.e. Faraday screen, protective coat for the higholtage cables, etc.). Conducting polymers have attracted attentionue to their electric, optical and thermal properties.

One of the intrinsic semiconductive polymers with useful prop-rties: high conductivity, transparency for the red and violetomain of spectrum, good elasticity and processability, superficialension, chemical, photochemical and electrochemical behavior, isolyaniline (PANI).

It is known that undoped state of PANI (insulator) is soluble inommon organic solvents, but the conducting form (doped form ofANI) is insoluble in almost all solvents, except concentrated sul-uric acid [1–4]. The lower processability is a major disadvantage

f PANI. In order to improve the solubility of the conductive form,ome routes are possible to overcome these problems: modificationf the polymer chain [5], usage of large anions as dopants [6–10] ory dispersing with suitable insulator polymer matrix [11].

∗ Corresponding author. Tel.: +40 256491818; fax: +40 256491824.E-mail addresses: nplesu@acad-icht.tm.edu.ro (N. Plesu),

lia@acad-icht.tm.edu.ro (G. Ilia).

379-6779/$ – see front matter © 2008 Elsevier B.V. All rights reserved.oi:10.1016/j.synthmet.2008.11.018

om the studied variables, the resistance is significantly influenced by theT.

© 2008 Elsevier B.V. All rights reserved.

The disadvantages of dispersing process are the loss of somemechanical and electrical properties. The main idea is to produceblends that maintain the processability and mechanical propertiesof conventional polymers and electric properties of the conductingpolymers.

The use of PANI as a conductive component in substitution formetallic particles or carbon black, decreases the percolation thresh-old and can solve some problems, like sloughing (for carbon black)and costs (for metallic fillers) [12,13]. In the field of corrosion pro-tection, conducting polymers especially PANI can be used either ascorrosion inhibitors or as protective coatings [14,15].

It is known that blends containing conducting polymers havepoor mechanical properties than polymer matrix, in this case acrylicresins [16]. It is desirable for the coating resulted from the paint topresent high electrical conductivity and good mechanical integrity(such as scratch resistance).

Some blends based on PANI were studied in more detail througha factorial design [14,17]. In this work, the blending process ofchemically synthesized PANI and commercially acrylic blends bymechanical mixing was studied through a factorial design, in orderto evaluate the relationship between blend composition-mixingparameter and coating properties.

The purpose of the present work was to optimize various param-eters, affecting the mechanical and electrical physical propertiesof the blends, such as hardness, elasticity and resistance, using an

experimental design approach.

The hardness is required in order to obtain a good resistance ofcoatings under a static load or to scratching and a good cohesion ofthe particles on the substrate. Films with higher hardness are moreeffective to support loading and possess less deformation. Elasticity

502 N. Plesu et al. / Synthetic Metals 159 (2009) 501–507

Table 1Composition and characteristics of blends samples.

No. crt. PANI, % Dispersing agent, %* Mixing time, min. Stiring speed, rot, min. Particle size, ×10−2 �m Hardness, min. Elasticity, mm Resistance, ×10−5 �**

1 5 3 10 500 78 163 2 8.062 5 3 10 1000 72 160 2.2 7.123 5 4.5 10 500 63 156 2.4 5.854 5 4.5 10 1000 55 151 2.6 3.575 5 3 40 500 64 157 2.1 2.616 5 3 40 1000 61 151 2.3 1.707 5 4.5 40 500 56 150 2.6 1.688 5 4.5 40 1000 44 149 2.4 1.309 15 3 10 500 58 167 2.1 1.15

10 15 3 10 1000 43 164 2.3 0.9411 15 4.5 10 500 34 160 2.4 0.8112 15 4.5 10 1000 30 154 2.2 0.6813 15 3 40 500 36 161 2.1 0.5714 15 3 40 1000 32 154 2.2 0.4815 15 4.5 40 500 36 153 2.2 0.4116 15 4.5 40 1000 30 152 2.1 0.3417 10 4 25 800 40 158 2.1 3.6018 10 4 25 800 42 155 2.1 3.15

2

ount i

eb

di

19 10 4 25 8000 10 4 25 800

* Represents the total quantity of dispersing agent from paste plus additional am** For dispersion.

nables the coating to tolerate and responds to the stresses caused

y environmental factors.

The processing parameters like concentration of doped PANI,ispersing additives, rotor speed and mixture time, have a strong

nfluence on coating characteristics. The aim of the study is to

Fig. 1. Pareto chart: (a) Pareto of Res, (b) Pareto

43 157 2.0 2.6044 154 2.0 1.66

n blending faze.

obtained coatings with high hardness, good elasticity and with

semiconductive properties.

Factorial designs are sufficient to estimate linear, and interac-tion models and they require a very low number of experimentalruns. Some advantages of using a factorial design are based on the

of H, (c) Pareto of EL and (d) Pareto of PS.

c Metals 159 (2009) 501–507 503

fmir

ht(ctr

t

ohtb

2

saat

p(pa

wpq

s

2

l(a0b

2

roa

tp

a

b

N. Plesu et al. / Syntheti

act that averages are more stable than single observations and theore data one average, the more reliable is the result. The main

mportance of the factorial design is the possibility to reveal theeal effect of each factor studied and their interactions.

The effects of polymer concentration upon blends propertiesave already been known [11–17]. The other important parame-ers like stirring speed (ST), mixing time (MT), dispersing agentDA) are considered for the estimation of connection between blendomposition-mixing parameter and coating properties. These con-rol factors are considered to influence the particle size andesistance of the dispersion, hardness and elasticity of pellicle.

The particle size of PANI indicates if in blends the phase separa-ion occurs or a homogenous well-mixed blend is formed.

The phase separation allowed the formation of distinct domainsf PANI in the polymer matrix. Some studies of PANI/PMMA filmsas shown that PANI forms distinct domains in the PMMA matrixhat is the blends phase separate but there is inter-connectivityetween the various PANI domains [18].

. Experimental

Doped PANI was obtained by oxidizing aniline in dilutetyrilphosphonic acid with ammonium peroxidisulfate as oxidizinggent. The aniline/oxidant molar ratio was 1 and the aniline/organiccid ratio 0.5. The procedure is the same for all samples accordingo previous reports [15].

The doped PANI powder was dispersed onto laboratory dis-ersing equipment (three rolls machine) using organic solventstoluene, butanol), dispersing additives and antideponants. Theaste contains doped PANI (38%), toluene (48%), acrylic acid (8%),ntideponants (1%) dispersing additives 3% and buthanol (2%).

Acrylic blends with doped PANI concentration rises from 5 to 15%ere obtained by dispersing different amounts of prior preparedaste in acrylic resins using a mechanical stirrer and additionaluantity of dispersing additives.

The composition and characteristics of blends samples are pre-ented in Table 1.

.1. Measurements

The hardness of the films was performed with Persosz pendu-um (according to STAS 2538-73) and Erichsen elasticity of filmsaccording to STAS 3046-68). The quantity of PANI acrylic dispersionpplied on the plate (80 mm × 120 mm) before drying was between.6–0.8 g and the thickness of pellicle between 0.05–0.06 mm. Thelends resistance was measured with the Rezistest Cella (Hungary).

.2. Design of experiments

The design of experiments (DoE) is a series of testes (calleduns) in which purposeful changes are made in the input variablef the process (so-called control factors) so that we may observend identify the reason of changes in the output response.

The first necessary step in design of experiments is to establishhe process control factors, with them adjustment range and therocess responses (sometimes called and objective functions).

For these reasons, the following process control factors and themdjustment range has been established:

a. Control Factors PANI (PANI concentration = 5–15), ST (stirringspeed = 500–1000), MT (mixing time = 10–40), DA (dispersingagent = 3–4.5).

. Objective functions (process responses): H (hardness). PS (particlesize) EL (elasticity) and Res (resistance).

Fig. 2. The main effects of control factors.

504 N. Plesu et al. / Synthetic Metals 159 (2009) 501–507

s

(

ie

3

fa

b(tif0oo

l

Aefpfe

M

TCc

N

Fig. 3. The influence of control factors.

A factorial design 22 with 4 central points was planed and 20yntheses were run (effectuated).

The runs array of the factorial design and the process responsesobjective functions) are presented in Table 1.

With the factorial design is possible to evaluate the variablesnfluence upon mechanical and electrical properties of blends andstablish an optimum formulation.

. Results and discussion

Based on the experimental data, the influence level of the controlactors and their interactions upon resistance: hardness, elasticitynd particle size can be selected (Fig. 1).

From statistical analysis of experimental results for acryliclends based on PANI, for each control factor the probability valuep) can be obtained. This value indicates if respectively control fac-or presents or not some significant influence upon characteristicnvestigated quality (objective functions). The frequent value usedor probability is p = 0.05, corresponding to the 5%. A value less than.05 (p < 0.05) indicates a significant influence of control factor uponbjective function. Not significant influence of control factor uponbjective function is present if the value is higher than 0.05.

In our case all control factors with a significant influence areocated over the line marked at 2.776 (p = 0.05).

The main effect of the control factors are presented in Fig. 2.horizontal line (parallel to the x-axis), indicates that no main

ffect is present (the control factor does not influence the objectiveunction). If the line is not horizontal, there may be a main effect

resent and in this case the control factors influence the objectiveunction. The greater is the slope of the line, the stronger is theffect.

With the increase of the PANI and dispersing agent (DA) content,T and ST, the Res of dispersion decreases. The high H (hardness)

able 2omposition and characteristics of blends samples for results a non-linear centralomposite type of experiments.

o. PANI MT PS*, ×10−2 H EL Res.**, ×10−5 �

1 20.0 35.0 38 163 2.1 0.482 22.1 40.0 37 166 2.0 0.363 7.9 40.0 58 158 2.3 1.634 15.0 40.0 35 160 2.2 0.515 15.0 47.1 33 158 2.2 0.436 10.0 45.0 32 153 2.3 0.867 20.0 45.0 32 163 2.1 0.358 15.0 40.0 34 159 2.4 0.399 15.0 32.9 35 152 2.3 0.61

10 10.0 35.0 42 154 2.1 0.7811 15.0 40.0 36 159 2.3 0.6212 15.0 40.0 35 160 2.2 0.5913 15.0 40.0 38 158 2.3 0.61

* Particle size of PANI.** For dispersion.

Fig. 4. The interaction between the control factors.

c Metals 159 (2009) 501–507 505

wr

doihd

cHi(t

tt

tt(

td

tei

pp

PMdPide

N. Plesu et al. / Syntheti

ere obtained with the increase of PANI content. The DA is a mate-ial that reduces the cohesive attraction between like particles.

The presence of the acrylic polymer between adjacent con-uctive particles reduces the conductivity of the coating. On thether hand, the use of DA, allows the conductive particles (PANI)n the resulting coating to contact one another thus resulting inigh conductivity and a slightly elastic pellicle, after the coating isrying.

The hardness of coatings increases with the increase of PANIontent due to the presence of rigid phenyl ring in PANI chains.ardness increases with decreasing particle size. The particle size

s influenced by the quantity of dispersing agent (DA), mixing timeMT) and stirring speed (ST). With the increase of DA, ST and MThe particle size decreases and as a result hardness decreases.

Because all control factors influence the objective function wery to estimate the order of influences for a better understanding ofhe phenomena.

For this purpose the sum of p values of each control factor uponhe objective function was calculated first for all control parame-er (Sum PS + H + EL + Res) and second for three objective functionsSum PS + H + Res). The results are presented in Fig. 3.

The obtained values illustrate that the influences of control fac-ors decrease in order PANI (concentration), mixing time (MT),ispersing agent (DA) and stirring speed (ST) (Fig. 3).

Another important aspect is the interaction among these con-rol factors. The interaction between the controls factors can bestimate from experimental design and the results are presentedn Fig. 4.

If the lines are parallel to each other, there is no interactionresent. With the increase of the deviation degree of line from beingarallel, the interactions among control factors increase.

The resistance is significant influenced by the two control factorsANI and MT. It means that besides PANI content the control factorT is essential. It is known that PANI aggregation causes the con-

uctance non-uniformity of the PANI film. With the increase of MT.ANI aggregates (produced as a result of strong polymer–polymernteractions) were destroyed and as a result PS decreases and con-uctive polymer becomes homogeny spread in the dispersion. Thelasticity (EL) is not affected by control factors; the mathematical

Fig. 6. Contour plots for central

Fig. 5. The values of objective function for optimum control factors.

model is not satisfactory, it’s approach is only 68.54% of experimentvalues.

Practically there are no significant interactions among controlfactors, the effects are additive not multiplicative with a singleexception in the resistance case. Only in the resistance case theinteraction PANI × MT is significant. This is a reason for the nextsupplementary set of non-linear experiments. The first factorialexperiment shows an optimum for: PANI = 15, DA = 3, MT = 40 andST = 864. The corresponding objective functions are presented inFig. 5.

In order to improve the results a non-linear central compositetype of experiments were run. The control factors were set to

1. PANI (concentration) in the range 10–20.2. Mixing time (MT) in the range 35–45.3. Dispersing agent (DA) constant and equal to the value optimized

a priory (3).

composite experiments.

506 N. Plesu et al. / Synthetic Metals 159 (2009) 501–507

Fig. 7. Surface plots for central composite experiments.

unctio

4

F

bf

(wdsppl

4

t

Fig. 8. The values of objective f

. Stirring speed (ST) constant and equal to the value optimized apriory (864–870).

The proposed experiments are presented in Table 2.The results of the central composite design are presented in

igs. 6 and 7.It was locking for a robust process, which is not so much affected

y the dispersion of the control factors. The control factors settingor a robust process in our case is presented also in Figs. 6 and 7.

On the contour plot (Fig. 6) the stabile optimal point is markedthe intersection of two lines in Fig. 6 and the black point in Fig. 7). Itas observed that around this point the level curves present a lowerensity. This proves that in this area there is not abrupt surface (highlope, see Fig. 7) and for small deviation of control factors, the dis-ersion of the process response is insignificant. In conclusion, therocess presents robustness and the important issue represents the

ack of the sensibility with the variation of control factors (Fig. 8).The optimum value found for PANI is 15.10 and for MT is 45.67.

. Conclusions

An alternative way from classical method, in optimization ofhe industrial processes, is design of experiments. From the fac-

n for optimum control factors.

torial design of blending process we concluded that the influencesof control factors decrease in order: PANI (concentration), mixingtime (MT), dispersing agent (DA) and stirring speed (ST). From thestudied variables, the resistance is significant influenced by thetwo control factors PANI and MT. Besides PANI content, the con-trol factor MT is essential. With the increase of MT, PANI aggregates(produced as a result of strong polymer-polymer interactions) weredestroyed and as a result PS decreases and conductive polymerbecomes homogeny spread in the dispersion.

The elasticity of coatings is not affected by control factors; themathematical model is not satisfactory, it includes only 68.54% fromexperiment values. Practically there are not significant interactionsamong control factors. The effects are additive not multiplicativewith a single exception in the resistance case.

References

[1] H.S. Nalwa, Handbook of Conductive Molecules and Polymers, vol. 2, John Wiley& Sons Ltd., London, 1997, p. 503–566.

[2] A.G. MacDiarmid, J.C. Chiang, A.F. Richter, Synth. Met. 18 (1987) 285.[3] D.W. Hatchett, M. Josowicz, J.J. Janata, Phys. Chem. B 103 (1999) 10992.[4] P.M. Beadle, Y.F. Nicolau, E. Banke, P. Ronnon, D. Djurado, Synth. Met. 95 (1998)

29.[5] W. Gazotti, M.-A. De Paoli, Synth. Met. 80 (1996) 263.[6] E. Erdem, M. Karakisla, M. Sacak, Eur. Polym. J. 40 (2004) 785.

c Meta

[[[

[[14] S. Sathiyanarayanan, S. Muthukrishnan, G. Venkatachari, D.C. Trivedi, Prog. Org.

N. Plesu et al. / Syntheti

[7] A. Ray, G.E. Asturias, D.L. Kershner, A.F. Richter, A.G. Mac Diarmid, A.J. Epstein,Synth. Met. 29 (1989) 141.

[8] Li. S. Dong, H.Y. Cao, Synth. Met. 29 (1989) 329.[9] T.L.A. Campos, D.F. Kersting, C.A. Ferreira, Surf. Coat. Technol. 122 (1999) 3.10] C.D.V. Minto, A. Vaughan, Polymer 38 (1997) 2638.11] J. Anand, S. Palaniappan, D.N. Sathyanayana, Polym. Sci. 23 (1998)993.12] E. Virtanen, J. Laakso, H. Ruohonen, K. Vaekiparta, H. Jaervinen, M. Jussila, P.

Passiniemi, J.-E. Oesterholm, Synth. Met. 84 (1997) 113.

[[[[

ls 159 (2009) 501–507 507

13] S. Mitzako, M.-A. De Paoli, Eur. Polym. J. 35 (1999) 1791.

Coat. 53 (2005) 297.15] N. Plesu, G. Ilia, A. Pascariu, G. Vlase, Synth. Met. 156 (2006) 230.16] J. Laska, K. Zak, A. Pron, Synth. Met 84 (1997) 117.17] K. Desai, C. Sung, Mat. Res. Soc. Symp. Proc. 788 (2004), LII.52-1-LII52-6.18] C.Y. Yang, Y. Cao, P. Smith, A.J. Heeger, Synth. Met. 53 (1993) 293.