Open Access

Optimizing Bi2O3 and TiO2to achieve the maximum non-linear electrical property of ZnO low voltage varistor

  • Yadollah Abdollahi1Email author,
  • Azmi Zakaria1Email author,
  • Raba’ah Syahidah Aziz2,
  • Siti Norazilah Ahmad Tamili2,
  • Khamirul Amin Matori1,
  • Nuraine Mariana Mohd Shahrani2,
  • Nurhidayati Mohd Sidek2,
  • Masoumeh Dorraj1 and
  • Seyedehmaryam Moosavi2
Chemistry Central Journal20137:137

DOI: 10.1186/1752-153X-7-137

Received: 19 May 2013

Accepted: 6 August 2013

Published: 10 August 2013

Abstract

Background

In fabrication of ZnO-based low voltage varistor, Bi2O3 and TiO2 have been used as former and grain growth enhancer factors respectively. Therefore, the molar ratio of the factors is quit important in the fabrication. In this paper, modeling and optimization of Bi2O3 and TiO2 was carried out by response surface methodology to achieve maximized electrical properties. The fabrication was planned by central composite design using two variables and one response. To obtain actual responses, the design was performed in laboratory by the conventional methods of ceramics fabrication. The actual responses were fitted into a valid second order algebraic polynomial equation. Then the quadratic model was suggested by response surface methodology. The model was validated by analysis of variance which provided several evidences such as high F-value (153.6), very low P-value (<0.0001), adjusted R-squared (0.985) and predicted R-squared (0.947). Moreover, the lack of fit was not significant which means the model was significant.

Results

The model tracked the optimum of the additives in the design by using three dimension surface plots. In the optimum condition, the molars ratio of Bi2O3 and TiO2 were obtained in a surface area around 1.25 point that maximized the nonlinear coefficient around 20 point. Moreover, the model predicted the optimum amount of the additives in desirable condition. In this case, the condition included minimum standard error (0.35) and maximum nonlinearity (20.03), while molar ratio of Bi2O3 (1.24 mol%) and TiO2 (1.27 mol%) was in range. The condition as a solution was tested by further experiments for confirmation. As the experimental results showed, the obtained value of the non-linearity, 21.6, was quite close to the predicted model.

Conclusion

Response surface methodology has been successful for modeling and optimizing the additives such as Bi2O3 and TiO2 of ZnO-based low voltage varistor to achieve maximized non-linearity properties.

Keywords

Optimization ZnO-varistor Modeling RSM Bi2O3 TiO2

Background

Varistors are nonlinear electro-devices with a ceramics microstructure that are used as protectors in distribution and energy transmission lines against voltage surge [1]. In the past four decades, varistors based on ZnO and SnO2 have attracted attention because of their excellent non-ohmic behavior and low leakage currents [2, 3]. However, ZnO-based varistor has been demanded along with the development of very-large-scale integration electronics because it exhibits high nonlinear current–voltage (I-V) characteristics in lower voltage ranges [4, 5]. The non-linearity is expressed by I = KVα where K is a constant, and 'α’ is nonlinear coefficient (alpha) [6]. The alpha originates from microstructure of the varistor ceramics which is composed of conductive n-type ZnO grains and small amount of a few metal oxide additives such as Bi2O3, TiO2, Co3O4, Mn2O3, Sb2O3 and Al2O3. The microstructure is made of ZnO grain surrounded by the melted additives as boundaries [4]. The boundaries contain of Bi-rich intergranular, metal oxide and secondary spinel phase and strictly influences on the alpha [4, 611]. The role of Bi2O3, as a former, is quite important since it provides the medium for liquid-phase sintering, enhances the growth of ZnO grains, and finally stables the nonlinear current–voltage characteristics of the varistor [12]. High sintering temperature is necessary for ZnO grain growth despite the fact that at this condition Bi2O3 tends to evaporate [13]. The melting point of Bi2O3 is 825°C, and the eutectic temperature of ZnO-Bi2O3 is only 740°C, thus a liquid phase is formed in the ZnO-Bi2O3 specimens below 800°C. As soon as the eutectic liquid is formed, the mass loss starts to increase which indicates the vaporization of Bi2O3[14]. The sharp lost weight was reported above 1100°C since there was no reported peaks of β-Bi2O3 at 1300°C [15, 16]. On the other hand, TiO2 increases reactivity of the Bi2O3-rich liquid phase with the solid ZnO during sintering process which prevents Bi2O3 vaporization [13, 1719]. The phase equilibrium and the temperature of liquid-phase formation are defined by the TiO2/Bi2O3 ratio [20]. According to the reports, the effect of TiO2 depends on Bi2O3 that means the additives interact in low-voltage varistor ceramics fabrication. To determine the effect of the interactions on the varistors’ electrical properties, the molar ratios of the additives must simultaneously be considered. To the best of our knowledge, there is no study on the interactions which optimize the ratio of the additives and maximize the alpha. Recently, response surface methodology (RSM) has been accepted for modeling and optimizing of input intractable variables to achieve maximum yield product as output for productive process [21]. RSM is known as a semi-empirical method because the process could be optimized by using experimental results, and a group of mathematical and statistical techniques [22]. In this work, RSM was used for modeling and optimizing of molar ratio of Bi2O3 and TiO2 as additives to achieve the maximum value of the alpha for low voltage varistor. The experiments were designed by central composite design (CCD) to obtain the empirical results (actual). The results were used for regression and fitting process to fine an appropriate model. The model was verified by several statistical techniques such as residual analysis, scaling residuals and prediction error sum of squares (PRESS). The model optimized the input additives and then maximized the alpha as output. In addition, the model predicted the desirable condition including minimum standard error and the maximum alpha which are validated by further experiments. The predicted samples were characterized by X-ray diffractometer (XRD), scanning electron microscope (SEM), variable pressure scanning electron microscope (VPSEM) and Energy-dispersive X-ray (EDX).

Experimental

Materials and methods

The commercial chemical, ZnO (99.99%), Bi2O3 (99.975%), TiO2 (99.9%), Sb2O3 (99.6%), Mn3O4 (98%), Co3O4 (99.7%) and Al(NO3)3 (100% ±2), were provided from Alfa Aesar as starting powders. The powders were weighed according to the experimental design of molar ratios (Table 1). The molar ratios of the powders were mixed, ground in dry form and then ball milled in acetone for 24 h. During ball milling, agglomeration was controlled by Zirconium oxide balls. After drying in hot oven for 8 h, the mixed powders were grounded and pressed into pellet forms of 10 mm in diameter and 0.70 mm thickness at 200 MPa by a uniaxial presser machine. The disks were sintered in a box furnace (CMTS Model HTS 1400) for holding time of 2 h at 1260°C. The heating and cooling rate were 5°C/min [23]. To determine DC current–voltage (I-V), both of the sintered sample surfaces were coated by silver electrodes. The I-V of the samples was measured by Keithley 236 source meter. The samples were scanned with dc voltage from 0 to 100 V in step size of 2.5 V. The alpha was calculated at J1 = 0.1 and J2 = 1 mA/cm2 by equation (1) as actual responses [6].
α = logJ 2 - logJ 1 / logE 2 - logE 1
(1)
Table 1

Experimental-design contain of the actual variables, and actual response and model predicted values of the alpha

Run

ZnO

TiO2

Bi2O3

Co3O4

Mn2O3

Sb2O3

Al(NO3)3

Alpha (Actual)

Alpha (Predicted)

1

96.50

1

1

0.5

0.5

0.5

0.00094

9.3

10.1

2

96.00

1.5

1

0.5

0.5

0.5

0.00094

3.9

3.9

3

96.00

1

1.5

0.5

0.5

0.5

0.00094

6.4

7.4

4

95.50

1.5

1.5

0.5

0.5

0.5

0.00094

10.5

10.6

5

96.35

0.896

1.25

0.5

0.5

0.5

0.00094

9

7.9

6

95.65

1.604

1.25

0.5

0.5

0.5

0.00094

5.6

5.8

7

96.35

1.25

0.896

0.5

0.5

0.5

0.00094

8.2

7.7

8

95.65

1.25

1.604

0.5

0.5

0.5

0.00094

11

10.5

9

96.00

1.25

1.25

0.5

0.5

0.5

0.00094

20

20

10

96.00

1.25

1.25

0.5

0.5

0.5

0.00094

19.6

20

11

96.00

1.25

1.25

0.5

0.5

0.5

0.00094

20.2

20

12

96.00

1.25

1.25

0.5

0.5

0.5

0.00094

20.7

20

13

96.00

1.25

1.25

0.5

0.5

0.5

0.00094

19.4

20

The breakdown voltage (Eb) was determined by measuring E at J = 0.75 mA/cm2 and the leakage current (JL) was determined evaluating J at 0.8Eb where J (mA/cm2) is the current density and E is the electrical field (V/mm). To characterize the microstructure, the both surfaces of samples were polished by aluminum oxide powder. Then, they were etched at 160°C under sintering time with heating and cooling rate, 10°C/min. Phase analysis was conducted using XRD (PANalytical, Philips-X’pert Pro PW3040/60) with CuKα source. The sample were radiated with Ni-filtered CuKα radiation (λ = 1.5428) within the 2θ scan range of 20–80°. Surface morphology and elemental analyses of sintered samples were studied under SEM (JEOL JSM 6400) and VPSEM (LEO 1455) which attached to EDX. The samples was mounted on Al stub using carbon paint and coated by gold layer. Average grains size of the ZnO in the varistor was evaluated by measuring 100 grains in SEMs images.

Experimental design

The experimental design was carried out by CCD that used Design-Expert software version 8.0.7.1, Stat-Ease Inc., USA [2426]. CCD is well fixed for fitting a quadratic surface that usually works well for optimization process [2729]. The variables number, level of variables and number of responses are determined as input of experimental design. In this case, the molar ratio of Bi2O3 and TiO2 was selected as effective variables in vicinity of their optimum while alpha was the response as output. The CCD transformed the variables and the response to the terms of code values (Table 2) because the units and range of variables were different. The coded values spaces are ±1 from the center (0.0) and the star points are usually located 'α’ distance from the center [29, 30]. In the design, there N experiment that includes the factorial points (2n), the axial points (2n), and the center points Co or replications as the equation N = 2n + 2n + Co which is 13, 4, 4 and 5 respectively. The replications are used to measure experimental error [31]. As a result, the experimental design is presented as a design matrix with 'n’ column and 'N’ row in Table 1. Where, each column corresponds to a particular variable, e.g. x1 and x2 which arranged in order to increase factor number from left to right. The rows are experiments runs because each one contains the descriptions of an experiment. Additionally, the design constructs a matrix of actual responses that obtained by the experiments.
Table 2

The variables and employed levels in the CCD for ZnO low voltage varistor fabrication

  

Level of variables

Symbol

Unit (%)

Low (-1)

Middle (0)

High (+1)

Bi2O3 (x1)

mol

1.0

1.25

1.5

TiO2 (x2)

mol

1.0

1.25

1.5

The RSM description

The RSM develops an adequate functional relationship between input variables and interested responses by low-degree approximation of the polynomial models such as the second degree model (Eq. 2) [32].
Y = β o + i = 1 n β i x i + i = 1 n β ii x i 2 + i = 1 n j = i + 1 n β ij x i x j + ϵ
(2)
where Y is the interested response, β 0 is the constant term, β i is the coefficient of the linear terms, β ii represents the coefficient of the quadratic terms, β ij is the coefficient of the interaction terms while xi are control variables and 'ϵ’ is a random experimental error [31]. For system with two factors, the model is described by equation (3),
Y 1 i = β 0 + β 1 x 1 i + β 11 x 1 i 2 + β 22 x 2 i 2 + β 12 x 1 i x 2 i + r 1 i
(3)

where Y1i is the experimental single response, x1 and x2 are the coded factors (Table 2), β0 is the intercept term, β1 and β2 are slopes with respect to each of the two factors, β11 and β22 are curvature terms, and β12 is the interaction term. To estimate the β’s, the fitting process provides the sufficient data by regression tools [33, 34]. In the process, the actual responses are fitted to the polynomial models by sequential model sum of squares (SMSS) [33, 34]. The SMSS compares the linear, two-factor interaction (2FI), quadratic and cubic models by using the statistical significance of adding new model terms, step by step in increasing order [35]. To select the provisional model, the lack of fit of those models is compared by minimum p-values and PRESS. The other assessments to select the best provisional model are maximum adjusted R-squared (RAdj) and predicted R-squared (RPred) [36]. The p-value is one of the most important evidences which was used to study significant effect of the parameters [33]. In addition, the lack-of-fit test diagnoses how well each terms of the full model fit the data that pillared by statistical parameters such as RAdj, RPred and PRESS [36, 37]. Therefore, the provisional model with minimum p-value and PRESS and also maximum RAdj, RPred is selected to investigate in details. The details are provided by using analysis of variance (ANOVA) which contains a collection of terms statistical evidences. The ANOVA determines the significance of intercept, linear, interaction and square terms of the provisional model by using minimum p-value. For more evaluation, the normality of residuals, constant error and residual outlier is checked by various diagnostic plots [38]. The validated model, the relationship between variables and response, is created in coded and actual variables. The model indicates the effect of linear, quadratic and the parameters interactions on the interested response. The effects are presented by estimated coefficients and the related positive and negative signs (+, -). The coefficients are specific weight of the parameters in the model while the signs (+) and (-) operate as synergistic and antagonistic effects on the response [39]. Then the optimization process investigates combination of variables levels that produces the maximum response to a surface area simultaneously. Moreover, the model predicts the yield of product in specific condition such as individual standard error, the range of variables and responses. The prediction could be performed by further experiments.

Results and discussion

Modeling

In the fitting process, the residuals are produced from difference between actual and predicted values. Standard error is a great tool to determine the residuals outlier and also the scope of models prediction [40]. Figure 1 indicates the standard error contour plot of the experimental design which displays Bi2O3 versus TiO2 molar ratio. The bright area has relatively low standard error that interested for the modeling and optimization process. However, the darker shading corners represent higher standard errors which are dangerous to extrapolation.
Figure 1

Contour plot of the experimental-design standard error with expanded axes, extrapolated area shaded.

Table 3 indicates that the SMSS compared four models to recommend a proper model [35, 36]. As shown, the quadratic model was suggested as the best provisional model. The suggestion based on the lowest standard deviation, p-value and PRESS and the highest RAdj, RPred values. Moreover, the RAdj (0.991) was in reasonable agreement with (<0.20) RPred (0.985) which confirmed the model sufficiency. Therefore, the authors selected the suggested model to investigate in details by using ANOVA.
Table 3

The sequential model fitting summary for the actual responses which shows statistics conformation of the regression process, DF is degree of freedom

Source

Sum of squares

DF

Mean square

F-Value

p-value

Remark

Sequential model sum of squares

     

Mean vs Total

2064.18

1

2064.18

-

-

 

Linear vs Mean

12.07

2

6.04

0.13

0.8818

 

2FI vs Linear

22.31

1

22.31

0.44

0.5216

 

Quadratic vs 2FI

447.11

2

223.55

356.58

< 0.0001

Suggested

Cubic vs Quadratic

1.56

2

0.78

1.38

0.3325

Aliased

Residual

2.83

5

0.57

-

-

 

Total

2550.06

13

196.16

-

-

 

Source

Sum of squares

DF

Mean square

F-Value

p-value

Remark

Lack of fit tests

      

Linear

472.78

6

78.80

307.73

< 0.0001

 

2FI

450.47

5

90.09

351.85

< 0.0001

 

Quadratic

3.36

3

1.12

4.38

0.0938

Suggested

Cubic

1.80

1

1.80

7.03

0.0569

Aliased

Pure Error

1.02

4

0.26

-

-

 

Source

Std.Dev.

R Adj

R Pred

R

PRESS

Remark

Model summary statistics

      

Linear

6.88

0.025

-0.170

-0.571

763.55

 

2FI

7.08

0.071

-0.239

-1.195

1066.45

 

Quadratic

0.79

0.991

0.985

0.947

25.53

Suggested

Cubic

0.75

0.994

0.986

0.760

116.85

Aliased

Table 4 shows the ANOVA of the provisional model which included the useful statistical evidences about the terms (x1, x2, x 1 x 2 , x12 and x22) in details. As shown, the prob > F of the terms was less than 0.05 which confirmed the high significance of the terms. Moreover, the model’s F-value was 153.6 that indicated great significance for the model. In addition, the very low value of the model p-value confirmed the significance. Futhermore, R-squared (R) provides a measure of how much variability in the observed response values can be explained by the experimental factors and their interactions. In this study, the R (0.991) indicated that the model was capable of accounting for more than 99.1% of the variability in the responses. The RAdj (0.985) was in reasonable agreement with (<0.20) the RPred (0.947) which confirmed the aptness of the model. The pure errors such as experimental errors were minimal as the lack of fit (0.094) was not significant or the model was fit well. Therefore, ANOVA confirmed the adequacy of the quadratic model that could be used to navigate the design space.
Table 4

Analysis of variance (ANOVA) for response surface quadratic model, MS is mean Square, DF is degree of freedom and SS is sum of squares while x 1 and x 2 introduce in Table 2

Source

SS

DF

MS

F-Value

p-value (Prob > F)

Suggestion

Model

481.5

5

96.3

153.6

< 0.0001

significant

 x1

4.6

1

4.6

7.4

0.0299

 

 x2

7.4

1

7.4

11.9

0.0108

 

 x1x2

22.3

1

22.3

35.6

0.0006

 

 x12

298.7

1

298.7

476.4

< 0.0001

 

 x22

205.5

1

205.5

327.7

< 0.0001

 

Residual

4.4

7

0.6

   

Lack of Fit

3.4

3

1.1

4.4

0.0938

not significant

Pure Error

1.0

4

0.3

 

< 0.0001

 

Cor Total

485.9

12

  

0.0299

 

Std.Dev.

R Adj

R Pred

R 2

PRESS

C.V.%

Adeq Precision

0.792

0.985

0.947

0.991

25.5

6.284

29.9

The normality of residuals, constant error and residual outliers were checked by various diagnostic plots. The normal probability plots of the studentized residuals as one of the most important diagnostic plots, was provided by software default (Figure 2). The plot presents percentage of normal% probability versus internal studentized residuals. The studentized residual is an important technique in the detection of residuals outliers in regressions [41]. As the plot demonstrates, the residuals followed a normal distribution that implies the points follow a straight line.
Figure 2

Normal plot of residuals for the whole model.

As another diagnosis, Figure 3 illustrates the predicted response values versus the actual response values and detects the values that are not easily predicted by the model. The data points were on the 45 degree line that means the values were not detected. As a result, the diagnosis of residuals reveals that there is no statistical problem in the model.
Figure 3

Studentized residuals versus predicted values to check the constant error.

The model presentation

The model expresses the relationship between responses of actual variables and the variables themselves. The validated model is presented in actual variables by equation (4),
Y = - 221.67 + 211.82 x 1 + 174.00 x 2 + 37.79 x 1 x 2 - 104.84 x 1 2 - 86.95 x 2 2
(4)

where the actual values of the variables x1 and x2 were shown in Table 2 and Y is the alpha. As shown, the parameters including linear (x1, x2), quadratic (x12, x22) and interaction (x1x2) affected on the interested response. The effects are presented by the individual coefficients and the related signs (+, -) in the model. The coefficients indicate the specific weight of the parameters in the model while the signs are synergistic (+) and antagonistic (-) effects of variables on the response (Y). The weights determine the importance of the parameters roles in the modeling. The model is able to optimize input variables and also approximately predict the response inside of the actual experimental region that confirmed by minimum standard error (Figure 1) [33]. Therefore, the model was used to optimize the molar ratio of Bi2O3 and TiO2 to achieve maximized alpha.

The model optimization

The model is able to optimize the variables by using canonical response and graphical plots. The canonical responses, local optimums, in terms of the code and actual variables were determined by differentiating the model (Eq. 4) as presented in equations (5 and 6),
Y / x 1 x 2 = 0
(5)
Y / x 2 x 1 = 0
(6)
where the terms were introduced in Table 2. Therefore, the optimum canonical amount of Bi2O3 and TiO2 were 1.195 and 1.025 respectively. At this optimum, the maximized alpha was 15.03. In fact, the optimization is a kind of the traditional methods, one-variable-at-a-time, because in each case, one of the variables was varied and others were constant. In graphical optimization, the model simultaneously considered the effect of Bi2O3 and TiO2 on the alpha. Figure 4 presents the three dimension response surface (3D) plot for the synergy between Bi2O3 (1–1.5 mol%) and TiO2 (1–1.5 mol%) which is standard error limitation area (Figure 1). As shown, the alpha increased within 1 to 1.25 mol% Bi2O3 and TiO2. However, when the amount of Bi2O3 and TiO2 was increased in excess of the optimum (1.25 mol%), the alpha decreased. Therefore, the optimum was determined in a surface area around 1.25 mol% for the both additives. The maximum alpha was 20.031 at center of the surface which indicated by a flag on top of Figure 4.
Figure 4

The effect of Bi 2 O 3 and TiO 2 on alpha that simultaneously presented by 3D response surface plot, the maximized alpha was 20.031.

The model predicted the maximized alpha in desirable condition of the additives and standard error which facilitated by default of software numerical option. The desirability is an objective function that uses mathematical methods to find the optimum condition [25]. The range of the function is from zero (outside of the limit area) to one (at the goal). The criteria for this case Bi2O3 (in range), TiO2 (in range), standard error (minimized) and alpha (maximized). The suggested solution was Bi2O3 (1.24 mol%), TiO2 (1.27 mol%), standard error (0.35), and alpha 20.03. The desirability of the solution was 0.981 which is close to 100% (at the goal). The solution was performed to confirm the prediction by validated experiment. The validated alpha was determined 21.6 which was quite close to the predicted alpha (20.03). Table 5 illustrated a summary of optimized molar ratios of Bi2O3 and TiO2 and also the related maximized alpha which obtained by canonical, graphical and numerical model optimization method.
Table 5

The summary of optimized input variables and obtained maximized alpha% by canonical, graphical, numerical methods and validation value of the photodegradation

Method

Bi2O3 (%mol)

TiO2 (%mol)

Alpha

Canonical (point)

1.195

1.025

15.03

Graphical (area)

Area around 1.25

Area around 1.25

20.03

Numerical (prediction)

1.24

1.27

20.03

Validated sample

1.24

1.27

21.6

The validated varistor

The validated sample was characterized as final varistor for this optimization process. Figure 5 demonstrates the SEM morphology of the sintered ceramic microstructure of the varistor. Figure 5a indicates the great homogeneity of ZnO grain size. The average grain size was 15 μm (Figure 5b).
Figure 5

The microstructure of the optimized varistor morphology, (a) SEM (b) distribution of ZnO grain size.

Figure 6 illustrates EDX spectra of a limited area of the etched varistor surfaces composition. As shown all additives particular Bi (1.0 weight%) and Ti (1.17 weight%) were detected in the selected area after sintering process at 1260°C.
Figure 6

The EDX of etched optimized sample surfaces.

Figure 7 shows the XRD pattern of the optimized sample which presented three phase of ZnO, spinel and metal oxide of additives. The composites were including ZnO (00-005-0664), Bi2O3 (00-002-0988), TiO2 (01-072-0020), MnO2 (00-003-1041), CoO (00-048-1719), Sb2O3 (01-075-1567), Al2O3 (00-004-0879) and Sb3Ti2O10 (00-028-0103). The number that mentioned in parenthesis are XRD reference code.
Figure 7

The XRD patterns of optimized varistor sample which include ZnO, additives and spinel.

The electrical properties of the varistor were basis of I-V characteristic measurement that shows breakdown voltage was 98 V/mm with non-linear coefficient 21.6. The leakage current was 0.013 mA/cm2. The stability of the varistor was measured by alpha recovery after removing the over voltage (Figure 8). As shown the stability was quit significant after fourth over voltage.
Figure 8

E-J characteristic curves of the optimized samples for first to fourth measurments.

Conclusions

This work reports modeling and optimization of the molar ratio of Bi2O3 and TiO2 by RSM. The fabrication was designed by CCD using two variables and a response. To obtain actual responses, the design was performed in laboratory by conventional fabrication methods. The actual responses were fitted into a quadratic model. The model was validated by ANOVA which provided evidences such as high F-value (153.6), very low p-value (<0.0001), Radj (0.985) and RPred (0.947). The results of the validation showed the model was significant. The model tracked the optimum of the designed additives by using 3D plots. In the optimum condition, the molars ratio of Bi2O3 and TiO2 were around 1.25 that maximized the alpha value at 20. Moreover, the model suggested a solution to predict the optimum amount of the additives. In this case, the condition of the solution included standard error of 0.35, Bi2O3 of 1.24, TiO2 of 1.27 and alpha of 20.03. The solution was tested by further experiments. As the validation test showed, the obtained value of the alpha (21.6) was very close to the predicted value (20.03). Therefore, RSM was succeeded in modeling of the additives in fabrication of zinc oxide based low voltage varistor to achieve maximum alpha.

Declarations

Acknowledgements

The author would like to express acknowledgement to Ministry of Higher Education Malaysia for granting this project under Research University Grant Scheme (RUGS) of Project No. 05-02-12-1878. The authors wish to thank Dr Pedram Lalbakhsh for polishing and proofreading the manuscript.

Authors’ Affiliations

(1)
Material Synthesis and Characterization Laboratory, Institute of Advanced Technology, Universiti Putra Malaysia
(2)
Department of Physics, Faculty Science, Universiti Putra Malaysia

References

  1. Ramírez M, Bueno P, Ribeiro W, Varela J, Bonett D, Villa J, Márquez M, Rojo C: The failure analyses on ZnO varistors used in high tension devices. J Mater Sci. 2005, 40: 5591-5596. 10.1007/s10853-005-1366-4.View ArticleGoogle Scholar
  2. Ramirez M, Bassi W, Bueno P, Longo E, Varela J: Comparative degradation of ZnO-and SnO2-based polycrystalline non-ohmic devices by current pulse stress. J Phys D Appl Phys. 2008, 41: 122002-122002. 10.1088/0022-3727/41/12/122002.View ArticleGoogle Scholar
  3. Ramírez MA, Bassi W, Parra R, Bueno PR, Longo E, Varela JA: Comparative electrical behavior at low and high current of SnO2 and ZnO based varistors. J Am Ceram Soc. 2008, 91: 2402-2404. 10.1111/j.1551-2916.2008.02436.x.View ArticleGoogle Scholar
  4. Levinson L, Philipp H: Zinc oxide varistors–a review. Am Ceram Soc Bull. 1986, 65: 639-Google Scholar
  5. Eda K: Zinc oxide varistors. IEEE Electr Insul Mag. 2002, 5: 28-30.View ArticleGoogle Scholar
  6. Bernik S, Zupancic P, Kolar D: Influence of Bi2O3/TiO2/Sb2O3 and Cr2O3 doping on low-voltage varistor ceramics. J Eur Ceram Soc. 1999, 19: 709-713. 10.1016/S0955-2219(98)00301-X.View ArticleGoogle Scholar
  7. Suzuki H, Bradt R: Grain growth of ZnO in ZnO-Bi2O3 ceramics with TiO2 addition. J Am Ceram Soc. 1995, 78: 1354-1360. 10.1111/j.1151-2916.1995.tb08494.x.View ArticleGoogle Scholar
  8. Clarke DR: Varistor ceramics. J Am Ceram Soc. 1999, 82: 485-502.View ArticleGoogle Scholar
  9. Pillai SC, Kelly JM, McCormack DE, O'Brien P, Ramesh R: The effect of processing conditions on varistors prepared from nanocrystalline ZnO. J Mater Chem. 2003, 13: 2586-2590. 10.1039/b306280e.View ArticleGoogle Scholar
  10. Elfwing M, Österlund R, Olsson E: Differences in wetting characteristics of Bi2O3 polymorphs in ZnO varistor materials. J Am Ceram Soc. 2000, 83: 2311-2314.View ArticleGoogle Scholar
  11. Lao Y-W, Kuo S-T, Tuan W-H: Effect of Bi2O3 and Sb2O3 on the grain size distribution of ZnO. J Electroceram. 2007, 19: 187-194. 10.1007/s10832-007-9187-2.View ArticleGoogle Scholar
  12. Peiteado M, Fernández Lozano JF, Caballero AC: Varistors based in the ZnO-Bi2O3 system: microstructure control and properties. J Eur Ceram Soc. 2007, 27: 3867-3872. 10.1016/j.jeurceramsoc.2007.02.046.View ArticleGoogle Scholar
  13. Peiteado M, De la Rubia M, Velasco M, Valle F, Caballero A: Bi2O3 vaporization from ZnO-based varistors. J Eur Ceram Soc. 2005, 25: 1675-1680. 10.1016/j.jeurceramsoc.2004.06.006.View ArticleGoogle Scholar
  14. Metz R, Delalu H, Vignalou J, Achard N, Elkhatib M: Electrical properties of varistors in relation to their true bismuth composition after sintering. Mater Chem Phys. 2000, 63: 157-162. 10.1016/S0254-0584(99)00227-8.View ArticleGoogle Scholar
  15. Wong J: Sintering and varistor characteristics of ZnO Bi2O3 ceramics. J Appl Phys. 1980, 51: 4453-4459. 10.1063/1.328266.View ArticleGoogle Scholar
  16. Yilmaz S, Ercenk E, Toplan HO, Gunay V: Grain growth kinetic in x TiO 2–6 wt.% Bi 2 O 3–(94- x) ZnO (x = 0, 2, 4) ceramic system. J Mater Sci. 2007, 42: 5188-5195. 10.1007/s10853-006-0581-y.View ArticleGoogle Scholar
  17. Toplan HÖ, Karakaş Y: Processing and phase evolution in low voltage varistor prepared by chemical processing. Ceram Int. 2001, 27: 761-765. 10.1016/S0272-8842(01)00027-X.View ArticleGoogle Scholar
  18. Yan MF, Heuer AH: Additives and interfaces in electronic ceramics. 1983, Columbus, OH: Amer Ceramic SocietyGoogle Scholar
  19. Peigney A, Andrianjatovo H, Legros R, Rousset A: Influence of chemical composition on sintering of bismuth-titanium-doped zinc oxide. J Mater Sci. 1992, 27: 2397-2405. 10.1007/BF01105049.View ArticleGoogle Scholar
  20. Bernik S, Daneu N, Rečnik A: Inversion boundary induced grain growth in TiO2 or Sb2O3 doped ZnO-based varistor ceramics. J Eur Ceram Soc. 2004, 24: 3703-3708. 10.1016/j.jeurceramsoc.2004.03.004.View ArticleGoogle Scholar
  21. Montgomery DC: Design and analysis of experiments. 2008, New York: John Wiley & Sons IncGoogle Scholar
  22. Khuri AI, Cornell JA: Response surfaces: designs and analyses. 1996, New York: CRCGoogle Scholar
  23. Bernik S: Microstructural and electrical characteristics of ZnO based varistor ceramics with varying TiO2/Bi2O3 ratio. Advances in science and technology. 1999, 151-158.Google Scholar
  24. Williges RC, Clark C: Illinois Univ Savoy Aviation Research Lab. Response surface methodology design variants useful in human performance research. 1971Google Scholar
  25. Myers RH, Anderson-Cook CM: Response surface methodology: process and product optimization using designed experiments. 2009, New Jersey: John Wiley and Sons, IncGoogle Scholar
  26. Clark C, Williges RC: DTIC Document, New York. Response surface methodology design variants useful in human performance research. 1971Google Scholar
  27. Deming S: Quality by design-part 5. Chemtech. 1990, 20: 118-120.Google Scholar
  28. Hader R, Park SH: Slope-rotatable central composite designs. Technometrics. 1978, 20: 413-417. 10.1080/00401706.1978.10489695.View ArticleGoogle Scholar
  29. Palasota JA, Deming SN: Central composite experimental designs: applied to chemical systems. J Chem Educ. 1992, 69: 560-10.1021/ed069p560.View ArticleGoogle Scholar
  30. Rubin IB, Mitchell TJ, Goldstein G: Program of statistical designs for optimizing specific transfer ribonucleic acid assay conditions. Anal Chem. 1971, 43: 717-721. 10.1021/ac60301a003.View ArticleGoogle Scholar
  31. Baş D, Boyacı İH: Modeling and optimization I: usability of response surface methodology. J Food Eng. 2007, 78: 836-845. 10.1016/j.jfoodeng.2005.11.024.View ArticleGoogle Scholar
  32. Khuri AI, Mukhopadhyay S: Response surface methodology. Wiley Interdiscip Rev Comput Stat. 2010, 2: 128-149. 10.1002/wics.73.View ArticleGoogle Scholar
  33. Anderson MJ, Whitcomb PJ: RSM simplified: optimizing processes using response surface methods for design of experiments. 2005, Virginia: Productivity PrGoogle Scholar
  34. Oehlert GW: A first course in design and analysis of experiments. 2000, New York: WH FreemanGoogle Scholar
  35. Deming S: Quality by design-part 3. Chemtech. 1989, 19: 249-255.Google Scholar
  36. Whitcomb PJ, Anderson MJ: RSM simplified: optimizing processes using response surface methods for design of experiments. 2004, New York: Productivity PressGoogle Scholar
  37. Allen DM: The relationship between variable selection and data agumentation and a method for prediction. Technometrics. 1974, 16: 125-127. 10.1080/00401706.1974.10489157.View ArticleGoogle Scholar
  38. Stuart A, Ord JK: Kendall’s advanced theory of statistics: vol 1. Distribution theory. 1994, London: ArnoldGoogle Scholar
  39. Abdollahi Y, Zakaria A, Abdullah AH, Masoumi HRF, Jahangirian H, Shameli K, Rezayi M, Banerjee S, Abdollahi T: Semi-empirical study of ortho-cresol photo degradation in manganese-doped zinc oxide nanoparticles suspensions. Chem Cent J. 2012, 6: 1-8. 10.1186/1752-153X-6-1.View ArticleGoogle Scholar
  40. Box GEP, Wilson K: On the experimental attainment of optimum conditions. J Roy Stat Soc B. 1951, 13: 1-45.Google Scholar
  41. Stuart A, Ord JK, Arnold S: Kendall’s advanced theory of statistics: vol 2A. Classical inference and the linear model. 1999, London: ArnoldGoogle Scholar

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