Optimization of deposition conditions of CdS thin films using response surface methodology

J Mater Sci: Mater Electron

Gökhan

2015

Self Cite

In this study, CuS thin films were deposited on glass substrates by SILAR method with different pH, dipping time and dipping cycles. For the first time, response surface methodology was used for optimization CuS thin film coating parameters. 5-level-3-factor central composite design was employed to evaluate the effects of the deposition parameters (pH, dipping time and dipping cycles) on the optical band gap of the films. The significant level of the main effects and the interactions were investigated by analysis of variance. The morphological, structural and optical properties of the films were investigated by SEM, XRD and UV-Vis. The optimum pH, dipping time and dipping cycles were found to be 11.5, 31 s and 15 cycles, respectively. Under these conditions, the experimental band gap of CuS was observed as 2.18 eV which was well in close agreement with predicted value (2.11 eV) by the model. Quality of the film was improved after chemometrics optimization.

“…The average crystallite sizes of the films were calculated by Debye-Scherrer equation [15] D ¼ kk=bcosh ð3Þ…”

Section: Structural and Morphological Propertiesmentioning

confidence: 99%

Section: Optical Propertiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization and modelling of preparation conditions of CuS thin films deposited by successive ionic layer adsorption and reaction (SILAR) method using response surface methodology

J Mater Sci: Mater Electron

Gökhan

2015

Self Cite

“…Response surface methodology (RSM) is a statistical approach to design experiments, to build models, to evaluate the effects of many factors and to find the optimal conditions for desirable responses and to reduce the number of required experiments [10][11][12][13][14][15]. Also, we have successfully used RSM to optimize of ethanol production conditions in our previous study [16].…”

Section: Introductionmentioning

confidence: 99%

Optimization and Modelling of Process Conditions Using Response Surface Methodology (RSM) for Enzymatic Saccharification of Spent Tea Waste (STW)

Göycincik

2015

Waste Biomass Valor

Self Cite

Spent tea waste (STW) is an important lignocellulosic waste as a cost-effective feedstock for ethanol production. The enzymatic hydrolysis of acid pretreated STW was investigated in this study. The effects of process parameters, including acid pretreatment time 26.4-93.6 min, b-glucosidase loading from 20 to 80 IU/g and cellulase loading from 11 to 45 IU/g on reducing sugar yield, were optimized by using central composite design of response surface methodology. The analysis of variance of data was examined by using response surface quadratic model. The valid model was used for optimization of the reducing sugar concentration during enzymatic hydrolysis. The optimum conditions for enzymatic saccharification were found to be acid pretreatment time of 27 min, b-glucosidase loading of 49 IU/g and cellulase loading of 12 IU/g. Maximum concentration of the reducing sugar under the optimum conditions was determined as 29.0 g reducing sugar/L.

“…Response surface methodology (RSM) is a statistical model widely used to study an aggregate effect of several variables and to seek optimum conditions for a multivariable system. A combination of factors generating a certain optimum response can be identified though factorial designs as well as RSM [12][13][14][15][16]. According to our best knowledge, RSM in combination with central composite design (CCD) used for optimization of conditions of fuel ethanol production using spent tea waste has not been investigated still yet.…”

Section: Introductionmentioning

confidence: 99%

Optimization of ethanol production from spent tea waste by Saccharomyces cerevisiae using statistical experimental designs

Göycincik

2014

Biomass Conv. Bioref.

Self Cite

The aim of this study was to investigate the prospect for the use of spent tea waste (STW), an important municipal waste, as a potential substrate to generate hydrolysates for fuel ethanol production. Acid pretreated STW was used as substrate for ethanol production. The critical variables that affected ethanol fermentation from STW were identified by Plackett-Burman designs and further optimized by using a five-level-three-factor central composite design of response surface methodology. The optimum conditions for ethanol fermentation were determined to be NH 4 Cl concentration of 2.7 g/L, yeast concentration of 11.7 g/L, and temperature of 42.8°C. Maximum concentration of reducing sugar and ethanol under the optimum conditions were 28.90 g reducing sugar/L and 12.72 g EtOH/L, respectively. Predicted ethanol concentration was obtained using quadratic polynomial equation. The predicted ethanol concentration was 13.38 g EtOH/L in the optimal conditions. Validity of the predicted model was confirmed using verification experiment (12.72 g EtOH/L).