Optimized synthesis and photovoltaic performance of TiO2 nanoparticles for dye-sensitized solar cell

• Published in: Particuology Vol. 11; no. 6; pp. 753 - 759
• Main Authors: Zainudin, Siti Nur Fadhilah, Markom, Masturah, Abdullah, Huda, Adami, Renata, Tasirin, Siti Masrinda
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2013
• Subjects: Analysis of variance; Calcination; Dye-sensitized solar cell; Hydrolysis precipitation; Mathematical models; Optimization; Photovoltaic cells; Response surface methodology; Solar cells; TiO2纳米粒子; Titanium dioxide; 光电性能; 功率转换效率; 合成; 性能优化; 扫描电子显微镜; 染料敏化太阳能电池; 纳米颗粒; Dye-sensitized solar cell; Response surface methodology; Titanium dioxide; Optimization; Hydrolysis precipitation
• ISSN: 1674-2001; 2210-4291
• DOI: 10.1016/j.partic.2012.07.010

This paper presents response surface methodology （RSM） as an efficient approach for modeling and optimizing TiO2 nanoparticles preparation via co-precipitation for dye-sensitized solar cell （DSSC） perfor- mance. Titanium （IV） bis-（acetylacetonate） di-isopropoxide （DIPBAT）, isopropanol and water were used as precursor, solvent and co-solvent, respectively. Molar ratio of water, aging temperature and calcina- tion temperature as preparation factors with main and interaction effects on particle characteristics and performances were investigated, Particle characteristics in terms of primary and secondary sizes, crys- tal orientation and morphology were determined by X-ray diffractometry （XRD） and scanning electron microscopy （SEM）. Band gap energy and power conversion efficiency of DSSCs were used for perfor- mance studies. According to analysis of variance （ANOVA） in response surface methodology （RSM）, all three independent parameters were statistically significant and the final model was accurate. The model predicted maximum power conversion efficiency （0.14%） under the optimal condition of molar ratio of DIPBAT-to-isopropanol-to-water of 1 ： 10：500, aging temperature of 36 C and calcination temperature of 400 ℃. A second set of data was adopted to validate the model at optimal conditions and was found to be 0.14 ± 0.015%, which was very close to the predicted value. This study proves the reliability of the model in identi（ving the optimal condition for maximum performance.