Deposition, characterizations and photoelectrochemical performance of nanocrystalline Cu–In–Cd–S–Se thin films by hybrid chemical process
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Nanocrystalline, uniform, pentanary mixed metal chalcogenide (PMMC) thin films of copper indium cadmium sulfoselenide (CuInCd(SSe)3) were successfully synthesized using simple, self-organized, arrested precipitation technique in an aqueous alkaline medium. The optical, structural, morphological, compositional and electrical properties of synthesized thin films were investigated as a function indium (In3+) concentration. An optical absorption study revealed that direct allowed transition and band gap energy decreases typically from 1.46 to 1.25 eV. The X-ray diffraction studies revealed that the PMMC thin films have a nanocrystalline nature and crystallite size increases with the increase in the In3+ concentration. Tuning of surface morphology from nanospheres to peas-like morphology with uniform, well-adhered distributed throughout the substrate surface were observed by field emission scanning electron microscopy micrographs. The high-resolution transmission electron microscopy images and selected area electron diffraction pattern were illustrated that compactly interconnected particles with nanocrystalline nature. Energy-dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy results confirmed that synthesized thin films had an appropriate chemical purity. The electrical conductivity and thermoelectric power measurement indicates that, the films have n-type conductivity. A photoelectrochemical conversion efficiency of 2.40% was achieved with a current density of 2.87 mA/cm2. The developed route may provide an alternative approach to synthesize multinary metal chalcogenide thin-film solar cell. Furthermore, we have developed a predictive model of a CICSSe thin-film solar cell using the artificial neural network. The proposed model is useful for the integrated development environment for the predictive modeling and design of high-efficient solar cells.
KeywordsSolar Cell Artificial Neural Network Model Field Emission Scanning Electron Microscopy Image Artificial Neural Network Architecture Copper Indium
One of the authors Dr. Kishorkumar V. Khot is very much thankful to Department of Science and Technology (DST), New Delhi, for providing DST-INSPIRE fellowship for financial support (Registration No. IF130751). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2009-0094055).
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