Synthesis and Characterization of Conducting Polyaniline Nanostructured Thin Films for Solar Cell Applications
Optical-quality transparent, conducting polyaniline (PANI) thin films are suitable candidates for efficient counter electrodes for high-performance solar cells. In the first part of this work, the synthesis of highly uniform and homogenous nanostructured PANI films is reported. The film properties w...
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description | Optical-quality transparent, conducting polyaniline (PANI) thin films are suitable candidates for efficient counter electrodes for high-performance solar cells. In the first part of this work, the synthesis of highly uniform and homogenous nanostructured PANI films is reported. The film properties were assessed via scanning electron microscopy, atomic force microscopy, optical profilometry, spectrophotometry, and conductimetry. Simultaneous modeling, optimization and physical characterization of the PANI nanostructured films have not received much attention in the literature. Hence, in the second part, a multi-objective optimization approach with three objectives, namely minimum film thickness, maximum transparency, and maximum conductivity, was performed based on artificial neural network models with a novel k-fold cross-validation technique. The developed models can accurately predict the film characteristics in a wide range of design variables with most residuals remarkably less than 1.0%. Furthermore, after optimization, conductivity was increased three-fold (~ 2.2 × 10
−1
S/cm) at a good level of transparency (~ 55%), which suit solar cell applications. |
doi_str_mv | 10.1007/s11837-020-04361-8 |
format | Article |
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−1
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−1
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In the first part of this work, the synthesis of highly uniform and homogenous nanostructured PANI films is reported. The film properties were assessed via scanning electron microscopy, atomic force microscopy, optical profilometry, spectrophotometry, and conductimetry. Simultaneous modeling, optimization and physical characterization of the PANI nanostructured films have not received much attention in the literature. Hence, in the second part, a multi-objective optimization approach with three objectives, namely minimum film thickness, maximum transparency, and maximum conductivity, was performed based on artificial neural network models with a novel k-fold cross-validation technique. The developed models can accurately predict the film characteristics in a wide range of design variables with most residuals remarkably less than 1.0%. Furthermore, after optimization, conductivity was increased three-fold (~ 2.2 × 10
−1
S/cm) at a good level of transparency (~ 55%), which suit solar cell applications.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11837-020-04361-8</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-1683-2368</orcidid></addata></record> |
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subjects | Advanced Coating and Thin Film Materials for Energy Aerospace and Biological Applications Algorithms Artificial neural networks Atomic force microscopy Chemistry/Food Science Earth Sciences Engineering Environment Film thickness Hydrochloric acid Morphology Multiple objective analysis Nanostructure Nanostructured materials Neural networks Optical properties Optimization Optimization techniques Photovoltaic cells Physics Polyanilines Polymers Scanning electron microscopy Solar cells Spectrophotometry Synthesis Temperature Thin films |
title | Synthesis and Characterization of Conducting Polyaniline Nanostructured Thin Films for Solar Cell Applications |
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