Machine learning analysis on performance of naturally-sensitized solar cells

Maddah, Hisham A.

doi:10.1016/j.optmat.2022.112343

Cited by 5 publications

(2 citation statements)

References 60 publications

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“…Arooj and Wang demonstrated a valuable attempt to optimize the sensitizers used in DSSC fabrication and report an efficiency of 17.30% by ML [259]. By understanding the characteristics of natural sensitized Maddah investigated to enhance the performance of DSSCs through ML [260]. Wen et al illustrated a quantitative structure-property relationship model by combining MLand computational quantum chemistry for exploring various organic dyes capable of being integrated in organic DSSCs [261].…”

Section: Machine Learning (Ml) In Dsscsmentioning

confidence: 99%

Recent advances in the development of flexible dye-sensitized solar cells: fabrication, challenges and applications-a review

Dawo¹,

Chaturvedi²

2023

Flex. Print. Electron.

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Flexible dye-sensitized solar cells (FDSSCs) show a huge potential for stretchable electronics and portable power sources due to their lightweight, handy, flexibility, cost-effective, and easy processing. This paper introduces basic operating principles and design opportunities for maximum efficiencies for FDSSCs. Flexible polymers or metal substrates, enabling cost reduction due to large volume production with roll to roll manufacturing technique. DSSCs achieved a PCE of 14.30 % on rigid conductive substrates, 10.28 % on flexible metal substrates, and 8 % on plastic substrates. A brief distinction has been made on different substrates, preparation of charge transfers materials, coating and printing techniques and processing methods for enhancing the performance of FDSSCs. We also highlight issues pertaining to progress in the stability of devices and the commercialisation of FDSCs technologies will be explained.

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Section: Machine Learning (Ml) In Dsscsmentioning

confidence: 99%

Recent advances in the development of flexible dye-sensitized solar cells: fabrication, challenges and applications-a review

Dawo¹,

Chaturvedi²

2023

Flex. Print. Electron.

View full text Add to dashboard Cite

show abstract

“…[18][19][20][21] The application of ML in the DSSC field has also been explored in recent years. [22][23][24][25][26] For instance, Xu et al adopted an artificial neural network to forecast absorption maxima for organic dyes in DSSCs based on a collection of 70 organic dyes with diverse structures. 27 In a similar vein, Venkatraman et al utilized supervised machine learning to ascertain whether dye adsorption on titania would induce changes in its absorption characteristics.…”

Section: Introductionmentioning

confidence: 99%