Prediction of Absorption Spectrum Shifts in Dyes Adsorbed on Titania.

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ID: 67418
2019
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Abstract
Dye adsorption on metal-oxide films often results in small to substantial absorption shifts relative to the solution phase, with undesirable consequences for the performance of dye-sensitized solar cells and optical sensors. While density functional theory is frequently used to model such behaviour, it is too time-consuming for rapid assessment. In this paper, we explore the use of supervised machine learning to predict whether dye adsorption on titania is likely to induce a change in its absorption characteristics. The physicochemical features of each dye were encoded as a numeric vector whose elements are the counts of molecular fragments and topological indices. Various classification models were subsequently trained to predict the type of absorption shift i.e. blue, red or unchanged (|Δλ| ≤ 10 nm). The models were able to predict the nature of the shift with a good likelihood (~80%) of success when applied to unseen data.
Reference Key
venkatraman2019predictionscientific Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Venkatraman, Vishwesh;Yemene, Amsalu Efrem;de Mello, John;
Journal Scientific reports
Year 2019
DOI
10.1038/s41598-019-53534-2
URL
Keywords
natural dye betacyanin dye sensitized solar cells (dssc's) prickly pear extract tio2 photoanode

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