Power Conversion Efficiency of Arylamine Organic Dyes for Dye-Sensitized Solar Cells (DSSCs) Explicit to Cobalt Electrolyte: Understanding the Structural Attributes Using a Direct QSPR Approach

Abstract

Post silicon solar cell era involves light-absorbing dyes for dye-sensitized solar systems (DSSCs). Therefore, there is great interest in the design of competent organic dyes for DSSCs with high power conversion efficiency (PCE) to bypass some of the disadvantages of silicon-based solar cell technologies, such as high cost, heavy weight, limited silicon resources, and production methods that lead to high environmental pollution. The DSSC has the unique feature of a distance-dependent electron transfer step. This depends on the relative position of the sensitized organic dye in the metal oxide composite system. In the present work, we developed quantitative structure-property relationship (QSPR) models to set up the quantitative relationship between the overall PCE and quantum chemical molecular descriptors. They were calculated from density functional theory (DFT) and time-dependent DFT (TD-DFT) methods as well as from DRAGON software. This allows for understanding the basic electron transfer mechanism along with the structural attributes of arylamine-organic dye sensitizers for the DSSCs explicit to cobalt electrolyte. The identified properties and structural fragments are particularly valuable for guiding time-saving synthetic efforts for development of efficient arylamine organic dyes with improved power conversion efficiency.

In silico designing of power conversion efficient organic lead dyes for solar cells using todays innovative approaches to assure renewable energy for future

“…37 The rationale behind the selection of the functional and basis set is demonstrated elsewhere. 25,29,38,39 Regarding descriptor selection, first we computed 32 quantummechanical descriptors exploring the Gaussian output files of DFT and TD-DFT calculations. Then DRAGON 6 40 software was employed for generation of 248 constitutional indices, ring descriptors, topological indices, connectivity indices, functional group counts, Atom-type E-state indices and 3D-Atom pairs from the optimized structures to recognize the crucial structural features liable for better PCE.…”

“…Recently, QSPR model has been used to predict PCE values for different types of solar cell. [22][23][24][25][26] Venkatraman and Alsberg 22 suggested that photovoltaic properties such as PCE of phenothiazine dyes could be predicted by QSPR with the use of eigenvalue like descriptors. In our ongoing research, we have already successfully employed QSPR method to model and suggest improvements for PCE of polymer-based solar cells 23 and AOD for DSSCs explicit to cobalt electrolytes.…”

“…In our ongoing research, we have already successfully employed QSPR method to model and suggest improvements for PCE of polymer-based solar cells 23 and AOD for DSSCs explicit to cobalt electrolytes. 25 Venkatraman et al 26 investigated de novo computational design methodology to design of coumarin-based dye sensitizer with improved properties for use in DSSCs. Li et al 24 established a unique cascaded QSPR model to predict PCE of AOD for DSSCs.…”

Modifying electronic properties of ICBA through chemical substitutions for solar cell applications

“…All geometry optimizations were performed using density functional theory (DFT) due to its broad and successful employment in organic materials studies [15][16][17][18][19] and even coupled with quantitative structure-properties relationship studies [20,21]. However, for good reliability of the results obtained by DFT, we must first choose the correlation and exchange functional that best suits to the system studied [16,22].…”

Accelerated Discovery of Potential Organic Dyes for Dye‐Sensitized Solar Cells by Interpretable Machine Learning Models and Virtual Screening

“…[22][23][24] Recently, QSPR models using ML and genetic algorithms for DSSCs have also been developed. [25][26][27][28][29][30] In 2015, Li et al [30] proposed a cascaded model with a two-level network using support vector machine (SVM) algorithm to predict PCE of DSSCs, where the short-circuit current density ( J SC ), open-circuit voltage (V OC ), and fill factor (FF) are the outputs of the first level and the inputs of the second level. In 2017, Kar et al [27,28] considered the effects of electrolytes and developed QSPR models for iodine-based and cobaltbased electrolytes, respectively.…”