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Ten novel fullerene-derivatives (FDs) of C60 and C70 had been designed as acceptor for polymer solar cell (PSC) by employing the quantitative structure-property relationship (QSPR) model, which was developed strategically with a reasonably big pool of experimental power conversion efficiency (PCE) data. The QSPR model was checked and validated with stringent parameter and reliability of predicted PCE values of all designed FDs. They were assessed by the applicability domain (AD) and process randomization test. The predicted PCE of FDs range from 7.96 to 23.01. The obtained encouraging results led us to the additional theoretical analysis of the energetics and UV-Vis spectra of isolated dyes employing Density functional theory (DFT) and Time-dependent-DFT (TD-DFT) calculations using PBE/6-31G(d,p) and CAM-B3LYP/6-311G(d,p) level calculations, respectively. The FD4 is the best C60-derivatives candidates for PSCs as it has the lowest exciton binding energy, up-shifted lowest unoccupied molecular orbital (LUMO) energy level to increase open-circuit voltage (VOC) and strong absorption in the UV region. In case of C70-derivatives, FD7 is potential candidate for future PSCs due to its strong absorption in UV-Vis region and lower exciton binding energy with higher VOC. Our optoelectronic results strongly support the developed QSPR model equation. Analyzing QSPR model and optoelectronic parameters, we concluded that the FD1, FD2, FD4, and FD10 are the most potential candidates for acceptor fragment of fullerene-based PSC. The outcomes of tactical molecular design followed by the investigation of optoelectronic features are suggested to be employed as a significant resource for the synthesis of FDs as an acceptor of PSCs.
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Evaluation of the degree of rate control via automatic differentiation
Abstract The degree of rate control (DRC) quantitatively identifies the kinetically relevant (sometimes known as rate‐limiting) steps of a complex reaction network. This concept relies on derivatives which are commonly implemented numerically, for example, with finite differences (FDs). Numerical derivatives are tedious to implement, and can be problematic, and unstable or unreliable. In this study, we demonstrate the use of automatic differentiation (AD) in the evaluation of the DRC. AD libraries are increasingly available through modern machine learning frameworks. Compared with the FDs, AD provides solutions with higher accuracy with lower computational cost. We demonstrate applications in steady‐state and transient kinetics. Furthermore, we illustrate a hybrid local‐global sensitivity analysis method, the distributed evaluation of local sensitivity analysis, to assess the importance of kinetic parameters over an uncertain space. This method also benefits from AD to obtain high‐quality results efficiently.
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- Award ID(s):
- 1921946
- PAR ID:
- 10370099
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AIChE Journal
- Volume:
- 68
- Issue:
- 6
- ISSN:
- 0001-1541
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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