Photocatalytic degradation of rhodamine B in the visible region using nanostructured CoAl2−xLaxO4 (x = 0, 0.01, 0.03, 0.07, and 0.09) series: Photocatalytic activity and DFT calculations

Mekky, Saleh D.; Sultan, Mohamed E.; Elsenety, Mohamed M.; Helal, Ahmed; El-Sayed, B. A.

doi:10.1016/j.inoche.2021.109176

Cited by 10 publications

(2 citation statements)

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“…Density functional theory (DFT) is a computational modeling technique for studying the electrical, optical, molecular, and structural properties of compounds. [ 31–36 ] Figure a–d provides an overview of the molecular orbitals for the excited state (lowest unoccupied molecular orbital (LUMO) + 1 (a); and LUMO (b)) and ground state (highest occupied molecular orbital [HOMO] (c); HOMO‐1 (d)) of the optimized geometrical structure of the DN‐F10 organic dye, as a result of computational calculation using DFT. The chemical structure of the organic DN‐F10 dye (D–π–A) contains three‐terminal chains around triphenylamine, as shown in Figure S1, Supporting Information.…”

Section: Introductionmentioning

confidence: 99%

“…Density functional theory (DFT) is a computational modeling technique for studying the electrical, optical, molecular, and structural properties of compounds. [31][32][33][34][35][36] The theoretical calculation shows that the conjugated system of triphenylamine has a remarkably high electron density at the HOMO level, which acts as an electron donor. In contrast, two-terminal aliphatic chains present low electron density at the HOMO or LUMO level.…”

Section: Introductionmentioning

confidence: 99%

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Passivation Engineering Using Ultrahydrophobic Donor–π–Acceptor Organic Dye with Machine Learning Insights for Efficient and Stable Perovskite Solar Cells

2023

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To prevent the degradation of perovskite solar cells (PSCs) and optimize the solar energy conversion process, a donor–π–acceptor (D–π–A) organic blue dye as a passivation layer and as a hole‐transporting layer is introduced. The terminal chains of D–π–A dye confer the ultrahydrophobic character (contact angle > 100°) of the interface layer, protecting the perovskite from ambient moisture while mitigating ionic diffusion in the device. The dye interlayer primarily improves the perovskite by reducing grain boundary defects. The perovskite/D–π–A architecture enhances the interfacial hole extraction, suppressing nonradiative carrier recombination and enabling power conversion efficiency (PCE) reaching 20.90%, outperforming by 2.05% the PCE of control cells. Unsealed PSCs retain 84% and 62% of their efficiency after photovoltaic operation for 1000 and 3000 h, respectively. Statistical correlation of bivariant and multivariant analyses of photovoltaic parameters is performed and Pearson's correlation identifies underlying patterns in experimental data collections. Machine learning (ML) of regression algorithms is used to predict the minimum errors and the coefficient of determination, which confirm the analysis quality. The linear regression ML model suggests the importance of photovoltaic parameters (Rs > Vmpp > Jsc > Voc > fill factor > Jmpp > Rsh) toward higher PCE. An efficient online prediction model is also developed to support the estimation of PCEs with high accuracy.

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Section: Introductionmentioning

confidence: 99%