优化构成芴芯的非富勒烯受体分子以提高有机太阳能电池的性能:一种理论方法论

IF 2.1 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Walid Taouali, Kamel Alimi
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引用次数: 0

摘要

背景为了寻找适用于有机太阳能电池的新型高性能材料,我们构建了一系列非富勒烯受体(NFAs),这些非富勒烯受体是从最近合成的受体分子 DICTIF 演化而来的。DICTIF 以芴为核心结构,其中 2-(2,3-二氢-3-氧代-1H-茚-1-亚基)丙二腈为末端基团。我们利用密度泛函理论(DFT)和时间依赖性-DFT(TD-DFT)模拟,模拟了五种受体分子改变 DICTIF 分子端基的影响,目的是探索它们的光电特性及其在有机太阳能电池(OSC)应用中的性能。事实证明,与合成的分子相比,设计的非富勒烯受体具有更高的效率,如平面几何形状和更窄的能隙(1.51 至 1.95 eV)。与参考分子(λmax = 578 nm)相比,所有定制分子的吸收都发生了红移(λmax = 583.5-711.4 nm)。为分析 NFA 的性能,还计算了各种决定性因素,如前沿分子轨道 (FMO)、激子结合能 (EB)、吸收最大值 (λmax)、开路电压 (VOC)、重组能 (RE)、过渡密度矩阵 (TDM)、还原密度梯度 (RDG) 和电子-空穴重叠。低重组能值有利于电荷移动,从而提高了所有设计分子的导电性。这项研究表明,我们量身定制的新型分子可能是制造高效光伏材料的合适候选分子。方法在测试了各种混合函数后,使用 DFT HSEH1PBE/6-31G(d) 理论水平分配了优化的几何结构。使用 TD-DFT MPW1PW91/6-31G(d) 理论水平研究了电子激发和吸收光谱。我们确定 HSEH1PBE/6-31G(d)理论水平计算出的 DICTIF 最高占有分子轨道(HOMO)和最低未占有分子轨道(LUMO)能级与相应的实验能级最为接近,而 TD-MPW1PW91//6-31G(d) 是探索电子激发和发现吸收最大值(λmax)的最合适理论水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Optimizing non-fullerene acceptor molecules constituting fluorene core for enhanced performance in organic solar cells: a theoretical methodology

Optimizing non-fullerene acceptor molecules constituting fluorene core for enhanced performance in organic solar cells: a theoretical methodology

Context

Looking for novel outstanding performance materials suitable for organic solar cells, we constructed a range of non-fullerene acceptors (NFAs) evolved from the recently synthesized acceptor molecule identified as DICTIF, structured around fluorene core where 2-(2,3-dihydro-3-oxo-1H-inden-1-ylidene) propanedinitrile presented the terminals end-groups. Employing density functional theory (DFT) and time dependent-DFT (TD-DFT) simulations, we have simulated the impact of altering the end groups of DICTIF molecule by five assorted acceptors molecules, for the purpose of exploring their opto-electronic properties and their performance in organic solar cell (OSC) applications. We proved that the designed non-fullerene acceptors provide enhanced efficiency compared to the synthesized molecule, such as planar geometries and narrower energy gap ranging from 1.51 to 1.95 eV. A red shift in absorption was observed for all tailored molecules (λmax = 583.5–711.4 nm) as compared to the reference molecule (λmax = 578 nm).Various decisive factors such as frontier molecular orbitals (FMOs), exciton binding energy (EB), absorption maximum (λmax), open circuit voltage (VOC), reorganization energies (RE), transition density matrix (TDM), reduced density gradient (RDG), and electron-hole overlap have also been computed for analyzing the performance of NFAs. Low reorganizational energy values facilitate charge mobility which improves the conductivity of all the designed molecules. This study showed that our novel tailored molecules might be suitable candidates for the fabrication of highly efficient photovoltaic materials.

Methods

After testing various hybrid functionals, optimized geometries were assigned using DFT HSEH1PBE/6-31G(d) level of theory. Electronic excitations and absorption spectra were investigated using the TD-DFT MPW1PW91/6-31G(d) level of theory. We ascertained that HSEH1PBE/6-31G(d) level of theory yield the closest calculated highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels of the DICTIF to the corresponding experimental ones and that TD-MPW1PW91//6-31G(d) was the most suitable level of theory for exploring electronic excitations and finding the maximum of absorption (λmax).

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来源期刊
Journal of Molecular Modeling
Journal of Molecular Modeling 化学-化学综合
CiteScore
3.50
自引率
4.50%
发文量
362
审稿时长
2.9 months
期刊介绍: The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling. Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry. Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.
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