4.6 Article

Evaluation of charge-transfer rates in fullerene-based donor-acceptor dyads with different density functional approximations

期刊

PHYSICAL CHEMISTRY CHEMICAL PHYSICS
卷 23, 期 9, 页码 5376-5384

出版社

ROYAL SOC CHEMISTRY
DOI: 10.1039/d0cp06510b

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资金

  1. Ministerio de Economia y Competitividad (MINECO) of Spain [CTQ2017-85341-P]
  2. Spanish government MICINN [PGC2018-098212-B-C22]
  3. Generalitat de Catalunya [2017SGR39]
  4. Spanish government [FPU17/02058]
  5. Consorci de Serveis Universitaris de Catalunya (CSUC)

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The authors analyzed the performance of donor-acceptor fullerene dyads in dye-sensitized solar cells, finding that the amount of exact exchange at short and long range electron-electron distances is key for successful predictions. Tuning these parameters significantly improves the performance of current density functional approximations.
The shift towards renewable energy is one of the main challenges of this generation. Dye-sensitized solar cells (DSSCs), based on donor-acceptor architectures, can help in this transition as they present excellent photovoltaic efficiencies yet cheap and simple manufacturing. For molecular heterojunction DSSCs, donor-acceptor pairs are linked in a covalent manner, which facilitates their tailoring and rational design. Nevertheless, reliable computational characterization of charge transfer rate constants (k(CT)) is needed to speed this development process up. In this context, the performance of time-dependent density functional theory for the calculation of k(CT) values in donor-acceptor fullerene-based dyads has not been benchmarked yet. Herein, we present a detailed analysis on the performance of seven well-known density functional approximations (DFAs) for this type of system, focusing on several parameters such as the reorganization energies (lambda), electronic couplings (V-DA), and Gibbs energies (Delta G0CT), as well as the final rate constants. The amount of exact exchange at short range (SR) and long range (LR) electron-electron distances (and the transition from the SR to LR) turned out to be key for the success of the prediction. The tuning of these parameters improves significantly the performance of current DFAs.

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