CoTCNQ as a Catalyst for CO2 Electroreduction: A First Principles r2SCAN Meta-GGA Investigation

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY Journal of Computational Chemistry Pub Date : 2024-12-16 DOI:10.1002/jcc.27528
Oliver J. Conquest, Yijiao Jiang, Catherine Stampfl
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Abstract

Using first principles calculations we investigate cobalt-coordinated tetracyanoquinodimethane (R-CoTCNQ) as a potential catalyst for the CO2 electroreduction reaction (CO2ERR). We determine that exchange–correlation functionals beyond the generalized gradient approximation (GGA) are required to accurately describe the spin properties of R-CoTCNQ, therefore, the meta-GGA r2SCAN functional is used in this study. The free energy CO2ERR reaction pathways are calculated for the reduced catalyst ([R-CoTCNQ]−1e) with reaction products HCOOH and HCHO predicted depending on our choice of electrode potential. Calculations are also performed for [R-CoTCNQ]−1e supported on a H-terminated diamond (1 1 0) surface with reaction pathways being qualitatively similar to the [R-CoTCNQ]−1e monolayer. The inclusion of boron-doping in the diamond support shows a slightly improved CO2ERR reaction pathway. Furthermore, structurally, supported R-CoTCNQ provide a high specific area of active Co active sites and could be promising catalysts for future experimental consideration.

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CoTCNQ作为CO2电还原催化剂:第一原理r2SCAN Meta-GGA研究
我们利用第一性原理计算研究了作为二氧化碳电还原反应(CO2ERR)潜在催化剂的钴配位四氰醌二甲烷(R-CoTCNQ)。我们认为需要超越广义梯度近似(GGA)的交换相关函数来准确描述 R-CoTCNQ 的自旋特性,因此本研究采用了元 GGA r2SCAN 函数。根据我们选择的电极电位,计算了还原催化剂([R-CoTCNQ]-1e)的 CO2ERR 自由能反应路径,并预测了反应产物 HCOOH 和 HCHO。此外,还对支撑在 H 端金刚石 (1 1 0) 表面上的 [R-CoTCNQ]-1e 进行了计算,其反应路径与 [R-CoTCNQ]-1e 单层反应路径基本相似。在金刚石支撑层中掺入硼元素后,CO2ERR 反应途径略有改善。此外,从结构上看,支撑的 R-CoTCNQ 提供了高活性 Co 活性位点的比面积,可能成为未来实验考虑的有前途的催化剂。
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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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