用于高效和选择性氯进化反应的碳纳米材料支撑铂复合电催化剂的理论研究。

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY Journal of Computational Chemistry Pub Date : 2024-07-17 DOI:10.1002/jcc.27466
Jewel Hossen, Naoki Nakatani
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引用次数: 0

摘要

氯是一种重要的化学物质,长期以来,人们一直使用尺寸稳定阳极(DSA)在氯碱工艺中生产氯。然而,DSA 的一些严重缺点激发了人们开发氯进化反应(CER)替代阳极的热情。在本研究中,我们重点研究了石墨烯和碳纳米管支撑的铂四苯基卟啉作为 CER 的电催化剂,并基于密度泛函理论对其进行了理论研究。研究结果表明,这些支撑基底具有潜在的 CER 电催化活性,通过 Cl* 而不是 ClO* 途径产生的热力学过电势非常低(0.012-0.028 V)。电子结构分析表明,电子通过铂中心从支撑物转移到吸附的氯,从而导致强烈的铂-氯相互作用。此外,由于氧进化过程的过电位和反应壁垒较高,支撑电催化剂对 CER 具有极佳的选择性。因此,我们的研究结果可为利用新兴碳纳米材料设计 CER 电催化剂铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Theoretical study on the carbon nanomaterial-supported Pt complex electrocatalysts for efficient and selective chlorine evolution reaction

Chlorine is an important chemical which has long been produced in chlor-alkali process using dimensionally stable anodes (DSA). However, some serious drawbacks of DSA inspire the development of alternative anodes for chlorine evolution reaction (CER). In this study, we focused on the graphene- and carbon nanotube-supported platinum tetra-phenyl porphyrins as electrocatalysts for CER, which have been theoretically investigated based on density functional theory. Our results reveal that the supported substrates possess potential CER electrocatalytic activity with very low thermodynamic overpotentials (0.012–0.028 V) via Cl* pathway instead of ClO*. The electronic structures analyses showed that electron transfer from the support to the adsorbed chlorine via the Pt center leads to strong Pt–Cl interactions. Furthermore, the supported electrocatalysts exhibited excellent selectivity toward CER because of high overpotentials and reaction barriers of oxygen evolution process. Therefore, our results may pave the way for designing CER electrocatalyst utilizing emerging carbon nanomaterials.

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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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