Mechanistic Insights into Water-Mediated CO2 Electrochemical Reduction Reactions on Cu@C2N Catalysts: A Theoretical Study

IF 13.5 2区 化学 Q1 CHEMISTRY, PHYSICAL 物理化学学报 Pub Date : 2024-01-01 DOI:10.3866/PKU.WHXB202303040
Hanyu Xu , Xuedan Song , Qing Zhang , Chang Yu , Jieshan Qiu
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Abstract

CO2 molecules can be converted into various fuels and industrial chemicals through electrochemical reduction, effectively addressing the problems of global warming, desertification, ocean acidification, and other adverse environmental changes and energy supply issues such as excessive utilization of nonrenewable fossil fuels. Generally, the pathway of the CO2 reduction reaction (CO2RR) involves multiple proton–electron pairs transferred to the reactants, resulting in the production of multiple reduction products. Here, protons are derived from water molecules under aqueous solvent conditions. Therefore, exploring the effect of water molecules on the proton–electron pair transfer process in CO2RRs is essential. In this study, we developed a water-mediated hydrogen shuttle model (H-shuttling) as a hydrogenation model to investigate the effect of water molecules on the proton–electron pair transfer process in CO2RRs and compared it with the widely used water-free direct hydrogenation model (H-transfer), wherein the hydrogen atom is used as a proton. Because copper is a metal electrode material capable of producing hydrocarbons from CO2 electroreduction with a high faraday efficiency, and nitrogen-doped graphene (C2N) exhibits excellent catalytic CO2 activation, we selected a single copper atom-embedded C2N (Cu@C2N) as the catalyst. Furthermore, to study the effect of graphene on the CO2RR activity of Cu@C2N/G, we selected a graphene-loaded Cu@C2N composite (Cu@C2N/G) as the catalyst because graphene was utilized as a substrate to boost the conductivity of the catalyst. In the two hydrogenation models, we investigated the mechanisms of CO2RRs on Cu@C2N and Cu@C2N/G catalysts through density functional theory calculations. Notably, in the H-shuttling model, the H atom combines with the water molecule to form H3O, which transfers one of its own H atoms to a reactant on the catalyst surface, yielding a reaction intermediate. The H-shuttling model enhances the interaction between the catalyst and intermediate. Graphene, as a substrate, transfers electrons to the Cu@C2N surface of the Cu@C2N/G catalyst, which is demonstrated by calculations of the Bader charge transferred between the reaction intermediate and catalyst, as well as the Gibbs free energy of the CO2 reduction elementary reaction. This effectively lowers the Gibbs free energy of the potential-determining step and enhances the CO2RR catalytic activity of Cu@C2N/G. Moreover the limiting potentials of the CO2RR and hydrogen evolution reaction are determined to obtain the activity and selectivity of the Cu@C2N and Cu@C2N/G catalysts. The results indicate that CO2 molecules on the Cu@C2N and Cu@C2N/G catalysts generate HCOOH at low applied potentials, and are able to produce CO, CH3OH, CH4, and H2 as the applied potentials increases.
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Cu@C2N催化剂上水介导的CO2电化学还原反应的机理研究
二氧化碳分子可以通过电化学还原转化为各种燃料和工业化学品,有效解决全球变暖、荒漠化、海洋酸化等不利环境变化问题和过度利用不可再生化石燃料等能源供应问题。通常,CO2还原反应(CO2RR)的途径是将多个质子-电子对转移到反应物上,从而产生多个还原产物。在这里,质子是在水溶液条件下由水分子产生的。因此,探索水分子对CO2RRs中质子-电子对转移过程的影响是必要的。在本研究中,我们建立了水介导的氢穿梭模型(h - shuttle)作为加氢模型,研究水分子对co2rs中质子-电子对转移过程的影响,并将其与广泛使用的以氢原子为质子的无水直接加氢模型(H-transfer)进行比较。由于铜是一种金属电极材料,能够以较高的法拉第效率从CO2电还原中产生碳氢化合物,并且氮掺杂石墨烯(C2N)具有优异的催化CO2活性,因此我们选择了单铜原子嵌入的C2N (Cu@C2N)作为催化剂。此外,为了研究石墨烯对Cu@C2N/G的CO2RR活性的影响,我们选择了石墨烯负载的Cu@C2N复合材料(Cu@C2N/G)作为催化剂,因为石墨烯被用作衬底来提高催化剂的导电性。在两种加氢模型中,我们通过密度泛函理论计算研究了Cu@C2N和Cu@C2N/G催化剂上co2rs的机理。值得注意的是,在H穿梭模型中,H原子与水分子结合形成h30, h30将自己的一个H原子转移到催化剂表面的反应物上,产生反应中间体。h -穿梭模型增强了催化剂和中间体之间的相互作用。石墨烯作为衬底,将电子转移到Cu@C2N/G催化剂的Cu@C2N表面,通过计算反应中间体和催化剂之间转移的Bader电荷以及CO2还原基本反应的吉布斯自由能证明了这一点。这有效地降低了势决定步骤的吉布斯自由能,提高了Cu@C2N/G的CO2RR催化活性。测定了CO2RR和析氢反应的极限势,得到了Cu@C2N和Cu@C2N/G催化剂的活性和选择性。结果表明,Cu@C2N和Cu@C2N/G催化剂上的CO2分子在低施加电位下生成HCOOH,随着施加电位的增大,能够生成CO、CH3OH、CH4和H2。下载:下载高清图片(92KB)下载:下载全尺寸图片
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来源期刊
物理化学学报
物理化学学报 化学-物理化学
CiteScore
16.60
自引率
5.50%
发文量
9754
审稿时长
1.2 months
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