High-level quantum chemistry exploration of reduction by group-13 hydrides: insights into the rational design of bio-mimic CO2 reduction

IF 2.9 Q3 CHEMISTRY, PHYSICAL
B. Chan, Masanari Kimura
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Abstract

In the present study, we have used computational quantum chemistry to explore the reduction of various types of substrates by group-13 hydrides. We use the high-level L-W1X method to obtain the energies for the constituent association and hydride transfer reactions. We find that the hydride transfer reactions are highly exothermic, while the preceding association reactions are less so. Thus, improving the thermodynamics of substrate association may improve the overall process. Among the various substrates, amine and imine show the strongest binding, while CO2 shows the weakest. Between the group-13 hydrides, alanes bind most strongly with the substrates, and they also have the most exothermic hydride transfer reactions. To facilitate CO2 binding, we have examined alanes with electron-withdrawing groups, and we indeed find CF3 groups to be effective. Drawing inspiration from the RuBisCO enzyme for CO2 fixation, we have further examined the activation of CO2 with two independent AlH(CF3)2 molecules, with the results showing an even more exothermic association. This observation may form the basis for designing an effective dialane reagent for CO2 reduction. We have also assessed a range of lower-cost computational methods for the calculation of systems in the present study. We find the DSD-PBEP86 double-hybrid DFT method to be the most suitable for the study of related medium-sized systems.
13族氢化物还原的高级量子化学探索:对生物模拟CO2还原的合理设计的见解
在本研究中,我们利用计算量子化学探索了13族氢化物对各种底物的还原作用。我们用高能级L-W1X方法得到了组份缔合反应和氢化物转移反应的能量。我们发现氢化物转移反应是高度放热的,而前面的缔合反应则不那么放热。因此,提高底物缔合的热力学可以改善整个过程。在各种底物中,胺和亚胺的结合最强,而CO2的结合最弱。在13族氢化物中,烷烃与底物结合最强烈,并且它们也有最多的放热氢化物转移反应。为了促进二氧化碳的结合,我们研究了带有吸电子基团的烷烃,我们确实发现CF3基团是有效的。受RuBisCO酶固定CO2的启发,我们进一步研究了两个独立的AlH(CF3)2分子对CO2的激活,结果显示出更大的放热关联。这一观察结果可作为设计一种有效的二烷试剂用于CO2还原的基础。在本研究中,我们还评估了一系列用于系统计算的低成本计算方法。我们发现DSD-PBEP86双混合DFT方法最适合于相关中型系统的研究。
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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来源期刊
CiteScore
3.70
自引率
11.50%
发文量
46
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