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叔胺捕获二氧化碳的计算研究

Computational Study of Carbon Dioxide Capture by Tertiary Amines.

作者信息

Pornjariyawatch Chalakon, Jitchum Varangkana, Assawatwikrai Krit, Leepakorn Pakanan, Probst Michael, Boekfa Bundet, Maihom Thana, Limtrakul Jumras

机构信息

Kasetsart University Laboratory School, Educational Research and Development Center, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand.

Division of Chemistry, Department of Physical and Material Sciences, Faculty of Liberal Arts and Science, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand.

出版信息

Chemphyschem. 2025 Jan 14;26(2):e202400754. doi: 10.1002/cphc.202400754. Epub 2024 Nov 11.

Abstract

The reaction mechanisms and corresponding structure-activity relationships of tertiary amines with respect to CO capture have been investigated using density functional theory (DFT) calculations. The reaction mechanism for CO capture via base-catalyzed hydration to form bicarbonate is proposed to proceed in a single step involving proton transfer and the formation of a carbon-oxygen bond. Based on the height of the reaction barriers, we suggest that amines containing side chains with the ethyl group, along with a single hydroxyl group, and cyclic structures, are especially active for CO capture. The activation barrier is shown to be a descriptor for predicting the experimental CO loading values. To enhance the prediction accuracy for CO loading, we employ the sure-independence screening and sparsifying operator (SISSO) method, which can scan a large pool of mathematical terms stemming from combining DFT-derived descriptors to select the superior ones. Thus, we can predict the CO loading with acceptable accuracy from the obtained mathematical expression. Since the computational workload of applying this expression is negligible, this facilitates high-throughput screening and accelerates the design of tertiary amines for CO capture.

摘要

利用密度泛函理论(DFT)计算研究了叔胺捕集CO的反应机理及相应的构效关系。提出了通过碱催化水合形成碳酸氢盐来捕集CO的反应机理,该反应机理通过涉及质子转移和碳 - 氧键形成的单一步骤进行。基于反应势垒的高度,我们认为含有乙基侧链、单个羟基和环状结构的胺对CO捕集特别活跃。活化势垒被证明是预测实验CO负载值的一个描述符。为了提高CO负载的预测准确性,我们采用了确信独立筛选和稀疏化算子(SISSO)方法,该方法可以扫描由DFT衍生描述符组合而成的大量数学项,以选择最优项。因此,我们可以从获得的数学表达式中以可接受的精度预测CO负载。由于应用该表达式的计算量可以忽略不计,这有利于高通量筛选并加速用于CO捕集的叔胺设计。

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