17O NMR spectroscopy reveals CO2 speciation and dynamics in hydroxide-based carbon capture materials.

IF 2.3 3区 化学 Q3 CHEMISTRY, PHYSICAL Chemphyschem Pub Date : 2024-11-20 DOI:10.1002/cphc.202400941
Benjamin Rhodes, Lars Schaaf, Mary Zick, Suzi Pugh, Jordon Hilliard, Shivani Sharma, Casey Wade, Phillip Milner, Gábor Csányi, Alexander Forse
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Abstract

Carbon dioxide capture technologies are set to play a vital role in mitigating the current climate crisis. Solid-state 17O NMR spectroscopy can provide key mechanistic insights that are crucial to effective sorbent development. In this work, we present the fundamental aspects and complexities for the study of hydroxide-based CO2 capture systems by 17O NMR. We perform static density functional theory (DFT) NMR calculations to assign peaks for general hydroxide CO2 capture products, finding that 17O NMR can readily distinguish bicarbonate, carbonate and water species. However, in application to CO2 binding in two test case hydroxide-functionalised metal-organic frameworks (MOFs):  MFU-4l and KHCO3-cyclodextrin-MOF, we find that a dynamic treatment is necessary to obtain agreement between computational and experimental spectra. We therefore introduce a workflow that leverages machine-learning forcefields to capture dynamics across multiple chemical exchange regimes, providing a significant improvement on static DFT predictions. In MFU-4l, we parameterise a two-component dynamic motion of the bicarbonate motif involving a rapid carbonyl seesaw motion and intermediate hydroxyl proton hopping. For KHCO3-CD-MOF, we combined experimental and modelling approaches to propose a new mixed carbonate-bicarbonate binding mechanism and thus, we open new avenues for the study and modelling of hydroxide-based CO2 capture materials by 17O NMR.

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17O NMR 光谱揭示了基于氢氧化物的碳捕获材料中的 CO2 分类和动力学。
二氧化碳捕集技术将在缓解当前气候危机方面发挥重要作用。固态 17O NMR 光谱可提供对有效开发吸附剂至关重要的关键机理认识。在这项工作中,我们介绍了利用 17O NMR 研究基于氢氧化物的二氧化碳捕集系统的基本方面和复杂性。我们进行了静态密度泛函理论 (DFT) NMR 计算,为一般氢氧化物二氧化碳捕获产物分配峰值,发现 17O NMR 可以轻松区分碳酸氢盐、碳酸盐和水物种。然而,在应用于两个氢氧化物功能化金属有机框架(MOFs)中的二氧化碳结合试验案例时: MFU-4l和 KHCO3-环糊精-MOF)中的二氧化碳结合情况时,我们发现必须进行动态处理,才能获得计算光谱与实验光谱之间的一致。因此,我们引入了一种工作流程,利用机器学习力场捕捉多种化学交换机制的动态,显著改善了静态 DFT 预测。在 MFU-4l 中,我们对碳酸氢盐基团的双组分动态运动进行了参数化,其中包括快速的羰基跷跷板运动和中间的羟基质子跳跃。对于 KHCO3-CD-MOF,我们结合了实验和建模方法,提出了一种新的碳酸盐-碳酸氢盐混合结合机制,从而为利用 17O NMR 研究和建模基于氢氧化物的二氧化碳捕获材料开辟了新的途径。
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来源期刊
Chemphyschem
Chemphyschem 化学-物理:原子、分子和化学物理
CiteScore
4.60
自引率
3.40%
发文量
425
审稿时长
1.1 months
期刊介绍: ChemPhysChem is one of the leading chemistry/physics interdisciplinary journals (ISI Impact Factor 2018: 3.077) for physical chemistry and chemical physics. It is published on behalf of Chemistry Europe, an association of 16 European chemical societies. ChemPhysChem is an international source for important primary and critical secondary information across the whole field of physical chemistry and chemical physics. It integrates this wide and flourishing field ranging from Solid State and Soft-Matter Research, Electro- and Photochemistry, Femtochemistry and Nanotechnology, Complex Systems, Single-Molecule Research, Clusters and Colloids, Catalysis and Surface Science, Biophysics and Physical Biochemistry, Atmospheric and Environmental Chemistry, and many more topics. ChemPhysChem is peer-reviewed.
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