CO2-CH4 杂水合物的机械特性和笼型转化:分子动力学和机器学习研究

Yu Zhang, Xintong Liu, Qiao Shi, Yongxiao Qu, Yongchao Hao, Yuequn Fu, Jianyang Wu, Zhisen Zhang
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摘要

用二氧化碳替代天然气水合物具有引人注目的双重优势,既能提取 CH4,又能封存二氧化碳。然而,这一过程与 CO2-CH4 杂水合物的机械稳定性密切相关。在本研究中,我们通过分子动力学(MD)模拟和机器学习(ML),报告了 CO2-CH4 杂水合物在单轴应变下的机械性能和笼型转变。结果表明,客体分子的占有率、CO2 与 CH4 的比例以及它们在杂水合物结构中的空间排列在很大程度上决定了 CO2-CH4 杂水合物的力学性能,包括杨氏模量、拉伸强度和临界应变。值得注意的是,在凝胶笼(尤其是 512 个小笼)中引入二氧化碳会削弱 CO2-CH4 杂水合物在力学性能方面的稳定性。在临界应变下,会形成非常规的凝胶笼,从而导致杂水合物断裂前的加载应力振荡。耐人寻味的是,发现了主要的笼型转变,如 51262 到 4151063 或 425864 以及 512 到 425861 笼型,其中 4151062 似乎是主要的中间笼型,能够转变为 4151063、425862、425863、512 和 51262 笼型,揭示了应变下杂水合物结构的动态性质。此外,利用 MD 数据开发的机器学习(ML)模型可以很好地预测杂水合物的机械性能,并强调了客体分子的空间排列对机械性能的关键影响。这些新开发的 ML 模型是准确预测杂水合物力学性能的宝贵工具。这项研究为了解杂水合物在应变作用下的力学性能和笼型转变提供了新的视角,对环境可持续利用 CO2-CH4 杂水合物具有重要意义。
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Mechanical properties and cage transformations in CO2-CH4 heterohydrates: a molecular dynamics and machine learning study
The substitution of natural gas hydrates with CO2 offers a compelling dual advantage by enabling the extracting of CH4 while simultaneously sequestering CO2. This process, however, is intricately tied to the mechanical stability of CO2-CH4 heterohydrates. In this study, we report the mechanical properties and cage transformations in CO2-CH4 heterohydrates subjected to uniaxial straining via molecular dynamics (MD) simulations and machine learning (ML). Results indicate that guest molecule occupancy, the ratio of CO2 to CH4 and their spatial arrangements within heterohydrate structure greatly dictate the mechanical properties of CO2-CH4 heterohydrates including Young’s modulus, tensile strength, and critical strain. Notable, the introduction of CO2 within clathrate cages, particularly within 512 small cages, weakens the stability of CO2-CH4 heterohydrates in terms of mechanical properties. Upon critical strains, unconventional clathrate cages form, contributing to loading stress oscillation before fracture of heterohydrates. Intriguingly, predominant cage transformations, such as 51262 to 4151063 or 425864 and 512 to 425861 cages, are identified, in which 4151062 appears as primary intermediate cage that is able to transform into 4151063, 425862, 425863, 512 and 51262 cages, unveiling the dynamic nature of heterohydrate structures under straining.Additionally, machine learning (ML) models developed using MD data well predict the mechanical properties of heterohydrates, and underscore the critical influence of the spatial arrangement of guest molecules on the mechanical properties. These newly-developed ML models serve as valuable tools for accurately predicting the mechanical properties of heterohydrates. This study provides fresh insights into the mechanical properties and cage transformations in heterohydrates in response to strain, holding significant implications for environmentally sustainable utilization of CO2-CH4 heterohydrates.
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