Chen Yu, Zhao Guobin, Yoon Sunghyun, Habibi Parsa, Hong Chang Seop, Li Song, Moultos Othonas A, Dey Poulumi, Vlugt Thijs J H, Chung Yongchul G
School of Chemical Engineering, Pusan National University, Busan 46241, Republic of Korea.
Engineering Thermodynamics, Process & Energy Department, Faculty of Mechanical Engineering, Delft University of Technology, Leeghwaterstraat 39, 2628 CB Delft, The Netherlands.
ACS Appl Mater Interfaces. 2024 Nov 13;16(45):61995-62009. doi: 10.1021/acsami.4c11953. Epub 2024 Oct 30.
Hydrogen is a clean-burning fuel that can be converted to other forms. of energy without generating any greenhouse gases. Currently, hydrogen is stored either by compression to high pressure (>700 bar) or cryogenic cooling to liquid form (<23 K). Therefore, it is essential to develop safe, reliable, and energy-efficient storage technology that can store hydrogen at lower pressures and temperatures. In this work, we systematically designed 2902 Mg-alkoxide-functionalized covalent-organic frameworks (COFs) and performed high-throughput (HT) computational screening for hydrogen storage applications at 111, 231, and 296 K. To accurately model the interaction between Mg-alkoxide sites and molecular hydrogen, we performed MP2 calculations to compute the hydrogen binding energy for different types of functionalized models, and the data were subsequently used to fit modified-Morse force field (FF) parameters. Using the developed FF models, we conducted HT grand canonical Monte Carlo (GCMC) simulations to compute hydrogen uptakes for both original and functionalized COFs. The generated data were subsequently used to evaluate the materials' gravimetric and volumetric storage performance at various temperatures (111, 231, and 296 K). Finally, we developed machine learning (ML) models to predict the hydrogen storage performance of functionalized structures based on the features of the original structures. The developed model showed excellent performance with a mean absolute error (MAE) of 0.061 wt % and 0.456 g/L for predicting the gravimetric and volumetric deliverable capacities, enabling a quick evaluation of structures in a hypothetical COF database. The screening results demonstrated that the Mg-alkoxide functionalization yields greater improvements in volumetric H storage capacities for COFs with smaller pores compared to those with larger (mesoporous) pores.
氢是一种清洁燃烧的燃料，可转化为其他形式的能量，且不产生任何温室气体。目前，氢气的储存方式要么是压缩至高压（>700巴），要么是低温冷却至液态（<23K）。因此，开发能够在较低压力和温度下储存氢气的安全、可靠且节能的储存技术至关重要。在这项工作中，我们系统地设计了2902种镁醇盐官能化的共价有机框架（COF），并在111K、231K和296K下对储氢应用进行了高通量（HT）计算筛选。为了准确模拟镁醇盐位点与分子氢之间的相互作用，我们进行了MP2计算，以计算不同类型官能化模型的氢结合能，随后将数据用于拟合修正的莫尔斯力场（FF）参数。使用开发的FF模型，我们进行了HT巨正则蒙特卡罗（GCMC）模拟，以计算原始和官能化COF的氢吸收量。随后，生成的数据用于评估材料在不同温度（111K、231K和296K）下的重量和体积储存性能。最后，我们开发了机器学习（ML）模型，以基于原始结构的特征预测官能化结构的储氢性能。所开发的模型表现出色，预测重量和体积可输送容量的平均绝对误差（MAE）分别为0.061 wt%和0.456 g/L，能够快速评估假设的COF数据库中的结构。筛选结果表明，与具有较大（中孔）孔的COF相比，镁醇盐官能化对具有较小孔的COF的体积储氢容量有更大的提升。
