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水系锌离子混合电容器中梯度多孔碳的设计原则:分子动力学与机器学习相结合的研究

Design Principles for Gradient Porous Carbon on Aqueous Zinc-Ion Hybrid Capacitors: A Combined Molecular Dynamic and Machine Learning Study.

作者信息

Zhang Yifeng, Tian Jie, Li Guanyu, Ji Dongyang, Sun Chen, Fan Zeng, Pan Lujun

机构信息

School of Physics, Dalian University of Technology, Dalian 116024, P. R. China.

出版信息

ACS Appl Mater Interfaces. 2025 Jan 15;17(2):3448-3456. doi: 10.1021/acsami.4c19397. Epub 2025 Jan 4.

Abstract

Gradient porous carbon has become a potential electrode material for energy storage devices, including the aqueous zinc-ion hybrid capacitor (ZIHC). Compared with the sufficient studies on the fabrication of ZIHCs with high electrochemical performance, there is still lack of in-depth understanding of the underlying mechanisms of gradient porous structure for energy storage, especially the synergistic effect of ultramicropores (<1 nm) and micropores (1-2 nm). Here, we report a design principle for the gradient porous carbon structure used for ZIHC based on the data-mining machine learning (ML) method. It is clarified that the combination of 0.6-0.9 nm ultramicropore and 1.6 nm micropore achieves the highest specific capacity. Molecular dynamic simulation was further employed to investigate the electric double-layer structures in several kinds of electrified gradient porous carbon electrode/electrolyte interface. It is found that the Zn ions in the 1.6 nm micropore balance the most charges of the electrode surface as the counterion with the modification of the solvation structure. Furthermore, the ML-based force field is trained and employed in the simulation of the ion charging dynamic in the gradient porous carbon electrode. Based on the free energy profile result, the remarkable benefit of the step-by-step desolvation process is found in the 0.86 and 1.6 nm gradient porous structure, which could be the origin of the enhanced ion charging dynamic and better capacity retention performance.

摘要

梯度多孔碳已成为包括水系锌离子混合电容器(ZIHC)在内的储能装置的潜在电极材料。与对具有高电化学性能的ZIHC制备的充分研究相比,对于梯度多孔结构储能的潜在机制仍缺乏深入了解,特别是超微孔(<1nm)和微孔(1-2nm)的协同效应。在此,我们基于数据挖掘机器学习(ML)方法报告了一种用于ZIHC的梯度多孔碳结构的设计原理。结果表明,0.6-0.9nm超微孔和1.6nm微孔的组合可实现最高比容量。进一步采用分子动力学模拟研究了几种带电梯度多孔碳电极/电解质界面的双电层结构。研究发现,1.6nm微孔中的锌离子作为抗衡离子,通过溶剂化结构的修饰平衡了电极表面的大部分电荷。此外,基于ML的力场经过训练并用于模拟梯度多孔碳电极中的离子充电动力学。基于自由能分布结果,在0.86和1.6nm梯度多孔结构中发现了逐步去溶剂化过程的显著优势,这可能是增强离子充电动力学和更好的容量保持性能的起源。

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