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用于先进电池应用的三氟甲磺酸钠盐包水电解质:基于第一性原理的分子动力学研究

Sodium Triflate Water-in-Salt Electrolytes in Advanced Battery Applications: A First-Principles-Based Molecular Dynamics Study.

作者信息

Rezaei Majid, Sakong Sung, Groß Axel

机构信息

Institute of Theoretical Chemistry, Ulm University, Oberberghof 7, 89081 Ulm, Germany.

Helmholtz Institute Ulm (HIU) for Electrochemical Energy Storage, Helmholtzstraße 11, 89069 Ulm, Germany.

出版信息

ACS Appl Mater Interfaces. 2024 Jun 26;16(25):32169-32188. doi: 10.1021/acsami.4c01449. Epub 2024 Jun 11.

Abstract

Offering a compelling combination of safety and cost-effectiveness, water-in-salt (WiS) electrolytes have emerged as promising frontiers in energy storage technology. Still, there is a strong demand for research and development efforts to make these electrolytes ripe for commercialization. Here, we present a first-principles-based molecular dynamics (MD) study addressing in detail the properties of a sodium triflate WiS electrolyte for Na-ion batteries. We have developed a workflow based on a machine learning (ML) potential derived from ab initio MD simulations. As ML potentials are typically restricted to the interpolation of the data points of the training set and have hardly any predictive properties, we subsequently optimize a classical force field based on physics principles to ensure broad applicability and high performance. Performing and analyzing detailed MD simulations, we identify several very promising properties of the sodium triflate as a WiS electrolyte but also indicate some potential stability challenges associated with its use as a battery electrolyte.

摘要

盐包水(WiS)电解质兼具安全性和成本效益,已成为储能技术中颇具前景的前沿领域。然而,仍迫切需要开展研发工作,以使这些电解质成熟到可商业化的程度。在此,我们展示了一项基于第一性原理的分子动力学(MD)研究,详细探讨了用于钠离子电池的三氟甲磺酸钠WiS电解质的性质。我们基于从头算MD模拟得出的机器学习(ML)势开发了一个工作流程。由于ML势通常仅限于训练集数据点的插值,几乎没有任何预测特性,因此我们随后基于物理原理优化了一个经典力场,以确保广泛的适用性和高性能。通过进行和分析详细的MD模拟,我们确定了三氟甲磺酸钠作为WiS电解质的几个非常有前景的特性,但也指出了其用作电池电解质时存在的一些潜在稳定性挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96e3/11212028/7ec9f448342c/am4c01449_0001.jpg

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