Collaborative Research: U.S.-Ireland R&D Partnership: Full Atomistic Understanding of Solid-Liquid Interfaces via an Integrated Experiment-Theory Approach

合作研究：美国-爱尔兰 R

• 批准号: 2137157
• 负责人: Narayana Aluru
• 金额: $ 29万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2137157&HistoricalAwards=false
• 关键词: Collaborative; Research; U.S.; Ireland; R&D

The worldwide deployment of renewable energy requires efficient electrochemical systems, such as batteries, supercapacitors, and fuel cells. In most of these systems, the energy conversion and storage processes rely crucially on the interface between solid electrodes and liquid electrolytes. However, the fundamental atomic and molecular structure at these electrified interfaces remains elusive. The goal of the project is to achieve an atomistic understanding of the structure and reaction dynamics of electrode-electrolyte interfaces, and provide design principles for various low-cost, carbon-based electrochemical systems. Through international collaborations with the University College Dublin and Ulster University, the PIs will develop an integrated experimental imaging - atomistic simulation method. The technical outcomes of the project will facilitate the design and engineering of efficient electrochemical energy conversion and storage systems. The educational efforts of the project will build and incorporate demo devices of electrochemical cells and materials imaging platforms into a series of education and outreach activities both domestically and internationally. The project will train the graduate and undergraduate students with skills in both experimental and simulation methods and provide them with an international collaborative research experience. The project will contribute to efforts to educate the public on the basic mechanisms of renewable energy conversion and storage.The project’s aim is to achieve a thorough atomistic understanding of electrochemical processes by determining the 3D structure of electrode-electrolyte interfaces, including both the surface of the solid electrodes and the liquid solvation layers. The project’s approach will integrate molecular dynamics and density functional theory simulations with 3D atomic-resolution force microscopy experiments to achieve a joint experiment-theory platform for precise understanding and prediction of electrochemical interfaces. The platform will be used to unravel the solvation layer structure that is responsible for energy storage in carbon-based supercapacitors, and the solvent-included atomistic kinetics of electrocatalytic reactions on single-atom catalysts. The project will produce fundamental models of solid-liquid interfaces that consider the inherent atomic-scale heterogeneities. Furthermore, the thorough determination of the atomistic interfacial structure and catalytic activities of single-atom catalysts will shed light on the unconventional scaling relationships of catalysts with nonuniform structures. This will be an important step towards a more predictive, molecular-level theory beyond the widely accepted "Sabatier Principle" for heterogeneous catalysis and electrocatalysis. The results will significantly foster the design and engineering of electrochemical interfaces for low-cost, highly efficient renewable energy applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

可再生能源的全球部署需要高效的电化学系统，例如电池、超级电容器和燃料电池。在大多数此类系统中，能量转换和存储过程主要依赖于固体电极和液体电解质之间的界面。然而，这些带电界面的基本原子和分子结构仍然难以捉摸。该项目的目标是实现对电极-电解质界面的结构和反应动力学的原子理解，并为各种低成本、碳基电化学系统提供设计原理。通过与都柏林大学学院和阿尔斯特大学的国际合作，PI 将开发一种集成的实验成像 - 原子模拟方法。该项目的技术成果将促进高效电化学能量转换和存储系统的设计和工程。该项目的教育工作将建立电化学电池演示装置和材料成像平台，并将其纳入一系列国内外教育和推广活动中。该项目将培养研究生和本科生的实验和模拟方法技能，并为他们提供国际合作研究经验。该项目将致力于向公众宣传可再生能源转换和存储的基本机制。该项目的目标是通过确定电极-电解质界面（包括固体电极表面和液体溶剂化层）的 3D 结构，实现对电化学过程的彻底原子理解。该项目的方法将把分子动力学和密度泛函理论模拟与3D原子分辨率力显微镜实验相结合，以实现联合实验理论平台，以精确理解和预测电化学界面。该平台将用于揭示负责碳基超级电容器能量存储的溶剂化层结构，以及单原子催化剂上电催化反应的包含溶剂的原子动力学。该项目将建立考虑固有原子尺度异质性的固液界面的基本模型。此外，对单原子催化剂的原子界面结构和催化活性的彻底测定将揭示具有非均匀结构的催化剂的非常规缩放关系。这将是迈向更具预测性的分子水平理论的重要一步，超越广泛接受的多相催化和电催化“萨巴蒂尔原理”。研究结果将极大地促进低成本、高效可再生能源应用的电化学接口的设计和工程。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement

限制中粗粒简单液体的数据驱动方法

• DOI: 10.1021/acs.jctc.3c00633
• 发表时间: 2023
• 期刊: Journal of Chemical Theory and Computation
• 影响因子: 5.5
• 作者: Nadkarni, Ishan;Wu, Haiyi;Aluru, Narayana R.
• 通讯作者: Aluru, Narayana R.

Activation of atomic transport via vibrational coupling-induced force fluctuations

• DOI: 10.1063/5.0160780
• 发表时间: 2023-08-28
• 期刊: APPLIED PHYSICS LETTERS
• 影响因子: 4
• 作者: Noh, Yechan;Aluru, N. R.
• 通讯作者: Aluru, N. R.

Topology-Based Phase Identification of Bulk, Interface, and Confined Water Using an Edge-Conditioned Convolutional Graph Neural Network

• DOI: 10.1021/acs.jpcc.2c07423
• 发表时间: 2022-04
• 期刊: The Journal of Physical Chemistry C
• 作者: A. Moradzadeh;H. Oliaei;N. Aluru

Deep learning-based quasi-continuum theory for structure of confined fluids

基于深度学习的承压流体结构准连续介质理论

• DOI: 10.1063/5.0096481
• 发表时间: 2022
• 期刊: The Journal of Chemical Physics
• 作者: Wu, Haiyi;Aluru, N. R.
• 通讯作者: Aluru, N. R.