CAREER: Understanding Electrochemical Metal Extraction in Molten Salts from First Principles

职业:从第一原理了解熔盐中的电化学金属萃取

基本信息

  • 批准号:
    2340765
  • 负责人:
  • 金额:
    $ 58.28万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2024
  • 资助国家:
    美国
  • 起止时间:
    2024-06-01 至 2029-05-31
  • 项目状态:
    未结题

项目摘要

The CAREER project addresses a critical climate change challenge: developing environmentally friendly methods for producing and recycling key metals such as nickel and cobalt, essential for clean energy technologies. As society shifts from fossil fuels to electric energy, increasing battery, electrolyzer, and fuel cell production is vital. This project contributes by investigating clean, electricity-powered metal extraction processes in molten salts by combining atomic-scale computer simulations with machine learning and data science. These computational tools will enable precise modeling of process steps, offering insights beyond experimental capabilities. This includes understanding mineral dissolution in molten salts and how electrolyte composition impacts energy needs for metal extraction, which is essential for effective process design. Besides advancing scientific research methods, clean electrolytic metal extraction processes promise substantial benefits for national health, prosperity, and welfare by advancing clean energy solutions and reducing fossil fuel dependency and, thereby, their detrimental impacts on health and the environment. Unlike conventional mining processes, clean electrolytic processes can be implemented domestically, reducing the reliance on international supply chains and, thus, enhancing national defense and economic stability. Additionally, the project establishes an annual winter school focusing on data science in electrochemical energy, targeting high-school seniors and undergraduates, especially from underrepresented minorities. This initiative advances education at the intersection of data/computational science and chemical engineering and raises awareness about the global impact of critical materials and sustainable energy practices, contributing to a diverse STEM pipeline.This project adopts a novel approach to studying high-temperature mineral electrolysis in molten salts, a crucial yet under-researched class of processes for clean metal extraction for metal production and recovery from electronics waste. It aims to understand key steps in molten-salt electrolysis through tailored modeling approaches: Electronic density-functional theory for atomic/electronic-scale properties that control redox potentials, first-principles surface-phase diagrams for surface/interfacial effects relevant for dissolution, and both ab initio molecular dynamics (MD) simulations and MD simulations based on machine-learning interatomic potentials for transport properties and solvation. An unsupervised learning approach is used to analyze trajectories from large-scale MD simulations, and computational predictions will be validated against experimental data from collaborators. This comprehensive study seeks to develop benchmarked methods and models for designing molten-salt electrolysis processes, with an initial focus on cobalt and nickel minerals relevant to lithium-ion batteries. These advances in computational process design and implementation have broad applications in computational chemical engineering and materials science, marking progress in integrating first principles theory with data science. Moreover, the project has significant broader impacts. It aids the transition to a clean energy economy by providing new degrees of freedom for the rational design of clean metal extraction processes that are needed for the electrification of industry and to overcome supply-chain challenges. Educationally, it integrates data science in chemical engineering and materials science, preparing students for interdisciplinary manufacturing challenges and fostering workforce development in a key yet underserved sector.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.
CAREER项目解决了一个关键的气候变化挑战:开发生产和回收镍和钴等关键金属的环保方法,这对清洁能源技术至关重要。随着社会从化石燃料转向电能,增加电池,电解槽和燃料电池的生产至关重要。该项目通过将原子级计算机模拟与机器学习和数据科学相结合,研究熔融盐中清洁的电力金属提取过程。这些计算工具将能够对工艺步骤进行精确建模,提供超越实验能力的见解。这包括了解熔盐中的矿物溶解以及电解质成分如何影响金属提取的能量需求,这对于有效的工艺设计至关重要。除了推进科学研究方法外,清洁电解金属提取工艺还通过推进清洁能源解决方案和减少化石燃料依赖性,从而减少其对健康和环境的有害影响,为国家健康,繁荣和福利带来了巨大利益。与传统采矿工艺不同,清洁电解工艺可以在国内实施,减少对国际供应链的依赖,从而增强国防和经济稳定性。此外,该项目还建立了一个年度冬季学校,重点关注电化学能源中的数据科学,目标是高中高年级学生和本科生,特别是来自代表性不足的少数民族。这一举措推动了数据/计算科学和化学工程交叉领域的教育,提高了人们对关键材料和可持续能源实践的全球影响的认识,为多样化的STEM管道做出了贡献。该项目采用了一种新的方法来研究熔融盐中的高温矿物电解,这是一种关键但研究不足的工艺,用于金属生产和电子废物回收的清洁金属提取。它旨在通过定制的建模方法来理解熔盐电解的关键步骤:控制氧化还原电位的原子/电子尺度性质的电子密度泛函理论,与溶解相关的表面/界面效应的第一原理表面相图,以及从头算分子动力学(MD)模拟和基于机器学习原子间势的MD模拟传输特性和溶剂化。无监督学习方法用于分析大规模MD模拟的轨迹,计算预测将根据合作者的实验数据进行验证。这项全面的研究旨在开发用于设计熔盐电解工艺的基准方法和模型,最初重点关注与锂离子电池相关的钴和镍矿物。这些在计算过程设计和实现方面的进展在计算化学工程和材料科学中有着广泛的应用,标志着第一性原理理论与数据科学相结合的进展。此外,该项目具有重大的广泛影响。它有助于向清洁能源经济过渡,为合理设计工业电气化和克服供应链挑战所需的清洁金属提取工艺提供了新的自由度。在教育方面,它将数据科学与化学工程和材料科学相结合,为学生应对跨学科制造挑战做好准备,并在一个关键但服务不足的领域促进劳动力发展。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值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

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Alexander Urban其他文献

Clinical and personal utility of genomic high-throughput technologies: perspectives of medical professionals and affected persons
基因组高通量技术的临床和个人效用:医疗专业人员和受影响人群的观点
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alexander Urban;M. Schweda
  • 通讯作者:
    M. Schweda
Sequentially firing neurons confer flexible timing in neural pattern generators.
连续放电的神经元赋予神经模式生成器灵活的时序。
Scalable training of neural network potentials for complex interfaces through data augmentation
通过数据增强对复杂界面的神经网络势进行可扩展训练
  • DOI:
    10.1038/s41524-025-01651-0
  • 发表时间:
    2025-05-28
  • 期刊:
  • 影响因子:
    11.900
  • 作者:
    In Won Yeu;Annika Stuke;Jon López-Zorrilla;James M. Stevenson;David R. Reichman;Richard A. Friesner;Alexander Urban;Nongnuch Artrith
  • 通讯作者:
    Nongnuch Artrith
Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes
硫化物固体电解质氧化降解机制的原子洞察
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Chuntian Cao;Matthew R. Carbone;Cem Komurcuoglu;Jagriti S. Shekhawat;Kerry Sun;Haoyue Guo;Sizhan Liu;Ke Chen;Seong;Yonghua Du;Conan Weiland;Xiao Tong;Dan Steingart;Shinjae Yoo;Nongnuch Artrith;Alexander Urban;Deyu Lu;Feng Wang
  • 通讯作者:
    Feng Wang
Formation of antiwaves in gap-junction-coupled chains of neurons.
在神经元间隙连接耦合链中形成反波。

Alexander Urban的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Alexander Urban', 18)}}的其他基金

Collaborative Research: C1: Learning the Universal Free Energy Function
合作研究:C1:学习通用自由能函数
  • 批准号:
    1940290
  • 财政年份:
    2020
  • 资助金额:
    $ 58.28万
  • 项目类别:
    Standard Grant

相似国自然基金

Understanding structural evolution of galaxies with machine learning
  • 批准号:
    n/a
  • 批准年份:
    2022
  • 资助金额:
    10.0 万元
  • 项目类别:
    省市级项目
Understanding complicated gravitational physics by simple two-shell systems
  • 批准号:
    12005059
  • 批准年份:
    2020
  • 资助金额:
    24.0 万元
  • 项目类别:
    青年科学基金项目

相似海外基金

Understanding and Improving Electrochemical Carbon Dioxide Capture
了解和改进电化学二氧化碳捕获
  • 批准号:
    MR/Y034244/1
  • 财政年份:
    2025
  • 资助金额:
    $ 58.28万
  • 项目类别:
    Fellowship
In situ TEM for understanding the electrochemical performance of iridium based catalysts
原位 TEM 了解铱基催化剂的电化学性能
  • 批准号:
    2905956
  • 财政年份:
    2023
  • 资助金额:
    $ 58.28万
  • 项目类别:
    Studentship
Atomistic understanding of the electrode potential effect on the electrochemical processes by the newly developed constant electrode potential QM/MM method
通过新开发的恒定电极电位 QM/MM 方法从原子角度理解电极电位对电化学过程的影响
  • 批准号:
    22KJ1854
  • 财政年份:
    2023
  • 资助金额:
    $ 58.28万
  • 项目类别:
    Grant-in-Aid for JSPS Fellows
Understanding electrochemical hydrogenation reactions over post-transition metal electrodes: the role of incidental mediators and metastable phases
了解后过渡金属电极上的电化学氢化反应:偶然介体和亚稳态相的作用
  • 批准号:
    2301381
  • 财政年份:
    2023
  • 资助金额:
    $ 58.28万
  • 项目类别:
    Standard Grant
Understanding the Structural Transformations of Aluminum Foil Anodes during Electrochemical De(alloying) for Sustainable Lithium-ion Batteries
了解可持续锂离子电池电化学脱(合金)过程中铝箔阳极的结构转变
  • 批准号:
    2321486
  • 财政年份:
    2023
  • 资助金额:
    $ 58.28万
  • 项目类别:
    Standard Grant
Understanding dynamic interfaces in electrochemical systems
了解电化学系统中的动态界面
  • 批准号:
    FT220100666
  • 财政年份:
    2023
  • 资助金额:
    $ 58.28万
  • 项目类别:
    ARC Future Fellowships
Designing porous carbon electrodes for high performance LIBs based on understanding electrochemical reactions in the pores
基于对孔内电化学反应的理解,设计高性能锂离子电池的多孔碳电极
  • 批准号:
    23H02048
  • 财政年份:
    2023
  • 资助金额:
    $ 58.28万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
An Electrochemical Approach to Understanding Coral Bleaching
了解珊瑚白化的电化学方法
  • 批准号:
    2894423
  • 财政年份:
    2023
  • 资助金额:
    $ 58.28万
  • 项目类别:
    Studentship
Understanding and controlling complex modified electrochemical interfaces using novel measurements and model systems
使用新颖的测量和模型系统理解和控制复杂的改性电化学界面
  • 批准号:
    RGPIN-2022-04419
  • 财政年份:
    2022
  • 资助金额:
    $ 58.28万
  • 项目类别:
    Discovery Grants Program - Individual
CAS: Understanding the Role of External Constraint on Electrochemical (De)alloying Mechanisms
CAS:了解外部约束对电化学(脱)合金机制的作用
  • 批准号:
    2209202
  • 财政年份:
    2022
  • 资助金额:
    $ 58.28万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了