Developing an atomic-scale computational framework to gain insights about the electrochemical double-layer for applications to renewable energy

开发原子级计算框架，以深入了解电化学双层在可再生能源中的应用

• 批准号: RGPIN-2020-07095
• 负责人: Chen, Leanne
• 金额: $ 2.11万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741591
• 关键词: Developing; atomic; scale; computational; framework

Coupling renewably generated electricity with inert starting materials to produce value-added chemicals and fuels could have potentially transformative benefits for society. However, current state-of-the-art energy storage and conversion devices such as batteries for electric vehicles and electrochemically generating carbon-based fuels are not yet efficient enough to justify the widespread implementation of these systems from an economic perspective. In order to design improved energy storage and conversion devices, researchers need to understand the fundamental mechanisms that govern energy transformation, yet this is still a challenge due to the complexity of these multi-component systems. Accurate computational simulations can give insight into chemical mechanisms where intermediates species are difficult to detect (for example, being too transient or too dilute) via current experimental techniques. Without a rigourous set of experimental data on reaction intermediates to guide computational chemists, the current challenge is to design computational simulations to understand the observables that are known-such as product formation rates-with the ultimate goal being making reliable predictions about how to improve the efficiency of a given energy storage or catalytic system. Hence, these simulations need to be extremely accurate both in terms of the fundamental interactions between individual atoms, and the model itself needs to be a detailed enough representation of reality. To meet the requirement of chemical accuracy, my research program will utilize quantum mechanical methods (which resolve the chemical system to the level of electrons) to map out reaction pathways in three different energy transformation applications: (i) aqueous metal-air batteries which could extend the range of electric vehicles to be on par with that of internal combustion engine vehicles, (ii) electrochemically converting CO2 to carbon-based fuels such as methane and ethanol to both close the carbon cycle and mitigate the effects of climate change, and (iii) applying statistical mechanical techniques to the cathodic half-reaction for water splitting (hydrogen evolution). The results from all three projects will be used to develop a more complete computational model for studying electrochemical processes. The long-term vision of my research program will provide researchers with a framework of computational simulations to study generalized electrochemical reactions, and take advantage of the efficiency offered by machine-learning approaches. The ultimate goal from developing this framework is to provide researchers with a comprehensive electrochemical model as well as a means to carry out efficient calculations that are of high enough quality such that theory/computation alone could make quantitative predictions of improved electrochemical energy storage systems.

将可再生电力与惰性原材料结合起来生产增值化学品和燃料可能会给社会带来潜在的变革性效益。然而，当前最先进的能量存储和转换设备，例如电动汽车电池和电化学生成碳基燃料，其效率还不足以证明从经济角度广泛实施这些系统的合理性。为了设计改进的能量存储和转换设备，研究人员需要了解控制能量转换的基本机制，但由于这些多组件系统的复杂性，这仍然是一个挑战。准确的计算模拟可以深入了解通过当前实验技术难以检测中间体物质（例如，太短暂或太稀释）的化学机制。如果没有一套严格的反应中间体实验数据来指导计算化学家，当前的挑战是设计计算模拟来了解已知的可观察数据（例如产物形成速率），最终目标是对如何改进反应中间体做出可靠的预测。给定能量存储或催化系统的效率。因此，这些模拟在单个原子之间的基本相互作用方面都需要极其准确，并且模型本身需要足够详细地表示现实。为了满足化学精度的要求，我的研究计划将利用量子力学方法（将化学系统解析为电子水平）来绘制三种不同能量转换应用中的反应路径：（i）水金属-空气电池，它可以将电动汽车的续航里程扩大到与内燃机汽车相当，(ii) 通过电化学方式将二氧化碳转化为甲烷和乙醇等碳基燃料，以关闭碳循环并减轻气候变化的影响，以及（ iii) 将统计机械技术应用于阴极水分解的半反应（析氢）。所有三个项目的结果将用于开发更完整的计算模型来研究电化学过程。我的研究计划的长期愿景将为研究人员提供一个计算模拟框架来研究广义电化学反应，并利用机器学习方法提供的效率。开发该框架的最终目标是为研究人员提供一个全面的电化学模型以及一种进行足够高质量的有效计算的方法，以便仅凭理论/计算就可以对改进的电化学储能系统进行定量预测。