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.

将可再生能源发电与惰性起始材料结合起来，生产增值化学品和燃料，可能对社会产生潜在的变革性效益。然而，目前最先进的能量存储和转换设备，如电动汽车电池和电化学产生碳基燃料，还不够有效，从经济角度来看，这些系统的广泛实施是合理的。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，但由于这些多组分系统的复杂性，这仍然是一个挑战。精确的计算模拟可以让我们深入了解通过现有实验技术难以检测到的中间产物（例如，太短暂或太稀）的化学机制。没有一组严谨的反应中间体实验数据来指导计算化学家，目前的挑战是设计计算模拟来理解已知的可观察到的东西，比如产物形成率，最终目标是对如何提高给定能量存储或催化系统的效率做出可靠的预测。因此，就单个原子之间的基本相互作用而言，这些模拟需要非常精确，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（将化学系统分解到电子水平）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，它可以延长电动汽车的行驶里程，使其与内燃机汽车相当；（ii）电化学将二氧化碳转化为碳基燃料，如甲烷和乙醇，以关闭碳循环并减轻气候变化的影响；（iii）将统计机械技术应用于水分解的阴极半反应（析氢）。所有三个项目的结果将用于开发一个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的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。开发这一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高效计算的手段，这样理论/计算就可以对改进的电化学储能系统进行定量预测。