Effects of electrode microstructure and Li2O2 growth on Li-air battery performance

锂(Li)-空气电池具有较高的可用能量密度，是一种很有前途的电动汽车和可再生能源储存电池解决方案。然而，目前的锂空气技术存在往返效率和能量容量较低的问题。正极微观结构和放电产物(过氧化锂，Li2O2)的形成对电池的性能有重要影响。该项目试图通过一个理论建模和实验相结合的综合项目来研究电极材料结构的影响以及对Li2O2形成过程对Li-Air电池性能的理解。开发高性价比、耐用和高能量密度的电池是迈向国家轻型车辆运输车队电气化的关键一步。虽然电池材料的发展是一个活跃的研究领域，但电极材料的结构设计主要是基于行业内的经验知识。这里开发的结构-性能-性能路径将提供关于电极结构如何以及为什么导致电池过早失效的基线理解，并将使虚拟设计新的电极结构以提高性能。该项目涉及两个工程学科和系，并建立了令人兴奋的合作，以实现锂空气电极设计的综合研究计划，为学生提供不同的培训和指导机会。该项目将为PI教授的能源和运输现象纳米制造课程提供新的课程材料和实验室演示。此外，PIS将继续积极指导本科生研究人员，并招募STEM的女性和代表性不足的少数族裔学生加入他们的研究小组。社区外展将通过费城科学节和Drexel GK-12/REU计划扩展到费城市中心的K-12教师和学生。该项目的目标是开发一个紧密集成的计划，包括新型电极制造、高保真多尺度建模、后期和现场表征、电化学测试和高性能计算，以探索电极微结构和Li2O2生长形态对电池性能的影响。该项目结合了Pi Sun在多尺度传输现象建模方面的专业知识和Co-Pi Kalra在制备用于电化学储能的新型纳米材料方面的专业知识。多尺度模型结合了孔尺度输运分辨模型和Li2O2生长相场模型，考虑了速率相关的Li2O2形态和形态相关的性质，能够模拟基于三维真实电极微结构的耦合生长、输运和电化学过程。综合实验程序为模型提供了几何/性质输入，并直接验证了模型对纳米级Li2O2形貌和电池性能的预测。经过验证的模型与支持图形处理单元(GPU)的计算相结合，将用于在多个周期内执行大规模动态模拟，以发现从多孔电极的结构设计到电池性能评估的知识途径。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。

The lithium (Li)-air battery, with its high usable energy density, is a promising battery solution for electric vehicles and renewable energy storage. However, current Li-air technology suffers from low round-trip efficiency and energy capacity. The cathode microstructure and the formation of the discharge product (lithium peroxide, Li2O2) can have a significant influence on the performance of the battery. The project seeks to examine the impact of the electrode's material structure and the understanding of the evolution of Li2O2 formation on Li-air cell performance via an integrated project that combines theoretical modeling and experiments. The development of cost-effective, long lasting, and high energy-density batteries is a crucial step towards the electrification of the nation's light-duty vehicle transportation fleet. While the development of battery materials is an active research area, the design of electrode material's structure has been mainly based on empirical knowledge within industry. The structure-property-performance pathway developed here will provide a baseline understanding of how and why the electrode architecture leads to premature battery failure and will enable virtual design of novel electrode structures for improved performance. This project involves two engineering disciplines and departments and builds an exciting collaboration to enable an integrated research program on Li-air electrode design that provides diverse training and mentoring opportunities for students. The project will enable new course materials and laboratory demonstrations for the Nanomanufacturing for Energy and Transport Phenomena courses that the PIs teach. In addition, the PIs will continue to actively mentor undergraduate researchers and recruit women and under-represented minority students in STEM into their research groups. The community outreach will be extended to Philadelphia inner-city K-12 teachers and students through the Philly Science Festival and the Drexel GK-12/REU programs.The goal of this project is to develop a closely integrated program that includes novel electrode fabrication, high fidelity multi-scale modeling, post-mortem and in-situ characterization, electrochemical testing, and high-performance computing to probe the effects of electrode microstructure and Li2O2 growth morphology on cell performance. The project combines the expertise of PI Sun on multi-scale modeling of transport phenomena and Co-PI Kalra on fabrication of novel nanomaterials for electrochemical energy storage. Combining the pore-scale transport resolved model with the phase-field model for Li2O2 growth, the multi-scale modeling approach accounts for rate-dependent Li2O2 morphology and morphology-dependent properties and is capable of simulating the coupled growth, transport, and electrochemistry based on 3D real electrode microstructures. The integrated experimental program provides geometry/property inputs to the model and directly validates the model predictions for both the Li2O2 morphology at the nanoscale and the battery performance at the cell level. The validated model combined with graphics processing unit (GPU)-enabled computing will be used to perform large-scale, dynamic simulations over many cycles to discover knowledge pathways from structural design of porous electrode to cell performance assessment.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.