Collaborative Research: Material Simulation-driven Electrolyte Designs in Intermediate-temperature Na-K / S Batteries for Long-duration Energy Storage

合作研究：用于长期储能的中温Na-K / S电池中材料模拟驱动的电解质设计

• 批准号: 2341994
• 负责人: Yuan Yang
• 金额: $ 38.63万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341994&HistoricalAwards=false
• 关键词: Collaborative; Research; Material; Simulation; driven

Long-duration energy storage technology (10 hours, LDES) is critical to the expansion of intermittent renewable energy (e.g., solar/wind). Conventional Na-S and K-S batteries are attractive for LDES due to their low cost and the use of earth-abundant elements. However, their deployment is severely hindered by their high operational temperature of 300-350oC and associated degradation and safety issues. This project will use materials design and simulation-driven approaches to develop innovative electrolytes to dissolve insoluble reaction products in Na-S and K-S batteries and advance knowledge on underlying dissolution mechanisms. Such novel electrolytes will enhance reaction kinetics so the operation temperature can be reduced to 60-120oC, which not only enhances thermal stability but also decreases operational costs. The new material systems from this project have the potential to be deployed for LDES, which enhances the economic competitiveness and sustainability of U.S. The project activities will integrate research and education, targeting students from K-12 to graduate school and promoting underrepresented communities' education through hands-on experiences, advising, and research integration across all levels. The primary challenge in traditional alkaline metal sulfur (AMS) batteries arises from the formation of solid M2S2 and M2S compounds during discharge (M = Na, K), which exhibit poor electrochemical kinetics. This limits the reversible redox range mainly to S/M2S3 reactions, reducing specific capacity and energy density. The goal of this project is to identify and develop new solvents that can dissolve M2S2/M2S readily to replace conventional ether electrolytes, which will in turn make M2S2/M2S electrochemically active. This will double the specific capacity of sulfur from 500 mAh/g in ether electrolytes to 1000-1500 mAh/g, along with a long cycle life. The project will utilize a simulation-driven approach to design electrolytes, such as combining molecular dynamics (MD) simulations and machine learning (ML). MD simulations calculate solvation free energy, and ML enables high-throughput screening for solvents with superior M2S2 and M2S solubilities. Promising candidates will be experimentally validated. After experimentally confirming the high-performance solvents, multi-scale/multi-modal characterizations will be used to understand the fundamental dissolution mechanisms, electrochemistry and transport in the proposed system comprehensively. An Ah-level prototype will be constructed and tested, and the cost of developed materials and devices will be analyzed for large-scale deployment. The advances in knowledge and research tools together will help develop next-generation batteries for long-duration energy storage.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.

长时间储能技术（10小时，LDES）对于间歇性可再生能源的扩展至关重要（例如，太阳能/风能）。传统的Na-S和K-S电池由于其低成本和使用地球上丰富的元素而对LDES有吸引力。然而，它们的部署受到300- 350 ℃的高工作温度以及相关的降解和安全问题的严重阻碍。该项目将使用材料设计和模拟驱动的方法来开发创新的电解质，以溶解Na-S和K-S电池中的不溶性反应产物，并推进对潜在溶解机制的了解。这种新型电解质将增强反应动力学，因此操作温度可以降低到60- 120 ℃，这不仅提高了热稳定性，而且降低了操作成本。该项目的新材料系统有可能被部署用于LDES，从而提高美国的经济竞争力和可持续性。该项目活动将整合研究和教育，针对从K-12到研究生院的学生，并通过实践经验，建议和各级研究整合来促进代表性不足的社区的教育。传统碱金属硫（AMS）电池的主要挑战来自于在放电期间形成固体M2 S2和M2 S化合物（M = Na，K），其表现出差的电化学动力学。这将可逆氧化还原范围主要限制为S/M2 S3反应，降低了比容量和能量密度。该项目的目标是识别和开发能够容易地溶解M2 S2/M2 S的新溶剂，以取代传统的醚电解质，这反过来又使M2 S2/M2 S具有电化学活性。这将使硫的比容量加倍，从醚电解质中的500 mAh/g增加到1000-1500 mAh/g，沿着具有长循环寿命。该项目将利用模拟驱动的方法来设计电解质，例如结合分子动力学（MD）模拟和机器学习（ML）。MD模拟计算溶剂化自由能，ML能够高通量筛选具有上级M2 S2和M2 S溶解度的溶剂。有希望的候选人将进行实验验证。在实验确认高性能溶剂后，将使用多尺度/多模态表征来全面了解所提出的系统中的基本溶解机制、电化学和传输。将建造和测试一个AH级原型，并将分析开发材料和设备的成本，以进行大规模部署。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。