Computationally Accelerated Discovery of Catalysts for Electrification of the Nitrogen Cycle

计算加速发现氮循环电气化催化剂

• 批准号: 2400339
• 负责人: Charles Musgrave
• 金额: $ 53.58万
• 依托单位: University of Colorado at Boulder
• 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: 2024-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2400339&HistoricalAwards=false
• 关键词: Computationally; Accelerated; Discovery; Catalysts; Electrification

With support from the Chemical Catalysis Program in the Division of Chemistry, Professor Charles Musgrave and his team at the University of Colorado-Boulder will use computational quantum chemical methods and machine learning to discover new electrocatalysts for carbon-free fertilizer synthesis. A significant shift to carbon-free fertilizer production could be on the horizon that would enable feeding the future population of the world using sustainable processes. Musgrave and his research group aims to accelerate this transformation by focusing on the synthesis of ammonia and ammonium nitrate, crucial components of fertilizers, through carbon-neutral electrochemical processes driven by renewable energy. By leveraging cutting-edge quantum chemical computational methods, Musgrave aims to accelerate the discovery and design of novel electrocatalysts that are essential for revolutionizing fertilizer production. Notably, this approach not only aims to enhance fertilizer synthesis but also to reduce nitrate levels, addressing the pressing issue of water contamination in global aquatic systems. Through the meticulous exploration of tens of thousands of candidate catalyst materials, the Musgrave research group aims to discover the key principles that govern the performance of electrocatalysts. This research has the potential to impact a wide array of electrocatalytic processes and will help in educating the future workforce in the increasingly important area of electrocatalysis. The central goal of this project is to accelerate the electrocatalytic carbon-neutral synthesis of ammonia and ammonium nitrate using renewable energy, with a particular focus on breaking the scaling relations that have long constrained electrocatalytic nitrogen reduction, nitrate reduction, and nitrogen oxidation reactions. To do this, the project will utilize grand canonical density functional theory methods that provide a quantum mechanical description of the electrified interface. This approach was chosen because it enables a fundamental and accurate description of electrochemical processes that occur at the electrocatalyst interface and how these processes change with the applied bias. These studies have the potential to enable a deeper understanding of the intricate processes underlying electrocatalysis. By delving into the electronic structure of electrocatalysts and their response to the applied potential, this research aims to uncover key insights into reaction energetics, relative kinetics, and the potential of unique materials to break scaling relations. The project scope encompasses a diverse range of materials within the binary and Chevrel chemical spaces, with the aim of facilitating the prediction of activity trends across various compositions and potentials. This comprehensive approach is intended to not only shed light on the transferability of scaling principles across different reactions and materials spaces, but also to inform the development of other electrocatalytic chemistries.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.

在化学系化学催化项目的支持下，科罗拉多大学博尔德分校的查尔斯·马斯格雷夫教授和他的团队将使用计算量子化学方法和机器学习来发现用于无碳肥料合成的新型电催化剂。 向无碳肥料生产的重大转变可能即将到来，这将使世界未来的人口能够使用可持续的工艺来养活。Musgrave和他的研究小组旨在通过可再生能源驱动的碳中性电化学过程，专注于合成氨和硝酸铵（肥料的关键成分）来加速这一转变。通过利用尖端的量子化学计算方法，马斯格雷夫的目标是加速发现和设计新型电催化剂，这些催化剂对于革新化肥生产至关重要。值得注意的是，这种方法不仅旨在提高肥料合成，而且还旨在降低硝酸盐水平，解决全球水生系统中水污染的紧迫问题。通过对数万种候选催化剂材料的细致探索，马斯格雷夫研究小组旨在发现控制电催化剂性能的关键原理。这项研究有可能影响广泛的电催化过程，并将有助于教育未来的劳动力在日益重要的电催化领域。该项目的中心目标是利用可再生能源加速氨和硝酸铵的电催化碳中和合成，特别关注打破长期限制电催化氮还原，硝酸盐还原和氮氧化反应的比例关系。为此，该项目将利用巨正则密度泛函理论方法，提供带电界面的量子力学描述。选择这种方法是因为它能够基本和准确地描述发生在电催化剂界面的电化学过程，以及这些过程如何随着施加的偏压而变化。这些研究有可能使人们更深入地了解电催化的复杂过程。通过深入研究电催化剂的电子结构及其对外加电位的响应，这项研究旨在揭示反应能量学，相对动力学以及独特材料打破标度关系的潜力的关键见解。该项目的范围包括二元和Chevrel化学空间内的各种材料，旨在促进对各种成分和潜力的活动趋势的预测。这一综合性方法不仅旨在阐明缩放原理在不同反应和材料空间中的可移植性，还旨在为其他电催化化学的发展提供信息。该奖项反映了NSF的法定使命，并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。