CAREER: Bayesian Model of Chemisorption for Adsorbate-Specific Tuning of Electrocatalysis

氨(NH3)最为人所知的是作为肥料的原料，但它与氧气的反应(称为氧化)在氨传感、废水处理和直接氨燃料电池等应用中是必需的-所有这些都是通过电化学进行的，通常由一种称为电催化剂的催化剂材料辅助。即使使用最先进的铂基电催化剂，氧化反应也效率低下，需要过多的电能。该项目将通过理论和计算手段，研究通过将铂与其他金属结合在被称为纳米合金的纳米颗粒中来提高能源效率和电化学氨氧化速率的可能性。预测的纳米合金成分将有助于指导更有效的电催化剂的设计，不仅适用于与氨有关的应用，也适用于广泛的能源和环境技术。该项目还将研究与教育和推广活动相结合，旨在激发高中生对STEM机会的兴趣，并培训本科生和研究生应用计算机模型来实现能源安全和环境管理。人工光合作用核心的电催化反应涉及多个质子耦合电子转移步骤。可以说，对于特定类型的催化剂，例如d-块金属，原子的吸附能与其氢化物种之间的标度关系限制了电/化学能量转换的效率。为了克服氨氧化反应的这些障碍，该项目将利用贝叶斯框架推进对吸附物-表面相互作用和金属-电解液界面上的催化过程的轨道水平的理解，为实现特定于吸附物的电催化调整铺平道路。关键反应物种的自由形成能将通过化学键的轨道方向微扰来选择性地调整，例如纳米合金化，从而降低限速N-N键形成或N-H解离步骤的活化势垒，而不会用吸附的N吸附原子毒化表面。催化理论、量子化学和机器学习将结合起来，揭示迟缓的NH3电氧化动力学的原子机制，并用机器学习的哈密顿建立化学吸附的贝叶斯模型。通过设计吸附物种与贝叶斯模型指导下的原子定制金属中心的相互作用来调节吸附物种，将进一步推动化学吸附理论及其在催化中的应用，使催化体系的设计能够具有物理上可解释的见解，而不是反复试验搜索。这项职业计划的教育部分旨在通过研究人员开发的移动设备应用程序iPhanes，进一步发展纳米工程结构中光子捕获的信息学。这一努力将激励学生在移动设备上使用材料信息学进行学习，展示对能源问题的多学科视角，并通过材料设计项目刺激学生的协作学习。这一“实验”将促进STEM领域女性、少数族裔和残疾人的招募和留住，并将激励学生终身学习和从事与先进的可再生能源和环境技术相关的职业。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。

Ammonia (NH3) is best known as a starting material for fertilizers, but its reaction with oxygen (called oxidation) is required in applications such as ammonia sensing, wastewater treatment, and direct ammonia fuel cells - all of which are carried out electrochemically, and usually assisted by a catalyst material called an electrocatalyst. Even with state-of-the-art platinum-based electrocatalysts, the oxidation reaction is inefficient and requires excess electrical energy. The project will investigate, through theoretical and computational means, the possibility of improving both the energy efficiency and the rate of electrochemical ammonia oxidation by combining platinum with other metals in nano-scale particles known as nano-alloys. The predicted nano-alloy compositions will help guide the design of more efficient electrocatalysts, not only for ammonia-related applications, but also for a broad range of energy and environmental technologies. The project also integrates research with educational and outreach initiatives designed to excite high-school students about STEM opportunities and train undergraduate and graduate students in the application of computer models for energy security and environmental stewardship.Electrocatalytic reactions at the core of artificial photosynthesis involve multiple proton-coupled electron transfer steps. Arguably, for a given type of catalysts, e.g., d-block metals, the scaling relations among adsorption energies of atoms and their hydrogenated species limit the efficiency of electrical/chemical energy conversion. To overcome those obstacles for the ammonia oxidation reaction, the project will utilize a Bayesian framework for advancing the orbital-level understanding of adsorbate-surface interactions and catalytic processes at the metal-electrolyte interfaces, paving the path toward adsorbate-specific tuning of electrocatalysis. The free formation energies of key reaction species will be selectively tuned via orbital-wise perturbation of chemical bonding, e.g., nano-alloying, such that the activation barrier of the rate-limiting N-N bond formation or N-H cleavage step is reduced without poisoning the surface with adsorbed N adatoms. Catalysis theory, quantum chemistry, and machine learning will be combined to unravel atomistic mechanisms of sluggish NH3 electro-oxidation kinetics and develop the Bayesian model of chemisorption with machine-learned Hamiltonians. Modulation of adsorbed species by engineering their interactions with atomically-tailored metal sites guided by the Bayesian models will further advance the theory of chemisorption and its applications in catalysis, enabling design of catalytic systems with physically interpretable insights rather than trial-and-error searches. The educational component of this CAREER plan aims to further develop the informatics for photon harvesting at nano-engineered structures, via a mobile device application, iPhanes, developed by the investigator. This effort will energize student learning using materials informatics on mobile devices, demonstrate a multidisciplinary perspective of energy issues, and stimulate the students' collaborative learning via materials design projects. This "experiment" will enhance recruitment and retention of women, minorities, and persons with disabilities in STEM fields, and will motivate the students towards lifelong learning and careers related to advanced renewable energy and environmental technologies.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.