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

职业：用于电催化吸附质特异性调节的化学吸附贝叶斯模型

基本信息

批准号：
1845531
负责人：
Hongliang Xin
金额：
$ 54.95万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845531&HistoricalAwards=false
关键词：
CAREER Bayesian Model Chemisorption Adsorbate

项目摘要

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.

氨（NH3）最著名的是肥料的起始材料，但是在氨气传感，废水处理和直接氨燃料电池等应用中，需要其与氧气（称为氧化）的反应（称为氧化），所有这些燃料电池都在电化学上进行，并且通常由称为电催化剂的催化材料辅助。即使使用最先进的铂金电催化剂，氧化反应也效率低，需要过量的电能。该项目将通过理论和计算手段来研究通过将铂与纳米级颗粒中的其他金属结合在一起，可以提高能源效率和电化学氨氧化速率，称为纳米合金。预测的纳米合金组合物将有助于指导更有效的电催化剂的设计，不仅是针对氨相关的应用，而且对于广泛的能源和环境技术。该项目还将研究与旨在激发高中生有关STEM机会的教育和宣传计划的融合，并培训本科生和研究生在应用计算机模型中用于能源安全和环境管理。可以说，对于给定类型的催化剂，例如D块金属，原子吸附能之间的缩放关系及其氢化物种限制了电/化学能转化的效率。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来促进轨道级别对吸附物 - 表面相互作用和催化过程的理解，从而在金属 - - 电解质界面上铺平了路径，从而铺平了路径的适应性特异性调节电骨质体的调节。关键反应物种的游离形成能将通过化学键合化的轨道扰动选择性调节，例如纳米合金，使得限制速率的N-N键形成的激活屏障或N-H H-H裂解步骤在不用吸附N n Adatoms中毒害表面而降低表面。催化理论，量子化学和机器学习将结合起来，以揭开缓慢的NH3电氧化动力学的原子机制，并使用机器学习的汉密尔顿人开发贝叶斯化学吸附模型。通过与贝叶斯模型引导的原子量的金属位点进行工程相互作用来调节吸附物种，将进一步推进化学吸附理论及其在催化中的应用理论，从而实现具有具有物理可解释的见解而不是试用和误用搜索的催化系统的设计。该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，进一步开发纳米工程结构的光子收集的信息学。这项工作将通过移动设备上的材料信息学为学生学习提供精力，展示了能量问题的多学科观点，并通过材料设计项目刺激学生的协作学习。该“实验”将增强妇女，少数群体和残疾人在STEM领域的招聘和保留，并将激励学生迈向与高级可再生能源和环境技术有关的终身学习和职业。该奖项反映了NSF的法规使命，并被认为是通过基金会的知识优点和广泛的Impactia的评估来进行评估。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Interpretable Machine Learning for Catalytic Materials Design toward Sustainability

DOI：
10.1021/accountsmr.3c00131
发表时间：
2023-11
期刊：
Accounts of Materials Research
影响因子：
14.6
作者：
Hongliang Xin;Tianyou Mou;H. Pillai;Shih-Han Wang;Yang Huang
通讯作者：
Hongliang Xin;Tianyou Mou;H. Pillai;Shih-Han Wang;Yang Huang

Ternary PtIrNi Catalysts for Efficient Electrochemical Ammonia Oxidation

DOI：
10.1021/acscatal.9b04670
发表时间：
2020-04-03
期刊：
ACS CATALYSIS
影响因子：
12.9
作者：
Li, Yi;Li, Xing;Wu, Gang
通讯作者：
Wu, Gang

Identification of Active Sites for Ammonia Electrosynthesis on Ruthenium

DOI：
10.1021/acsenergylett.2c02175
发表时间：
2022-11
期刊：
ACS Energy Letters
影响因子：
22
作者：
Lin Hu;H. Pillai;Corbin Feit;Kaige Shi;Zhengning Gao;P. Banerjee;Hongliang Xin;Xiaofeng Feng
通讯作者：
Lin Hu;H. Pillai;Corbin Feit;Kaige Shi;Zhengning Gao;P. Banerjee;Hongliang Xin;Xiaofeng Feng

Catalyst design with machine learning