Manufacturing USA: Computational Screening of Metal Oxides for Alkane Dehydrogenation to Olefins

烯烃是化学工业的重要组成部分，一条很有前途的途径是在金属氧化物上对烷烃进行脱氢。这种反应的发生是由于氧化物的酸碱表面官能性，但其潜在的机制尚不清楚。这一知识差距一直阻碍着设计活性和强大的催化剂的努力，这些催化剂可以有效地将天然气（如轻烃）转化为增值化学品（如烯烃），优化天然气升级，并降低高能耗脱氢化学反应的成本。该研究旨在通过与RAPID制造研究所的研究人员合作，利用基于第一性原理的计算技术和实验验证，阐明适用于轻烷烃脱氢的金属氧化物催化剂的构效关系，从而填补这一知识空白。通过加速催化剂的发现，将轻烃从天然气有效转化为增值化学品，该项目有可能对化工行业产生积极影响。开发有效的工艺，减少化学品生产的能源投入和相关成本，并利用丰富的天然气储量，可以对社会和美国经济产生积极影响。提出的研究计划包括应用周期性密度泛函理论计算、从头算分子动力学模拟、多尺度过程建模和机器学习，以开发新的构效关系，将烷烃脱氢活性描述为氧化物刘易斯酸碱性质的函数。这些关系将能够快速筛选各种金属氧化物，每种氧化物的不同方面和表面酸碱位点的范围，以确定主要的脱氢机制和金属氧化物上最活跃的位点，作为其路易斯酸碱强度的函数。该项目还将通过确定哪些表面部位会被强物种吸附而中毒，甚至在脱氢温度升高的情况下也会中毒，并将在现实实验条件下研究催化剂表面动力学。最终目标是通过避免在实验室中进行试错实验来推进催化剂的发现，解决与工艺强化相关的知识差距，并支持快速制造研究所正在进行的转化研究。该提案的教育和推广部分包括：(a)让研究生和本科生参与研究，重点关注来自代表性不足背景的学生；(b)为相关课程（化学动力学、催化等）生成材料；(d)通过卡内基科学中心和匹兹堡大学的INVESTING NOW计划，使STEM推广到K-12学生。学生将接受计算化学、分子模拟、催化、过程强化、科学计算、机器学习和高性能计算方面的培训。该奖项反映了美国国家科学基金会的法定使命，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。

A promising route to olefins that are important building blocks for the chemical industry is thedehydrogenation of alkanes on metal oxides. This reaction occurs due to oxide acid-base surface functionalities, but the underlying mechanism is poorly understood. This knowledge gap has been impeding efforts to design active and robust catalysts that efficiently convert natural gas (i.e. light hydrocarbons) to value-added chemicals (i.e. olefins), optimize natural gas upgrading, and reduce the cost of the energy-demanding dehydrogenation chemistries. The proposed research aims to fill this knowledge gap by elucidating structure-activity relationships on metal oxide catalysts applicable to the dehydrogenation of light alkanes, using first-principles-based computational techniques and experimental verification through collaboration with researchers from the RAPID Manufacturing Institute. The project has the potential to positively impact the chemical industry by accelerating catalyst discovery for the efficient conversion of light hydrocarbons from natural gas to value-added chemicals. The development of efficient processes that reduce the energy input and associated cost for chemicals production, and utilize the abundant natural gas reserves, can have a positive impact on society and the US economy.The proposed research plan includes application of periodic Density Functional Theory calculations, ab-initio molecular dynamics simulations, multi-scale process modeling and machine learning to develop novel structure-activity relationships that describe the alkane dehydrogenation activity as a function of the Lewis acid-base properties of the oxides. These relationships will enable the rapid screening of a wide range of metal oxides, different facets on each oxide and a gamut of surface acid-base sites, to identify the prevailing dehydrogenation mechanism and the most active sites on the metal oxides, as function of their Lewis acid-base strength. The project will also create poisoning maps by identifying which surface sites will be poisoned by strong species adsorption, even at elevated dehydrogenation temperatures, and will study catalyst surface dynamics under realistic experimental conditions. The ultimate objective is to advance catalyst discovery by avoiding trial-and-error experimentation in the laboratory, address knowledge gaps pertinent to process intensification, and support translational research being conducted in the RAPID Manufacturing Institute. The educational and outreach components of the proposal include: (a) engaging graduate and undergraduate students in research, with a focus on students from under-represented backgrounds, (b) generating material for relevant courses (Chemical Kinetics, Catalysis, etc.) and (d) enabling STEM outreach to K-12 students through the Carnegie Science Center and University of Pittsburgh's INVESTING NOW program. Students will be trained on computational chemistry, molecular simulations, catalysis, process intensification, scientific computation, machine learning and high-performance computing.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.