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

美国制造：用于烷烃脱氢制烯烃的金属氧化物的计算筛选

基本信息

批准号：
1920623
负责人：
Ioannis Bourmpakis
金额：
$ 35.5万
依托单位：
University of Pittsburgh
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：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1920623&HistoricalAwards=false
关键词：
Manufacturing USA Computational Screening Metal

项目摘要

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.

烯烃是化学工业的重要组成部分，一条很有前途的路线是烷烃在金属氧化物上的脱氢。这种反应是由于氧化物的酸碱表面功能而发生的，但其潜在的机制却知之甚少。这种知识差距阻碍了设计活性和耐用催化剂的努力，这些催化剂有效地将天然气（即轻质烃）转化为增值化学品（即烯烃），优化天然气提质，并降低高能耗脱氢化学品的成本。拟议的研究旨在通过阐明适用于低碳烷烃脱氢的金属氧化物催化剂的结构-活性关系，使用基于第一原理的计算技术和实验验证，通过与RAPID制造研究所的研究人员合作，填补这一知识空白。该项目有可能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，从而对化学工业产生积极影响。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。拟议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，以开发新型结构-活性关系，将烷烃脱氢活性描述为氧化物的刘易斯酸碱性质的函数。这些关系将使得能够快速筛选宽范围的金属氧化物、每种氧化物上的不同面和表面酸碱位点的范围，以确定占主导地位的脱氢机制和金属氧化物上的最活性位点，作为它们的刘易斯酸碱强度的函数。该项目还将通过确定哪些表面位点将被强物种吸附中毒来创建中毒地图，即使在升高的脱氢温度下，并将在现实的实验条件下研究催化剂表面动力学。最终目标是通过避免实验室中的试错实验来推进催化剂发现，解决与过程强化相关的知识缺口，并支持RAPID制造研究所正在进行的转化研究。该提案的教育和外联部分包括：（a）让研究生和本科生参与研究，重点是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）编写材料。以及（d）通过卡内基科学中心和匹兹堡大学的INVESTING NOW计划，将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等方面的培训。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。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Computational Screening of Lewis Acid Catalysts for the Ene Reaction between Maleic Anhydride and Polyisobutylene

DOI：
10.1021/acs.iecr.0c04860
发表时间：
2021-01-13
期刊：
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
影响因子：
4.2
作者：
Morales-Rivera, Cristian A.;Proust, Nico;Mpourmpakis, Giannis
通讯作者：
Mpourmpakis, Giannis

Mechanistic understanding of methane-to-methanol conversion on graphene-stabilized single-atom iron centers

DOI：
10.1039/d1cy00826a
发表时间：
2021-07
期刊：
Catalysis Science & Technology
影响因子：
5
作者：
S. Hong;Giannis Mpourmpakis
通讯作者：
S. Hong;Giannis Mpourmpakis

Understanding and Optimizing the Behavior of Al- and Ru-Based Catalysts for the Synthesis of Polyisobutenyl Succinic Anhydrides

DOI：
10.1021/acs.iecr.2c02003
发表时间：
2022-09-22
期刊：
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
影响因子：
4.2
作者：
Morales-Rivera, Cristian A.;Cormack, Glenn;Mpourmpakis, Giannis
通讯作者：
Mpourmpakis, Giannis

Multiscale modeling reveals aluminum nitride as an efficient propane dehydrogenation catalyst

多尺度建模揭示氮化铝是一种高效的丙烷脱氢催化剂

DOI：
10.1039/d2cy02173k
发表时间：
2023
期刊：
Catalysis Science & Technology
影响因子：
5
作者：
Abdelgaid, Mona;Miu, Evan V.;Kwon, Hyunguk;Kauppinen, Minttu M.;Grönbeck, Henrik;Mpourmpakis, Giannis
通讯作者：
Mpourmpakis, Giannis

CuNi bimetallic nanocatalyst enables sustainable direct carboxylation reactions