Fundamental Acid/Base Properties of Hydrocarbon Carbon-Hydrogen (C-H) Bonds and Metal-Element Active Sites

北德克萨斯大学的Thomas R. Cundari教授获得了化学系化学催化项目的奖励，以计算研究将轻烷烃（如甲烷或天然气、乙烷等）转化为更有价值的商品化学品和燃料（如汽油）的新型催化方法。将轻烷烃转化为更有价值的产品通常需要使用含金属的催化剂来加速其转化为特定的理想产品。昆达里教授的学生们寻求新的途径来转化这种丰富的国内能源资源，而不需要危险、苛刻或昂贵的化学催化剂。先进的计算机模拟甲烷反应的结果与实验合作者的研究相结合，以确定这一重要化学转化的有前途的催化剂。Cundari Group学生生成的大型数据集与新兴的机器学习技术相结合，以识别用于甲烷转化的新型化学实体。从事这项研究的早期职业科学家在一个具有明显社会效益的领域获得了具有重要理论与实验相互作用的前沿研究经验。北德克萨斯大学的Thomas R. Cundari教授获得了化学系化学催化项目的奖励，研究了烷烃C-H键和金属配体活性位点的酸和碱性质的基本重要性。脂肪碳氢键的活化和功能化是商品和精细化学合成中均相和多相催化剂的重要挑战。Cundari小组的学生使用量子力学和机器学习建模来辨别催化功能化的程度，而不使用超酸/超碱试剂。Cundari小组的模拟与实验紧密结合，通过建立重要催化和化学性质的准确性和精度基准，进一步有价值的化学社区。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。

Professor Thomas R. Cundari of the University of North Texas is supported by an award from the Chemical Catalysis program in the Division of Chemistry to computationally study novel catalytic methods for upgrading light alkanes (e.g., methane or natural gas, ethane, etc.) to more valuable commodity chemicals and fuels (e.g. gasoline). The conversion of light alkanes to more valuable products generally requires the use of metal-containing catalysts to speed up their conversion to specific desirable products. Professor Cundari's students pursue novel routes for conversion of this abundant domestic energy resource that do not require dangerous, harsh or expensive chemical catalysts. The results of advanced computer simulations of methane reactions are integrated with the research of experimental collaborators to identify promising catalysts for this important chemical transformation. The large data sets generated by Cundari Group students are integrated with emerging machine learning techniques to identify novel chemical entities for methane conversion. Early-career scientists engaged in this research gain experience in cutting-edge research with significant theory-experiment interplay in an area with obvious societal benefit.Professor Thomas R. Cundari of the University of North Texas is supported by an award from the Chemical Catalysis program in the Division of Chemistry to investigate the fundamental importance of acid and base properties of alkane C-H bonds and metal-ligand active sites, respectively. Activation and functionalization of aliphatic C-H bonds is an important challenge in commodity and fine chemical synthesis, for homogeneous and heterogeneous catalysts. Cundari Group students employ quantum mechanical and machine learning modeling to discern the extent of catalytic functionalization without using super-acid/-base reagents. Simulations by the Cundari Group are closely integrated with experiment and are further valuable chemistry community by establishing benchmarks for accuracy and precision of important catalytic and chemical properties.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.