CAREER: Confinement Effects & Emergent Reactivity in Multifunctional Metal-Organic Framework (MOF)-Based Catalysts

在化学系化学催化项目的支持下，堪萨斯州立大学的Tendai Gadzikwa教授正在研究其物理和化学结构以酶的活性位点为模型的材料。酶是我们最有效和最具选择性的催化剂，可以将反应加速许多数量级，并从多种可能性中产生单一产物。通过研究与酶具有相同结构特征的合成材料的性质和反应性，Gadzikwa博士的实验室将确定与催化有关的结构-功能相关性。所获得的见解将应用于设计和建造更高效的催化剂，从而减少化学反应的能源消耗和废物产生，这是化学工业的两个主要可持续发展目标。Gadzikwa支持扩大化学科学的参与，重点是在堪萨斯州的STEM项目中保留代表性不足的少数民族学生。在这方面的努力包括教师指导，专业发展机会，外展活动，并在当地NOBCChE章的社区和领导力的培养。这些研究旨在探索内，和上，多功能金属有机框架（MOF）材料意想不到的反应性，与使用获得的见解设计和构建更有效的酶启发的催化剂的目标。酶通过固定在有限空间内的多个精确定位的化学官能团起作用，协同作用以活化、定向和稳定反应物种。已知紧密接近的多个官能团干扰彼此的物理性质，从而影响反应性。在机器学习的帮助下，Gadzikwa实验室将阐明在多功能MOF中观察到的意外反应性的分子起源。从这些研究中确定的结构-功能关系将为多功能MOF基受限催化剂的设计提供信息。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。

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Tendai Gadzikwa of Kansas State University is studying materials whose physical and chemical structure are modeled after the active sites of enzymes. Enzymes are our most efficient and selective catalysts, accelerating reactions by many orders of magnitude and producing a single product out of many possibilities. By studying the properties and reactivities of synthetic materials that share structural features with enzymes, Dr. Gadzikwa’s laboratory will ascertain structure-function correlations as they relate to catalysis. The insights gained will be applied to the design and construction of more efficient catalysts, thereby reducing energy consumption and waste generation from chemical reactions, two major sustainability goals for the chemical industry. Dr. Gadzikwa supports broadening participation in the chemical sciences, with a focus on the retention of underrepresented minority students in STEM programs at Kansas State. Efforts in this regard include faculty mentorship, professional development opportunities, outreach activities, and the fostering of community and leadership in the local NOBCChE chapter.These studies are directed at exploring unexpected reactivity within, and upon, multifunctional metal-organic framework (MOF) materials, with the goal of using the insights gained to design and construct more efficient enzyme-inspired catalysts. Enzymes operate via multiple, precisely located, chemical functionalities immobilized within confined space, acting synergistically to activate, orient, and stabilize reacting species. The multiple functional groups in close proximity are known to perturb each other’s physical properties, thereby affecting reactivity. With the aid of machine learning, the Gadzikwa Lab will elucidate the molecular origins of unexpected reactivity that has been observed in multifunctional MOFs. The structure-function relationships that are determined from these investigations will inform the design of multifunctional MOF-based confined catalysts.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.