CAS: Photocatalysis Without Metals: Design Rules for Organic Photoredox Chemistry

在化学系化学结构、动力学和机理-A（CSDM-A）项目的支持下，Shaama M。Sharada和她在南加州大学（USC）的研究小组正在使用计算方法研究有机光氧化还原催化剂。越来越需要制定减轻气候变化不利影响的战略。碳捕获和利用提供了捕获和转化人为二氧化碳（CO2）为有用的燃料和化学品的手段。利用阳光进行二氧化碳转化至关重要，因为打破二氧化碳中的键需要高能量输入。与传统的重金属热催化剂相比，使用有机光活化分子或发色团作为催化剂来促进这种转化降低了成本和毒性问题。开发光氧化还原催化循环的基本机械图是解锁这些材料潜力的重要第一步。Sharada博士和她的研究团队的目标是使用量子化学方法和机器学习来生成二氧化碳转化的机理见解和催化剂设计规则。设计规则预计将有更广泛的影响超越二氧化碳的利用，因为这些催化剂发现在有机合成，水分解，药物输送和生物杀灭剂的应用。Sharada小组用于研究的开源建模软件也可以作为高中生强大的视觉学习辅助工具。由于通过与教师的合作可以实现持久的影响，因此计划为霍桑数学与科学学院的教师提供暑期实习，培训他们使用建模方法，并支持环境科学，化学和生物学课程的教案设计。有机发色团的光氧化还原催化循环很难单独使用实验进行研究，因为会产生几种自由基中间体，复杂的溶剂化效应、变化的产物分布和低的催化剂周转数。尽管在电荷转移过程的治疗方法的进步，有机生色团的研究主要集中在光电应用，而光氧化还原循环的机理研究仍然有限。这项工作的目的是开拓适应的基础原则在多相催化-Sabatier原则-光氧化还原系统，并确定先天的权衡，管理催化活性和营业额（或耐降解）。目标是表征激发态发色团和电子供体之间形成的激基复合物，然后猝灭，并确定有利于完全猝灭和电荷分离的因素维斯相对于通过Birch还原的发色团降解。为此，将采用多种量子化学方法，包括密度泛函理论（DFT）、约束DFT、时间依赖DFT以及基态、激发态和溶剂化体系的能量分解分析。降低降解可能性的因素将与那些也提高电子转移到CO2的速率的因素并列，以确定这些性能指标的权衡。光氧化还原系统的特性-溶剂，电子供体和发色团的性质-导致活性和降解之间的所需平衡，预计将出现。计算工作将得到南加州大学合作者的实验瞬态吸收光谱研究的支持。最后，这些设计规则将被纳入机器学习方法，通过快速搜索具有所需特性的发色团的广阔化学空间来加速发现。该奖项反映了NSF的法定使命，并通过使用基金会的智力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 support from the Chemical Structure, Dynamics, and Mechanisms-A (CSDM-A) Program in the Division of Chemistry, Shaama M. Sharada and her group at the University of Southern California (USC) are using computational methods to study organic photoredox catalysts. There is growing need for strategies to mitigate the adverse impacts of climate change. Carbon capture and utilization offers means to trap and transform anthropogenic carbon dioxide (CO2) into useful fuels and chemicals. Harnessing sunlight to carry out CO2 conversion is essential because breaking the bonds in CO2 requires high energy input. Using organic light-activated molecules, or chromophores, as catalysts to facilitate this transformation lowers cost and toxicity concerns compared to traditional, heavy metal thermal catalysts. Developing a fundamental mechanistic picture of the photoredox catalytic cycle is an essential first step toward unlocking the potential of these materials. Dr. Sharada and her research team aim to use quantum chemistry methods and machine learning to generate mechanistic insights and catalyst design rules for CO2 conversion. The design rules are expected to have broader impact beyond carbon dioxide utilization as these catalysts find applications in organic synthesis, water-splitting, drug delivery, and biocides. Open-source modeling software used by the Sharada group for research can also serve as powerful visual learning aids for high school students. Since lasting impact can be achieved through partnership with teachers, summer externships are planned for teachers at the Hawthorne Math and Science Academy, to train them in the use of modeling methods and support the design of lesson plans for environmental sciences, chemistry, and biology classes.Photoredox catalytic cycles with organic chromophores are difficult to study using experiments alone owing to the generation of several radical intermediates, complex solvation effects, varied product distributions, and low catalyst turnover numbers. Despite methodological advances in the treatment of charge transfer processes, organic chromophore studies largely focus on optoelectronic applications while mechanistic studies of photoredox cycles remain limited. This work aims to pioneer the adaptation of a foundational principle in heterogeneous catalysis – the Sabatier principle – to photoredox systems and identify innate trade-offs that govern catalytic activity and turnover number (or resistance to degradation). The goal is to characterize exciplexes formed between the excited-state chromophore and electron donor prior to quenching and determine factors that favor complete quenching and charge separation vis-à-vis chromophore degradation via Birch reduction. To this end, multiple quantum chemistry methods are to be employed, including density functional theory (DFT), constrained DFT, time-dependent DFT, and energy decomposition analysis for ground, excited-state, and solvated systems. Factors that lower the likelihood of degradation will be juxtaposed with those that also enhance the rates of electron transfer to CO2 to identify trade-offs underlying these performance metrics. Characteristics of the photoredox system – solvent, electron donor, and chromophore properties – that lead to the desired balance between activity and degradation are expected to emerge. The computational work will be supported by experimental transient absorption spectroscopy studies by a collaborator at USC. Finally, these design rules will feed into machine learning methods to accelerate discovery, by rapidly searching the vast chemical space for chromophores that possess desired characteristics.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.