Research Initiation Award: Establishing the Fundamentals of Spin-Crossover Behavior and Catalysis in Single-Chain Nanoparticle Metallopolymers

研究启动奖为历史悠久的黑人学院和大学的初级和职业生涯中期教师提供支持，这些教师正在建立新的研究计划或重新定向和重建现有的研究计划。预计该奖项有助于进一步提高教师的研究能力和有效性，改善所在机构的研究和教学，并让本科生参与研究经验。黑人妇女在STEM中是一个代表性明显不足的群体，纠正这一问题的一个重要方法是在黑人妇女的学术生涯早期为其提供培训和研究机会。这里提出的研究项目是由斯佩尔曼学院的本科生进行的：黑人女性正在努力在STEM建立自己的地位。除了在计算科学、无机化学和聚合物化学领域为他们提供宝贵的科学培训外，这项研究还为他们提供了网络和专业发展机会。这项研究的重点是使用计算化学来模拟和更好地理解某些含金属聚合物的行为。研究结果本身也可以通过改进催化剂和新的多功能材料的设计，对人类社会产生重大的有益影响。聚合物科学的首要目标之一是开发具有蛋白质功能和能力的材料，但不受其脆性或复杂合成的限制。一类可能满足这些标准的材料是单链纳米颗粒(SCNP)金属聚合物。研究发现，含有金属的SCNPs可以作为许多反应的有效催化剂，这表明它们可能是潜在的“人造酶”。利用现代计算化学技术(主要是密度泛函理论和QM/MM模拟)对几种具有催化活性的SCNP金属聚合物的反应机理进行了计算，以帮助阐明它们的反应路径的原子细节以及这些反应是如何受到它们的聚合物特性的影响的。自旋态也是金属酶活性的一个重要特征，也可能影响SCNP酶模拟物的机理和催化行为。因此，正在对SCNP模型系统进行系统的建模，以确定支配SCNP金属聚合物中自旋态能级的基本原理。这项工作的目标是开发分子设计策略，以合理地将自旋交叉(SCO)行为纳入SCNPs。通过建立一个路线图，将SCO固有的量子力学状态切换特性与SCNP系统的动态和模块化行为相结合，未来可以开发出具有蛋白质特征的新型、柔软、多功能材料。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness, improve research and teaching at the home institution, and involves undergraduate students in research experiences. Black women are a significantly under-represented group in STEM, and one important way to remedy this is to provide training and research opportunities to Black women early in their academic careers. The research projects proposed here are carried out by undergraduates at Spelman College: Black women who are working to establish themselves in STEM. In addition to providing them with valuable scientific training in the fields of computational science, inorganic chemistry, and polymer chemistry, this research provides them with networking and professional development opportunities as well. This research is focused on using computational chemistry to model and better understand the behavior of certain metal-containing polymers. The results of the research itself can also have significantly beneficial impacts on human society by leading to the design of improved catalysts and new multifunctional materials. One of the overarching goals of polymer science is to develop materials that have the functions and capabilities of proteins but are not limited by their fragility or complex synthesis. A class of materials that can potentially fulfill these criteria are single-chain nanoparticle (SCNP) metallopolymers. It has been found that metal containing SCNPs can act as effective catalysts for numerous reactions, suggesting that they could be potential “artificial enzymes”. Using modern computational chemistry techniques (chiefly density functional theory and QM/MM simulations) the mechanisms of several catalytically active SCNP metallopolymers are being computed to help elucidate the atomistic details of their reaction pathways and how these are influenced by their polymeric features. Spin-state is also known to be an important feature in metalloenzyme reactivity and could also affect the mechanistic and catalytic behavior of SCNP enzyme mimics as well. Therefore, systematic modeling of SCNP model systems is being performed to determine the underlying principles that govern spin-state energetics in SCNP metallopolymers. The goal of this work is to develop molecular design strategies for rationally incorporating spin-crossover (SCO) behavior into SCNPs. By establishing a roadmap for combining the inherently quantum mechanical state-switching properties of SCO with the dynamic and modular behavior of SCNP systems, novel, soft, multifunctional materials with protein-like characteristics can be developed in the future.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.