Collaborative Research: Cyclopropenimine-Mediated Organo-Carbonation Polymerizations

This collaborative project will explore how polymers can be synthesized using carbon dioxide as part of their core structure, and computational techniques will be used to guide the selection of molecular building blocks and optimal reaction conditions. Another important goal is to avoid the use of any metal-based catalysts that can be difficult to remove from the resulting products, and instead, all-organic catalysts and reagents will be designed and synthesized. The research team will employ a feedback loop strategy where calculations and artificial intelligence (AI) based methods will be used in conjunction with experimental findings to refine the predictive capabilities for AI-driven discovery. The expected outcomes can gainfully impact how polyurethanes and polycarbonates are made, especially targeting applications in advanced manufacturing and consumer products. In turn, new chemistry-focused, artificial intelligence models will be developed and translated to unprecedented synthetic methods to access new classes of polymers. Central to this collaboration are the values of evidence-based tutelage, partnership, and inclusivity for all aspiring scientists. These are qualities that cultivate genuine belonging, setting the foundation to develop a skilled and creative workforce. Beyond recruiting talent, the researchers participating in this program will be able to architect sustained pathways to encourage other individuals through outreach—from early classroom experiences all the way to early career professionals. Activating carbon dioxide (CO2) and directly polymerizing it from its gaseous state is challenging to achieve, especially using all-organic reagents. This project will advance sustainable polymer chemistry by implementing artificial intelligence (AI) and computational methods to develop metal-free reagents and catalysts capable of upcycling CO2, into value-added polymers. By focusing on cyclopropenimine (CPI) superbases, this research aims to overcome current limitations in CO2 utilization and create a machine-learning-assisted platform for the synthesis of versatile polyurethanes and polycarbonates—polymers that can be engineered for circularity. The overarching goal of this collaborative project is to create fundamental, mechanistic knowledge about the chemistry of CPI through a feedback loop of experiments, theory, and machine learning. This work is driven by two main objectives. First, it seeks to develop step-growth polymerizations using CPIs to convert CO2 directly into polyurethanes. Such approach circumvents the need for traditional, often hazardous, precursors. Second, the project will explore the catalytic reactivity of CPIs in copolymerizations of CO2 with epoxides. The high-throughput quantum calculations on the CPI-catalysts will be added to the open-access database (VERDE materials DB). The VERDE materials DB will be leveraged as cyberinfrastructure to train AI-models to broaden the range of substrates and reactions compatible with CPI superbases, thus accelerating the rational design of new catalysts for CO2 polymerizations for advanced manufacturing of functional polymers. 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.