ML-Assisted in situ monitoring of Flow Reactions for Sustainable Solvent Design
Noah Malmstadt,
Professor of Chemical Engineering and Materials Science,
University of Southern California
The transition to a sustainable chemical manufacturing infrastructure requires a new approach to solvents: solvent waste must be recycled to reactor feed, closing the material cycle. Ionic liquid (IL) solvents represent a special class of low-volatility, generally safe solvents that are particularly easy to recycle. Recent work from our lab has demonstrated that automated millifluidic flow chemistry routes to IL recycling can reduce their total process cost to the point where they are competitive with traditional volatile organic solvents.
This talk discusses an exemplary application of IL solvents: the fabrication of colloidal catalyst nanoparticles (NPs). While the capacity to produce metallic NPs in ILs has been known for decades, we know little about the mechanism of these reactions and, in particular, how solvent choice can guide this mechanism. To discover the mechanism of Pt NP fabrication in ILs, we have constructed a flow reactor with in-line spectrophotometric monitoring of the products. To determine reaction component concentration from the complex spectral data, we have implemented a machine learning (ML) algorithm that can determine concentration. By measuring product concentration as a function of residence time, we are able to determine the IL solvent-dependent reaction kinetics.
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