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Chew, Alex K.

Publications and source records attributed to Chew, Alex K..

3 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Recycling of multilayer plastic packaging materials by solvent-targeted recovery and precipitation

Many plastic packaging materials manufactured today are composites made of distinct polymer layers (i.e., multilayer films). Billions of pounds of these multilayer films are produced annually, but manufacturing inefficiencies result in large, corresponding postindustrial waste streams. Although relatively clean (as opposed to municipal wastes) and of near-constant composition, no commercially practiced technologies exist to fully deconstruct postindustrial multilayer film wastes into pure, recyclable polymers. Here, we demonstrate a unique strategy we call solvent-targeted recovery and precipitation (STRAP) to deconstruct multilayer films into their constituent resins using a series of solvent washes that are guided by thermodynamic calculations of polymer solubility. We show that the STRAP process is able to separate three representative polymers (polyethylene, ethylene vinyl alcohol, and polyethylene terephthalate) from a commercially available multilayer film with nearly 100% material efficiency, affording recyclable resins that are cost-competitive with the corresponding virgin materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant–solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable accurate predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water–cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid, high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗