Bayesian Optimization of Environmentally Sustainable Graphene Inks Produced by Wet Jet Milling.

Liquid phase exfoliation (LPE) of graphene is a potentially scalable method to produce conductive graphene inks for printed electronic applications. Among LPE methods, wet jet milling (WJM) is an emerging approach that uses high-speed, turbulent flow to exfoliate graphene nanoplatelets from graphite in a continuous flow manner. Unlike prior WJM work based on toxic, high-boiling-point solvents such as n-methyl-2-pyrollidone (NMP), this study uses the environmentally friendly solvent ethanol and the polymer stabilizer ethyl cellulose (EC). Bayesian optimization and iterative batch sampling are employed to guide the exploration of the experimental phase space (namely, concentrations of graphite and EC in ethanol) in order to identify the Pareto frontier that simultaneously optimizes three performance criteria (graphene yield, conversion rate, and film conductivity). This data-driven strategy identifies vastly different optimal WJM conditions compared to literature precedent, including an optimal loading of 15 wt% graphite in ethanol compared to 1 wt% graphite in NMP. These WJM conditions provide superlative graphene production rates of 3.2 g hr-1 with the resulting graphene nanoplatelets being suitable for screen-printed micro-supercapacitors. Finally, life cycle assessment reveals that ethanol-based WJM graphene exfoliation presents distinct environmental sustainability advantages for greenhouse gas emissions, fossil fuel consumption, and toxicity.

Systematic Coarse-graining of Epoxy Resins with Machine Learning-Informed Energy Renormalization.

A persistent challenge in predictive molecular modeling of thermoset polymers is to capture the effects of chemical composition and degree of crosslinking (DC) on dynamical and mechanical properties with high computational efficiency. We established a new coarse-graining (CG) approach that combines the energy renormalization method with Gaussian process surrogate models of the molecular dynamics simulations. This allows a machine-learning informed functional calibration of DC-dependent CG force field parameters. Taking versatile epoxy resins consisting of Bisphenol A diglycidyl ether combined with curing agent of either 4,4-Diaminodicyclohexylmethane or polyoxypropylene diamines, we demonstrated excellent agreement between all-atom and CG predictions for density, Debye-Waller factor, Young's modulus and yield stress at any DC. We further introduce a surrogate model enabled simplification of the functional forms of 14 non-bonded calibration parameters by quantifying the uncertainty of a candidate set of high-dimensional/flexible calibration functions. The framework established provides an efficient methodology for chemistry-specific, large-scale investigations of the dynamics and mechanics of epoxy resins.