Publications by authors named "Aubrey Toland"

Ring-opening enthalpy (Δ) is a fundamental thermodynamic quantity controlling the polymerization and depolymerization of an important class of recyclable polymers, namely, those created from ring-opening polymerization (ROP). Highly accurate first-principles-based computational methods to compute Δ are computationally too demanding to efficiently guide the design of depolymerizable polymers. In this work, we develop a generalizable machine-learning model that was trained on experimental measurements and reliably computed simulation results of Δ (the latter provides a pathway to systematically increase the chemical diversity of the data).

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Artificial intelligence-based methods are becoming increasingly effective at screening libraries of polymers down to a selection that is manageable for experimental inquiry. The vast majority of presently adopted approaches for polymer screening rely on handcrafted chemostructural features extracted from polymer repeat units-a burdensome task as polymer libraries, which approximate the polymer chemical search space, progressively grow over time. Here, we demonstrate that directly "machine learning" important features from a polymer repeat unit is a cheap and viable alternative to extracting expensive features by hand.

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A central challenge in the development of next-generation sustainable materials is to design polymers that can easily revert back to their monomeric starting material through chemical recycling to monomer (CRM). An emerging monomer class that displays efficient CRM are thiolactones, which exhibit rapid rates of polymerization and depolymerization. This report details the polymerization thermodynamics for a series of thiolactone monomers through systematic changes to substitution patterns and sulfur heteroatom incorporation.

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Ring-opening polymerization (ROP) enthalpy Δ is an important thermodynamic property controlling the polymerization of cyclic monomers. While Δ can be measured, computing Δ for realistic polymer systems with an error of ≃5-10 kJ/mol is critical for designing new monomer systems for depolymerizable polymers. We have developed a first-principles computational scheme in which multiple challenges in computing Δ are resolved definitively including extensive exploration of conformational states and adequately addressing finite size effects.

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Materials science has made progress in maximizing or minimizing the thermal conductivity of materials; however, the thermal effusivity-related to the product of conductivity and capacity-has received limited attention, despite its importance in the coupling of thermal energy to the environment. Herein, we design materials that maximize the thermal effusivity by impregnating copper and nickel foams with conformal, chemical-vapor-deposited graphene and octadecane as a phase change material. These materials are ideal for ambient energy harvesting in the form of what we call thermal resonators to generate persistent electrical power from thermal fluctuations over large ranges of frequencies.

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Thermal diodes, or devices that transport thermal energy asymmetrically, analogous to electrical diodes, hold promise for thermal energy harvesting and conservation, as well as for phononics or information processing. The junction of a phase change material and phase invariant material can form a thermal diode; however, there are limited constituent materials available for a given target temperature, particularly near ambient. In this work, we demonstrate that a micro and nanoporous polystyrene foam can house a paraffin-based phase change material, fused to PMMA, to produce mechanically robust, solid-state thermal diodes capable of ambient operation with Young's moduli larger than 11.

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