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Hydrogen gas is a promising renewable energy storage medium when produced via water electrolysis, but this process is limited by the sluggish kinetics of the anodic oxygen evolution reaction (OER). Herein, we used a microkinetic model to investigate promoting the OER using programmable oxide catalysts (i.e., forced catalyst dynamics). We found that programmable catalysts could increase current density at a fixed overpotential (100-600× over static rates) or reduce the overpotential required to reach a fixed current density of 10 mA cm-2 (45-140% reduction vs static). In our kinetic parametrization, the key parameters controlling the quality of the catalytic ratchet were the O*-to-OOH* and O*-to-OH* activation barriers. Our findings indicate that programmable catalysts may be a viable strategy for accelerating the OER or enabling lower-overpotential operation, but a more accurate kinetic parametrization is required for precise predictions of performance, ratchet quality, and resulting energy efficiency. 

Thin films of elemental metals play a very important role in modern electronic nano-devices as conduction pathways, spacer layers, spin-current generators/detectors, and many other important functionalities. In this work, by exploiting the chemistry of solid metal-organic source precursors, we demonstrate the molecular beam epitaxy synthesis of elemental Ir and Ru metal thin films. The synthesis of these metals is enabled by thermodynamic and kinetic selection of the metal phase as the metal-organic precursor decomposes on the substrate surface. Film growth under different conditions was studied using a combination of in situ and ex situ structural and compositional characterization techniques. The critical role of substrate temperature, oxygen reactivity, and precursor flux in tuning film composition and quality is discussed in the context of precursor adsorption, decomposition, and crystal growth. Computed thermodynamics quantifies the driving force for metal or oxide formation as a function of synthesis conditions and changes in chemical potential. These results indicate that bulk thermodynamics are a plausible origin for the formation of Ir metal at low temperatures, while Ru metal formation is likely mediated by kinetics. 

Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories.