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Eric Montufar-Morales

Publications and source records attributed to Eric Montufar-Morales.

2 recordsLinked to original sources

Activating Basal Planes in Transition Metal Dichalcogenides for CO2 Reduction to CO through Alloying

Transition metal dichalcogenides (TMDCs) have emerged as highly tunable platforms for electrocatalysis, particularly for the CO2 reduction reaction (CO2RR). While TMDC edge sites exhibit catalytic activity, the basal plane is typically inert, severely limiting the overall active site density. In this work, we show that sulfur vacancies activate the basal plane of 1H-TMDCs, while concurrent solid-solution alloying provides a mechanism to broadly tune intermediate adsorption energies. We evaluate a library of quasi-binary TMDC sulfide alloys comprising V, Nb, Ta, Mo, and W, screening them by stability, defect energetics, and competitive selectivity to identify the most effective catalysts for CO2RR. Electronic-structure analysis reveals that a d-band center closer to the Fermi energy weakens intermediate binding by leaving key bonding states unoccupied above the Fermi energy. Our calculations identify (Nb,Ta)S2 as a promising catalyst with low sulfur vacancy formation energies, near-optimal CO2RR intermediate binding, and selectivity against the hydrogen evolution reaction. Overall, this work establishes a rational design framework for TMDC alloy catalysts through the simultaneous use of defect engineering and alloying.

cond-mat.mtrl-sci↗

Mic-hackathon 2024: Hackathon on Machine Learning for Electron and Scanning Probe Microscopy

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1

cond-mat.mtrl-sci↗