In this work, we implement a facile microwaveassisted synthesis method to yield three binary Chevrel-Phase chalcogenides (Mo 6 X 8 ; X = S, Se, Te) and investigate the effect of increasing chalcogen electronegativity on hydrogen evolution catalytic activity. Density functional theory predictions indicate that increasing chalcogen electronegativity in these materials will yield a favorable electronic structure for proton reduction. This is confirmed experimentally via X-ray absorption spectroscopy as well as traditional electrochemical analysis. We have identified that increasing the electronegativity of X in Mo 6 X 8 increases the hydrogen adsorption strength owing to a favorable shift in the pband position as well as an increase in the Lewis basicity of the chalcogen, thereby improving hydrogen evolution reaction energetics. We find that Mo 6 S 8 exhibits the highest hydrogen evolution activity of the Mo 6 X 8 series of catalysts, requiring an overpotential of 321 mV to achieve a current density of 10 mA cm −2 ECSA , a Tafel slope of 74 mV per decade, and an exchange current density of 6.01 × 10 −4 mA cm −2 ECSA . Agreement between theory and experiment in this work indicates that the compositionally tunable Chevrel-Phase chalcogenide family is a promising framework for which electronic structure can be predictably modified to improve catalytic small-molecule reduction reactivity.
Presented herein is an investigation of a promising ternary metal sulfide catalyst that is capable of electrochemically converting CO2 to liquid and gas fuels such as methanol and hydrogen.
The
Chevrel phase (CP) is a class of molybdenum chalcogenides that
exhibit compelling properties for next-generation battery materials,
electrocatalysts, and other energy applications. Despite their promise,
CPs are underexplored, with only ∼100 compounds synthesized
to date due to the challenge of identifying synthesizable phases.
We present an interpretable machine-learned descriptor (H
δ) that rapidly and accurately estimates decomposition
enthalpy (ΔH
d) to assess CP stability.
To develop H
δ, we first used density
functional theory to compute ΔH
d for 438 CP compositions. We then generated >560 000 descriptors
with the new machine learning method SIFT, which provides an easy-to-use
approach for developing accurate and interpretable chemical models.
From a set of >200 000 compositions, we identified 48 501
CPs that H
δ predicts are synthesizable
based on the criterion that ΔH
d <
65 meV/atom, which was obtained as a statistical boundary from 67
experimentally synthesized CPs. The set of candidate CPs includes
2307 CP tellurides, an underexplored CP subset with a predicted preference
for channel site occupation by cation intercalants that is rare among
CPs. We successfully synthesized five of five novel CP tellurides
attempted from this set and confirmed their preference for channel
site occupation. Our joint computational and experimental approach
for developing and validating screening tools that enable the rapid
identification of synthesizable materials within a sparse class is
likely transferable to other materials families to accelerate their
discovery.
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