High-Throughput Exploration of Metal Vanadate Thin-Film Systems (M-V-O, M = Cu, Ag, W, Cr, Co, Fe) for Solar Water Splitting: Composition, Structure, Stability, and Photoelectrochemical Properties

Solution Combustion Synthesis and Characterization of Magnesium Copper Vanadates

Magnesium vanadate (MgV2O6) and its alloys with copper vanadate were synthesized via the solution combustion technique. Phase purity and solid solution formation were confirmed by a variety of experimental techniques, supported by electronic structure simulations based on density functional theory (DFT). Powder X-ray diffraction combined with Rietveld refinement, laser Raman spectroscopy, diffuse reflectance spectroscopy, and high-resolution transmission electron microscopy showed single-phase alloy formation despite the MgV2O6 and CuV2O6 end members exhibiting monoclinic and triclinic crystal systems, respectively. DFT-calculated optical band gaps showed close agreement in the computed optical bandgaps with experimentally derived values. Surface photovoltage spectroscopy, ambient-pressure photoemission spectroscopy, and Kelvin probe contact potential difference (work function) measurements confirmed a systematic variation in the optical bandgap modification and band alignment as a function of stoichiometry in the alloy composition. These data indicated n-type semiconductor behavior for all the samples which was confirmed by photoelectrochemical measurements.

High-throughput exploration of activity and stability for identifying photoelectrochemical water splitting materials

The experimental high-throughput (HT) exploration for a suitable solar water splitting photoanode has greatly relied on photoactivity as the sole descriptor to identify a promising region within the searched composition space. Although activity is essential, it is not sufficient for describing the overall performance and excludes other pertinent criteria for photoelectrochemical (PEC) water splitting. Photostability in the form of (photo)electrocatalyst dissolution must be tracked to illustrate the intricate relation between activity and stability for multinary photoelectrocatalysts. To access these two important metrics simultaneously, an automated PEC scanning flow cell coupled to an inductively coupled plasma mass spectrometer (PEC-ICP-MS) was used to study an Fe-Ti-W-O thin film materials library. The results reveal an interrelation between composition, photocurrent density, and element-specific dissolution. These structure-activity-stability correlations can be represented using data science tools like principal component analysis (PCA) in addition to common data visualization approaches. This study demonstrates the importance of addressing two of the most important catalyst metrics (activity and stability) in a rapid and parallel fashion during HT experiments to adequately discover high-performing compositions in the multidimensional search space.

Combinatorial Screening of Cu-W Oxide-Based Photoanodes for Photoelectrochemical Water Splitting

Metal oxide libraries for photoanodes for the oxygen evolution reaction (OER) were generated by printing a metal salt solution in an array layout, followed by calcination to yield 22 ternary metal oxide systems. The libraries included a ternary metal cation system based on CuWO₄ with one out of eight transition or posttransition metal ions Cr, Mn, Fe, Co, Ni, Zn, Bi, and Ga in different overall atomic ratios. The photocatalyst libraries were screened by scanning photoelectrochemical microscopy for the highest anodic photocurrents. Array elements that showed promising performance were printed in another set of eight libraries with smaller increments of overall composition. Improved performance with respect to CuWO₄ was found for Ga, Co, and Ni as the third element. A comparison of the most active composition of those arrays within one library showed the highest activity for Cu₄₈Ga₃W₄₉O_x. Printing spots of identical composition (Cu₄₈Ga₃W₄₉O_x, Cu₄₄Ni₉W₄₇O_x, and Cu₄₄Co₉W₄₇O_x) over a larger area facilitated further characterization by X-ray photoelectron spectroscopy ultraviolet photoelectron spectroscopy (UPS), X-ray diffraction, scanning electron microscopy, chopped light voltammetry, and scanning electrochemical microscopy for the OER. High and stable steady-state photocurrents were generated in a photoelectrochemical cell for all three electrodes even at a low constant bias voltage. The best overall photoanode composition Cu₄₈Ga₃W₄₉O_x showed currents that were 36 times higher than the currents of the binary Cu₅₀W₅₀O_x system. Significant n-doping was found by UPS valence band spectra for Ga-containing materials.

Bayesian Optimization of High-Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction*

Active, selective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High‐entropy alloys (HEAs) offer a vast compositional space for tuning such properties. Too vast, however, to traverse without the proper tools. Here, we report the use of Bayesian optimization on a model based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag‐Ir‐Pd‐Pt‐Ru and Ir‐Pd‐Pt‐Rh‐Ru. The discovered optima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag–Pd, Ir–Pt, and Pd–Ru binary alloys. This study offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys which has been determined to be on the order of 50 for ORR on these HEAs.

Phase-Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries

Exploring the vast compositional space offered by multicomponent systems or high entropy materials using the traditional route of materials discovery, one experiment at a time, is prohibitive in terms of cost and required time. Consequently, the development of high-throughput experimental methods, aided by machine learning and theoretical predictions will facilitate the search for multicomponent materials in their compositional variety. In this study, high entropy oxides are fabricated and characterized using automated high-throughput techniques. For intuitive visualization, a graphical phase-property diagram correlating the crystal structure, the chemical composition, and the band gap are introduced. Interpretable machine learning models are trained for automated data analysis and to speed up data comprehension. The establishment of materials libraries of multicomponent systems correlated with their properties (as in the present work), together with machine learning-based data analysis and theoretical approaches are opening pathways toward virtual development of novel materials for both functional and structural applications.