Combinatorial synthesis of oxysulfides in the lanthanum-bismuth-copper system

Establishing synthesis methods for a target material constitutes a grand challenge in materials research, which is compounded with use-inspired specifications on the format of the material. Solar photochemistry using thin film materials is a promising technology for which many complex materials are being proposed, and the present work describes application of combinatorial methods to explore the synthesis of predicted La–Bi–Cu oxysulfide photocathodes, in particular alloys of LaCuOS and BiCuOS. The variation in concentration of three cations and two anions in thin film materials, and crystallization thereof, is achieved by a combination of reactive sputtering and thermal processes including reactive annealing and rapid thermal processing. Composition and structural characterization establish composition-processing-structure relationships that highlight the breadth of processing conditions required for synthesis of LaCuOS and BiCuOS. The relative irreducibility of La oxides and limited diffusion indicate the need for high temperature processing, which conflicts with the temperature limits for mitigating evaporation of Bi and S. Collectively the results indicate that alloys of these phases will require reactive annealing protocols that are uniquely tailored to each composition, motivating advancement of dynamic processing capabilities to further automate discovery of synthesis routes

Analyzing machine learning models to accelerate generation of fundamental materials insights

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data

Bi Alloying into Rare Earth Double Perovskites Enhances Synthesizability and Visible Light Absorption

A high throughput combinatorial synthesis utilizing inkjet printing of precursor inks was used to rapidly evaluate Bi-alloying into double perovskite oxides for enhanced visible light absorption. The fast visual screening of photo image scans of the library plates identifies 4-metal oxide compositions displaying an increase in light absorption, which subsequent UV–vis spectroscopy indicates is due to bandgap reduction. Structural characterization by X-ray diffraction (XRD) and Raman spectroscopy demonstrates that the visually darker composition range contains Bi-alloyed Sm₂MnNiO₆ (double perovskite structure), of the form (Bi,Sm)₂MnNiO₆. Bi alloying not only increases the visible absorption but also facilitates crystallization of this structure at the relatively low annealing temperature of 615 °C. Investigation of additional seven combinations of a rare earth (RE) and a transition metal (TM) with Bi and Mn indicates that Bi-alloying on the RE site occurs with similar effect in the family of rare earth oxide double perovskites

Enhanced Bulk Transport in Copper Vanadate Photoanodes Identified by Combinatorial Alloying

The impact of alloying on the performance of β-Cu₂V₂O₇ photoanodes was investigated using inkjet printing of composition libraries containing 1,809 Cu₂V₂O₇-based photoanodes. Six elements (Zr, Ca, Hf, Gd, La, and Lu) were alloyed and pairwise co-alloyed at concentrations up to 7 at % into Cu-rich, stoichiometric, and Cu-deficient host Cu₂V₂O₇. A 1.7-fold increase in oxygen evolution photocurrent in pH 9.2 electrolyte was obtained by alloying Ca into β-Cu₂V₂O₇. Experiments employing a hole scavenger to better characterize bulk charge separation and transport revealed a 2.2-fold increase in photoactivity via alloying with Hf, Zr, and La, which increased to 2.7-fold upon co-alloying these elements with Ca. Concurrent with increased photoactivity is substantially decreased photon absorption between 1.5 and 2 eV, a range reported to coincide with high exciton absorption in β-Cu₂V₂O₇, motivating further exploration of whether these co-alloy compositions may destabilize the excitonic state that appears to have limited performance to date

Enhanced Bulk Transport in Copper Vanadate Photoanodes Identified by Combinatorial Alloying

The impact of alloying on the performance of β-Cu₂V₂O₇ photoanodes was investigated using inkjet printing of composition libraries containing 1,809 Cu₂V₂O₇-based photoanodes. Six elements (Zr, Ca, Hf, Gd, La, and Lu) were alloyed and pairwise co-alloyed at concentrations up to 7 at % into Cu-rich, stoichiometric, and Cu-deficient host Cu₂V₂O₇. A 1.7-fold increase in oxygen evolution photocurrent in pH 9.2 electrolyte was obtained by alloying Ca into β-Cu₂V₂O₇. Experiments employing a hole scavenger to better characterize bulk charge separation and transport revealed a 2.2-fold increase in photoactivity via alloying with Hf, Zr, and La, which increased to 2.7-fold upon co-alloying these elements with Ca. Concurrent with increased photoactivity is substantially decreased photon absorption between 1.5 and 2 eV, a range reported to coincide with high exciton absorption in β-Cu₂V₂O₇, motivating further exploration of whether these co-alloy compositions may destabilize the excitonic state that appears to have limited performance to date

Alkaline-stable nickel manganese oxides with ideal band gap for solar fuel photoanodes

Combinatorial (photo)electrochemical studies of the (Ni–Mn)O_x system reveal a range of promising materials for oxygen evolution photoanodes. X-ray diffraction, quantum efficiency, and optical spectroscopy mapping reveal stable photoactivity of NiMnO_3 in alkaline conditions with photocurrent onset commensurate with its 1.9 eV direct band gap. The photoactivity increases upon mixture with 10–60% Ni_6MnO_8 providing an example of enhanced charge separation via heterojunction formation in mixed-phase thin film photoelectrodes. Density functional theory-based hybrid functional calculations of the band edge energies in this oxide reveal that a somewhat smaller than typical fraction of exact exchange is required to explain the favorable valence band alignment for water oxidation

Multi-component background learning automates signal detection for spectroscopic data

Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond

Alkaline-stable nickel manganese oxides with ideal band gap for solar fuel photoanodes

Combinatorial (photo)electrochemical studies of the (Ni–Mn)O_x system reveal a range of promising materials for oxygen evolution photoanodes. X-ray diffraction, quantum efficiency, and optical spectroscopy mapping reveal stable photoactivity of NiMnO_3 in alkaline conditions with photocurrent onset commensurate with its 1.9 eV direct band gap. The photoactivity increases upon mixture with 10–60% Ni_6MnO_8 providing an example of enhanced charge separation via heterojunction formation in mixed-phase thin film photoelectrodes. Density functional theory-based hybrid functional calculations of the band edge energies in this oxide reveal that a somewhat smaller than typical fraction of exact exchange is required to explain the favorable valence band alignment for water oxidation