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

Discovery of New Oxygen Evolution Reaction Electrocatalysts by Combinatorial Investigation of the Ni–La–Co–Ce Oxide Composition Space

We report a new family of earth-abundant electrocatalysts for the oxygen evolution reaction (OER) discovered via high-throughput screening of 1771 discrete metal oxide compositions covering the nickel–lanthanum–cobalt–cerium composition space. The catalytic performance of each of these compositions was measured under conditions applicable to distributed solar fuel generation using a three-electrode scanning-drop electrochemical cell. These high-throughput measurements show enhanced activity for catalyst compositions containing 20–65 metal atom % Ce. The catalytic activity and stability of a representative highly active composition (Ni_(0.1)La_(0.1)Co_(0.3)Ce_(0.5))O_x was verified by standard rotating-disc electrochemistry. Catalysts of this composition showed stable operational performance at 10 mA cm^(−2) for 2 h and survived a 100 h endurance test in a testbed electrolyzer

Identification of optimal solar fuel electrocatalysts via high throughput in situ optical measurements

Many solar fuel generator designs involve illumination of a photoabsorber stack coated with a catalyst for the oxygen evolution reaction (OER). In this design, impinging light must pass through the catalyst layer before reaching the photoabsorber(s), and thus optical transmission is an important function of the OER catalyst layer. Many oxide catalysts, such as those containing elements Ni and Co, form oxide or oxyhydroxide phases in alkaline solution at operational potentials that differ from the phases observed in ambient conditions. To characterize the transparency of such catalysts during OER operation, 1031 unique compositions containing the elements Ni, Co, Ce, La, and Fe were prepared by a high throughput inkjet printing technique. The catalytic current of each composition was recorded at an OER overpotential of 0.33 V with simultaneous measurement of the spectral transmission. By combining the optical and catalytic properties, the combined catalyst efficiency was calculated to identify the optimal catalysts for solar fuel applications within the material library. The measurements required development of a new high throughput instrument with integrated electrochemistry and spectroscopy measurements, which enables various spectroelectrochemistry experiments

Tracking materials science data lineage to manage millions of materials experiments and analyses

In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research

Combinatorial screening yields discovery of 29 metal oxide photoanodes for solar fuel generation

Combinatorial synthesis combined with high throughput electrochemistry enabled discovery of 29 ternary oxide photoanodes, 15 with visible light response for oxygen evolution. Y₃Fe₅O₁₂ and trigonal V₂CoO₆ emerge as particularly promising candidates due to their photorepsonse at sub-2.4 eV illumination

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

Development of solar fuels photoanodes through combinatorial integration of Ni-La-Co-Ce oxide and Ni-Fe-Co-Ce oxide catalysts on BiVO_4

The development of an efficient, stable photoanode to provide protons and electrons to the (photo)cathode remains a primary materials challenge in the establishment of a scalable technol. for solar fuels generation. The typical photoanode architecture consists of a semiconductor light absorber coated with a metal oxide that serves a combination of functions, including corrosion protection, electrocatalysis, light trapping, hole transport, and elimination of deleterious recombination sites. To provide a more efficient exploration of metal oxide coatings for a given light absorber, we introduce a high throughput methodol. wherein a uniform BiVO_4 library is coated with 858 unique metal oxides covering a range of metal oxide loadings and the full Ni-La-Co-Ce oxide or Ni-Fe-Co-Ce oxide psuedo-quaternary compn. spaces. Photoelectrochem. characterization of each photoanode reveals that approx. one third of the coatings lower the photoanode performance while select combinations of metal oxide compn. and loading provide up to a 14-fold increase in the max. photoelectrochem. power generation for oxygen evolution in pH 13 electrolyte. Particular Ce-rich coatings also exhibit an anti-reflection effect that further amplifies the performance, yielding a 20-fold enhancement in power conversion efficiency compared to bare BiVO_4. By use of in situ optical spectroscopy and comparisons between the metal oxide coatings and their extrinsic optical and electrocatalytic properties, we present a suite of data-driven discoveries, including compn. regions which form optimal interfaces with BiVO_4 and photoanodes that are suitable for integration with a photocathode due to their excellent power conversion and solar transmission efficiencies. The initial high throughput discoveries were extended and validated through follow-up high throughput investigations and conventional photoelectrochem. measurements. The high throughput experimentation and informatics provides a powerful platform for both identifying the pertinent interfaces for further study and discovering high performance photoanodes for incorporation into efficient water splitting devices