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

High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis

High-throughput experimentation provides efficient mapping of composition–property relationships, and its implementation for the discovery of optical materials enables advancements in solar energy and other technologies. In a high throughput pipeline, automated data processing algorithms are often required to match experimental throughput, and we present an automated Tauc analysis algorithm for estimating band gap energies from optical spectroscopy data. The algorithm mimics the judgment of an expert scientist, which is demonstrated through its application to a variety of high throughput spectroscopy data, including the identification of indirect or direct band gaps in Fe_2O_3, Cu_2V_2O_7, and BiVO_4. The applicability of the algorithm to estimate a range of band gap energies for various materials is demonstrated by a comparison of direct-allowed band gaps estimated by expert scientists and by automated algorithm for 60 optical spectra

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

A STUDY ON THE FUSION REACTION 139La + 12C AT 50 MeV/u WITH THE VUU EQUATION

Recently Bownan et al. found that in the reaction 139La + 12C at 50 MeV/u a compound nucleus is formed. We simulate this reaction with a numerical solution of the VUU equation and indeed find that for a central collision the system fuses and equilibrates after 90 fm/c

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 catalysts on BiVO_4

The development of an efficient photoanode remains the primary materials challenge in the establishment of a scalable technology for solar water splitting. 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 date, such coatings have been mostly limited to simple materials such as TiO_2 and Co-Pi, with extensive experimental and theoretical effort required to provide an understanding of the physics and chemistry of the semiconductor-coating interface. To provide a more efficient exploration of metal oxide coatings for a given light absorber, we introduce a high throughput methodology wherein a uniform BiVO_4 thin film is coated with 858 unique metal oxides covering a range of metal oxide loadings and the full Ni–La–Co–Ce oxide quaternary composition space. Photoelectrochemical characterization of each photoanode reveals that approximately one third of the coatings lower the photoanode performance while select combinations of metal oxide composition and loading provide up to a 14-fold increase in the maximum photoelectrochemical 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 BiVO4. 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 composition regions which form optimal interfaces with BiVO4 and photoanodes that are suitable for integration with a photocathode due to their excellent power conversion and solar transmission efficiencies. 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

The Simplest Little Higgs

We show that the SU(3) little Higgs model has a region of parameter space in which electroweak symmetry breaking is natural and in which corrections to precision electroweak observables are sufficiently small. The model is anomaly free, generates a Higgs mass near 150 GeV, and predicts new gauge bosons and fermions at 1 TeV.Comment: 13 pages + appendix, typos corrected, version to appear in JHE

Mn_2V_2O_7: An Earth Abundant Light Absorber for Solar Water Splitting

Complex oxide β-Mn_2V_2O_7 is identified as exhibiting near-optimal band energetics for solar fuel applications among known metal oxides. Experiments, corroborated by theory, indicate a bandgap near 1.8 eV. The calculations predict that β-Mn_2V_2O_7 has well-aligned band edge energies for the hydrogen evolution reaction and oxygen evolution reaction. Photoelectrochemical measurements indicate appreciable photocurrent, corroborating the predictions

A clinically aligned experimental approach for quantitative characterization of patient-specific cardiovascular models

Recent improvements in computational tools opened the possibility of patient-specific modeling to aid clinicians during diagnosis, treatment, and monitoring. One example is the modeling of blood flow for surgical planning, where modeling can help predict the prognosis. Computational analysis is used to extract hemodynamic information about the case; however, these methods are sensitive to assumptions on blood properties, boundary conditions, and appropriate geometry accuracy. When available, experimental measurements can be used to validate the results and, among the modalities, ultrasound-based methods are suitable due to their relative low cost and non-invasiveness. This work proposes a procedure to create accurate patient-specific silicone replicas of blood vessels and a power Doppler compatible experimental setup able to simulate and measure realistic flow conditions. The assessment of silicone model geometry shows small discrepancies between these and the target geometries (median of surface error lies within 57 µm and 82 μm). Power Doppler measurements were compared against computational fluid dynamics results, showing discrepancies within 10% near the wall. The experimental approach offers a setup to quantify flow in in vitro systems and provide more accurate results where other techniques (e.g., particle image velocimetry and particle tracking velocimetry) have shown limitations due to the interference of the interface