High-throughput on-the-fly scanning ultraviolet-visible dual-sphere spectrometer

Mitrović, Slobodan; Cornell, Earl; Marcin, Martin; Jones, Ryan J. R.; Newhouse, Paul F.; Suram, Santosh K.; Jin, Jian Xun; Gregoire, John M.

doi:10.1063/1.4905365

Cited by 37 publications

(39 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…29 Automated data processing to calculate the directallowed Tauc signal was recently described in detail. 30 Structural characterization of select compositions was performed via Raman spectroscopy (Renishaw inVia Reflex) and X-ray diffraction (XRD, Bruker DISCOVER D8).…”

Section: Materials Characterizationmentioning

confidence: 99%

Combinatorial alloying improves bismuth vanadate photoanodes via reduced monoclinic distortion

Newhouse

Guevarra

Umehara

et al. 2018

Energy Environ. Sci.

View full text Add to dashboard Cite

Improving the efficiency of solar-power oxygen evolution is both critical for development of solar fuels technologies and challenging due to the broad set of properties required of a solar fuels photoanode. Bismuth vanadate, in particular the monoclinic clinobisvanite phase, has received substantial attention and has exhibited the highest radiative efficiency among metal oxides with a band gap in the visible range. Efforts to further improve its photoelectrochemical performance have included alloying one or more metals onto the Bi and/or V sites, with progress on this frontier stymied by the difficulty in computational modelling of substitutional alloys and the high dimensionality of co-alloying composition spaces. Since substituional alloying simultaneously changes multiple materials properties, understanding the underlying cause for performance improvements is also challenging, motivating our application of combinatorial materials science techniques to map photoelectrochemical performance of 948 unique bismuth vanadate alloy compositions comprising 0 to 8% alloys of P, Ca, Mo, Eu, Gd, and W along with a variety of compositions from each pairwise combination of these elements. Upon identification of substantial improvements in the (Mo,Gd) co-alloying space, structural mapping was performed to reveal a remarkable correlation between performance and a lowered monoclinic distortion. First-principles density functional theory calculations indicate that the improvements are due to a lowered hole effective mass and hole polaron formation energy, and collectively, our results identify the monoclinic distortion as a critical parameter in the optimization and understanding of bismuth vanadate-based photoanodes. IntroductionThe production of fuels via photoelectrochemistry (PEC) has been recognized as a promising method for capturing and storing intermittent solar energy, in particular as renewable transportation fuels. 1 Regardless of the fuel to be generated, the oxygen evolution reaction at the photoanode is critical; hence development of stable, efficient, n-type semiconductors composed of earth abundant elements and producible by scalable, cost-effective methods has received increasing research attention. [2][3][4] Bismuth vanadate, BiVO4, in particular has become the oxide semiconductor serving as the platform to develop and demonstrate a toolkit of techniques for improving and understanding the fundamental properties and photoelectrochemical performance of novel oxide-based semiconductors, despite its non-optimal bandgap. 2,5 As recently reviewed, the methods of improving the properties include heterojunction formation, alloying or doping, hydrogen or nitrogen annealing, nanostructuring to enhance charge collection, microstructuring for photon management, and surface coating to passivate surface defects and enhance catalysis. 3,4,[6][7][8][9] The highest performing BVO-based photoanodes reported to date have all incorporated several of these strategies in order to produce photocurrent densities in the range of app...

show abstract

Section: Materials Characterizationmentioning

confidence: 99%

Combinatorial alloying improves bismuth vanadate photoanodes via reduced monoclinic distortion

Newhouse

Guevarra

Umehara

et al. 2018

Energy Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Both approaches are commonly used in combinatorial materials science where accelerated synthesis techniques include cosputtering, 6 co-evaporation, 10 ink-jet printing, 38 combinatorial ball-milling, 39 high-throughput hydrothermal synthesis, 40,41 and bulk ceramic hot-pressing. 42 Similarly, the acceleration of the characterization of materials properties and evaluation of performance for a target functionality have been the focus of extensive methods development in the past two decades, with notable demonstrations including electrochemical testing, [43][44][45][46] X-ray diffraction, [47][48][49] processing, 9,50,51 optical spectroscopy, 52,53 electric properties, 65,66 shape memory, 13,54 and phase dynamics. 9 These advancements in experiment automation have undoubtedly led to discoveries that would not have been made in the same time frame using traditional techniques.…”

Section: Automation and Parallelizationmentioning

confidence: 99%

Progress and prospects for accelerating materials science with automated and autonomous workflows

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…58 Basic quality control and plate alignment is done via screening of optical properties via platebed scanning 24 and ultravioletvisible spectroscopy. 24,59 Structural characterization data are generated at synchrotron experiments at Stanford Synchrotron Radiation Lightsource 60 or an in-house Bruker diffractometer 61 that is coupled to subsequent automated analysis. 27 Compositional analysis is performed via techniques such as x-ray fluorescence 62 where applicable.…”

Section: Experimental Techniquesmentioning

confidence: 99%

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

High-throughput on-the-fly scanning ultraviolet-visible dual-sphere spectrometer

Cited by 37 publications

References 17 publications

Combinatorial alloying improves bismuth vanadate photoanodes via reduced monoclinic distortion

Combinatorial alloying improves bismuth vanadate photoanodes via reduced monoclinic distortion

Progress and prospects for accelerating materials science with automated and autonomous workflows

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

Contact Info

Product

Resources

About