Engineering topics
Publications and source records attributed to Suram, Santosh K..
We present an automated platform for studying electrode corrosion as a function of electrochemical history and electrolyte composition using Pt ORR as a proof-of-concept relevant to next-generation phosphoric acid fuel cells.
Event-based data workflows powered by cloud computing can help accelerate the development of materials acceleration platforms while fostering the ideals of extensibility and interoperability in materials chemistry research.
We present a generalizable database architecture ESAMP that captures the complete provenance associated with a material. We demonstrate this architecture and provenance based machine learning on one of the largest experimental materials databases.
Graph representations of hierarchical knowledge, including experiment provenances, will help usher in a new era of data-driven materials science.
In this perspective, we highlight results of a research consortium devoted to advancing understanding of oxygen reduction reaction (ORR) catalysis as a means to inform fuel cell science. We demonstrate how targeted collaborations between different institutions from academic, national lab, and industry backgrounds and different scientific disciplines like theory, experiment, and characterization can yield unique insights into fuel cell catalysts. We comment on such insights into material designs for platinum-group-metal alloys, transition metal oxides, and non-traditional materials including metal–organic frameworks; systems that have served as the foundational building blocks for our consortium. We also motivate a renewed focus on catalyst durability in light of emerging technological requirements and paths forward in understanding in situ and operando electrochemical stability. Lastly, we describe new frontiers ORR research can take and how emerging artificial intelligence tools can assist researchers in capturing data, selecting new experiments, and guiding characterization to accelerate the design and discovery of fuel cell catalysts. A main goal of sharing this perspective is to discuss the rationale for our future research plans based on our consortium work. However, we also hope to illustrate both the potential impact of a collaborative strategy with the hopes of inspiring a higher degree of Industry-Academia-National Laboratory collaboration and encourage other centers and consortiums to distill and share their findings in a similar perspective-type article. Together we hope to enable the fuel cell research community to engage in a discussion of strategies for research and accelerated development of catalysts with improved activity and stability.
An oxygen evolution reaction catalyst is a ternary metal oxide that includes Mn and is represented by Mn u Sb v O w in the rutile crystal phase and M x Mn y O z where M is selected from the group consisting of Ca, Ni, Sr, Zn, Mg, Ni, Ba, Co and where u/(u+v) is greater than 33%.
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Various samples are generated on a substrate. The samples each includes or consists of one or more analytes. In some instances, the samples are generated through the use of gels or through vapor deposition techniques. The samples are used in an instrument for screening large numbers of analytes by locating the samples between a working electrode and a counter electrode assembly. The instrument also includes one or more light sources for illuminating each of the samples. The instrument is configured to measure the photocurrent formed through a sample as a result of the illumination of the sample.
Benchmarking metrics for materials discovery via sequential learning are presented, to assess the efficacy of existing algorithms and to be scientific in our assessment of accelerated science.
Abstract 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.
Abstract
Here, we reort that photoelectrocatalysis of the oxygen evolution reaction remains a primary challenge for development of tandem-absorber solar fuel generators due to the lack of a photoanode with broad solar spectrum utilization, a large photovoltage, and stable operation. Bismuth vanadate with a 2.4-2.5 eV band gap has shown the most promise becauses its photoactivity down to 0.4 V vs RHE is sufficiently low to couple to a lower-gap photocathode for fuel synthesis. Through development of photoanodes based on the FeWO 4 structure, in particular, Fe-rich variants with addition of about 6% Bi, we demonstrate the same 0.4 V vs RHE turn-on voltage with a 2 eV band gap metal oxide, enabling a 2-fold increase in the device efficiency limit. Combinatorial exploration of materials composition and processing facilitated synthesis of n-type variants of this typical p-type semiconductor that exhibit much higher photoactivity than previous implementations of FeWO 4 in solar photochemistry. Lastly, the photoanodes are particularly promising for solar fuel applications given their stable operation in acid and base.
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 6 MnO 8 providing an example of enhanced charge separation via heterojunction formation in mixed-phase thin film photoelectrodes. Further, 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.
From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery
© 2017 American Chemical Society. Oxynitrides with the photoelectrochemical stability of oxides and desirable band energetics of nitrides comprise a promising class of materials for solar photochemistry. Challenges in synthesizing a wide variety of oxynitride materials has limited exploration of this class of functional materials, which we address using a reactive cosputtering combined with rapid thermal processing method to synthesize multi-cation-multi-anion libraries. We demonstrate the synthesis of a LaxTa1-xOyNzthin film composition spread library and its characterization by both traditional thin film materials characterization and custom combinatorial optical spectroscopy and X-ray absorption near edge spectroscopy (XANES) techniques, ultimately establishing structure-chemistry-property relationships. We observe that over a substantial La-Ta composition range the thin films crystallize in the same perovskite LaTaON2structure with significant variation of anion chemistry. The relative invariance in optical band gap demonstrates a remarkable decoupling of composition and band energetics so that the composition can be optimized while retaining the desirable 2 eV band gap energy. We also demonstrate the intercalation of diatomic nitrogen into the La3TaO7structure, which gives rise to a direct-allowed optical transition at 2.2 eV, less than half the value of the oxide's band gap. These findings motivate further exploration of the visible light response of this material that is predicted to be stable over a wide range of electrochemical potential.
Solar-driven oxygen evolution is a critical technology for renewably synthesizing hydrogen- and carbon-containing fuels in solar fuel generators. New photoanode materials are needed to meet efficiency and stability requirements, motivating materials explorations for semiconductors with (i) band-gap energy in the visible spectrum and (ii) stable operation in aqueous electrolyte at the electrochemical potential needed to evolve oxygen from water. Motivated by the oxygen evolution competency of many Mn-based oxides, the existence of several Bi-containing ternary oxide photoanode materials, and the variety of known oxide materials combining these elements with Sm, we explore the Bi-Mn-Sm oxide system for new photoanodes. Through the use of a ferri/ferrocyanide redox couple in high-throughput screening, BiMn 2 O 5 and its alloy with Sm are identified as photoanode materials with a near-ideal optical band gap of 1.8 eV. Using density functional theory-based calculations of the mullite Bi 3+ Mn 3+ Mn 4+ O 5 phase, we identify electronic analogues to the well-known BiVO 4 photoanode and demonstrate excellent Pourbaix stability above the oxygen evolution Nernstian potential from pH 4.5 to 15. Lastly, our suite of experimental and computational characterization indicates that BiMn 2 O 5 is a complex oxide with the necessary optical and chemical properties to be an efficient, stable solar fuel photoanode.
We present a first-principles-based formalism to provide a quantitative measure of the thermodynamic instability and propensity for electrochemical stabilization, passivation, or corrosion of metastable materials in aqueous media. Here, we demonstrate that this formalism can assess the relative Gibbs free energy of candidate materials in aqueous media as well as their decomposition products, combining solid and aqueous phases, as a function of pH and potential. On the basis of benchmarking against 20 stable as well as metastable materials reported in the literature and also our experimental characterization of metastable triclinic-FeVO 4 , we present quantitative estimates for the relative Gibbs free energy and corresponding aqueous regimes where these materials are most likely to be stable, form inert passivating films, or steadily corrode to aqueous species. Furthermore, we show that the structure and composition of the passivating films formed on triclinic-FeVO 4 are also in excellent agreement with the Point Defect Model, as proposed by the corrosion community.
Various samples are generated on a substrate. The samples each includes or consists of one or more analytes. In some instances, the samples are generated through the use of gels or through vapor deposition techniques. The samples are used in an instrument for screening large numbers of analytes by locating the samples between a working electrode and a counter electrode assembly. The instrument also includes one or more light sources for illuminating each of the samples. The instrument is configured to measure the photocurrent formed through a sample as a result of the illumination of the sample.