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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Recrystallization mechanisms of Zircaloy-4 alloy annealed by electric current
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Recent development of thermoelectric nanofibers and their composites
This review deals with the recent development of thermoelectric (TE) nanofibers and their composite materials. Typical processing and manufacturing technologies for preparing nanofibers are introduced. Specifically, electrospinning, electrochemical oxidation, and liquid phase chemical deposition approaches are discussed. Various kinds of TE nanofibers are introduced one-by-one. Several important inorganic and organic nanofibers including carbon-based nanofibers, conducting polymer-based nanofibers, and transition metal oxide-based nanofibers with significant thermoelectric responses are shown. Thermoelectric properties of some important nanofibers are compared. The applications of TE nanofibers for energy conversion, thermal imaging and temperature sensing are illustrated. Perspectives and concluding remarks are presented.
Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data
Abstract Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.
Tandem bulk oxygen diffusion and surface reactions in reducible metal oxides control redox cycle dynamics
The interplay between bulk oxygen diffusion and surface reactions in reducible metal oxides is key in heterogeneous catalysts, but direct measurements of oxygen mobility, transient kinetics, and in situ spectroscopies have been lacking. Here, we reveal complex dynamic behavior of ceria-zirconia by H 2 using transient kinetics via mass spectrometry and in situ Raman and near-ambient pressure x-ray photoelectron spectroscopies. Molecular dynamics simulations with a machine learning potential delineate competitive oxygen diffusion mechanisms, with an optimal mobility at intermediate reductions. We expose a compensation between vacancy availability and lattice distortion at intermediate to high reductions and Frenkel defects at low reductions, underscoring a potential deficiency of 16 O/ 18 O exchange experiments in deducing oxygen mobility. Vacancies in proximity require electron localization on Ce atoms further away. The continuous replenishment of surface oxygen results in a varying reduction rate, with H 2 dissociation being the rate-limiting step. Multiscale transient simulations, consistent with experiments, indicate catalysts of potentially spatially varying oxidation states. The approach is broadly applicable to reducible oxide materials.
GaSb doping facilitates conduction band convergence and improves thermoelectric performance in n-type PbS
P-type lead chalcogenides have superior thermoelectric performance because they exhibit the energy convergence of several valence bands. However, despite the existence of two conduction bands, there has been no report about conduction band (CB) convergence for n-type counterparts because of the large energy difference between them. Therefore, new strategies are required to manipulate the CBs if enhancing the electrical transport performance of n-type lead chalcogenides is to be achieved. PbS is a highly attractive member of the lead chalcogenides because of its high earth-abundance and low cost. Here, we report that the introduction of GaSb can successfully dope the PbS matrix with Ga and Sb atoms occupying the Pb site in its rock salt structure. GaSb doping leads to conduction band convergence and enlarged effective density of state mass for n-type PbS. This effect results in superior power factor and decreased lattice thermal conductivity caused by the soft phonon modes and point defect scattering of phonons. Consequently, a record-high average power factor PF avg of ~20.4 μW cm –1 K –2 and figure of merit ZT avg of ~0.84 in the temperature range of 400 K to 923 K were obtained, higher than any n- and p-type PbS-based thermoelectric materials.
2018 LDRD Annual Report (Argonne National Laboratory)
Argonne National Laboratory’s Laboratory Directed Research and Development (LDRD) program encourages the development of novel technical concepts, enhances the Laboratory’s research and development (R&D) capabilities, and enables pursuit of strategic laboratory goals. Argonne’s LDRD projects are proposal based and peer reviewed, supporting ideas that require advanced exploration so they can be sufficiently developed to pursue support through normal programmatic channels. Among the aims of the projects supported by the LDRD program are the establishment of engineering proofs of principle, assessment of design feasibility for prospective facilities, development of instrumentation or computational methods or systems, and discoveries in fundamental science and exploratory development. All LDRD projects have demonstrable ties to one or more of the science, energy, environment, and national security missions of the U.S. Department of Energy (DOE) and its National Nuclear Security Administration (NNSA), and many are also relevant to the missions of other federal agencies that sponsor work at Argonne. A natural consequence of the more “applied” type projects is their concurrent relevance to industry. The LDRD program is managed in overarching portfolios, each containing multiple projects each fiscal year. The LDRD Prime portfolio is further divided into strategic focus areas aligned with Argonne’s strategic plan. The largest component of Argonne’s program is LDRD Prime, which emphasizes R&D explicitly aligned with Laboratory major initiatives in support of Argonne’s strategic plan. The choice of Focus Areas under the LDRD Prime component reflects the major initiatives; the state of development of relevant technical fields; the potential value of advancing those fields to DOE/NNSA and the nation; and the compatibility of the fields with existing facilities, capabilities, and staff expertise at Argonne. Focus Areas with projects that ended in FY18 are: Advanced Computing, Biological and Environmental Science Capability Development, Energy Manufacturing Science and Engineering, Hard X-ray Sciences, Materials and Chemistry, Securing Energy and Critical Resources, and The Universe as Our Laboratory (ULab).
A System for Standardizing and Combining U.S. Environmental Protection Agency Emissions and Waste Inventory Data
The U.S. Environmental Protection Agency (USEPA) provides databases that agglomerate data provided by companies or states reporting emissions, releases, wastes generated, and other activities to meet statutory requirements. These databases, often referred to as inventories, can be used for a wide variety of environmental reporting and modeling purposes to characterize conditions in the United States. Yet, users are often challenged to find, retrieve, and interpret these data due to the unique schemes employed for data management, which could result in erroneous estimations or double-counting of emissions. To address these challenges, a system called Standardized Emission and Waste Inventories (StEWI) has been created. The system consists of four python modules that provide rapid access to USEPA inventory data in standard formats and permit filtering and combination of these inventory data. When accessed through StEWI, reported emissions of carbon dioxide to air and ammonia to water are reduced approximately two- and four-fold, respectively, to avoid duplicate reporting. StEWI will greatly facilitate the use of USEPA inventory data in chemical release and exposure modeling and life cycle assessment tools, among other things. To date, StEWI has been used to build the recent USEEIO model and the baseline electricity life cycle inventory database for the Federal LCA Commons.