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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Author Correction: An autonomous laboratory for the accelerated synthesis of inorganic materials

Following publication of this article, concerns were raised about the unambiguous identification of the compound structures using diffraction as well as the original claims of material novelty. We acknowledge that the original claims of material novelty were subject to misinterpretation—their intention was to indicate that the materials were new to the prediction platform, not necessarily new to science. The article text has been updated to reflect this in the HTML and PDF versions of the article.

Szymanski, Nathan J. [University of California, Be↗
DOE OSTI2020

Polycaprolactone: A Promising Addition to the Sequential Infiltration Synthesis Polymer Family Identified through In Situ Infrared Spectroscopy

Infiltration of inorganic oxides inside polymers using sequential infiltration synthesis (SIS) is an effective method for creating materials for a broad range of applications. The reactions between various polymer functional groups and organometallic/inorganic precursors are unique, which makes it essential to understand the specific interactions for a range of precursors and polymers to enable predictive process design and to extend the utility of SIS to applications. In this paper, in situ Fourier transform infrared spectroscopy (FTIR) measurements have been performed during Al 2 O 3 and TiO 2 SIS in three different homopolymers: poly(methyl methacrylate) (PMMA), poly(epsilon-caprolactone) (PCL), and poly(2-vinylpyridine) (P2VP). From the FTIR intensity changes after precursor exposure and during the subsequent purge times, it is demonstrated quantitatively that the interaction dynamics and the stability of the intermediate complexes of these polymers with the metal precursors are substantially different. A key finding from this comparative study is that PCL interacts far more strongly with metal precursors, even though its carbonyl (C=O) and ester (C-O-R) functional groups are similar to those of the more weakly interacting PMMA. This behavior suggests that additional factors beyond the identity of the functional groups dictate how polymers interact with metal compounds in SIS. PCL, which has not previously been reported in SIS processes, may be an attractive polymeric template for implementing SIS with improved uniformity and cost-effectiveness.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine learning guided discovery of ternary compounds involving La and immiscible Co and Pb elements

Abstract Ternary compounds with an immiscible pair of elements are relatively unexplored but promising for novel quantum materials discovery. Exploring what third element and its ratio that can be added to make stable ternary compounds out of an immiscible pair of elements remains a great challenge. In this work, we combine a machine learning (ML) method with ab initio calculations to efficiently search for the energetically favorable ternary La-Co-Pb compounds containing immiscible elements Co and Pb. Three previously reported structures are correctly captured by our approach. Moreover, we predict a ground state La 3 CoPb compound and 57 low-energy La-Co-Pb ternary compounds. Attempts to synthesize La 3 CoPb via multiple techniques produce mixed or multi-phases samples with, at best, ambiguous signals of the predicted lowest-energy La 3 CoPb and the second lowest-energy La 18 Co 28 Pb 3 phases. The calculated results of Gibbs free energy are consistent with experiments, and will provide very useful guidance for further experimental synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Predicted Ferromagnetism in Discovered Co–Bi Binary Phases

Binary solid-state materials offer unique insight into how the interplay of factors such as stoichiometry and bonding interactions affects magnetism and electronic properties. In this work, we considered systems where a transition metal provides the spin moment and a heavy main group element bolsters strong spin–orbit coupling. Within this context, cobalt, a known component of permanent magnets, and bismuth, functionally the heaviest element stable to radioactive decay, form a compelling combination. The Co–Bi system has been previously shown to exhibit superconductivity in a phase recovered from high pressure. We expected the Co−Bi system could also be ferromagnetic, resulting in two sets of compounds within one chemical system, one superconducting and one ferromagnetic. Subsequently, we investigated the Co–Bi system through both experimental and theoretical approaches to discover new candidates for permanent magnets. Ab initio random structure searching calculations identified five new compounds with diverse structural motifs that may form at higher pressures than previously reported. Experimental high-pressure synthesis yielded four compounds: α-CoBi, α-CoBi 2 , β-CoBi, and β-CoBi 2 . Three of these phases, α-CoBi 2 , β-CoBi, and β-CoBi 2 , were consistent with the calculated structures, corresponding to a 60% success rate for our structure search and underscoring the strength of combining computation with experiment. Theory predicts β-CoBi and β-CoBi2 are ferromagnetic, with β-CoBi possessing larger magnetocrystalline anisotropy energy than familiar permanent magnets such as CoPt and Nd–Fe–B. These results suggest the Co–Bi system could be a platform for understanding the factors that underpin magnetism and, to an extent, superconductivity in a chemically simple binary system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Thaw depth, soil moisture, and vegetation height, Teller and Kougarok sites, Seward Peninsula, Alaska, 2022

Thaw depth, soil moisture, and vegetation height sampled from locations on the Teller MM27, Kougarok MM80, and Kougarok MM82 NGEE-Arctic sites, Seward Peninsula, Alaska. These data were collected in support of the ongoing NGEE-Arctic and NASA ABoVE data synthesis work. Samples were collected in July 2022, including 6 transects covering the entire Teller MM27 watershed and 4 transects covering 4 thaw ponds at Kougarok MM80 and MM82. This data package includes sample information and thaw depth, soil moisture, vegetation height data (.csv). Metadata files include data descriptions (_dd.csv) for tabular data and file level metadata (.csv). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Understanding polaronic transport in complex oxides by combining precise synthesis and first-principles many-body theory

In complex oxides, charge carriers often couple strongly with lattice vibrations to form polarons–entangled electron–phonon quasiparticles whose transport properties remain difficult to characterize. Experimental access to intrinsic polaronic transport requires ultraclean samples, while theoretical description demands methods beyond low-order perturbation theory. Here, we show a predictive theory–experiment workflow to study polaron transport in complex oxides. Focusing on a prototypical polaronic oxide, anatase TiO 2 , we combine growth of high-quality oxygen-vacancy-doped films using hybrid molecular beam epitaxy with a first-principles electron–phonon diagrammatic Monte-Carlo (FEP-DMC) framework recently developed for accurate polaron predictions. Our films exhibit record-high electron mobility for anatase TiO 2 , in excellent agreement with FEP-DMC calculations conducted prior to experiment, which predict a room-temperature mobility of 45 ± 15 cm −2 V −1 s −1 and a mobility-temperature scaling of μ ∝ T −1.9 ± 0.077 . Microscopic analysis using scanning transmission electron microscopy and x-ray photoelectron spectroscopy reveals the role of oxygen vacancies in modulating transport at lower temperatures. FEP-DMC further provides quantitative insight into polaron formation energy, phonon cloud distribution, lattice distortion around the polaron, and the polaronic contribution to mobility. Together, these results provide a deeper microscopic understanding of large-polaron transport in a complex oxide and provide the blueprint to characterize other polaronic materials.

anatase TiO2↗
DOE OSTI2023

Synthesis of Hf 0.75 Ta 0.25 B 2 for self-coating TPS

Ultra-high temperature ceramic materials (UHTCs) are important for designing high-performance aerospace vehicles that can withstand repeated exposures to high temperatures. UHTCs mixed with silicides often have well-controlled oxidation due to the formation of protective silicates, which create a regenerative outer protective layer. Borides are known to have high melting points, high hardness and reasonably good oxidation resistance. In this study, Hf 0.75 Ta 0.25 B 2 (HTB) is proposed as a potential alternative to YSZ protective coatings and ZrB 2- SiC composites via the formation of Hf 6 Ta 2 O 17 (HTO) passivation layer. The objective of this paper is to explore the parameter space of HTB synthesis via borocarbothermal (BCTR). Effects on particle size, phase purity, and residual oxygen content were analyzed with parameters of atmosphere composition, reactant grain sizes, and differing reaction pathways. The BCTR of HfO 2 and Ta 2 O 5 were analyzed to predict HTB behavior. It was shown that both one-step and two-step reaction routes can yield HTB, but two-step yields a purer product. Nano B 4 C produced finer HTB and facilitated reaction completion. Using a reducing atmosphere also enhanced reaction completion

36 MATERIALS SCIENCE↗
DOE OSTI2020

Synthesis of Honeycomb-Structured Beryllium Oxide via Graphene Liquid Cells

Using high-resolution transmission electron microscopy and electron energy-loss spectroscopy, we demonstrate that beryllium oxide crystallizes in the planar hexagonal structure in a graphene liquid cell by a wet-chemistry approach. These liquid cells can feature van-der-Waals pressures up to 1 GPa, producing a miniaturized high-pressure container for the crystallization in solution. The thickness of as-received crystals is beyond the thermodynamic ultra-thin limit above which the wurtzite phase is energetically more favorable according to the theoretical prediction. The crystallization of the planar phase is ascribed to the near-free-standing condition afforded by the graphene surface. Our calculations show that the energy barrier of the phase transition is responsible for the observed thickness beyond the previously predicted limit. These findings open a new door for exploring aqueous-solution approaches of more metal-oxide semiconductors with exotic phase structures and properties in graphene-encapsulated confined cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Mechanistic modeling of in vitro transcription incorporating effects of magnesium pyrophosphate crystallization

The in vitro transcription (IVT) reaction used in the production of messenger RNA vaccines and therapies remains poorly quantitatively understood. Mechanistic modeling of IVT could inform reaction design, scale-up, and control. In this work, we develop a mechanistic model of IVT to include nucleation and growth of magnesium pyrophosphate crystals and subsequent agglomeration of crystals and DNA. To help generalize this model to different constructs, a novel quantitative description is included for the rate of transcription as a function of target sequence length, DNA concentration, and T7 RNA polymerase concentration. The model explains previously unexplained trends in IVT data and quantitatively predicts the effect of adding the pyrophosphatase enzyme to the reaction system. The model is validated on additional literature data showing an ability to predict transcription rates as a function of RNA sequence length.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Crystallization of binary nanocrystal superlattices and the relevance of short-range attraction

The synthesis of binary nanocrystal superlattices (BNSLs) enables the targeted integration of orthogonal physical properties, such as photoluminescence and magnetism, into a single superstructure, unlocking a vast design space for multifunctional materials. However, the formation mechanism of BNSLs remains poorly understood, restricting the prediction of the structure and properties of superlattices. Here we use a combination of in situ scattering and molecular simulation to elucidate the self-assembly of two common BNSLs (AlB 2 and NaZn 13 ) through emulsion templating. Our self-assembly experiments reveal that no intermediate structures precede the formation of the final binary phases, indicating that their formation proceeds through classical nucleation. Using simulations, we find that, despite the formation of AlB 2 and NaZn 13 typically being attributed to entropy, their self-assembly is most consistent with the nanocrystals possessing short-range interparticle attraction, which we find can accelerate nucleation kinetics in BNSLs. Here, we also find homogeneous, classical nucleation in simulations, corroborating our experiments. These results establish a robust correspondence between experiment and theory, paving the way towards prediction of BNSLs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Epitaxial growth, magnetoresistance, and electronic band structure of GdSb magnetic semimetal films

Motivated by observations of extreme magnetoresistance (XMR) in bulk crystals of rare-earth monopnictide (RE-V) compounds and emerging applications in novel spintronic and plasmonic devices based on thin-film semimetals, we have investigated the electronic band structure and transport behavior of epitaxial GdSb thin films grown on III-V semiconductor surfaces. The Gd 3 + ion in GdSb has a high spin S=7/2 and no orbital angular momentum, serving as a model system for studying the effects of antiferromagnetic order and strong exchange coupling on the resulting Fermi surface and magnetotransport properties of RE-Vs. Here, we present a surface and structural characterization study mapping the optimal synthesis window of thin epitaxial GdSb films grown on III-V lattice-matched buffer layers via molecular-beam epitaxy. To determine the factors limiting XMR in RE-V thin films and provide a benchmark for band-structure predictions of topological phases of RE-Vs, the electronic band structure of GdSb thin films is studied, comparing carrier densities extracted from magnetotransport, angle-resolved photoemission spectroscopy (ARPES), and density-functional theory (DFT) calculations. ARPES shows a hole-carrier rich, topologically trivial, semimetallic band structure close to complete electron-hole compensation, with quantum confinement effects in the thin films observed through the presence of quantum-well states. DFT-predicted Fermi wave vectors are in excellent agreement with values obtained from quantum oscillations observed in magnetic field-dependent resistivity measurements. An electron-rich Hall coefficient is measured despite the higher hole-carrier density, attributed to the higher electron Hall mobility. The carrier mobilities are limited by surface and interface scattering, resulting in lower magnetoresistance than that measured for bulk crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

waveSZ: A Hardware-Algorithm Co-Design of Efficient Lossy Compression for Scientific Data

Error-bounded lossy compression is critical to the success of extreme-scale scientific research because of ever-increasing volumes of data produced by today's high-performance computing (HPC) applications. Not only can error-controlled lossy compressors significantly reduce the I/O and storage burden but they can retain high data fidelity for post analysis. Existing state-of-the-art lossy compressors, however, generally suffer from relatively low compression and decompression throughput (up to hundreds of megabytes per second on a single CPU core), which considerably restrict the adoption of lossy compression by many HPC applications especially those with a fairly high data production rate. In this paper, we propose a highly efficient lossy compression approach based on field programmable gate arrays (FPGAs) under the state-of-the-art lossy compression model SZ. Our contributions are fourfold. (1) We adopt a wavefront memory layout to alleviate the data dependency during the prediction for higher-dimensional predictors, such as the Lorenzo predictor. (2) We propose a co-design framework named WAVESZ based on the wavefront memory layout and the characteristics of SZ algorithm and carefully implement it by using high-level synthesis. (3) We propose a hardware-algorithm co-optimization method to improve the performance. (4) We evaluate our proposed WAVESZ on three real-world HPC simulation datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and FPGAs. Experiments show that our WAVESZ can improve SZ's compression throughput by 6.9x similar to 8.7x over the production version running on a state-of-the-art CPU and improve the compression ratio and throughput by 2.1x and 5.8x on average, respectively, respectively, compared with the state-of-the-art FPGA design.

Compression Ratio↗
DOE OSTI2024

Active Learning for Rapid Targeted Synthesis of Compositionally Complex Alloys

The next generation of advanced materials is tending toward increasingly complex compositions. Synthesizing precise composition is time-consuming and becomes exponentially demanding with increasing compositional complexity. An experienced human operator does significantly better than a novice but still struggles to consistently achieve precision when synthesis parameters are coupled. The time to optimize synthesis becomes a barrier to exploring scientifically and technologically exciting compositionally complex materials. This investigation demonstrates an active learning (AL) approach for optimizing physical vapor deposition synthesis of thin-film alloys with up to five principal elements. We compared AL-based on Gaussian process (GP) and random forest (RF) models. The best performing models were able to discover synthesis parameters for a target quinary alloy in 14 iterations. We also demonstrate the capability of these models to be used in transfer learning tasks. RF and GP models trained on lower dimensional systems (i.e., ternary, quarternary) show an immediate improvement in prediction accuracy compared to models trained only on quinary samples. Furthermore, samples that only share a few elements in common with the target composition can be used for model pre-training. We believe that such AL approaches can be widely adapted to significantly accelerate the exploration of compositionally complex materials.

Chemistry↗
DOE OSTI2023

2.3.4.501 - Synthesis and Analysis of Performance-Advantaged Bioproducts

This project focuses on the synthesis and analysis of performance-advantaged bioproducts (PABPs). We have established collaborations with other BETO-funded projects and academic and industrial collaborators to source new molecules that have promising manufacturing pathways and that could be serve as performance-advantaged biochemicals or biopolymers. We conduct synthesis and characterization of biochemicals and biopolymers alongside techno-economic analysis and life cycle assessment to estimate their cost and environmental impacts relative to incumbent materials. As part of the project, Profs. Linda Broadbelt and Brent Shanks are developing computational pathway prediction tools to identify optimal production pathways for bio-based compounds via biological and chemo-catalytic transformations. When coupled to the polyID tool from the Inverse Design project, these tools will ultimately enable a narrowing of design space for PABPs. From FY21-FY23, we described a framework for benchmarking PAPBs, estimated the energy and GHG emissions for commodity organic chemicals, developed performance-advantaged nylons and polyesters from beta-ketoadipic acid, and produced lignin-based plasticizers. We have shown that aromatic amines can be used in performance thermosets and that polyhydroxyalkanoates with crosslinked side chains can exhibit rubber-like properties, along with repair and degradability. We are working actively with industry partners on scale-up and validation of multiple PABPs.

BIOMASS FUELS↗
DOE OSTI2025

Comparing approaches for introducing polycyclic aromatic hydrocarbons to Ge(001) to seed graphene nanoribbon synthesis by CH 4 chemical vapor deposition

Here, we evaluate two approaches for introducing polycyclic aromatic hydrocarbons (PAHs) to a graphene catalyst substrate as part of a two-step chemical vapor deposition (CVD) process for growing graphene nanoribbons (GNRs). In this process, PAHs first form graphene-like seeds on Ge, and then GNRs are subsequently evolved from these PAH-derived seeds via substrate-mediated anisotropic growth kinetics during the CVD of CH4. The first seeding approach sublimes controlled doses of PAH thin films into the CVD chamber, while the second delivers PAHs directly from the vapor phase at set concentrations. Using these two approaches, we measure the dependence of GNR density on PAH dose and compare the experimental results with predictions from a rate model of PAH diffusion, desorption, and clustering. We find that PAHs that more strongly adsorb to the catalyst surface (generally larger PAHs) desirably remain more individualized prior to GNR evolution whereas smaller, more weakly bound PAHs aggregate into larger clusters with various sizes on Ge that are undesirable for synthesizing more monodisperse GNRs. As a result, this work is important because it offers a framework that enables the rational selection and design of seed molecules, advancing anisotropic GNR CVD synthesis.

CVD↗
DOE OSTI2020

Contrasting responses to climate change at Himalayan treelines revealed by population demographics of two dominant species

Abstract Alpine treelines are expected to shift upward due to recent climate change. However, interpretation of changes in montane systems has been problematic because effects of climate change are frequently confounded with those of land use changes. The eastern Himalaya, particularly Langtang National Park, Central Nepal, has been relatively undisturbed for centuries and thus presents an opportunity for studying climate change impacts on alpine treeline uncontaminated by potential confounding factors. We studied two dominant species, Abies spectabilis (AS) and Rhododendron campanulatum (RC) , above and below the treeline on two mountains. We constructed 13 transects, each spanning up to 400 m in elevation, in which we recorded height and state (dead or alive) of all trees, as well as slope, aspect, canopy density, and measures of anthropogenic and animal disturbance. All size classes of RC plants had lower mortality above treeline than below it, and young RC plants (<2 m tall) were at higher density above treeline than below. AS shows little evidence of a position change from the historic treeline, with a sudden extreme drop in density above treeline compared to below. Recruitment, as measured by size–class distribution, was greater above treeline than below for both species but AS is confined to ~25 m above treeline whereas RC is luxuriantly growing up to 200 m above treeline. Synthesis . Evidence suggests that the elevational limits of RC have shifted upward both because (a) young plants above treeline benefited from facilitation of recruitment by surrounding vegetation, allowing upward expansion of recruitment, and (b) temperature amelioration to mature plants increased adult survival. We predict that the current pure stand of RC growing above treeline will be colonized by AS that will, in turn, outshade and eventually relegate RC to be a minor component of the community, as is the current situation below the treeline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

One Pot Synthesis of Cyan Emitting CdZnSSe Quantum Dots for Human Centric Lighting

A one pot synthesis of blue and green emissive CdZnSSe quantum dots (QDs) from thio- and selenoureas and Cd and Zn carboxylates is optimized using high throughput robotic optimization. A large set of spectral data (N = 192) is used to train machine learning models that accurately predict the photoluminescence emission wavelength (λmax) and full-width half-maximum, and the relative photoluminescence quantum yield (PLQY) from the S:Se and Zn:Cd stoichiometries and reaction time. ZnS shells are deposited on the crude QD heterostructures using 4-tert-butylbenzyl mercaptan, a more reactive source of sulfide that enables shell growth below the temperature where ion diffusion in the QD can broaden its optical spectrum (≤275 °C). These optimized procedures provide gram quantities of blue-green emitting QDs (PLQY = 85–99%) in a single reaction vessel. A solid state lighting device (4260 K) that incorporates cyan emissive QDs achieved a higher luminous efficacy of 179 lm/W and melanopic daylight efficiency ratio (0.71) than existing commercial human centric lighting devices.

Jordan, Abraham J↗