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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Rapid Bayesian High Entropy Alloy Designs Fabricated via Wire Arc Additive Manufacturing

Purpose: This project seeks to demonstrate a new high-throughput (rapid) alloy design technique applied to creating new high entropy alloys (HEAs) for extreme environments. High entropy alloys shift the design paradigm from being focused on a single principal element (e.g. nickel-based alloys) to target alloys that include high atomic fractions (X >10%) of multiple elements. These HEA materials can exhibit sluggish diffusion and enhanced corrosion resistance, ideal for potential applications in advanced ultra supercritical (A-USC) steam cycles for power generation. Scope: The addition of multiple elements in high atomic fractions creates an enormous design space that cannot easily be investigated by traditional material design strategies such as designed of experiments (DOE). This project utilizes a Bayesian machine learning algorithm that has been modified to work with calculation of phase diagrams (CALPHAD) software. This Bayesian algorithm reduces manual inputs and increase the likelihood of achieving an optimal solution. Compositional inputs to this algorithm will be assessed using existing material property models for high temperature strength and corrosion resistance. The target for alloy performance will be a 15% (~100 ⁰C) increase in allowable service temperature beyond heat-resistant stainless steels while maintaining or improving alloy cost and corrosion resistance. Haynes 230 was selected as a baseline, which is 57 wt% Ni with 22 wt% Cr 14 wt% W, and 2 wt% Mo as solid solution strengtheners. In addition to rapid design via Bayesian machine learning, the alloys were rapidly fabricated using a multi-wire arc additive manufacturing (mWAAM) technique which allows for precise control of alloy composition and assessing of alloy design “windows” to study composition effects. Build speeds for wire-arc additive processes are among the highest for additive technologies enabling rapid and reliable sample fabrication when compared to conventional methods such as arc button melting. The mWAAM samples will be rapidly characterized via instrumented indentation for room temperature modulus and strength and for elevated temperature strength via hot hardness tests. After being screened with hardness testing, potential alloys will be further evaluated with conventional microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to assess agreement with modeling results. The most promising compositions will also be evaluated by printing full sized tensile specimens for mechanical behavior tests at elevated temperatures. Results: Bayesian machine learning of a single performance function was initially used to optimize five performance metrics: 1) single phase stability, 2) yield strength, 3) creep resistance (low diffusion coefficient), 4) freezing range (weldability), and 5) material cost. The single performance function was suboptimal as assumptions had to be made about the results while formulating the optimization. A goal-oriented Bayesian optimization strategy (Hanaoka, 2021) was implemented with CALPHAD for use with the five metrics above. This multi-objective Bayesian optimization (MOBO) enabled the design of NiCrCoFe alloys with V and W additions. A base composition of NiCoCr was selected as Ni provides a stable FCC matrix, Cr aids corrosion/oxidation resistance, and Co is a solid-solutions strengthener that also improves creep by increasing the activation energy. Fe helps reduce diffusion coefficients and cost. Finally, V and W were selected for their reasonable solubility and high atomic misfit to aid in solid solution strengthening. Cracking of the mWAAM specimens was an early issue, and the Easton solidification cracking model (Easton et al., 2014a) was selected for addition to the MOBO function. High performing alloys fabricated by mWAAM included Ni 28 Cr 25 Co 26 Fe 15 V 8 and Ni 62 Cr 18 Co 1 Fe 3 W 15 . It was observed that even after adapting the mWAAM process for W, the W did not fully dissolve. To fully evaluate the Ni 62 Cr 18 Co 1 Fe 3 W 15 composition, a cored wire (80-20 NiCr sheath/powder core) was manufactured and printed via WAAM, and HIP’ing was utilized to homogenize and densify the printed alloy. The V and W alloys produced met metrics 1 (solid solution), 4 (solidification cracking), and 5 (cost). However, an unmodeled mechanism of thermal stress cracking was identified in the WAAM produced materials, perhaps exacerbated by the lack of grain boundary strengthening elements (B, C). Conclusions & Recommendations: A high-throughput (rapid) alloy design technique was applied to designing and manufacturing new high entropy alloys (HEAs) for extreme environments utilizing MOBO and mWAAM. The developed process was rapid and effective in addressing the mechanisms included in the model. The lack of grain boundary strengthening element additions (e.g., B, C) was a simplification that likely produced thermal stress cracking that turned into a large part of the investigation. Additions on the order of 0.005 wt% B and 0.05 wt% C likely would have minimized thermal stress grain boundary cracking. Overall, the high throughput design strategy is promising for rapid design of metrics-driven alloys for advanced ultra supercritical (A-USC) steam cycles for power generation. The MOBO and mWAAM process could be commercialized to accelerate metrics-driven alloy design. In addition, the cored-wire process utilized for scale-up is a promising high-volume process for WAAM alloy development and scale-up.

36 MATERIALS SCIENCE↗
DOE OSTI2025

dCache: The Storage System of Choice for Data-Intensive Applications

The ever-increasing volumes of data produced by modern scientific facilities like EuXFEL and LHC put significant stress on data management infrastructure operated by laboratories and research centers. The challenges to be addressed span the entire data life cycle, from ingest and efficient data analysis to long-term preservation, typically involving large tape libraries. dCache, a storage system developed in collaboration between the Deutsches Elektronen-Synchrotron (DESY), Fermi National Accelerator Laboratory, and Nordic e-Infrastructure Collaboration (NeIC), is designed to manage a large number of disk servers and to facilitate transparent data migration to and from archival storage. Its multifaceted approach offers a unified method to support a variety of scientific use cases with the same storage infrastructure, including high-throughput data ingest, data sharing over wide area networks, efficient access from HPC clusters, and long-term data preservation on tertiary storage. Initially developed for high energy physics (HEP) experiments, dCache is now used by various scientific communities, including astrophysics, biomedical research, and life sciences, each having specific requirements. This paper presents architecture, deployment strategies, performance and scalability enhancements, and recent advancements in dCache addressing the needs of scientific communities. Finally, we touch on the development and release process, ensuring the software’s high quality.

DCache↗
DOE OSTI2025

Sensitivity analysis of thermal contact conductance modeling to inform MiniFuel irradiation capsule designs

The MiniFuel irradiation platform has been developed by Oak Ridge National Laboratory as a flexible, high-throughput separate effects testing capability within the High Flux Isotope Reactor (HFIR). Finite element thermal models are relied upon to design MiniFuel experiments to achieve a specific time-averaged irradiation temperature for experimental objectives. A previous study identified that uncertainty in the component heat generation rates and thermal contact conductance (TCC) model are the most significant contributors to predicted fuel temperature variance. To address both sources of uncertainty, this work performs sensitivity analysis on the TCC model to identify high-impact, high-uncertainty parameters that contribute to fuel temperature variance. The TCC model is analyzed in increasing detail, first using a standalone Python code, then again after coupling Python to the BISON fuel performance code. Furthermore, the parameters with the largest contributions to fuel temperature variance which can be reduced through design changes are identified as the initial subcapsule gas pressure, contact pressure between the fuel and dish, and the effective surface roughness of the interface. A set of design recommendations for future capsule designs has been established and applied to reduce the previously quantified average fuel temperature uncertainty ranges of ± 40 °C in the HFIR vertical experiment facilities (VXF) and ± 80 °C in the removable beryllium (RB) reflector to approximately ± 32 °C and ± 53 °C, respectively. This equates to a 21 % and 33 % reduction in the uncertainty range of the average fuel temperature for VXF and RB, respectively.

BISON↗
DOE OSTI2026

Robust Rapid Cellular Metabolite Sensing Using Benchtop NMR and SABRE-Hyperpolarized [1- 13 C]Pyruvate

Hyperpolarized NMR has emerged as a powerful analytical technique to significantly enhance targeted NMR signals, improving the sensitivity for investigations of unique chemical and biological dynamics. Here, we demonstrate the use of a hyperpolarization strategy based on Signal Amplification By Reversible Exchange (SABRE) to generate highly reproducible doses of a hyperpolarized [1- 13 C]pyruvate probe for benchtop characterization of yeast metabolism. This method allows rapid, scalable, and benchtop preparation of biocompatible hyperpolarized solutions suitable for live-cell experiments. We show that this production can be dove-tailed into a modular, compact workflow to characterize real-time metabolism in cell cultures, using Saccharomyces cerevisiae (Baker’s yeast) as a model organism. With high temporal resolution, we show that this method can resolve the conversion of hyperpolarized [1- 13 C]pyruvate into oxidative decarboxylation products CO 2 and bicarbonate. This conversion exhibits sustained and detectable metabolic activity for over 300 s after introduction of the agent to the cells. We model the metabolite kinetics to show decarboxylation activity and derive estimates of the pH over time from the CO 2 and bicarbonate (carbonic acid buffer system) equilibrium to probe changes in the cellular environment during active metabolism. These results highlight the utility of benchtop SABRE-hyperpolarized [1- 13 C]pyruvate as a scalable, specific probe for metabolic phenotyping of living cells using compact, low-cost instrumentation well-suited for future high-throughput applications across microbial engineering, drug response profiling, and dynamic metabolic screening.

fungi↗
DOE OSTI2026

Conductivity Spectroscopy for Investigation and Discovery of Photovoltaic Materials

Conductivity spectroscopy is an extremely powerful set of methods for probing the properties of optoelectronic materials, especially photovoltaics, where photoconductivity is one of the best spectroscopic proxies for performance. Despite this power, they are substantially less commonly used than time-resolved photoluminescence (for instance) because they tend to be more expensive to implement (THz) and/or require specialized knowledge (GHz) to construct instruments, which are not widely available. The goal of this review is to illustrate the utility of these experiments in the discovery and study of photovoltaic absorber materials and simultaneously make them more accessible to the community by providing a central tutorial resource. We provide a comprehensive review of how conductivity spectroscopy has developed over the past decade and been applied in the discovery and development of photovoltaic materials, with a primary focus on emerging solution-processable technologies. Along the way we aim to demystify conductivity spectroscopy with focused tutorial sections that explain the physical models used to fit the data and illustrate how to think about “high-frequency conductivity”.

14 SOLAR ENERGY↗
DOE OSTI2025

A Flat Analyzer Polycapillary Spectrometer for X-ray Absorption Spectroscopy of Dilute Transition Metals at X-ray-Free Electron Lasers

X-ray absorption spectroscopy (XAS) coupled with highly intense pulses from an X-ray-free electron laser (XFEL) can be used to track ultrafast chemical dynamics. Nonetheless, measurements for dilute samples (≤1 mM) have been exceptionally challenging, as scattering background signals dominate over the sample’s X-ray fluorescence. We show that femtosecond time-resolved XAS measurements of sub-millimolar transition metal solutions are now possible at the Linac Coherent Light Source (LCLS) using a high-throughput polycapillary XAS spectrometer, designed, developed, and commissioned at LCLS and the Stanford Synchrotron Radiation Lightsource (SSRL). The instrument integrates three polycapillary optics that collect and collimate X-ray fluorescence emitted from the interaction point with a high solid angle. X-ray collimated fluorescence is then selectively diffracted by coupled graphite analyzer crystals. As a result, the contribution of scattered photons is suppressed. Experiments at the Pt L 3 -edge on 0.1 mM aqueous K 2 PtCl 6 (delivered via a 100 μm liquid jet) were successfully performed in the laser pump X-ray probe configuration at the LCLS XCS and XPP instruments. We report the transient spectra of hexachloroplatinate within the first 10 ps after a 266 nm photoexcitation. We observed a short-lived reduced intermediate (≈2 ps). The polycapillary X-ray spectrometer at LCLS now enables the efficient study of the dynamics of ultradilute transition metals in solution. This capability opens the door to investigating plasmonic systems, photocatalysts, enzymes, and other scarce and dilute samples.

LCLS↗
DOE OSTI2024

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

X-ray continuum spectroscopy of inertial confinement fusion implosions at the National Ignition Facility

A methodology for measuring x-ray continuum spectra of inertial confinement fusion (ICF) implosions is described. The method relies on the use of ConSpec, a high-throughput spectrometer using a highly annealed pyrolytic graphite crystal , which measures the spectra in the ≃20–30 keV range. Due to its conical shape, the crystal is sagittally focusing a Bragg-reflected x-ray spectrum into a line, which enhances the recorded x-ray emission signal above the high neutron-induced background accompanying ICF implosions at the National Ignition Facility. To improve the overall measurement accuracy, the sensitivity of the spectrometer measured in an off-line x-ray laboratory setting was revised. The error analysis was expanded to include the accuracy of the off-line measurements, the effect of the neutron-induced background, as well as the influence of possible errors in alignment of the instrument to the ICF target. Here we demonstrate how the improved methodology is applied in the analysis of ConSpec data with examples of a relatively low-neutron-yield implosion using a tritium–hydrogen–deuterium mix as a fuel and a high-yield deuterium–tritium (DT) implosion producing high level of the background. In both cases, the shape of the measured spectrum agrees with the exponentially decaying spectral shape of bremsstrahlung emission to within ±10%. In the case of the high-yield DT experiment, non-monotonic deviations slightly exceeding the measurement uncertainties are observed and discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A hybrid neural architecture: Online attosecond x-ray characterization

The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 μs inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2026

MapsTorch : automatic differentiation for X-ray fluorescence data analysis

X-ray fluorescence (XRF) is a popular spectroscopy technique for elemental analysis. Spectrum fitting and parameter tuning are at the core of XRF analysis and are conventionally manually intensive, especially for synchrotron experiments involving large amounts of diverse samples. This work introduces the automatic differentiation (AD) technique to XRF and an open-source package called MapsTorch. By transforming an analytical model of the XRF spectrum into a differentiable computation graph with AD, MapsTorch enables robust optimization of parameters and elemental intensities. We evaluate MapsTorch by conducting computational experiments on a large number of historical synchrotron XRF datasets and compare its performance with the currently practiced fitting tool NLopt. The results show that MapsTorch consistently achieves high-quality fits and often leads to better fitting quality than NLopt, particularly in tasks such as initial spectrum fitting and elemental intensity refinement. The robust performance of MapsTorch paves the way for developing automated and high-throughput XRF data analysis workflows to handle the increasing data volumes expected from next-generation synchrotron facilities.

X-ray fluorescence↗
DOE OSTI2024

High-Throughput Data Processing at FRIB Using ESnet

Real-time or nearly real-time (nearline) data processing methods are critical tools as detector technologies and data acquisition (DAQ) systems allow for higher data rates and volumes. The introduction of the energy sciences network (ESnet), a U.S. Department of Energy (DOE) supported high-speed network for scientific research, creates opportunities to leverage the computing power of DOE facilities like the National Energy Research Scientific Computing Center (NERSC). As a first step toward realizing a DOE Office of Science Integrated Research Infrastructure (IRI) pattern, an automated workflow was developed to remotely process data obtained from a nuclear physics experiment at the Facility for Rare Isotope Beams (FRIB) at NERSC with data transferred between FRIB and NERSC over ESnet. The workflow demonstrated the ability to process one week’s worth of experimental data in approximately 90 min and was used successfully for nearline analysis during a recently completed FRIB experiment. Here, a summary of the workflow development and results of recent demonstrations will be presented.

Data processing↗
DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

High-Throughput Directed Evolution of Marine Microalgae and Phototrophic Consortia for Improved Biomass Yields (Final Report)

Primary project achievements include using selective pressures (O 2 , light, temperature) and developing culturing regimes for the diatom Nitzschia inconspicua str. hildebrandi to attain enrichments with an ~90% increase in areal biomass productivity relative to the parental strain under pond-mimicking conditions with high O 2 stress in laboratory bioreactors. The resulting strain (GAI-337) was tested further for dilution time, culture density, CO 2 supplementation, pH, temperature, and dissolved O 2 concentration under outdoor pond-mimicking conditions to improve areal productivities. These experiments yielded an optimum harvest and dilution time just after sunset, ~0.45 g AFDW L -1 initial culture density for maximal productivities, no requirement for CO 2 supplementation or pH control, maximal performance under a diel temperature curve going from 24 °C at night to 36 °C during the day, and benefits from some O 2 removal from the culture by bubbling with air. Using pond-mimicking laboratory bioreactors, N. inconspicua GAI-337 achieved ~42 g AFDW m -2 d -1 . Nutrient limitation experiments resulted in a biomass composition that equated to ~160 Gallons of Gasoline Equivalent energy per ton AFDW, highlighting the potential of GAI-337 as a promising renewable fuel feedstock strain. Genome resequencing has revealed genome alterations potentially contributing to the improved growth of GAI-337 in the laboratory. Based on the comparative analyses of the GAI-337 and GAI-229 (reference) strains, we identified 144 single nucleotide substitutions that resulted in amino acid change, 7 single nucleotide substitutions that resulted in protein truncation; 5 deletions; and 1 frameshift mutation. From the mutations that potentially affect expression of functionally annotated genes, particular interest was noted for an interferon-induced 6-16 family protein that may be involved in the host immune response against microbe invasion; the chaperone protein DnaK, which may function to protect the folding of proteins within the cell; and SPRY domain protein that is found in many eukaryotic proteins important in cell signaling pathways. Transcriptome analysis revealed over 1000 genes with increased transcript levels. Many of these and many of the genes with mutations are not yet functionally annotated and an increased bioinformatics effort is necessary to more completely analyze the Nitzschia inconspicua genome. Adaptive laboratory evolution (ALE) was performed for over 300 days using consecutive 0.5°C temperature increases in a constant temperature incubator to attain greater thermal tolerance in Nitzschia inconspicua. The adapted strain was able to grow at a constant temperature of 37.5°C; whereas this constant temperature was lethal to the parental control, which had an upper temperature boundary of 35.5°C prior to adaptive evolution. Several high-temperature clonal isolates were obtained from the evolved population following ALE, and increased temperature tolerance was observed in clonal adapted cultures. The final temperature adaptation was maintained through cryopreservation and was observed in multiple clonal isolates, including multiple clonal isolates with significantly increased cell size, indicating the potential occurrence of a sexual cycle during the clonal isolation process. A survey of Nannochloropsis strains was conducted for tolerances to high pH and high bicarbonate media. Nannochloropsis granulata showed promising growth in diel bioreactors and was successfully grown at the GAI Kauai farm site in long-term growth campaigns. Co-culturing using Nitzschia inconspicua, Nannochloropsis and a cyanobacterium were assembled in the laboratory to determine if productivity synergies could be attained. Although all strains grew well in the laboratory high-bicarbonate media individually, the cyanobacterium quickly outgrew the other strains in the laboratory consortium pushing the co-culture away from a diverse (and potentially synergistic assemblage) phototroph culture towards a monoculture dominated by the cyanobacterium. Several outdoor growth campaigns were conducted, with productivities ranging between 10-20 g/m 2 /d of biomass. The best performing strain in the laboratory (GAI-337) did not outperform reference strains at the Kauai farm under the conditions used. Addition growth campaigns are necessary under conditions that result in higher biomass (>20 g/m 2 /d) and that attain higher O 2 levels are likely necessary. Initial data indicate that the thermally adapted strain did slightly better than the control strain at higher temperatures; however, additional campaigns are necessary to establish statistical significance. In summary, Nitzschia inconspicua is able to attain exemplary biomass and lipid yields in the laboratory bioreactors. Strain evolution to both O 2 and temperature resulted in targeted strain improvements. Additional outdoor campaigns are necessary to determine if laboratory improvements translate to the field.

09 BIOMASS FUELS↗
DOE OSTI2024

Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Introduction The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped. Methods To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources. Results Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments. Discussion We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Genetics & Heredity↗
DOE OSTI2024

A Portable Miniature Cryogenic Environment for In Situ Neutron Diffraction

Neutron diffraction instruments offer a platform for materials science and engineering studies at extended temperature ranges far from ambient. As one of the widely used neutron sample environment types, cryogenic furnaces are usually bulky and complex, and they may need hours of beamtime overhead for installation, configuration, cooling, and sample change, etc. To reduce the overhead time and expedite experiments at the state-of-the-art high-flux neutron source, we developed a low-cost, miniature, and easy-to-use cryogenic environment (77–473 K) for in situ neutron diffraction. A travel-size mug serves for the environment where the samples sit inside. Immediate cooling and an isothermal dwell at 77 K are realized on the sample by direct contact with liquid N 2 in the mug. The designed Al inserts serve as the holder of samples and heating elements, alleviate the thermal gradient, and clear neutron pathways. Both a single-sample continuous measurement and multi-sample high-throughput measurements are demonstrated in this environment. High-quality and refinable in situ neutron diffraction patterns are acquired on model materials. The results quantify the orthorhombic-to-cubic phase transformation process in LiMn 2 O 4 and differentiate the anisotropic lattice thermal expansions and bond length evolutions between rhombohedral perovskite oxides with composition variation.

47 OTHER INSTRUMENTATION↗
NASA NTRS2009

Catalysts for Efficient Production of Carbon Nanotubes

Several metal alloys have shown promise as improved catalysts for catalytic thermal decomposition of hydrocarbon gases to produce carbon nanotubes (CNTs). Heretofore almost every experiment on the production of carbon nanotubes by this method has involved the use of iron, nickel, or cobalt as the catalyst. However, the catalytic-conversion efficiencies of these metals have been observed to be limited. The identification of better catalysts is part of a continuing program to develop means of mass production of high-quality carbon nanotubes at costs lower than those achieved thus far (as much as $100/g for purified multi-wall CNTs or $1,000/g for single-wall CNTs in year 2002). The main effort thus far in this program has been the design and implementation of a process tailored specifically for high-throughput screening of alloys for catalyzing the growth of CNTs. The process includes an integral combination of (1) formulation of libraries of catalysts, (2) synthesis of CNTs from decomposition of ethylene on powders of the alloys in a pyrolytic chemical-vapor-decomposition reactor, and (3) scanning- electron-microscope screening of the CNTs thus synthesized to evaluate the catalytic efficiencies of the alloys. Information gained in this process is put into a database and analyzed to identify promising alloy compositions, which are to be subjected to further evaluation in a subsequent round of testing. Some of these alloys have been found to catalyze the formation of carbon nano tubes from ethylene at temperatures as low as 350 to 400 C. In contrast, the temperatures typically required for prior catalysts range from 550 to 750 C.

Sun, Ted X.↗
NASA NTRS2009

Life Science Research in Outer Space: New Platform Technologies for Low-Cost, Autonomous Small Satellite Missions

We develop integrated instruments and platforms suitable for economical, frequent space access for autonomous life science experiments and processes in outer space. The technologies represented by three of our recent free-flyer small-satellite missions are the basis of a rapidly growing toolbox of miniaturized biologically/biochemically-oriented instrumentation now enabling a new generation of in-situ space experiments. Autonomous small satellites (~ 1 50 kg) are less expensive to develop and build than fullsize spacecraft and not subject to the comparatively high costs and scheduling challenges of human-tended experimentation on the International Space Station, Space Shuttle, and comparable platforms. A growing number of commercial, government, military, and civilian space launches now carry small secondary science payloads at far lower cost than dedicated missions; the number of opportunities is particularly large for so-called cube-sat and multicube satellites in the 1 10 kg range. The recent explosion in nano-, micro-, and miniature technologies, spanning fields from telecommunications to materials to bio/chemical analysis, enables development of remarkably capable autonomous miniaturized instruments to accomplish remote biological experimentation. High-throughput drug discovery, point-of-care medical diagnostics, and genetic analysis are applications driving rapid progress in autonomous bioanalytical technology. Three of our recent missions exemplify the development of miniaturized analytical payload instrumentation: GeneSat-1 (launched: December 2006), PharmaSat (launched: May 2009), and O/OREOS (organism/organics exposure to orbital stresses; scheduled launch: May 2010). We will highlight the overall architecture and integration of fluidic, optical, sensor, thermal, and electronic technologies and subsystems to support and monitor the growth of microorganisms in culture in these small autonomous space satellites, including real-time tracking of their culture density, gene expression, and metabolic activity while in the space environment. Flight data and results will be presented from GeneSat-1, which tracked gene expression levels of GFP-labeled E. coli and from PharmaSat, which monitored the dose dependency of an antifungal agent against S. cerevisiae. The O/OREOS SESLO instrument, which will study the effects of radiation and microgravity upon the viability and growth characteristics of B. subtilis and the halophile Halorubrum chaoviatoris for periods of 0 - 6 months in space, will be described as well. The ongoing expansion of the small satellite toolbox of biological technologies will be summarized.

Ricco, Antonio J.↗
NASA NTRS2005

Glass Frit Filters for Collecting Metal Oxide Nanoparticles

Filter disks made of glass frit have been found to be effective as means of high-throughput collection of metal oxide particles, ranging in size from a few to a few hundred nanometers, produced in gas-phase condensation reactors. In a typical application, a filter is placed downstream of the reactor and a valve is used to regulate the flow of reactor exhaust through the filter. The exhaust stream includes a carrier gas, particles, byproducts, and unreacted particle-precursor gas. The filter selectively traps the particles while allowing the carrier gas, the byproducts, and, in some cases, the unreacted precursor, to flow through unaffected. Although the pores in the filters are much larger than the particles, the particles are nevertheless trapped to a high degree: Anecdotal information from an experiment indicates that 6-nm-diameter particles of MnO2 were trapped with greater than 99-percent effectiveness by a filtering device comprising a glass-frit disk having pores 70 to 100 micrometer wide immobilized in an 8-cm-diameter glass tube equipped with a simple twist valve at its downstream end.

Ackerman, John↗