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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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589 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.

Petzold, Christopher [Lawrence Berkeley National L↗
DOE OSTI2023

A novel statistical methodology for quantifying the spatial arrangements of axons in peripheral nerves

A thorough understanding of the neuroanatomy of peripheral nerves is required for a better insight into their function and the development of neuromodulation tools and strategies. In biophysical modeling, it is commonly assumed that the complex spatial arrangement of myelinated and unmyelinated axons in peripheral nerves is random, however, in reality the axonal organization is inhomogeneous and anisotropic. Present quantitative neuroanatomy methods analyze peripheral nerves in terms of the number of axons and the morphometric characteristics of the axons, such as area and diameter. In this study, we employed spatial statistics and point process models to describe the spatial arrangement of axons and Sinkhorn distances to compute the similarities between these arrangements (in terms of first- and second-order statistics) in various vagus and pelvic nerve cross-sections. We utilized high-resolution transmission electron microscopy (TEM) images that have been segmented using a custom-built high-throughput deep learning system based on a highly modified U-Net architecture. Our findings show a novel and innovative approach to quantifying similarities between spatial point patterns using metrics derived from the solution to the optimal transport problem. We also present a generalizable pipeline for quantitative analysis of peripheral nerve architecture. Our data demonstrate differences between male- and female-originating samples and similarities between the pelvic and abdominal vagus nerves.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Discovery top-down proteomics in symbiotic soybean root nodules

Proteomic methods have been widely used to study proteins in complex biological samples to understand biological molecular mechanisms. Most well-established methods (known as bottom-up proteomics, BUP) employ an enzymatic digestion step to cleave intact proteins into smaller peptides for liquid chromatography (LC) mass spectrometry (MS) detection. In contrast, top-down proteomics (TDP) directly characterizes intact proteins including all possible post-translational modifications (PTMs), thus offering unique insights into proteoform biology where combinations of individual PTMs may play important roles. We performed TDP on soybean root nodules infected by the symbiotic Bradyrhizobium japonicum in both the wildtype bacterium and a nifH- mutant, which lacks the ability to fix nitrogen in the soybean root nodule. TDP captured 1648 proteoforms derived from 313 bacterial genes and 178 soybean genes. Leghemoglobin, the most abundant protein in the sample, existed in many truncated proteoforms. Interestingly, these truncated proteoforms were considerably more abundant in the wildtype relative to the nifH- mutant, implicating protease activity as an important factor in nitrogen fixation. Proteoforms with various PTMs and combinations thereof were identified using an unrestricted open modification search. This included less common PTMs such as myristoylation, palmitoylation, cyanylation, and sulfation. In parallel, we collected high resolution MS imaging (MSI) data of intact proteins and biopolymers (<20 kDa due to current technical limitations) from sections of the soybean root nodules using matrix-assisted laser desorption/ionization (MALDI) coupled to high resolution Orbitrap. Several detected proteoforms exhibited unique spatial distributions inside the infection zone and cortex, suggesting functional compartmentalization in these regions. A subset of peaks from the MALDI-MSI were assigned to proteoforms detected in TDP LCMS data based on matching accurate masses. Many of the proteins detected in both LCMS and MALDI-MSI are currently uncharacterized in UniProt: the PTM and spatial information presented here will be valuable in understanding their biological functions. Taken together, our study demonstrates how untargeted TDP approach can provide unique insights into plant proteoform biology. On-going technology developments are expected to further improve TDP coverage for more comprehensive high-throughput analysis of proteoforms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Large-Scale Synthesis of Semiconducting Cu(In,Ga)Se2 Nanoparticles for Screen Printing Application

During the last few decades, the interest over chalcopyrite and related photovoltaics has been growing due the outstanding structural and electrical properties of the thin-film Cu(In,Ga)Se2 photoabsorber. More recently, thin film deposition through solution processing has gained increasing attention from the industry, due to the potential low-cost and high-throughput production. To this end, the elimination of the selenization procedure in the synthesis of Cu(In,Ga)Se2 nanoparticles with following dispersion into ink formulations for printing/coating deposition processes are of high relevance. However, most of the reported syntheses procedures give access to tetragonal chalcopyrite Cu(In,Ga)Se2 nanoparticles, whereas methods to obtain other structures are scarce. Herein, we report a large-scale synthesis of high-quality Cu(In,Ga)Se2 nanoparticles with wurtzite hexagonal structure, with sizes of 10–70 nm, wide absorption in visible to near-infrared regions, and [Cu]/[In + Ga] ≈ 0.8 and [Ga]/[Ga + In] ≈ 0.3 metal ratios. The inclusion of the synthesized NPs into a water-based ink formulation for screen printing deposition results in thin films with homogenous thickness of ≈4.5 µm, paving the way towards environmentally friendly roll-to-roll production of photovoltaic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fabricating Silver Nanowire–IZO Composite Transparent Conducting Electrodes at Roll-to-Roll Speed for Perovskite Solar Cells

This study addresses the challenges of efficient, large-scale production of flexible transparent conducting electrodes (TCEs). We fabricate TCEs on polyethylene terephthalate (PET) substrates using a high-speed roll-to-roll (R2R) compatible method that combines gravure printing and photonic curing. The hybrid TCEs consist of Ag metal bus lines (Ag MBLs) coated with silver nanowires (AgNWs) and indium zinc oxide (IZO) layers. All materials are solutions deposited at speeds exceeding 10 m/min using gravure printing. We conduct a systematic study to optimize coating parameters and tune solvent composition to achieve a uniform AgNW network. The entire stack undergoes photonic curing, a low-energy annealing method that can be completed at high speeds and will not damage the plastic substrates. The resulting hybrid TCEs exhibit a transmittance of 92% averaged from 400 nm to 1100 nm and a sheet resistance of 11 Ω/sq. Mechanical durability is tested by bending the hybrid TCEs to a strain of 1% for 2000 cycles. The results show a minimal increase (<5%) in resistance. The high-throughput potential is established by showing that each hybrid TCE fabrication step can be completed at 30 m/min. We further fabricate methylammonium lead iodide solar cells to demonstrate the practical use of these TCEs, achieving an average power conversion efficiency (PCE) of 13%. The high-performance hybrid TCEs produced using R2R-compatible processes show potential as a viable choice for replacing vacuum-deposited indium tin oxide films on PET.

14 SOLAR ENERGY↗
DOE OSTI2021

A Custom High-Throughput Optical Mapping Instrument (OMI) for Accelerated Stress Testing of PV Module Materials

The performance and integrity of photovoltaic (PV) modules and materials may be assessed through optical characterizations. We have developed a custom Optical Mapping Instrument (OMI) to improve measurement throughput time, facilitate automated measurements, and obtain spatial information relative to commercial benchtop spectrophotometer instruments. Both the key components and the methods used to improve the measurements are described. The measurement precision for the OMI is benchmarked for hemispherical transmittance relative to a previous round-robin study for commercial spectrophotometers. Example mapping measurements and their implications are given for specimens from other studies, including: (1) the depletion of the UV absorber is revealed for specimens beyond a certain size after standardized artificial ultraviolet (UV) weathering, (2) an optical degradation by internal haze formation is confirmed following Damp Heat (85°C/85% relative humidity) testing, (3) the better uniformity of the wear pattern for falling sand abrasion is used to select the DIN 52348 fixture over the ASTM D968 fixture, and (4) the local (millimeter-scale) heterogeneity of surface contamination in the soiling-prone location of Dubai relative to Kuwait. The results of the UV study have already been used to improve the standardized weathering of encapsulants. The example for Damp Heat testing is given to increase awareness of a degradation believed to be ubiquitously occurring in safety and design-type qualification testing of PV modules.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2023

Design and Operation of a Multi-Bed Catalytic Micro-Reactor for the Study of Co-Processing of Bio-Oils with VGO

An industry wide shift from fossil-based fuel to renewable fuel sources including biomass, municipal waste, and plastics will require new process monitoring methods to minimize transitional risks including off specification product formation and catalyst deactivation. This project aims to provide a machine learning based process monitoring tool composed of online, slipstream mass spectra for use in biomass refineries and co-processing in existing refineries allowing operators to monitor product qualities and adjust process conditions accordingly. In order to maximize the robustness of the tool, large volumes of data must be collected to fine tune model parameters which consists of both micro and pilot scale mass spectral data. Micro-scale data is collected with a multi-tube micro-reactor housing up to six catalysts in horizontal beds, coupled with a molecular beam mass spectrometer. A pyrolizer equipped with an auto-sampler streamlines the micro-scale data collection process. This type of pyrolizer/micro-reactor configuration does not exist on the market, and therefore had to be created for the purposes of this project. The design and commissioning of this reactor will be presented in detail. This reactor set-up is highly flexible and increases throughput of analysis. For catalyst testing, each bed can be individually selected simply by turning valves. For catalyst reduction and regeneration, simultaneous flow through all six beds is used. The reproducibility of the system was first assessed with whole biomass pyrolysis along with pyrolysis of calibration standards. Initial work on this system evaluated two FCC catalysts, equilibrium catalyst (E-cat), and a proprietary catalyst from Johnson Matthey specifically design for co-processing of bio-oil with vacuum gas oil (VGO). This work used model compounds and VGO which illuminated differences in products produced by the catalysts.

biomass↗
DOE OSTI2022

Multiplication of freestanding III-V semiconductor membranes from a single wafer by alternating growth with amorphous graphene interlayer

Freestanding single-crystalline III-V compound semiconductors are important building blocks for functional devices due to their high electron mobilities, a wide range of bandgaps, and excellent optoelectronic properties. Despite efforts to produce such membranes by detaching epitaxial layers from donor wafers, current methods suffer from either slow processes or poor material quality. Here, we demonstrate a technique to grow and harvest multiple epitaxial membranes with extremely high throughput at the wafer scale. For this, a process to directly grow amorphous graphene on III-V substrates in metal-organic chemical vapor deposition reactors is developed, which enables an advanced remote epitaxy scheme comprised of multiple alternating layers of amorphous graphene and III-V epitaxial layers that can be formed by a single epitaxy run. Each epilayer in the multi-stack structure is then harvested by layer-by-layer peeling, producing multiple freestanding membranes with unprecedented throughput from a single wafer. Because amorphous graphene provides a weak van der Waals interface that allows peeling at the interface without damaging the epilayer or the substrate, wafers can be reused for subsequent membrane production. Therefore, this work represents a meaningful step toward high-throughput and low-cost production of single-crystal membranes that can be heterointegrated.

Han, Ne Myo↗
DOE OSTI2022

Development of a Laser Ultrasonics-based Approach for Rapid Screening of High Entropy Alloys

This project utilized a laser ultrasonic technique to systematically study temperature-induced evolution of material properties in a set of interrelated binary alloys and a high entropy alloy (HEA) fabricated using arc-melting and spark plasma sintering processes. This technique involved the use of a nanosecond duration, high-intensity pulsed laser to thermo-elastically generate ultrasonic waves that propagate in the bulk of the metal alloy. Sub-nanometer-scale displacements associated with the propagating bulk ultrasonic waves were detected along the epicenter on the opposite surface of the sample using a 1 GHz bandwidth photorefractive interferometer. Phase transformations and microstructural changes were inferred from the temperature-dependent trends in the bulk acoustic velocities and features in the ultrasonic epicentral waveforms measured in the binary alloys. These inferences were then correlated with electron/optical microscopy observations and predictions using calculations of phase diagrams (CALPHAD). The results showed that the laser-generated ultrasonic pulses were strongly influenced by changes in material microstructure and could accurately track thermally driven phase transformations and detect the presence of microscale heterogeneities (grain boundaries, dendritic structures, etc.) in the set of binary alloy samples. The measurement approach was then applied to a quinary HEA sample for estimating phase transition temperature and determining microstructural heterogeneity. The rapid, non-contact and non-destructive ultrasonic testing approach demonstrated here is amenable to high throughput combinatorial investigations that can be applied to graded composition HEAs produced using advanced manufacturing methods. When paired with atomistic simulations and CALPHAD modeling, this approach can overcome the bottlenecks faced by current material characterization methods in efficiently screening the vast discovery space of HEAs that spans over a hundred million unique quinary alloy compositions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A laser ultrasonics-based approach for rapid screening of high entropy alloys

This project demonstrates a laser ultrasonics-based characterization methodology for rapid metallic materials design and discovery via in situ determination of phases, microstructures and elastic properties with respect to temperature. Ultrasonic waves are strongly affected by material microstructure, and therefore serve as a facile means to probe elastic properties, phase content and their size distributions and volume fractions. In this study, a laser ultrasonic technique is used to systematically study the evolution of properties in a set of interrelated simple binary alloys and high entropy alloys (HEA). Phase transformations and microstructural changes inferred from the ultrasonic signals will be correlated with electron microscopy data and predictions using calculations of phase diagrams (CALPHAD). The non-contact and non-destructive ultrasonic testing approach developed in this study could overcome several limitations associated with current material characterization methods for materials discovery. It is expected that laser ultrasonics-based methodology developed in this work will be utilized to evaluate novel graded composition HEAs currently being developed at the Idaho National Laboratory (INL) using advanced manufacturing methods based on direct energy deposition and spark plasma sintering processes, and contribute to accelerate the discovery of HEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A laser ultrasonics-based approach for rapid screening of high entropy alloys

The primary objective of this seed project is to develop a laser ultrasonics-based characterization methodology for rapid metallic materials design and discovery via in situ determination of phases, microstructures and elastic properties with respect to temperature. Ultrasonic waves are strongly affected by material microstructure, and therefore, serve as a facile means to probe elastic properties, phase content and their size distributions and volume fractions. In this study, a laser ultrasonic technique will be used to systematically study the evolution of properties in a set of interrelated simple binary alloys and high entropy alloys (HEA). Phase transformations and microstructural changes inferred from the ultrasonic signals will be correlated with electron microscopy data and predictions using calculations of phase diagrams (CALPHAD). The non-contact and non-destructive ultrasonic testing approach developed in this study could overcome several limitations associated with current material characterization methods for materials discovery. It is expected that the products of the proposed work will be utilized to evaluate novel graded composition HEAs currently being developed at the Idaho National Laboratory (INL) using advanced manufacturing methods based on direct energy deposition and spark plasma sintering processes, and contribute to accelerate the discovery of HEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advances in Molecular Beam Epitaxy Growth of Ultra-Wide Bandgap Ga2O3 Based Alloys

Gallium oxide (Ga2O3) is an emerging ultra-wide bandgap semiconductor material that has attracted attention for its potential to outperform existing SiC and GaN based devices operating at high breakdown voltages and high temperature. Isovalent alloying of In and Al in Ga2O3 provides the ability to engineer bandgap energy and strain of the material. Alloying with Al increases the bandgap energy and the theoretically achievable Baliga's figure of merit, a key measure of a material's ultimate performance limits for high power switching devices. Alloying with In introduces compressive strain and can be used to counteract the tensile strain of Al incorporation. The resulting (AlxGa1-x-yIny)2O3 alloy can be lattice-matched to commercially available Ga2O3 wafers and has a tunable bandgap energy greater than that of Ga2O3, 4.76 eV. Such lattice-matched material can be grown arbitrarily thick without the detrimental effects of elastic strain and relaxation, making it suitable for high voltage diodes and transistors. However, efforts to synthesize isovalent alloys are complicated by their tendency to phase separate into corundum Al2O3 or bixbyite In2O3. Literature reports of the quaternary (AlxGa1-x-yIny)2O3 are limited to <1% unintentional indium incorporation in In-catalyzed (AlxGa1-x)2O3. The primary limitation to quaternary growth is the limited incorporation of indium at elevated growth temperatures. This limited incorporation is due to both the volatility of indium oxide and Al and Ga cation exchange reactions which replace indium in In2O3. We report on the development of a novel high-throughput molecular beam epitaxy (MBE) technique to screen the growth conditions for the ternary alloy (InyGa1-y)2O3, and the application of these findings to the first successful synthesis of phase pure monoclinic (AlxGa1-x-yIny)2O3 by MBE. By leveraging the unique sub-oxide chemistry of Ga2O3 and in-situ monitoring of crystal properties by reflection high-energy electron diffraction (RHEED), a cyclical growth and etch-back method is developed and applied to rapidly characterize the (InyGa1-y)2O3 growth space. This cyclical method provides approximately 10x increase in experimental throughput and up to 46x improvement in Ga2O3 substrate utilization. Appropriate growth conditions for monoclinic (InyGa1- y)2O3 are identified by machine learning analysis of RHEED patterns and targeted growths are characterized ex-situ to confirm improved In incorporation. These growth conditions are then combined with established (AlxGa1-x)2O3 growth conditions to grow quaternary (AlxGa1-x-yIny)2O3 with Al mole fractions ranging from 1.4% - 24.4% and In mole fractions ranging from 3.1% to 15.5%. The chemical and optical properties of the alloys are investigated by XRD, XPS, and spectroscopic ellipsometry. A lattice-matched (AlxGa1-x-yIny)2O3 alloy is examined by 4D-STEM and the chemical and physical uniformity of Al and In incorporation are discussed.

alloy↗
DOE OSTI2024

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗