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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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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A High-Throughput MEMS-Based Differential Scanning Calorimeter for Direct Thermal Characterization of Antibodies

Calorimeters, which can be used for rapid thermal characterization of biomolecules, are getting intense attention in drug development. This paper presents a novel MEMS-based differential scanning calorimeter (DSC) for direct thermal characterization of protein samples. The DSC consisted of a pair of temperature sensors made by vanadium oxide (VOx) film with a temperature coefficient of resistivity of –0.025/K at 300 K, a microfluidic device with high thermal insulation (2.8 K/mW), and a Peltier heater for linear temperature scanning. The DSC exhibited high sensitivity (6.1 µV/µW), low noise (0.4 µW), high scanning rate (45 K/min), and low sample consumption volume (0.63 µL). The MEMS DSC was verified by measuring the temperature-induced denaturation of lysozyme at different pH, and then used to study the thermal stability of a monoclonal antibody (mAb), an antigen-binding fragment (Fab), and a dual variable domain immunoglobulin (DVD-Ig) at pH = 6. The results showed that lysozyme is a stable protein in the pH range of 4.0–8.0. The protein stability study revealed that the transition temperatures of the intact Fab fragment, mAb, and DVD proteins were comparable with conformational stability results obtained using conventional commercial DSC. These studies demonstrated that the MEMS DSC is an effective tool for directly understanding the thermal stability of antibodies in a high-throughput and low-cost manner compared to conventional calorimeters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Material Discovery and High Throughput Exploration of Ru Based Catalysts for Low Temperature Ammonia Decomposition

High throughput experimentation has the capability to generate massive, multidimensional datasets, allowing for the discovery of novel catalytic materials. Here, we show the synthesis and catalytic screening of over 100 unique Ru-Metal-K based bimetallic catalysts for low temperature ammonia decomposition, with a Ru loading between 1–3 wt% Ru and a fixed K loading of 12 wt% K, supported on γ-Al2O3. Bimetallic catalysts containing Sc, Sr, Hf, Y, Mg, Zr, Ta, or Ca in addition to Ru were found to have excellent ammonia decomposition activity when compared to state-of-the-art catalysts in literature. Furthermore, the Ru content could be reduced to 1 wt% Ru, a factor of four decrease, with the addition of Sr, Y, Zr, or Hf, where these secondary metals have not been previously explored for ammonia decomposition. The bimetallic interactions between Ru and the secondary metal, specifically RuSrK and RuFeK, were investigated in detail to elucidate the reaction kinetics and surface properties of both high and low performing catalysts. The RuSrK catalyst had a turnover frequency of 1.78 s−1, while RuFeK had a turnover frequency of only 0.28 s−1 under identical operating conditions. Based on their apparent activation energies and number of surface sites, the RuSrK had a factor of two lower activation energy than the RuFeK, while also possessing an equivalent number of surface sites, which suggests that the Sr promotes ammonia decomposition in the presence of Ru by modifying the active sites of Ru.

McCullough, Katherine (ORCID:0000000197525740)↗
DOE OSTI2021

PlasmidMaker: a Versatile, Automated, and High Throughput End-to-End Platform for Plasmid Construction

Plasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high-throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute ( Pf Ago)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate ~100 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments within 3 days. PlasmidMaker should greatly expand the potential of synthetic biology.

Enghiad, Behnam↗
DOE OSTI2026

Robust and High Throughput Tools for Characterization of Wet Cellulosic Biogas Feedstocks and Co-Products: Cooperative Research and Development Final Report, CRADA Number CRD-21-21472

This project will develop standardized analytical procedures to support understanding of the chemical composition of wet manure feedstocks, and the digestate formed following anaerobic digestion of organic waste to produce renewable natural gas (RNG). The developed methods will be validated to improve component mass closure, eliminate ambiguities around key constituents, and document the precision and accuracy of the methods. Following validation of the analytical laboratory methods for characterization of wet organic waste feedstocks and digestate, NLR will use the same samples to build and validate near-infrared (NIR) models for high throughput analysis, potentially deployable at commercial facilities, enabling increased production of low carbon intensity RNG from wet organic wastes.

09 BIOMASS FUELS↗
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 OSTI2021

The Tracking Machine Learning challenge : Throughput phase

This paper reports on the second "Throughput" phase of the Tracking Machine Learning (TrackML) challenge on the Codalab platform. As in the first "Accuracy" phase, the participants had to solve a difficult experimental problem linked to tracking accurately the trajectory of particles as e.g. created at the Large Hadron Collider (LHC): given O($10^5$) points, the participants had to connect them into O($10^4$) individual groups that represent the particle trajectories which are approximated helical. While in the first phase only the accuracy mattered, the goal of this second phase was a compromise between the accuracy and the speed of inference. Both were measured on the Codalab platform where the participants had to upload their software. The best three participants had solutions with good accuracy and speed an order of magnitude faster than the state of the art when the challenge was designed. Although the core algorithms were less diverse than in the first phase, a diversity of techniques have been used and are described in this paper. The performance of the algorithms are analysed in depth and lessons derived.

Amrouche, Sabrina↗
DOE OSTI2021

Apparatus for high-throughput sequential tensile testing and methods thereof

The present invention relates, in part, to an apparatus configured to test a plurality of test samples within a sample cartridge. Such an apparatus can facilitate high-throughput tensile testing of such test samples. Also described herein are methods for using such an apparatus and for testing such test samples.

Boyce, Brad↗
DOE OSTI2024

In-line, High-Throughput Quality Monitoring for Fuel Cell and Electrolyzer Components Based on Transmission and Reflection Imaging

During the manufacturing of fuel cell and electrolyzer membranes and membrane electrode assemblies (MEAs), real-time, in-line, high-throughput optical-based quality monitoring methods are essential for detecting defects and monitoring thickness variations, thus improving the performance and increasing the durability of fuel cell and electrolyzer in the hydrogen industry. For the MEAs with very opaque coatings, optical transmission-based imaging has been developed and applied in the Roll-to-Roll system using a flashlight and a high-sensitivity CCD camera. We observed high signal-to-noise ratio images while the Roll-to-Roll system ran at 5 ft/min. The entire sample image could quickly be recovered from the discrete frames using customized Python codes for automatic frame cropping and stitching. We detected significant non-uniformities in our experimental MEAs specimen. For fuel cell and low-temperature electrolysis (LTE) transparent membranes, we used optical reflectance hyperspectral imaging with interference fringe-based thickness mapping. We set up a hyperspectral camera to measure various rolls of commercial membranes. The measurement results are analyzed to find the thickness distribution of each roll and to check for defects. Transmission and reflection imaging-based quality monitoring techniques demonstrated in this project can be widely used in the mass production environment to improve the production yield and performance of hydrogen devices.

DIRECT ENERGY CONVERSION,ENGINEERING↗
DOE OSTI2024

High-Throughput Custom Monitoring for the Mu2e TDAQ System

In this project we are studying the application of programmable network hardware to provide a custom monitoring capability for the Mu2e Trigger and Data Acquisition System (TDAQ) system. The goal of the Mu2e experiment is to search for a charged-lepton flavor violating processes where a negative muon converts into an electron in the field of an aluminum nucleus. This experiment is intended to improve by four orders of magnitude the search sensitivity reached so far. We have a working prototype of a system that provides high-throughput, custom monitoring for the Mu2e TDAQ system. The custom Mu2e network packet header format is parsed as it crosses the network switch. Parsing extracts bits that convey information about error states at read-out controllers (ROCs). This information is periodically relayed to the switch controller, which in turn alerts experiment operators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Revisit the VEC rule in high entropy alloys (HEAs) with high-throughput CALPHAD approach and its applications for material design-A case study with Al–Co–Cr–Fe–Ni system

Valence electron concentration (VEC) was treated as a useful parameter to predict solid solution phases, and the VEC rule was proposed for high entropy alloys (HEAs). However, this empirical rule has its limitations, which restricts its applications for phase predictions in HEAs. In this paper, we revisited the empirical VEC rule with the HT-CALPHAD approach in the Al–Co–Cr–Fe–Ni system. Our investigation showed that more than 90% compositions are observed to have BCC structures when 5.7≤VEC≤7.2 and we got 100% FCC structures when VEC≥8.4. Meanwhile, we proposed a data screening procedure to classify and discover the new HEAs. The concepts of average density (ρHEA), highest BCC/FCC temperatures ($T$ $^{max}_{BCC/FCC}$), and temperature ranges (ΔT BCC/FCC ) were introduced as useful data screening criteria to down-screen the candidate alloy compositions for specific engineering applications. In the current work, we have identified three HEA categories (refractory BCC HEAs, light-weight BCC HEA and refractory FCC HEAs) in the Al–Co–Cr–Fe–Ni system and proposed the best candidate in each group. Here, our investigation showed the HEA non-equiatomic composition space provides ample opportunities for the discovery of the next-generation HEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity in silico at scale. However, as polycrystalline and nanoscale catalysts increasingly dominate industry, computational screening tools must be adapted to these uses. In this work, we demonstrate the effectiveness of computational adsorption energy screening in nanocatalysis by comparing a multisite adsorption energy prediction workflow against a large experimental dataset of hydrogen evolution activities over bimetallic nanoparticles. Comparing 16 million hydrogen adsorption energy predictions with the hydrogen evolution activity of 5300 experiments across 84 monometallic and bimetallic systems, we discover that favorable adsorption energies are a necessary condition for experimental activity, but other factors often determine trends in practice. Finally, about half of the bimetallic search space can be excluded from experimental screens using hydrogen adsorption predictions, but this method may become significantly more powerful when combined with other screening tools.

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

Rapid and high-throughput determination of sorghum ( Sorghum bicolor ) biomass composition using near infrared spectroscopy and chemometrics

Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models’ effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.

09 BIOMASS FUELS↗
DOE OSTI2022

siRNA high throughput screening identifies regulators of chloropicrin and hydrogen fluoride injury in human corneal epithelial cell models

Corneal injuries induced by various toxicants result in similar clinical presentations such as corneal opacity and neovascularization. Many studies suggest that several weeks post-exposure a convergence of the molecular mechanisms drives these progressive pathologies. However, chemical agents vary in toxicological properties, and early molecular responses are anticipated to be somewhat dissimilar for different toxicants. In this report we chose 3120 targets from the Dharmacon Human Druggable genome to screen for chloropicrin (CP) and hydrogen fluoride (HF) corneal injury as we hypothesized that targets identified in vitro may be effective as therapeutic targets in future studies. Human immortalized corneal epithelial cells (SV40-HCEC) were used for screening. Cell viability and IL-8 were analyzed to down-select hits into validation studies, where multiplex cytokine analysis and high content analysis were performed to understand toxicant effect and target function. Some endpoints were also evaluated in a second human immortalized corneal epithelial cell line, TCEpi. Over 20 targets entered validation studies for CP and HF; of these, only three targets were shared: NR3C1, RELA, and KMT5A. These findings suggest that early molecular responses to different toxicants may be somewhat distinctive and present dissimilar targets for possible early intervention.

60 APPLIED LIFE SCIENCES↗