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Results for “High throughput”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

High Throughput, High Yield Fabrication of High Quantum Efficiency Back-Illuminated Photon Counting, Far UV, UV, and Visible Detector Arrays

In this paper we discuss the high throughput end-to-end post fabrication processing of high performance delta-doped and superlattice-doped silicon imagers for UV, visible, and NIR applications. As an example, we present our results on far ultraviolet and ultraviolet quantum efficiency (QE) in a photon counting, detector array. We have improved the QE by nearly an order of magnitude over microchannel plates (MCPs) that are the state-of-the-art UV detectors for many NASA space missions as well as defense applications. These achievements are made possible by precision interface band engineering of Molecular Beam Epitaxy (MBE) and Atomic Layer Deposition (ALD).

spectroscopy↗
NASA NTRS2014

Delta-Doping at Wafer Level for High Throughput, High Yield Fabrication of Silicon Imaging Arrays

Systems and methods for producing high quantum efficiency silicon devices. A silicon MBE has a preparation chamber that provides for cleaning silicon surfaces using an oxygen plasma to remove impurities and a gaseous (dry) NH3 + NF3 room temperature oxide removal process that leaves the silicon surface hydrogen terminated. Silicon wafers up to 8 inches in diameter have devices that can be fabricated using the cleaning procedures and MBE processing, including delta doping.

Hoenk, Michael E.↗
NASA NTRS2014

Space Link Extension Protocol Emulation for High-Throughput, High-Latency Network Connections

New space missions require higher data rates and new protocols to meet these requirements. These high data rate space communication links push the limitations of not only the space communication links, but of the ground communication networks and protocols which forward user data to remote ground stations (GS) for transmission. The Consultative Committee for Space Data Systems, (CCSDS) Space Link Extension (SLE) standard protocol is one protocol that has been proposed for use by the NASA Space Network (SN) Ground Segment Sustainment (SGSS) program. New protocol implementations must be carefully tested to ensure that they provide the required functionality, especially because of the remote nature of spacecraft. The SLE protocol standard has been tested in the NASA Glenn Research Center's SCENIC Emulation Lab in order to observe its operation under realistic network delay conditions. More specifically, the delay between then NASA Integrated Services Network (NISN) and spacecraft has been emulated. The round trip time (RTT) delay for the continental NISN network has been shown to be up to 120ms; as such the SLE protocol was tested with network delays ranging from 0ms to 200ms. Both a base network condition and an SLE connection were tested with these RTT delays, and the reaction of both network tests to the delay conditions were recorded. Throughput for both of these links was set at 1.2Gbps. The results will show that, in the presence of realistic network delay, the SLE link throughput is significantly reduced while the base network throughput however remained at the 1.2Gbps specification. The decrease in SLE throughput has been attributed to the implementation's use of blocking calls. The decrease in throughput is not acceptable for high data rate links, as the link requires constant data a flow in order for spacecraft and ground radios to stay synchronized, unless significant data is queued a the ground station. In cases where queuing the data is not an option, such as during real time transmissions, the SLE implementation cannot support high data rate communication.

Computer Networking↗
DOE OSTI2024

HTESP (High-throughput electronic structure package): A package for high-throughput ab initio calculations

High-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron–phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different ab initio calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, in this work, we introduce a software package named “HTESP” (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Design and Validation of a High-Throughput Reductive Catalytic Fractionation Method

Reductive catalytic fractionation (RCF) is a promising method to extract and depolymerize lignin from biomass, and bench-scale studies have enabled considerable progress in the past decade. RCF experiments are typically conducted in pressurized batch reactors with volumes ranging between 50 and 1000 mL, limiting the throughput of these experiments to one to six reactions per day for an individual researcher. Here, we report a high-throughput RCF (HTP-RCF) method in which batch RCF reactions are conducted in 1 mL wells machined directly into Hastelloy reactor plates. The plate reactors can seal high pressures produced by organic solvents by vertically stacking multiple reactor plates, leading to a compact and modular system capable of performing 240 reactions per experiment. Using this setup, we screened solvent mixtures and catalyst loadings for hydrogen-free RCF using 50 mg poplar and 0.5 mL reaction solvent. The system of 1:1 isopropanol/methanol showed optimal monomer yields and selectivity to 4-propyl substituted monomers, and validation reactions using 75 mL batch reactors produced identical monomer yields. To accommodate the low material loadings, we then developed a workup procedure for parallel filtration, washing, and drying of samples and a 1H nuclear magnetic resonance spectroscopy method to measure the RCF oil yield without performing liquid-liquid extraction. As a demonstration of this experimental pipeline, 50 unique switchgrass samples were screened in RCF reactions in the HTP-RCF system, revealing a wide range of monomer yields (21-36%), S/G ratios (0.41-0.93), and oil yields (40-75%). These results were successfully validated by repeating RCF reactions in 75 mL batch reactors for a subset of samples. We anticipate that this approach can be used to rapidly screen substrates, catalysts, and reaction conditions in high-pressure batch reactions with higher throughput than standard batch reactors.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2025

High-throughput synthesis of high-entropy alloys via parallelized electric field assisted sintering

Materials discovery and design is an expensive and time-consuming process, though necessary to advance many engineering fields. In this work, a novel tooling design is utilized in conjunction with electric field assisted sintering (EFAS) to effectively create a new high-throughput synthesis technique: parallelized EFAS. Through this technique, a wide range of material compositions and geometries can be synthesized in parallel as isolated samples or as part of contiguous arrays. Multiple tooling designs are explored to examine both the flexibility and limitations of the technique. A series of increasing complex alloys is produced simultaneously using in situ alloying, beginning with pure Ni and adding equimolar constituents up to the septenary high-entropy alloy AlCoCrCuFeMnNi. Microstructural characterization reveals each sample is effectively fully dense and chemically homogenous while exhibiting phases in agreement with CALPHAD predictions. Scalability of parallelized EFAS is then experimentally demonstrated and the implications for materials discovery and automation are discussed.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

High throughput screening of high entropy spinel electrolytes for multivalent batteries

High-entropy (HE) design emerged as a promising path for discovering multivalent superionic conductors. This work provides a computational exploration of the synthesizability of HE spinel-based electrolytes among the typical chemical space. Design principles have been established, while experimental synthesis has supported the stability rules predicted by computational data.

25 ENERGY STORAGE↗
DOE OSTI2025

High-throughput computational framework for high-order anharmonic thermal transport in cubic and tetragonal crystals

Accurate first-principles prediction of lattice thermal conductivity (κL) remains challenging in identifying materials with extreme thermal behavior. While the harmonic approximation with three-phonon scattering (HA + 3ph) is now routine, reliable κL prediction often requires higher-order anharmonic effects, including self-consistent phonon renormalization, three- and four-phonon scattering, and off-diagonal heat flux (SCPH + 3, 4ph + OD). We present a state-of-the-art high-throughput workflow that unifies these effects and apply it to 773 cubic and tetragonal crystals spanning diverse chemistries and structures. From 562 dynamically stable compounds, we assess the hierarchical impacts of higher-order anharmonicity. For around 60% of materials, HA + 3ph predictions closely match those from SCPH + 3, 4ph + OD. SCPH generally increases κ L , by over 8 times in extreme cases, whereas four-phonon scattering universally suppresses κ L , sometimes to 15% of the HA + 3ph value. Off-diagonal contributions are negligible in high-κ L systems but can rival diagonal terms in highly anharmonic low-κ L compounds. We highlight four case studies, Rb 2 TlAlH 6 , Cu 3 VSe 4 , CuBr, and KTlCl 4 , that exhibit distinct extreme behaviors. This work delivers not only a robust workflow for high-fidelity κL dataset but also a quantitative framework to determine when higher-order effects are essential. The hierarchy of κ L results, from the HA + 3ph to SCPH + 3, 4ph + OD level, offers a scalable, interpretable route to discovering next-generation extreme thermal materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Using scalable computer vision to automate high-throughput semiconductor characterization

Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Science & Technology - Other Topics↗
DOE OSTI2025

Orthorhombic Structures as Inorganic Halide Perovskite Models for High-Throughput DFT Investigations

Although high-symmetry Pm-3m space group cubic models are computationally efficient for high-throughput density functional theory (DFT) calculations of inorganic ternary (ABX 3 ) halide perovskites (HPs), they frequently predict band gaps (E g ) that disagree with experiment. Conversely, while low-symmetry cubic polymorphous networks (PN) comprised of 160 to 320 atoms incur significantly greater computational cost, they predict E g ’s that are more closely aligned with experiment. Here, in this study, we compare the DFT total energies and E g ’s predicted by four high-symmetry structure models (Pnma orthorhombic, R3m trigonal, P4/mbm tetragonal, and Pm-3m cubic) to cubic PNs for 5 experimentally characterized ternary HPs and find that the orthorhombic model computes Eg’s with the smallest MAD of 0.23 eV relative to the PNs. Pair distribution functions and DFT-computed total energies show that octahedral tilting, which is present in the 20-atom orthorhombic and 160-atom cubic PN models but not in the 5-atom cubic models, stabilizes all 5 compositions in our benchmarking set. We also find that imposing PN constraints when generating and optimizing these orthorhombic structures by fixing the unit cell lattice vectors and displacing the atoms prior to ionic relaxation with DFT, which we call the orthorhombic surrogate model (OSM), lowers the MAD of E g predictions to 0.09 eV. Our OSM predicts the PN band gaps of an additional 95 theoretical inorganic ternary HPs with MAD of 0.08 eV, supporting its usage in high-throughput DFT investigations to closely estimate PN band gaps with much less computational expense.

14 SOLAR ENERGY↗
DOE OSTI2026

High-throughput small-angle X-ray scattering reveals effective structure factor transitions linked to high-concentration antibody viscosity

High-concentration monoclonal antibody (mAb) formulations are often constrained by elevated viscosity, largely driven by protein–protein interactions, which complicates manufacturing and limits subcutaneous delivery. Early viscosity risk assessment is essential during discovery, yet traditional measurements require large sample volumes, and lack high-throughput capability. Here, we develop a high-throughput small-angle X-ray scattering (SAXS) protocol to detect mAb self-association at dilute concentrations, enabling early predictive insights into high-concentration viscosity. Synchrotron SAXS measurements were conducted for 21 mAbs formulated in a histidine buffer at pH 6.0. An initial subset of 10 mAbs analyzed across 1–150 mg/mL revealed that effective structure factor transitions in the low-q region, indicative of interparticle interactions, consistently emerged below 25 mg/mL. Subsequently, 11 additional mAbs were analyzed at 1–25 mg/mL using automated liquid handling and flow cells to enable high-throughput screening. High-viscosity mAbs exhibited detectable low-q upturns at concentrations ≤10 mg/mL, whereas low-viscosity mAbs showed downturns. A classification criterion based on effective structure factor transitions accurately classified all high- and low-viscosity mAbs at 150 mg/mL, offering a scalable, sample-efficient alternative to conventional methods. These results extend recent findings on the concentration-dependent sensitivity of SAXS to short-range attractions, demonstrating that they can emerge at lower concentrations than previously reported. This study presents the most comprehensive and diverse SAXS dataset for mAbs reported to date within a single formulation, providing a valuable resource for developing and validating coarse-grained models that can more accurately capture intermolecular interactions governing high-concentration solution behavior, thereby enabling rational antibody engineering and improved developability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-throughput reaction discovery for Cs–Pb–Br nanocrystal synthesis

High-throughput reaction discovery is necessary to understand complex reaction spaces for inorganic nanocrystal synthesis. Here, we implemented a high-throughput continuous flow millifluidic reactor to perform reaction discovery for Cs–Pb–Br nanocrystal synthesis using a ligand assisted reprecipitation (LARP)-type approach. 3D-printed flow resistors enable the screening of up to 16 different mixing ratios within a single 90 s run, allowing for >270 different precursor concentration ratios to be quickly tested to explore the phase space that results in CsPbBr 3 , Cs 4 PbBr 6 , a biphasic mixture, or no product. To construct a full phase map from these high-throughput experiments, a neural network was trained and validated to predict the product composition (~500 000 points in precursor concentration space). The phase map predicts product composition/phase as a function of Cs–Pb–Br feed ratio. As a result, this approach demonstrates how high-throughput flow chemistry can be used in tandem with machine learning to rapidly explore nanocrystal reaction spaces in flow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

An Innovative High Throughput Genome Releaser for Rapid and Efficient PCR Screening

High-throughput PCR screening is vital in synthetic biology and metabolic engineering as it allows researchers to rapidly analyze and detect numerous targeted genetic mutation in the genome. Current challenges for high-throughput PCR screening in synthetic biology include efficiently preparing genomic DNA, optimizing protocols for diverse sample types, managing contamination risks, and effectively analyzing the large volumes of data generated while ensuring consistent and accurate results. In this study, we present the development of a High Throughput Genome Releaser (HTGR), an innovative device addressing common challenges in screening PCR. This genome DNA releaser is designed based on a squash method for rapid, cost-effective, and efficient DNA release, optimized for subsequent PCR reactions. After experimenting with various synthetic materials, we selected a plastic that closely replicates the smooth surface and compression properties of microscope slides, ensuring reliable performance. We engineered a device featuring a 96-Well Plate and a shear applicator, operable both manually and automatically, and compatible with standard liquid-handling robot platform. This compatibility enhances ease of use in high-throughput PCR workflows. Additionally, we developed software to support its automatic functions. Our results demonstrated that the specially engineered 96-Well Plate and HTGR can effectively squash fungal spores , which release enough genome DNA for PCR screening. The genome releaser facilitates the preparation of PCR-amplifiable genomic DNA substrate from 96 samples within minutes, eliminates the need for extraction buffers, and is adaptable to a wide range of microorganisms and cells, which could significantly advance biomanufacturing processes.

Yuan, Guoliang [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2026

High-throughput oxidation screening and down-selection of refractory high entropy alloys in the Al-Cr-Mo-Nb-Ta-Ti system

Rapid experimentation and characterization are ever-present needs in the discovery of high entropy alloys. High entropy alloy systems are difficult to survey with systematic composition sweeps using traditional synthesis methods. The number of distinct compositions in even a four-element system is experimentally intractable. Exploration of these, and higher-element systems, necessitates thermodynamic prediction coupled with an automated sample creation method and a rapid screening methodology to effectively down-select alloys with targeted properties. As a result, a high-throughput method for evaluating the oxidation performance of refractory high entropy alloys was developed and tested. The six-element system of aluminum, chromium, molybdenum, niobium, tantalum, and titanium was evaluated for single phase stability and short-duration oxidation resistance. Target compositions were initially determined via thermodynamic predictions of single-phase stability across a wide temperature range. A twenty-five-sample build plate was produced using directed energy deposition additive manufacturing. After fabrication, the twenty-five 1 cm 3 samples were heat treated and characterized for composition and phase identification. The build plate was exposed to a high temperature oxidizing environment at 1000 °C for three hours. After oxidation, the composition, morphology, and chemistry of the oxides formed were characterized. Of the twenty-five samples produced, nine exhibited a favorable oxidation response, from which a single-phase BCC alloy at a composition of Al 13 Cr 7 Mo 19 Nb 18 Ta 26 Ti 17 was identified as the alloy with the most protective oxidation coating with a thin, adherent oxide scale. Finally, the complete experimental down-selection—from machine setup to final alloy identification—required approximately 45 labor hours, demonstrating a rapid validation for alloy discovery.

Additive manufacturing↗
DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗
DOE OSTI2025

Characterization of Performance Degradation Mechanisms in Low-Cost High Throughput DI-O3 Layer for Passivated Contact Silicon Solar Cells

Characterization and mitigating performance limiting defects in Silicon (Si) PV is one of key areas to be addressed to improve PV hardware costs and energy yield in order to lower the levelized cost of energy (LCOE) of installed PV cost to $0.02/kWh. As Si PV cells efficiencies have surpassed 22% and approaching 23%, the recombination at the metal contacts have become the focus point to be addressed. Passivated contact technologies—having a heterojunction with a band-gap larger than silicon between the metal and silicon—have emerged as a great potential for future highand ultrahigh-efficiency solar cells, as it concurrently reduces recombination and increases carrier selectivity, by incorporating thin films within the contact structure. Passivated contact Si solar cell technologies use a wide variety of tunnel layers—playing a crucial role to passivate metal contacts and tunnel charge carriers—including stoichiometric silicon oxide (SiO 2 ) grown by thermal oxidation and Low-Pressure Chemical Vapor Deposition (LPCVD) technique and silicon oxide (SiO x ) by hot nitric acid. However, thorough investigations on understanding the failure and performance degradation mechanisms associated with tunnel layers are still limited to date. Unlocking those degradation characteristics in crucial tunnel layers could improve the reliability and energy yield of passivated contact Si solar cells. Besides, the technique of growing aforementioned tunneling layers are low throughput, and requires high temperature processes and/or a vacuum environment. In this project, we investigated the performance degradation mechanisms of a low-cost high-throughput ozonated oxide (DI-O 3 ) tunnel layer for the passivated contact Si solar cells.

14 SOLAR ENERGY↗
DOE OSTI2026

High-throughput validation of phase formability and simulation accuracy of Cantor alloys

High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ~1000 °C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement between computation and experiment exists, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement.

36 - MATERIALS SCIENCE↗