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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Stochastic room temperature creep of 316 L stainless steel

The creep behavior of 316 L stainless steel at room temperature was evaluated as a function of time and applied stress using a new high-throughput approach. Several common creep models were evaluated against the observations, leading to deeper analysis of a stress-dependent modified logarithmic creep model. Within this model, multiple sources of uncertainty were compared. Aleatoric stochastic variation between samples under nominally identical conditions was identified as the primary contributor to uncertainty in creep response. Under any particular set of conditions, the sample-to-sample variability in creep strain was as high as a factor of two, highlighting the engineering importance of characterizing large statistical datasets. The model's extrapolation capabilities were assessed by comparing predictions derived from calibration on partial, shorter-duration subsets of the data. In conclusion, these findings underscore the importance of accounting for stochastic effects in predictive modeling of aging phenomena.

High-throughput↗
DOE OSTI2021

Metagenomic strategies identify diverse integron–integrase and antibiotic resistance genes in the Antarctic environment

The objective of this study is to identify and analyze integrons and antibiotic resistance genes (ARGs) in samples collected from diverse sites in terrestrial Antarctica. Integrons were studied using two independent methods. One involved the construction and analysis of intI gene amplicon libraries. In addition, we sequenced 17 metagenomes of microbial mats and soil by high-throughput sequencing and analyzed these data using the IntegronFinder program. As expected, the metagenomic analysis allowed for the identification of novel predicted intI integrases and gene cassettes (GCs), which mostly encode unknown functions. However, some intI genes are similar to sequences previously identified by amplicon library analysis in soil samples collected from non-Antarctic sites. ARGs were analyzed in the metagenomes using ABRIcate with CARD database and verified if these genes could be classified as GCs by IntegronFinder. We identified 53 ARGs in 15 metagenomes, but only four were classified as GCs, one in MTG12 metagenome (Continental Antarctica), encoding an aminoglycoside-modifying enzyme (AAC(6´)acetyltransferase) and the other three in CS1 metagenome (Maritime Antarctica). One of these genes encodes a class D β-lactamase (blaOXA-205) and the other two are located in the same contig. One is part of a gene encoding the first 76 amino acids of aminoglycoside adenyltransferase (aadA6), and the other is a qacG2 gene.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Laser Ablation-Capillary Absorption Spectroscopy: A novel approach for high throughput and increased spatial resolution measurements of δ 13 C in plant-soil systems

Spatial and temporal heterogeneity of nutrient exchange within the rhizosphere is a topic of increasing interest, although challenging to study due to limits in existing analytical capabilities. Here, we developed and demonstrated a new approach applying laser ablation sample introduction to capillary absorption spectroscopy (LA-CAS) to characterize carbon isotopic distribution within plant tissues, rhizosphere, and soil. We exposed switchgrass plants to 13 CO 2 to allow tracing of 13 C-labelled photosynthates within plant biomass and into the associated soil. The LA-CAS methods we describe leverage continuous measurements of a sample stream derived from laser ablation line scans (10-25 µm in width) over a sample surface which enables the user to produce an isotope map with a) higher data density or b) over larger spatial areas versus previously existing techniques. This versatility of LA-CAS is assessed through testing of a range of laser parameters (spot size, scan rates) on various materials (soil, plant biomass/tissues, and rhizosphere). Here, we demonstrate the ability of LA-CAS to provide near instantaneous $δ$ 13 C measurements over isotopically distinct surfaces to enable high spatially resolved mapping of 13 C-labelled material within the rhizosphere. Applying LA-CAS analysis to plant biomass, we observed higher $δ$ 13 C values concentrated within phloem structures, consistent with localized photosynthate transport. When mapping across the rhizosphere, 13 C-enriched soil was typically present within 5-10µm of root boundaries with a steep spatial increase in $δ$ 13 C when the scan approached the middle of the root. As with all LA approaches, care is required to ensure accurate results as phenomena linked to ablation, combustion, and isotopic measurement can impart artifacts if not carefully controlled. Still, taken as a whole, our demonstrations highlight the increased sample throughput, improved data density, and enhanced $δ$ 13 C capability of LA-CAS versus other LA techniques and emphasize the role this method can play in future plant and rhizosphere related studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Proteomic Analysis of Exosomes for Discovery of Protein Biomarkers for Prostate and Bladder Cancer

Extracellular vesicles (EVs) are released by nearly all cell types as part of normal cell physiology, transporting biological cargo, including nucleic acids and proteins, across the cell membrane. In pathological states such as cancer, EV-derived cargo may mirror the altered state of the cell of origin. Exosomes are the smaller, 50–150 nanometer-sized EVs released from fusion of multivesicular endosomes with the plasma membrane. Exosomes play important roles in cell-cell communication and participate in multiple cancer processes, including invasion and metastasis. Therefore, proteomic analysis of exosomes is a promising approach to discover potential cancer biomarkers, even though it is still at an early stage. Herein, we critically review the advances in exosome isolation methods and their compatibility with mass spectrometry (MS)-based proteomic analysis, as well as studies of exosomes in pathogenesis and progression of prostate and bladder cancer, two common urologic cancers whose incidence rates continue to rise annually. As urological tumors, both urine and blood samples are feasible for noninvasive or minimally invasive analysis. A better understanding of the biological cargo and functions of exosomes via high-throughput proteomics will help provide new insights into complex alterations in cancer and provide potential therapeutic targets and personalized treatment for patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

High-throughput surface characterization to identify porosity defects in additively manufactured 316L stainless steel

In the present study, a high throughput (HT) methodology is applied to rapidly assess the surface characteristics of 83 additively manufactured 316L stainless steel. The variation in surface roughness (S a ) showed a good correlation with a dimensionless number (π) and provided direct linkages to the internal porosity defects. Lack-of-fusion regime relates to high surface roughness (S a > 5 µm), low dimensionless number (π < 61), and the presence of cavities in between the melt pool tracks. Balling regime correlates to high surface roughness (S a > 5 µm), intermediate dimensionless number (61 < π < 146), and non-uniform melt pool track width. Keyhole regime shows low surface roughness (S a < 5 µm), high dimensionless number (π > 146), and a curved melt pool track. This approach accelerates the process parameter discovery and minimizes the porosity defects for the LPBF process. The impact of the defects on the as-processed tensile mechanical properties demonstrates that samples with porosity exhibit up to 10% less tensile strength and 30% less ductility than optimal samples.

316L stainless steel↗
DOE OSTI2021

Photonic Curing of Nickel Oxide Transport Layer and Perovskite Active Layer for Flexible Perovskite Solar Cells: A Path Towards High-Throughput Manufacturing

High-throughput roll-to-roll (R2R) manufacturing of perovskite solar cells (PSCs) is currently limited by thermal processes that take tens of minutes each, translating to impractically long annealing tools at high web speeds. In addition, PSCs are usually made with metal oxide transport layer materials that require high temperatures for thermal annealing. Here, we demonstrate the fabrication of PSCs using photonic curing, instead of thermal annealing, to convert NiO x directly from sol-gel precursors for hole transport layers and to crystallize methylammonium lead iodide (MAPbI 3 ) active layers on flexible Willow ® Glass substrates. Photonic curing uses short, intense pulses of light to process materials at a high speed, hence it is compatible with R2R manufacturing. We achieved power conversion efficiencies (PCEs) of 11.7% in forward-scan and 10.9% in reverse-scan for PSCs made with photonic cured NiO x and MAPbI 3 films. Furthermore, both NiO x and MAPbI 3 films could be processed with a single photonic curing pulse, with a web speed of 5.7 m/min, and still produce PCEs comparable to thermally annealed control samples. Based on the single-pulse photonic curing condition for each film, we project a web speed of 26 m/min, laying a pathway to high-throughput production of perovskite solar modules.

14 SOLAR ENERGY↗
DOE OSTI2025

Quantifying dispersity in size and shape of nanoparticles from small-angle scattering data using machine learning based CREASE

Here, we use machine learning (ML) enhanced computational reverse engineering analysis of scattering experiments (CREASE) to interpret small-angle X-ray scattering (SAXS) data obtained from a system of nanoparticles without a priori knowledge of their exact shapes (e.g. spheres or ellipsoids), sizes (0.5–50 nm) and distributions. The SAXS measurements yielded three categories of scattering profiles exhibiting 'strong', 'weak' and 'no' features. Diminishing features (e.g. broadening or disappearing peaks) in scattering profiles have always been attributed to the presence of significant dispersity in the system. Such featureless SAXS data are not suitable for traditional analysis using analytical models. If one were to fit a relevant analytical model (e.g. the lmfit analytical model for polydisperse spheres) to these 'weak' and 'no' SAXS profiles from our nanoparticle systems, one would obtain non-unique interpretations of the data. Relying on electron microscopy to identify the distributions of nanoparticle shapes and sizes is also unfeasible, especially in high-throughput synthesis and characterization loops. In such situations, to identify the distributions of particle sizes and shapes that could be present in the sample, one must rely on methods like ML-CREASE to interpret the data quickly and output all relevant interpretations about the structure present in the system. The ML-CREASE optimization loop takes the experimental scattering profile as input and outputs multiple candidate solutions whose computed scattering profiles match the SAXS profile input. The ML-CREASE method outputs distributions of relevant structural features, such as the volume fraction of the nanoparticles in the system and the mean and standard deviation of the particle size and aspect ratio, assuming a type of distribution (e.g. normal, log-normal) for size and aspect ratio. We find that, for the SAXS profiles analyzed here, accounting for the shape dispersity along with size dispersity of the nanoparticles using ML-CREASE improved the match between the computed scattering profiles and input experimental profiles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

A small-volume, high-throughput approach for surface tension and viscosity measurements of liquid fuels

An ongoing effort looking at bioblendstocks to function as drop-in replacements or blending components for gasoline has identified a large number of candidate fuels. This work documents an approach for rapid screening of candidate fuels using relatively small sample sizes ($\mathcal{O}(\mu\,{\rm l})$) that targets two key physical properties of liquid fuels—surface tension and viscosity. The approach utilizes shape oscillation dynamics of single droplets generated by a piezo-electric device and their decay over time. Strobed imaging of the oscillation process is used along with image processing, edge detection, and data analysis to capture the decay of the oscillation over time. The time constant of the decay process along with oscillation frequency are then used to estimate viscosity and surface tension using a theory for small amplitude droplet oscillations. Measurements are obtained for primary reference fuels (isooctane and n-heptane) as well as candidate fuels from four bio-derived functional groups of interest. Measurement results for surface tension and viscosity are correlated with literature data as well as measurements from standard reference instruments. The measurement results show that the droplet oscillation based approach is capable of reproducing surface tension and viscosity values for the tested fuels within deviations of 7% and 13% respectively from literature data. Results are obtained using an average of 5 µl per fuel within about 20 s, thus demonstrating a small-volume, high-throughput approach that can be used for screening of candidate bioblendstock fuels.

09 BIOMASS FUELS↗
DOE OSTI2026

Total Dissolved Nitrogen and Ammonia Data for the East River Watershed, Colorado (2015-2025)

This data package contains mean values for total dissolved nitrogen (TDN) and ammonia concentrations for water samples taken from the East River Watershed in Colorado. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. TDN was analyzed using a Shimadzu Total Nitrogen Module (TNM-1) combined with the TOC-VCSH analyzer (Shimadzu Corporation, Japan). TNM-1 is a non-specific measurement of total nitrogen (TN). All nitrogen species in samples are combusted to nitrogen monoxide and nitrogen dioxide, then reacted with ozone to form an excited state of nitrogen dioxide. Upon returning to ground state, light energy is emitted. Then, TDN is measured using a chemiluminescence detector. Ammonia was determined using a Lachat's QuikChem 8500 Series 2 Flow Injection Analysis System (LACHAT Instruments, QuckChem 8500 series 2, Automated Ion Analyzer, Loveland, Colorado). When ammonia in water samples is heated (60 degrees C) with salicylate and hypochlorite in an alkaline phosphate buffer, an emerald green color is produced which is proportional to the ammonia concentration. The color is intensified by the addition of nitroprusside. Ethylenediaminetetraacetic acid (EDTA) is added to the buffer to prevent the interference of metal ions (Ca, Mg, and Fe etc.). Ammonia-N is then determined by LACHAT flow injection and a colorimetric assay at an absorbance wavelength 660 nm. (Reference: LACHAT Instruments: QuickChem Method 90-107-06-3-A, Determination of Ammonia by Flow Injection Analysis (High Throughput, Salicylate Method/DCIC) (Multi Matrix method). Written by Lynn Egan (Application group), February 08, 2011.) All files are labeled by location and variable, and data reported are the mean values upon replicate measurements. All samples were analyzed under a rigorous quality assurance and quality control (QA/QC) process as detailed in the methods. This data package contains (1) a zip file (tdn_ammonia_data_2015-2025.zip) containing a total of 299 files: 298 data files of ammonia and TDN data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v7_20260901_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v7_20260901_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; (4) PDF and docx files for the determination of Method Detection Limits (MDLs) for TDN data, which has been updated in 2026-08; and (5) PDF and docx files for the detemination of Method Detection Limits (MDLs) for Ammonia and the Interferences by LACHAT Flow Injection Analysis. Missing values within the anion data files are noted as either "-9999" or "0.0" for not detectable (N.D.) data. There are a total of 105 locations containing TDN and Ammonia-N data. Update 2020-10-07: Updated the data files to remove times from the timestamps, so that only dates remain. The data values have not changed. Update 2021-04-11: Added Determination of Method Detection Limits (MDLs) for DIC, NPOC and TDN Analyses and Determination of Method Detection Limit for Ammonia and the Interferences by LACHAT Flow Injection Analysis documents, which can be accessed as PDFs or with Microsoft Word.Update on 6/10/2022: versioned updates to this dataset was made along with these changes: (1) updated total dissolved nitrogen and ammonia data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) restructure of units to comply with CSV reporting format requirements, (5) added -9999 for empty numerical cells, and (6) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv). Update on 2022-12-20: Updates were made to both the data files and reporting format specific files. Units were listed incorrectly, but have been fixed to reflect correct units (ug/L). File level metadata (flmd) and data dictionary (dd) files were updated to reflect the updated versions of these files. Available data was added up until 2022-06-01. Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-01-05. The file level metadata and data dictionary files were updated to reflect the additional data added. Update on 2024-03-11: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-10-27. Further, revisions to the data files were made to remove incorrect data points (from 1970 and 2001). The reporting format specific files were updated to reflect the additional data added. Revised versions of the PDF and docx files for determination of MDLs for TDN were added to replace previous versions. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available TDN and Ammonia-N data was added, up until the end of WY2024 (September 30, 2024). International Generic Sample Numbers (IGSNs), when registered, were added to the data files. The reporting format specific files were updated to reflect the additional data added. Update on 2026-09-01: Updates were made to both the data files and reporting format specific files. New available TDN and Ammonia-N data was added, up until the end of WY2025 (September 30, 2025). Updated versions, as of 2026-08-10, of the PDF and docx files for determination of MDLs for TDN data were added to this dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Rapid Generation of Tandem Mass Spectrometry Reference Libraries Using Immediate Drop-on-Demand Liquid Handling Coupled to an Open Port Sampling Interface

In metabolomics, tandem MS (MS2) fragmentation libraries are important for the identification of unknown features, but generating these libraries takes many valuable hours of instrument and operator time. Here, an immediate droplet-on-demand/open port sampling interface was used to rapidly acquire tandem MS of standards arrayed in a 96-well plate format. A workflow was developed for automated, high-throughput control of MS2 library generation. Pure standard mass spectral libraries were collected on Orbitrap and Q-TOF mass spectrometers for 192 compounds using 6 different collision energies with a throughput of 4 and 7.8 s/spectrum, respectively. Libraries were acquired using different solvent additives, precursor adducts, and ion polarities.

Cahill, John [ORNL] (ORCID:0000000298664010)↗
DOE OSTI2022

Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices

Polarizable nanoparticles are of interest in materials science because of their rich and complex phase behavior that can be used to engineer nanostructured materials with long-range crystalline order. To understand and rationally navigate the design space of polarizable nanoparticles for self-assembling highly ordered superlattices, we developed a coarse-grained computational model to describe the nanoparticle-nanoparticle interactions in implicit solvent and employ the computationally efficient image method to model many-body polarization interactions. We conducted high-throughput virtual screening over a five-dimensional particle design space spanned by temperature, particle size, particle charge, particle dielectric, and solvent dielectric using enhanced sampling molecular dynamics calculations within an active learning framework to efficiently map out the regions of thermodynamic stability of the self-assembled aggregates. We validate our predictions in comparisons against small angle x-ray scattering measurements of gold nanoparticles surface functionalized with metal chalcogenide ligands. Lastly, we use our validated phase maps to computationally design switchable nanostructured materials capable of triggered assembly and disassembly as a function of temperature and solvent dielectric with potential applications as sensors, smart windows, optoelectronic devices, and in medical diagnostics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2021

Robotic sample changers for macromolecular X-ray crystallography and biological small-angle X-ray scattering at the National Synchrotron Light Source II

In this work, we present two robotic sample changers integrated into the experimental stations for the macromolecular crystallography (MX) beamlines AMX and FMX, and the biological small-angle scattering (bioSAXS) beamline LiX. They enable fully automated unattended data collection and remote access to the beamlines. The system designs incorporate high-throughput, versatility, high-capacity, resource sharing and robustness. All systems are centered around a six-axis industrial robotic arm coupled with a force torque sensor and in-house end effectors (grippers). They have the same software architecture and the facility standard EPICS-based BEAST alarm system. The MX system is compatible with SPINE bases and Unipucks. It comprises a liquid nitrogen dewar holding 384 samples (24 Unipucks) and a stay-cold gripper, and utilizes machine vision software to track the sample during operations and to calculate the final mount position on the goniometer. The bioSAXS system has an in-house engineered sample storage unit that can hold up to 360 samples (20 sample holders) which keeps samples at a user-set temperature (277 K to 300 K). The MX systems were deployed in early 2017 and the bioSAXS system in early 2019.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The NanoSIMS-HR: The Next Generation of High Spatial Resolution Dynamic SIMS

The high lateral resolution and sensitivity of the NanoSIMS 50 and 50L series of dynamic SIMS instruments have enabled numerous scientific advances over the past 25 years. Here, in this study, we report on the NanoSIMS-HR, the first major upgrade to the series, and analytical tests in a suite of sample types, including an aluminum sample containing silicon crystals, microalgae, and plant roots colonized with a symbiotic fungus. Significant improvements have been made in the Cs + ion source, high voltage (HV) control, stage reproducibility, and other aspects of the instrument that affect performance. The modified design of the NanoSIMS-HR thermal-ionization Cs + source enables a 5 pA primary ion beam to be focused into a 100 nm spot, a ~2.5-fold increase compared to Cs + sources on previous instruments (~2 pA at 100 nm). The brightness of the new Cs + source enables an ultimate lateral resolution as high as 30 nm and improved detection limits for a given analysis area. Sample stage movement accuracy is higher than 500 nm, enabling many-fold higher throughput automated analyses. With the new HV control, the primary ion beam impact energy can be reduced from 16 to 2 keV, which enables higher depth resolution during depth profiling (a 2-fold improvement), albeit with a 5-fold decrease in lateral resolution. In the NanoSIMS-HR, the secondary ion column and detection system are identical to those used in the previous series, and the isotopic analysis performance is as precise as in previous NanoSIMS instruments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

High-throughput Single-Cell Proteomics and Transcriptomics from the Same Cells with a Nanoliter-Scale Spin-Transfer Approach

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

multi 'omics↗
DOE OSTI2024

High Throughput Genome Releaser

In this study, we present the development of a High Throughput Genome Releaser, an innovative device addressing common challenges in screening PCR. This genome DNA releaser is designed for rapid, cost-effective, and efficient DNA extraction, optimized for subsequent PCR reactions. Our experimentation with various synthetic materials led us to select a particular type of plastic that mirrors the properties of glass cover slides, providing a smooth surface and effective compression capabilities. We engineered a 96-well device equipped with a 96-well plate and a top rod, operable both manually and automatically, which is compatible with widely used liquid-handling robot decks. This compatibility enhances ease of use in high-throughput PCR setups. Additionally, we developed software to support its automatic functions. The genome releaser facilitates the extraction of PCR-amplifiable genomic DNA from 96 samples within minutes, eliminates the need for extraction buffers, and is adaptable to a wide range of microorganisms and cells. This versatility could significantly advance biomanufacturing processes.

42 ENGINEERING↗
DOE OSTI2025

High-throughput spin-bath characterization of spin defects in semiconductors

Detailed knowledge of the local environments of spin defects in semiconductors, such as nitrogenvacancy (NV) centers in diamond or divacancies in silicon carbide, is crucial for optimizing control and entanglement protocols in quantum sensing and information applications. However, at present a direct experimental characterization of individual defect environments is not scalable, as conventional spin-bath measurements are time consuming and difficult to automate. Achieving high-throughput characterization requires short experiments to probe the spin bath. However, with fewer and noisier measurements, the inverse problem of recovering spin-bath properties from measured data becomes ill posed, with multiple spin baths having a high likelihood of yielding the same data. In this work, we present a set of computational tools to resolve the ill-posed inverse problem of recovering the atomic positions and hyperfine couplings of random nuclei surrounding spin defects from sparse, noisy experimental coherence data, which can be obtained in hours. Here, we use a trans-dimensional Bayesian approach that incorporates ab initio data to yield full posterior distributions over nuclear spin environments, enabling robust recovery from limited data. We also provide practical tools and guidelines to determine the limits of detectability for hyperfine couplings under specific dynamical decoupling sequences and sampling conditions. In addition, we demonstrate how the tools developed here, in combination with ab initio simulations of spin baths, can guide the design of efficient experimental protocols for application-specific high-throughput screening. To showcase the utility of our approach, we apply it to design fast dynamical decoupling experiments to characterize the spin baths often individual NV centers in diamond. While the primary focus is on accelerating spin-bath characterization of spin defects, this Bayesian approach also lays the foundation for digital-twin studies of spin defects, where a virtual model of the spin-defect system evolves in real time with ongoing experimental measurements. Together, the set of tools we designed and applied paves the way for scalable deployment of spin defects in semiconductors for quantum sensing and information applications.

Bayesian methods↗
DOE OSTI2026

APPL Hyperspectral_Imaging_Dataset_for_Heritability_Analysis_in_Populus_trichocarpa

This dataset contains hyperspectral imaging data collected at the Advanced Plant Phenotyping Laboratory (APPL) at Oak Ridge National Laboratory. Natural variants of Populus trichocarpa were imaged using a high-throughput hyperspectral phenotyping pipeline to quantify spectral reflectance traits for downstream quantitative genetics analyses. The dataset includes hyperspectral image files and derived reflectance data products suitable for extracting spectral features across the measured wavelength range (e.g., VNIR and/or SWIR, depending on instrument configuration), along with associated sample metadata (e.g., genotype identifiers, experimental design factors, and imaging run identifiers). These data were generated to support analyses of broad-sense heritability of hyperspectral traits and their relationships with biochemical phenotypes (including lignin traits from Py-MBMS).

APPL↗