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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 OSTI2022

GaN vertical-channel junction field-effect transistors with regrown p-GaN by metal organic chemical vapor deposition (MOCVD)

Fabricating a vertical-channel junction field-effect transistor includes forming an unintentionally doped GaN layer on a bulk GaN layer by metalorganic chemical vapor deposition, forming a Cr/SiO 2 hard mask on the unintentionally doped GaN layer, patterning a fin by electron beam lithography, defining the Cr and SiO 2 hard masks by reactive ion etching, improving a regrowth surface with inductively coupled plasma etching, removing hard mask residuals, regrowing a p-GaN layer, selectively etching the p-GaN layer, forming gate electrodes by electron beam evaporation, and forming source and drain electrodes by electron beam evaporation. The resulting vertical-channel junction field-effect transistor includes a doped GaN layer, an unintentionally doped GaN layer on the doped GaN layer, and a p-GaN regrowth layer on the unintentionally doped GaN layer. Portions of the p-GaN regrowth layer are separated by a vertical channel of the unintentionally doped GaN layer.

Zhao, Yuji↗
DOE OSTI2024

Detailed Characterization of CZT Detector Response for Improved Coded-Aperture Imaging Performance

Gamma-ray imaging is a powerful method for locating and quantifying sources of radiation. The coded-aperture technique demonstrates superior angular resolution in comparison to other methods (e.g., Compton reconstruction). In this method, a mask constructed of highly attenuating material encodes the scene as a shadow pattern on a position-sensitive detector; this pattern can then be used to recreate the origin(s) of incident radiation. This is typically done through convolution of the mask and shadow patterns. Iterative methods which attempt to reconstruct the observed shadow pattern using a weighted combination of simulated patterns may also be employed. In either case, errors in event position reconstruction due to detector imperfections alter the shadow pattern and will therefore degrade system performance and may introduce imaging artifacts. These effects can be mitigated with a detailed understanding of such errors – allowing for the generation of representative simulations that include the errors and/or correction of raw imager data to remove the errors. We present a calibration process for a commercially available cadmium zinc telluride (CZT) gamma imager which provides a comprehensive characterization of the spatial and energy dependence of event reconstruction. By illuminating a mask featuring a regular grid of pinholes with a calibration source, the localized response of the detector can be measured with fine granularity. These local responses are combined to generate a full detector response map which can be used to distort simulations in a manner that is representative of the observed detector data. Details of the calibration procedure and an assessment of the impact of its end products on the performance of iterative imaging methods will be presented.

Ziock, Klaus-Peter↗
DOE OSTI2024

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology

Abstract The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum . The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Adaptive language model training for molecular design

Abstract The vast size of chemical space necessitates computational approaches to automate and accelerate the design of molecular sequences to guide experimental efforts for drug discovery. Genetic algorithms provide a useful framework to incrementally generate molecules by applying mutations to known chemical structures. Recently, masked language models have been applied to automate the mutation process by leveraging large compound libraries to learn commonly occurring chemical sequences (i.e., using tokenization) and predict rearrangements (i.e., using mask prediction). Here, we consider how language models can be adapted to improve molecule generation for different optimization tasks. We use two different generation strategies for comparison, fixed and adaptive. The fixed strategy uses a pre-trained model to generate mutations; the adaptive strategy trains the language model on each new generation of molecules selected for target properties during optimization. Our results show that the adaptive strategy allows the language model to more closely fit the distribution of molecules in the population. Therefore, for enhanced fitness optimization, we suggest the use of the fixed strategy during an initial phase followed by the use of the adaptive strategy. We demonstrate the impact of adaptive training by searching for molecules that optimize both heuristic metrics, drug-likeness and synthesizability, as well as predicted protein binding affinity from a surrogate model. Our results show that the adaptive strategy provides a significant improvement in fitness optimization compared to the fixed pre-trained model, empowering the application of language models to molecular design tasks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2021

Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images

We propose a new framework, PlasmodiumVF-Net, to analyze thick smear microscopy images for a malaria diagnosis on both image and patient-level. Our framework detects whether a patient is infected, and in case of a malarial infection, reports whether the patient is infected by Plasmodium falciparum or Plasmodium vivax. PlasmodiumVF-Net first detects candidates for Plasmodium parasites using a Mask Regional-Convolutional Neural Network (Mask R-CNN), filters out false positives using a ResNet50 classifier, and then follows a new approach to recognize parasite species based on a score obtained from the number of detected patches and their aggregated probabilities for all of the patient images. Reporting a patient-level decision is highly challenging, and therefore reported less often in the literature, due to the small size of detected parasites, the similarity to staining artifacts, the similarity of species in different development stages, and illumination or color variations on patient-level. We use a manually annotated dataset consisting of 350 patients, with about 6000 images, which we make publicly available together with this manuscript. Our framework achieves an overall accuracy above 90% on image and patient-level.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

2D Exotic Optical Lattice via a Digital‐Coding Circular Airy Beam

Optical lattices have been widely used from classical to quantum physics. The tunable and scalable fabrication of lattices would be of great significance in lattice‐based multipartite applications. This work demonstrates first that a circular Airy beam (CAB), which has the peculiar properties of self‐healing and abrupt autofocusing, can be used to generate two‐dimensional (2D) optical lattices in propagation when encoded by a programmable spatial mask, resulting in the formation of large‐scale and tunable optical lattices with both axis and axial symmetry, and even high‐orbital kaleidoscope shapes. The efficient diffraction of CAB during the spatial crosstalk with the mask enables the realization of tunable lattices with rich periodicity and complexity. The study shows a flexible method to manipulate lattices with large‐scale and versatile structures for potential applications in integrated and scalable optical and photonic devices.

Sun, Peisheng↗
DOE OSTI2021

Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

In this work, we propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we present a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel DataBase, demonstrating the flexibility of the technique.

36 MATERIALS SCIENCE↗
DOE OSTI2023

NeRVI: Compressive neural representation of visualization images for communicating volume visualization results

We present NeRVI, a new deep-learning approach that compresses a large collection of visualization images generated from time-varying data for communicating volume visualization results. Based on an image-based implicit neural representation, our approach represents tens of thousands of high-resolution rendering images parametrized by different parameters via a hybrid model of multilayer perceptrons and convolutional neural networks. Here, our model predicts images and corresponding masks, and the masks are utilized for loss computation and network training to capture fine structural details and small components. In conjunction with model quantization and weight encoding, NeRVI yields highly compact compressive neural representations while preserving the image fidelity well. We demonstrate the effectiveness of NeRVI with isosurface rendering and direct volume rendering images generated from multiple data sets and compare NeRVI with other state-of-the-art deep learning-based (InSituNet, SIREN, NeRF, and NeRV) methods. Quantitative and qualitative results show that NeRVI provides an alternative solution that augments domain scientists' ability to manage, represent, and communicate scientific visualization output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Overcoming small minirhizotron datasets using transfer learning

Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique to help with training when available datasets are limited. In this paper, we investigated the effect of pre-trained features from the massives-cale, irrelevant ImageNet dataset and a relatively moderate-scale, but relevant peanut root dataset on switchgrass root imagery segmentation applications. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. Deep neural networks based on the U-net architecture were used with different pre-trained features as initialization for automated, precise pixel-wise root segmentation in minirhizotron imagery. We observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset were more effective than features pre-trained on the large but unrelated ImageNet dataset. Here, we achieved high quality segmentation on peanut root dataset with 99.04% accuracy at the pixel-level and overcame errors in human-labeled ground truth masks. By applying transfer learning technique on limited switchgrass dataset with features pre-trained on peanut dataset, we obtained 99% segmentation accuracy in switchgrass imagery using only 21 images for training (fine tuning). Furthermore, the peanut pre-trained features can help the model converge faster and have much more stable performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

EHR-BERT: A BERT-based model for effective anomaly detection in electronic health records

Objective: Physicians and clinicians rely on data contained in electronic health records (EHRs), as recorded by health information technology (HIT), to make informed decisions about their patients. The reliability of HIT systems in this regard is critical to patient safety. Consequently, better tools are needed to monitor the performance of HIT systems for potential hazards that could compromise the collected EHRs, which in turn could affect patient safety. In this paper, we propose a new framework for detecting anomalies in EHRs using sequence of clinical events. This new framework, EHR-Bidirectional Encoder Representations from Transformers (BERT), is motivated by the gaps in the existing deep-learning related methods, including high false negatives, sub-optimal accuracy, higher computational cost, and the risk of information loss. EHR-BERT is an innovative framework rooted in the BERT architecture, meticulously tailored to navigate the hurdles in the contemporary BERT method; thus, enhancing anomaly detection in EHRs for healthcare applications.Methods: The EHR-BERT framework was designed using the Sequential Masked Token Prediction (SMTP) method. This approach treats EHRs as natural language sentences and iteratively masks input tokens during both training and prediction stages. This method facilitates the learning of EHR sequence patterns in both directions for each event and identifies anomalies based on deviations from the normal execution models trained on EHR sequences.Results: Extensive experiments on large EHR datasets across various medical domains demonstrate that EHR-BERT markedly improves upon existing models. It significantly reduces the number of false positives and enhances the detection rate, thus bolstering the reliability of anomaly detection in electronic health records. This improvement is attributed to the model’s ability to minimize information loss and maximize data utilization effectively.Conclusion: EHR-BERT showcases immense potential in decreasing medical errors related to anomalous clinical events, positioning itself as an indispensable asset for enhancing patient safety and the overall standard of healthcare services. The framework effectively overcomes the drawbacks of earlier models, making it a promising solution for healthcare professionals to ensure the reliability and quality of health data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Emergence of exchange bias and giant coercive field enhancement by internal magnetic frustration in La 0.67 Sr 0.33 MnO 3 thin films

We have studied the influence of controlled defects on the magnetic properties of La 0.67 Sr 0.33 MnO 3 (LSMO) thin films. We used 100 nm thick epitaxial LSMO films, which were grown on SrTiO 3 substrates and introduced stoichiometry changes and defects using oxygen ion irradiation through nanoporous alumina masks. Oxygen irradiation through the mask creates cascades of defects in spatially-modulated regions in the plane of the LSMO film. The magnetic properties of the samples were compared before and after the irradiation. We have found that irradiation reduces the ferromagnetic ordering temperature, decreases the total magnetization, enhances the coercivity, and induces exchange bias below 50 K. The coercivity enhancement is dramatically higher below 50 K and can be associated with exchange bias. These results can be explained by the formation of Mn-rich antiferromagnetic or ferrimagnetic phases within the bombarded regions that are exchange coupled to the non-irradiated - ferromagnetic host.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Formation of tunable diamond micro- and nanopillars for field effect enhancement applications

Here, we developed a process for the fabrication of tunable single crystal diamond micro- and nanopillars, with tip widths ranging from 40 to 460 nm, densities ranging from 0.5 to 53.5 pillars/μm 2 , and heights greater than 4.5 μm. A self-assembled Au nanodot ensemble etch mask was formed from an annealed Au thin film. The nanodot diameter and density can be tuned using the initial film thickness. The pillars were etched from the nanodot mask using an RIE O 2 plasma, which has infinite selectivity for the diamond when applied at low RF powers (50 W). Finally, the pillars can be sharpened to ~40 nm tip widths by annealing in air at 650 °C. These pillars can be used for applications such as field effect enhancement of diamond photocathode devices, enhancement of optical emission from N-V centers, and antireflective coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2021

SHarD: A beam dynamics simulation code for dielectric laser accelerators based on spatial harmonic field expansion

In order to demonstrate acceleration of electrons to relativistic scales by an on chip dielectric laser accelerator (DLA), a ponderomotive focusing scheme capable of capturing and transporting electrons through nanometer-scale apertures over extended interaction lengths has been proposed. Here we present a Matlab-based numerical code (SHarD) utilizing a spatial harmonic expansion of the fields within the dielectric structure to simulate the evolution of the beam phase space distribution in this scheme. The code can be used to optimize key-parameters for the accelerator performance such as the final energy, transverse spot size evolution and total number of electrons accelerated through currently fabricated structures. Eventually, the simulation model will be applied to inform the phase mask profile to be added to a pulse front tilt drive laser pulse using a liquid crystal mask in the experimental setup being assembled at UCLA Pegasus Laboratory.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A rotor-based multileaf collimator for beam shaping

We introduce a new style of multileaf collimator which employs rotors with angularly dependent radius to control the masking aperture: a rotor-based multileaf collimator (RMLC). Using a padlock-inspired mechanism, a single motor can set dozens of rotors, i.e. leaves, independently. This is especially important for an ultra-high vacuum (UHV) compatible MLC, since this reduces the number of actuators and vacuum feedthroughs required by more than an order of magnitude. This new RMLC will complement previous work employing a UHV compatible MLC with an emittance exchange beamline to create arbitrarily shaped beams on demand. A feed-forward control system which abstracts away the complexity of the RMLC operation, and is adaptable to real beamline conditions, is discussed and demonstrated in simulation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Observation of tantalum deposition and growth on TiB2 and ZrB2 from PISCES-RF deuterium and helium plasma exposures

Deuterium and helium plasma exposures on bulk TiB2 and ZrB2 samples were performed using the PISCES-RF linear plasma device. 40 and 90 eV deuterium ion plasma exposures were performed at 240 and 800 °C sample temperatures, and 80 eV helium ion plasma exposures were performed at 800 °C sample temperatures. Following plasma exposures, it was discovered that two plasma conditions (90 eV deuterium and 80 eV helium at 800 °C) resulted in thick (>200 nm) tantalum-rich (>10 at%) surface features on the targets, presumably from tantalum sourced from a tantalum adapter mask or cap used as part of the target holder. This work aims to characterize these tantalum-rich features and examine the mechanisms of impurity deposition.Plasma-induced surface morphology of the tantalum-rich surface layers depends on plasma properties and target temperature and chemistry. Greater titanium sputtering compared to zirconium resulted in more distinct surface features in the TiB2 samples compared to the ZrB2 samples via increased, prompt deposition onto tantalum surface impurities. There is still uncertainty as to why thick tantalum deposition only occurred under some plasma exposure conditions but not others; it is likely due to tantalum sputtering by a combination of boron molecules from the targets and carbon-impurities in the tantalum mask or targets. Impurity driven surface features are a well-documented phenomena in samples exposed to plasma from linear plasma device facilities—this work confirms the occurrence of this and emphasizes the need for chemistry characterization of isolated post-mortem surface features in plasma-exposed samples.

Nuckols, Lauren↗
DOE OSTI2025

Stress accommodation in nanoscale dolan bridges designed for superconducting qubits

Josephson junctions are the principal circuit element in numerous superconducting quantum information devices and can be readily integrated into large-scale electronics. However, device integration at the wafer scale necessarily depends on having a reliable, high-fidelity, and high-yield fabrication method for creating Josephson junctions. When creating Al/AlO x based superconducting qubits, the standard Josephson junction fabrication method relies on a sub-micron suspended resist bridge, known as a Dolan bridge, which tends to be particularly fragile and can often times fracture during the resist development process, ultimately resulting in device failure. In this work, we demonstrate a unique Josephson junction lithography mask design that incorporates stress-relief channels. Our simulation results show that the addition of stress-relief channels reduces the lateral stress in the Dolan bridge by more than 70% for all the bridge geometries investigated. In practice, our novel mask design significantly increased the survivability of the bridge during device processing, resulting in 100% yield for over 100 Josephson junctions fabricated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗