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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Upcycling disposable face masks into fuel range iso-alkanes through hydropyrolysis coupled with vapor-phase hydrocracking

The COVID-19 pandemic has resulted in an alarming accumulation of plastic waste. Herein, an integrated hydropyrolysis and hydrocracking process was performed to upcycle disposable masks into fuel-range iso-alkanes over carbon supported ruthenium (Ru/C). Experimental results indicated that catalyst type significantly affected product distribution during the hydropyrolysis and vapor-phase hydrocracking of disposable masks. Compared with zeolites-induced catalytic cascade process where up to ~25.9 wt% yield of aromatic hydrocarbons such as toluene and xylenes were generated, a ~82.7 wt% yield of desirable iso-alkanes with a high C 5 –C 12 gasoline selectivity of 95.5% was obtained over Ru/C under 550 °C hydropyrolysis temperature and 300 °C hydrocracking temperature at 0.2 MPa H 2 . Further, the cascade hydropyrolysis and hydrocracking process also exhibited high adaptability and flexibility in upcycling single-use syringes, food packaging, and plastic bags, generating 79.1, 81.6, and 80.3 wt% yields of fuel range iso/n-alkanes, respectively. This catalytic cascade hydrotreating process provides an efficient and effective approach to convert pandemic-derived plastic waste into gasoline-range fuel products.

09 BIOMASS FUELS↗
DOE OSTI2025

Shadow masks predictions in SPARC tokamak plasma-facing components using HEAT code and machine learning methods

Here, this work uses machine learning (ML) to complement HEAT (Heat flux Engineering Analysis Toolkit) by developing 3-D footprint surrogate models for fast and accurate heat load calculations in the divertor of the SPARC tokamak. The focus is on shadowed regions, or magnetic shadows, caused by the 3-D geometry of plasma-facing components (PFCs). ML classifiers are employed to create a surrogate model for HEAT generated shadow masks, predicting these shadow masks and divertor heat flux profiles based on a diverse range of equilibria and only the plasma current, safety factor(q95) at the edge, and magnetic flux angles as input parameters. The ultimate goal is to integrate the model for real-time control and future operational decisions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Freestanding high-aspect-ratio gold masks for low-energy, phase-based x-ray microscopy

High-resolution, x-ray phase contrast microscopy, a key technique with promising potential in biomedical imaging and diagnostics, is based on narrow-slit high-aspect-ratio gold gratings. Here we present the development, fabrication details, and experimental testing of the freestanding 10 μm thick gold membrane masks with an array of 0.9–1.5 μm void slit apertures for a novel low-energy x-ray microscope. The overall mask size is 4 mm × 4 mm, with a grating pitch of 7.5 μm, 6.0–6.6 μm wide gold bars are supported by 3 μm wide crosslinks at 400 μm intervals. The fabrication process is based on gold electroplating into a silicon mold coated with various thin films to form a voltage barrier, plating base, and sacrificial layer, followed by the mold removal to obtain the freestanding gold membrane with void slit apertures. We discuss key aspects for the materials and processes, including gold structures homogeneity, residual stresses, and prevention of collapsing of the grid elements. We further demonstrate the possibility to obtain high-resolution, high contrast 2D images of biological samples using an incoherent, rotating anode x-ray tube.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2019

Highly Absorptive Pupil Mask Fabricated with Black Silicon

Many of NASA’s direct imaging of exoplanet missions and projects require fabricated coronagraph masks to control scattering and diffraction of light. The designed, patterned mask intended for the coronagraphic testbeds are highly absorptive in the visible range on non-metallic regions. In this work, we employed the cryogenic etching process to fabricate black silicon (BSi) to achieve a high aspect ratio (HAR) structures with higher etch rate than conventional reactive ion etching (REI). Recent bidirectional reflectance distribution function (BRDF) measurements of uniformly etched BSi on silicon wafer show highly diffusive BSi with a specular reflective component in the orders of seven magnitudes lower than the total hemispherical reflectance when the polarized or non-polarized incident beam is used.

black-silicon↗
DOE OSTI2022

Targeted masking enables stable cycling of LiNi 0.6 Co 0.2 Mn 0.2 O 2 at 4.6V

Layered LiNi x Co y Mn 1–x-y O 2 (NCM, or NCMxy(1-x-y)) is a dominant family of cathode materials for lithium-ion batteries (LIBs) due to its high energy density. Among all NCM cathode materials, NCM622 possess the optimal energy density at high potential (≥ 4.6 V vs. Li/Li + ). However, the practical application of NCM622 at high voltage (≥ 4.6 V) is limited by its parasitic reactions and associated safety concerns. Completely physical isolation has been considered as the main approach to mitigate the parasitic reaction. It has also been previously demonstrated that the interface reaction has active site selectivity, and that the reactivity of the active sites can effectively suppressed by blocking the chemically active sites. Herein, a targeted masking by LiFePO 4 @C nanoplates is reported to unlock the stable performance of NCM622 up to 4.6 V vs. Li/Li + . The (targeted masked-NCM622)|graphite pouch cell shows 86.5% capacity retention after 1000 cycles and its maximum temperature during thermal runaway is dramatically reduced from 570 °C to 415 °C. Systematic in/ex situ characterizations, first-principles calculations and half/pouch targeted LiFePO 4 @C covers the surface of NCM partcell evaluation prove that PO 4 3- is preferentially adsorbed on transition metal sites, stabilizing both the transition metal ions and oxygen ions on the surface against the ethylene carbonate-containing traditional electrolyte even under high voltage (≥ 4.6 V vs. Li/Li + ). Furthermore, this work opens up new venue for rational design of high-performance cathode materials through a low-cost and scalable decoration process, and reveal a new understanding of interfacial activity of materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask

Abstract Seasonal‐to‐decadal climate prediction is crucial for decision‐making in a number of industries, but forecasts on these timescales have limited skill. Here, we develop a data‐driven method for selecting optimal analogs for seasonal‐to‐decadal analog forecasting. Using an interpretable neural network, we learn a spatially‐weighted mask that quantifies how important each grid point is for determining whether two climate states will evolve similarly. We show that analogs selected using this weighted mask provide more skillful forecasts than analogs that are selected using traditional spatially‐uniform methods. This method is tested on two prediction problems using the Max Planck Institute for Meteorology Grand Ensemble: multi‐year prediction of North Atlantic sea surface temperatures, and seasonal prediction of El Niño Southern Oscillation. This work demonstrates a methodical approach to selecting analogs that may be useful for improving seasonal‐to‐decadal forecasts and understanding their sources of skill.

Geology↗
DOE OSTI2025

Image masks of global ship tracks for NASA MODIS data products

Ship tracks, long thin artificial cloud features formed from the pollutants in ship exhaust, are satellite-observable examples of aerosol-cloud interactions (ACI) that can lead to increased cloud albedo and thus increased solar reflectivity, phenomena of interest in solar radiation management. In addition to ship tracks being of interest to meteorologists and policy makers, their observed cloud perturbations provide benchmark evidence of ACI that remain poorly captured by climate models. To broadly analyze the effects of ship tracks, high-resolution satellite imagery data highlighting their presence are required. To support this, we provide a hand labelled dataset to serve as a benchmark for a variety of subsequent analyses. Established from a previous dataset that identified ship track presence using NASA’s MODIS Aqua satellite imager, our first-of-its-kind dataset is comprised of image masks: capturing full ship track regions, including their contours, emission points and dispersive patterns. In total, 300 images, or around 2,500 masked ship tracks, observed under varying conditions are provided, and may facilitate training of machine learning algorithms to automate extraction.

Atmospheric dynamics↗
DOE OSTI2021

Single-shot x-ray phase-contrast and dark-field imaging based on coded binary phase mask

We introduce a coded-mask-based multi-contrast imaging method for high-resolution phase-contrast and dark-field imaging. The method uses a binary phase mask designed to provide an ultra-high-contrast pattern and reference-free single-shot measurement and an algorithm based on maximum-likelihood optimization and automatic differentiation to perform simultaneous reconstruction of absorption, phase, and dark-field object images. Further, we demonstrate that the method has great potential for real-time quantitative phase imaging and wavefront sensing when combined with deep learning.

Qiao, Zhi (ORCID:0000000286285320)↗
DOE OSTI2025

Coded-mask-based wavefront sensing technique for APS nanofocusing beamline diagnostics

Here, we extend our recently developed coded-mask wavefront sensing technique to enable single-shot measurements of nanofocused x-ray beams. This method accurately reconstructs the focal beam profile by backpropagating the wavefront measured downstream of the beam focus. To validate its performance, we benchmarked it against the conventional fluorescence wire scan method, successfully measuring ∼120 nm focal spots at the 28-ID-B beamline of the Advanced Photon Source using a polymeric compound refractive lens. The results highlight the effectiveness of coded-mask wavefront sensing for high-precision beam profiling and its application as a real-time wavefront monitoring tool.

Shi, Xianbo [Argonne National Laboratory (ANL), Ar↗
DOE OSTI2021

Large aperture and durable glass-engraved optical metasurfaces using nanoparticle etching masks: prospects and future directions

Abstract Metasurfaces (MSs), or surfaces consisting of engineered spatially oriented sub-wavelength elements—also known as meta-atoms—are quickly becoming a burgeoning field of interest due to their seemingly unparalleled control over the propagation of light. This article will discuss the emergence of a MS technology that is based on dewetting thin metal films to form dry etching masks for MS fabrication. The novelty this process presents is the ability to spatially vary mask features—which translates into spatially varying index of the finished surface—in a way that, rather than writing subwavelength elements one-by-one, writes entire areas at once to provide a route to easily scale the process up to meter-sized optics. Glass-engraved MSs have shown to be laser damage resistant, with damage thresholds close to the parent substrate damage thresholds. Furthermore, these surfaces have proven to be mechanically durable and capable of handling normal loads two orders of magnitude higher than that of an average fingerprint pressure. This indicates that accidental handling contact is not detrimental to the MS optical performance. Also discussed are the foreseeable technological challenges, potential solutions for those obstacles, and some currently unexplored directions for future efforts.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series

In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection. With the increasing availability of sensor data from industrial systems, being able to detecting anomalies from streams of multivariate time series data is of significant importance. Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned normal representations can empower anomaly detection as the model has learned to capture certain key underlying data regularities. A typical formulation is to learn a predictive model, i.e., use a window of time series data to predict future data values. In this paper, we propose an alternative self-supervised learning task. By randomly masking a portion of the inputs and training a model to estimate them using the remaining ones, MAD is an improvement over the traditional left-to-right next step prediction (NSP) task. Our experimental results demonstrate that MAD can achieve better anomaly detection rates over traditional NSP approaches when using exactly the same neural network (NN) base models, and can be modified to run as fast as NSP models during test time on the same hardware, thus making it an ideal upgrade for many existing NSP-based NN anomaly detection models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Automating Genetic Algorithm Mutations for Molecules Using a Masked Language Model

Inspired by the evolution of biological systems, genetic algorithms have been applied to generate solutions for optimization problems in a variety of scientific and engineering disciplines. For a given problem, a suitable genome representation must be defined along with a mutation operator to generate subsequent generations. Unlike natural systems which display a variety of complex rearrangements (e.g. mobile genetic elements), mutation for genetic algorithms commonly utilizes only random point-wise changes. Furthermore, generalizing beyond point-wise mutations poses a key difficulty as useful genome rearrangements depend on the representation and problem domain. To move beyond the limitations of manually defined point-wise changes, here we propose the use of techniques from masked language models to automatically generate mutations. As a first step, common subsequences within a given population are used to generate a vocabulary. The vocabulary is then used to tokenize each genome. A masked language model is trained on the tokenized data in order to generate possible rearrangements (i.e. mutations). In order to illustrate the proposed strategy, we use string representations of molecules and use a genetic algorithm to optimize for drug-likeness and synthesizability. Finally, our results show that moving beyond random point-wise mutations accelerates genetic algorithm optimization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Development of hard masks for reactive ion beam angled etching of diamond

Diamond offers good optical properties and hosts bright color centers with long spin coherence times. Recent advances in angled-etching of diamond, specifically with reactive ion beam angled etching (RIBAE), have led to successful demonstration of quantum photonic devices operating at visible wavelengths. However, larger devices operating at telecommunication wavelengths have been difficult to fabricate due to the increased mask erosion, arising from the increased size of devices requiring longer etch times. We evaluated different mask materials for RIBAE of diamond photonic crystal nanobeams and waveguides, and how their thickness, selectivity, aspect ratio and sidewall smoothness affected the resultant etch profiles and optical performance. We found that a thick hydrogen silesquioxane (HSQ) layer on a thin alumina adhesion layer provided the best etch profile and optical performance. The techniques explored in this work can also be adapted to other bulk materials that are not available heteroepitaxially or as thin films-on-insulator.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Custom surface reflectance, shade mask, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study (2025)

This dataset contains land surface reflectance estimates and additional derived products generated from NEON Imaging Spectrometer (NIS) data collected in the Upper Gunnison river basin during June and July of 2025. Data was collected over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). These products were derived from radiance and LiDAR data collected by the NEON Airborne Observation Platform (AOP) campaign funded by the Colorado Headwaters Ecological Spectroscopy Study (CHESS) (doi:10.15485/3017965). Products include per-pixel surface reflectance (rfl) and reflectance uncertainty (rfl_unc), observational data (obs), canopy equivalent water thickness (ewt), and shade masks. Atmospheric correction was performed per flightline using the ISOFIT (Imaging Spectrometer Optimal FITting) optimal estimation framework to estimate surface reflectance and the associated per-band reflectance uncertainty. Reflectance retrievals achieved a mean absolute error of 1.5% across diverse validation surfaces (see validation report.pdf). Equivalent water thickness was calculated from surface reflectance using the Beer–Lambert absorption of liquid water. Shade masks were generated based on the geometry between the sun angle, ground surface, and sensor at the time of flight. Data products are provided per-flightline and as mosaics for each domain. Flightline data products are provided as ENVI-formatted binary files (rfl, rfl_unc, ewt) and GeoTIFFs (shade). Reflectance and uncertainty mosaics are provided as tiled NetCDFs, while all other mosaicked products are provided as cloud-optimized GeoTIFFs. These formats are supported by common geospatial software (e.g., QGIS, ArcGIS, ENVI) and programmatic libraries in Python (e.g., rasterio, xarray, spectral, netCDF4) and R (e.g., terra, ncdf4). Processing workflows were designed to be equivalent to those used to generate the 2018 CHESS campaign airborne imaging spectroscopy data products (doi:10.15485/3013527). All outputs were co-registered to a common spatial grid to support time series analyses. 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 Acknowledgment: Data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). Computational research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2021

High-Resolution Scanning Coded-Mask-Based X-ray Multi-Contrast Imaging and Tomography

Near-field X-ray speckle tracking has been used in phase-contrast imaging and tomography as an emerging technique, providing higher contrast images than traditional absorption radiography. Most reported methods use sandpaper or membrane filters as speckle generators and digital image cross-correlation for phase reconstruction, which has either limited resolution or requires a large number of position scanning steps. Recently, we have proposed a novel coded-mask-based multi-contrast imaging (CMMI) technique for single-shot measurement with superior performance in efficiency and resolution compared with other single-shot methods. We present here a scanning CMMI method for the ultimate imaging resolution and phase sensitivity by using a coded mask as a high-contrast speckle generator, the flexible scanning mode, the adaption of advanced maximum-likelihood optimization to scanning data, and the multi-resolution analysis. Scanning CMMI can outperform other speckle-based imaging methods, such as X-ray speckle vector tracking, providing higher quality absorption, phase, and dark-field images with fewer scanning steps. Scanning CMMI is also successfully demonstrated in multi-contrast tomography, showing great potentials in high-resolution full-field imaging applications, such as in vivo biomedical imaging.

Qiao, Zhi (ORCID:0000000233928732)↗
NASA NTRS1966

Silver-palladium braze alloy recovered from masking materials

Method for recovering powdered silver-palladium braze alloy from an acrylic spray binder and rubber masking adhesive used in spray brazing is devised. The process involves agitation and dissolution of masking materials and recovery of suspended precious metal particles on a filter.

Cierniak, R.↗