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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Binary pseudo-random array standard for extreme ultraviolet lithography tool characterization

Extreme ultraviolet (EUV) imaging tools play a crucial role in EUV lithography. Achieving high accuracy in EUV metrology is essential for advanced semiconductor manufacturing. A thorough characterization of the instrumentation in use is required. Binary pseudo-random arrays (BPRAs) are an established standard for calibrating and characterizing optical instruments in the frequency domain. Here, we expand the BPRA standard to applications in EUV imaging. To extend the technology to the EUV spectral range, a high-resolution BPRA target with the smallest feature size of 40 nm is developed. The EUV BPRA target establishes an in situ and portable calibration and alignment standard for EUV imaging. The target is patterned by means of electron-beam lithography, using a nickel absorber with a thickness of 39 nm. The substrate is a 4″ silicon wafer with a molybdenum/silicon multilayer coating. To demonstrate the efficacy of the target and develop the instrument calibration protocol, the target is imaged on the Sharp Hyper-NA Actinic Reticle Review Project EUV mask microscope. Power spectral density (PSD) data are presented. The characteristics of the imaging system are imprinted on the PSD. The modulation transfer function is extracted from the PSD data. A partially coherent imaging model is used as a reference to the experimental results.

BPRA↗
DOE OSTI2022

Polarized, V-Shaped, and Conjoined Biscoumarins: From Lack of Dipole Moment Alignment to High Brightness

Eleven conjoined coumarins possessing a chromeno[3,4-c]chromene-6,7-dione skeleton have been synthesized via the reaction of electron-rich phenols with esters of coumarin-3-carboxylic acids, catalyzed by either Lewis acids or 4-dimethylaminopyridine. Furthermore, Michael-type addition to angular benzo[f]coumarins is possible, leading to conjugated helical systems. Arrangement of the electron-donating amino groups at diverse positions on this heterocyclic skeleton makes it possible to obtain π-expanded coumarins with emission either sensitive to, or entirely independent of, solvent polarity with large Stokes shifts. Computational studies have provided a rationale for moderate solvatochromic effects unveiling the lack of collinearity of the dipole moments in the ground and excited states. Depending on the functional groups present, the obtained dyes are highly polarized with dipole moments of ~14 D in the ground state and ~20–25 D in the excited state. Strong emission in nonpolar solvents, in spite of the inclusion of a NO 2 group, is rationalized by the fact that the intramolecular charge transfer introduced into these molecules is strong enough to suppress intersystem crossing yet weak enough to prevent the formation of dark twisted intramolecular charge transfer states. Photochemical transformation of the dye possessing a chromeno[3,4-c]pyridine-4,5-dione scaffold led to the formation of a spirocyclic benzo[g]coumarin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Magnetic structures and excitations in sawtooth olivine chalcogenides Mn 2 SiX 4 (X = S, Se)

The Mn lattice in olivine chalcogenide Mn 2 SiX 4 (X = S, Se) compounds forms a sawtooth, which is of special interest in magnetism owing to the possibility of realizing flat bands in magnon spectra, a key component in magnonics. In this work, we investigate the Mn 2 SiX 4 olivines using magnetic susceptibility, and X-ray and neutron diffraction. We have determined the average and local crystal structures of Mn 2 SiS 4 and Mn 2 SiSe 4 using synchrotron X-ray, neutron diffraction, and X-ray total scattering data followed by Rietveld and pair distribution function analyses. It is found from the pair distribution function analysis that the Mn triangle that constitutes the sawtooth is isosceles in Mn 2 SiS 4 and Mn 2 SiSe 4 . The temperature evolution of magnetic susceptibility of Mn 2 SiS 4 and Mn 2 SiSe 4 shows anomalies below 83 K and 70 K, respectively, associated with magnetic ordering. From the neutron powder diffraction measurements the magnetic space groups of Mn 2 SiS 4 and Mn 2 SiSe 4 are found to be Pnma and Pnm'a', respectively. Here, we find that the Mn spins adopt a ferromagnetic alignment on the sawtooth in both Mn 2 SiS 4 and Mn 2 SiSe 4 but along different crystallographic directions for the S and the Se compounds. From the temperature evolution of Mn magnetic moments obtained from refining neutron diffraction data, the transition temperatures are accurately determined as T N (S) = 83(2) K and T N (Se) = 70.0(5) K. Broad diffuse magnetic peaks are observed in both the compounds, and are prominently seen close to T N , suggesting the presence of a short-range magnetic order. The magnetic excitations studied using inelastic neutron scattering reveal a magnon excitation with an energy corresponding to approximately 4.5 meV in both S and Se compounds. Spin correlations are observed to persist up to 125 K much above the ordering temperature and we suggest the possibility of short-range spin correlations responsible for this.

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

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
DOE OSTI2021

Tailoring Interfaces in Solid-State Batteries Using Interfacial Thermochemistry and Band Alignment

Solid-state lithium ion batteries have enhanced thermal stability compared to batteries using liquid electrolytes. Although their practical implementation is limited by undesired side reactions between the electrode and electrolyte, different modification strategies have been proposed to stabilize these reactive interfaces. These approaches have been primarily based upon bulk materials properties, however, which may not necessarily be representative of the chemistry at the solid/solid interface. In this work, first principles calculations and X-ray scattering experiments are used to elucidate molecular-level reactivity and develop design principles for tailored interfaces based on surface and interfacial properties. Because of its well-known instability, the interface between the Li metal anode and the lithium lanthanum titanate (LLTO) solid electrolyte is applied as a model system. Density functional theory calculations are used to describe bulk, surface, and interfacial thermochemistry of the Li/LLTO system, and interfacial reconstruction is probed using ab initio molecular dynamics. These simulations of the Li/LLTO interface demonstrate facile decomposition concomitant with reduction of Ti 4+ ions. Based on further insights from computational analysis of the surface band edge positions, La 2 O 3 is proposed as an interlayer coating and is shown to provide an energetic barrier for interfacial charge transfer and decomposition reactions from X-ray reflectivity measurements and theoretical calculations. The findings in this work suggest that design strategies based on surface and interfacial properties can be used to kinetically stabilize electrode/electrolyte interfaces in solid-state batteries.

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

Improved Self-Assembly of P3HT with Pyrene-Functionalized Methacrylates

A block copolymer with discotic liquid crystalline behavior was synthesized using Grignard metathesis polymerization (GRIM) and initiators for continuous activator regeneration atom transfer radical polymerization (ICAR-ATRP). A novel discotic liquid crystalline mesogen, 6-(pyren-1-yloxy)hexyl methacrylate (PyMA), comprises a block that is attached to regioregular poly(3- hexylthiophene) (rr-P 3 HT) generated by GRIM and subjected to end-group modification. Due to the continuous regeneration of Cu + in the reaction mixture in ICAR-ATRP compared to conventional methods, the synthesis was successfully performed with less catalyst. The purity and yield of the final product are increased by eliminating rigorous post-synthesis purification. Stacked pyrene units have contributed to the enhanced long-range π-π interactions and aligning of the P 3 HT block as observed in thin-film X-ray diffraction (XRD). Furthermore, field-effect mobilities in the order of 10 -2 cm 2 V -1 s -1 in bottom-gate, top-contact organic field-effect transistors (OFETs) suggest an enhancement in charge transport due to the discotic electron-rich pyrene units that help mitigate the insulating effect of the methacrylate backbone. The formation of uniform microdomains of P 3 HT-bpoly(PyMA) observed with tapping mode atomic force microscopy (TMAFM) on the channel regions of OFETs indicates the unique packing of the block copolymer in comparison to pristine P 3 HT. Thermotropic properties of the novel discotic mesogen in the presence and absence of P3HT were observed with both the poly(3-hexylthiophene)-b-poly(6-(pyren-1-yloxy)hexyl methacrylate) (P3HT-b-poly(PyMA)) block copolymer and poly(6-(pyren-1-yloxy)hexyl methacrylate) (poly(PyMA)) homopolymer using polarized optical microscopy (POM) and differential scanning calorimetry (DSC).

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

Henry’s Solubility and Diffusion Coefficients for 29 Volatile Organic Compounds in Polydimethylsiloxane Sylgard 184 at 293 K

Two-dimensional (2D) inverse gas chromatography (IGC) enables simultaneous determination of Henry’s solubility and Fickian diffusion coefficients for volatile organic compounds (VOCs) in polymer films. This technique offers a significant advantage over traditional cylindrical column IGC by providing precise control and measurement of the film thickness (here, 0.064 ± 0.002 mm), which is the critical length scale for accurate diffusivity determination. We apply this methodology to characterize VOC transport in Sylgard 184, a widely used polydimethylsiloxane (PDMS)-based polymer containing substantial silica filler content. At room temperature (20 °C), we measured solubility and diffusion coefficients for 29 common VOCs spanning diverse chemical functionalities, including alkanes, aromatics, chlorinated solvents, ketones, esters, and alcohols. Comparison with literature data for pure PDMS reveals that VOC solubility in Sylgard 184 is generally higher; for most non-hydrogen-bonding compounds it remains within a factor of 2 of pure PDMS, whereas alcohols are enhanced by roughly 1.8 to 3.7 times, which we attribute to favorable interactions with residual silanol groups on the silanized silica filler. Diffusion coefficients range from 1.0 × 10 –6 cm 2 /s (n-undecane) to 8.9 × 10–5 cm 2 /s (acetonitrile) and align well with extrapolated literature values for PDMS. This study provides essential thermodynamic and transport data for predicting VOC permeation in Sylgard 184 while demonstrating the utility of 2D IGC as a robust technique for characterizing rubbery polymer membranes across diverse industrial applications.

organic↗
DOE OSTI2024

The deep-acceptor nature of the chalcogen vacancies in 2D transition-metal dichalcogenides

Abstract Chalcogen vacancies in the semiconducting monolayer transition-metal dichalcogenides (TMDs) have frequently been invoked to explain a wide range of phenomena, including both unintentional p-type and n-type conductivity, as well as sub-band gap defect levels measured via tunneling or optical spectroscopy. These conflicting interpretations of the deep versus shallow nature of the chalcogen vacancies are due in part to shortcomings in prior first-principles calculations of defects in the semiconducting two-dimensional TMDs that have been used to explain experimental observations. Here we report results of hybrid density functional calculations for the chalcogen vacancy in a series of monolayer TMDs, correctly referencing the thermodynamic charge transition levels to the fundamental band gap (as opposed to the optical band gap). We find that the chalcogen vacancies are deep acceptors and cannot lead to n-type or p-type conductivity. Both the (0/−1) and (−1/−2) transition levels occur in the gap, leading to paramagnetic charge states S = 1 / 2 and S = 1, respectively, in a collinear-spin representation. We discuss trends in terms of the band alignments between the TMDs, which can serve as a guide to future experimental studies of vacancy behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Noble Gas Transport in the MSRE

This study explores the relationship between the physicochemical parameter known as Henry’s gas constant and gas transport across a two-layer film interface. The investigation utilized the Gibbs free energy, incorporating surface and volume terms to elucidate trends in enthalpy and entropy. Notably, our findings align with experimental data and offer predictive insights into the Henry’s gas constants for helium and krypton, which hold significance for future experiments and theoretical developments. Furthermore, this study enhances the Gibbs free energy theory pertaining to the liquid–gas interface. It underscores the substantial contribution of noble gases in this region to volumetric energy as temperature increases. Additionally, we employed Monte Carlo simulations to analyze the effective thermal neutron multiplication factor, denoted as k eff . Our analysis reveals a linear correlation between graphite density and uniform density as a function of temperature. For the 1D Molten Salt Reactor Experiment (MSRE) system, we employed the Mole code to conduct heat and mass transfer calculations. These computations enable us to ascertain the distribution of fuel temperature based on coefficients and thermal properties. We also studied delayed neutron precursors during fuel cycling, taking into account the drift of cycling fuel through Mole–Griffin coupling. Our model represents k eff and β eff across various volume flow rates and salinity compositions. Finally, this study leveraged xenon-135 for continuous on-line monitoring of fuel salts to investigate the impact of steady-state xenon-135 on the MSRE and to better understand its distribution. These efforts build upon previous research related to removal processes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Glass-fiber-reinforced polymeric film as an efficient protecting layer for stable Li metal electrodes

With numerous reports on protecting films for stable lithium (Li) metal electrodes, the key attributes for how to construct these efficient layers have rarely been fully investigated. Here, we report a rationally designed hybrid protective layer (HPL) with each component aligning with one key attribute; i.e., cross-linked poly(dimethylsiloxane) (PDMS) enhances flexibility, polyethylene glycol (PEG) provides homogeneous ion-conducting channels, and glass fiber (GF) affords mechanical robustness. A significant improvement of the electrochemical performance of HPL-modified electrodes can be achieved in Li/HPL@Cu half cells, HPL@Li/HPL@Li symmetric cells, and HPL@Li/LiFePO 4 full cells. Even with an industrial standard LiFePO 4 cathode (96.8 wt % active material), the assembled cell still exhibits a capacity retention of 90% after 100 cycles at 1 C. More importantly, the functionality of each component has been studied comprehensively via electrochemical and physical experiments and simulations, which will provide useful guidance on how to construct efficient protective layers for next-generation energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2024

Partial wrapping of single-stranded DNA by replication protein A and modulation through phosphorylation

Abstract Single-stranded DNA (ssDNA) intermediates which emerge during DNA metabolic processes are shielded by replication protein A (RPA). RPA binds to ssDNA and acts as a gatekeeper to direct the ssDNA towards downstream DNA metabolic pathways with exceptional specificity. Understanding the mechanistic basis for such RPA-dependent functional specificity requires knowledge of the structural conformation of ssDNA when RPA-bound. Previous studies suggested a stretching of ssDNA by RPA. However, structural investigations uncovered a partial wrapping of ssDNA around RPA. Therefore, to reconcile the models, in this study, we measured the end-to-end distances of free ssDNA and RPA–ssDNA complexes using single-molecule FRET and double electron–electron resonance (DEER) spectroscopy and found only a small systematic increase in the end-to-end distance of ssDNA upon RPA binding. This change does not align with a linear stretching model but rather supports partial wrapping of ssDNA around the contour of DNA binding domains of RPA. Furthermore, we reveal how phosphorylation at the key Ser-384 site in the RPA70 subunit provides access to the wrapped ssDNA by remodeling the DNA-binding domains. These findings establish a precise structural model for RPA-bound ssDNA, providing valuable insights into how RPA facilitates the remodeling of ssDNA for subsequent downstream processes.

Biochemistry & Molecular Biology↗
DOE OSTI2023

Prediction of dislocation - grain boundary interactions in FCC aluminum bicrystals using a modified continuum criterion and machine learning methods

Mechanical properties of metals such as strength and toughness are strongly correlated to complex interactions between various defects in the crystalline structure. While elementary interactions between these defects have been investigated using recent micro- and nano-characterization techniques, understanding of the detailed interaction mechanisms has hardly been obtained. To understand defect-driven plasticity at various time and length scales, it is necessary to formulate a general guideline to predict both the interaction type (transmission or reflection) and the dislocation's subsequent slip system after the interaction. Many criteria based on the geometric alignment of the defects have been developed to predict this phenomenon, but these have yet to be found to be accurate when applied to general data sets of grain boundaries (GBs). With this motivation, we conduct a systematic study using molecular dynamics (MD) models of bicrystals to analyze defect interaction process between a prismatic dislocation loop and eleven different grain boundaries of the following character: three tilt, three twist, and five mixed. Based on the MD observations, two new prediction methods are developed: the first is a new data-driven parametric score function based on the classical geometric criteria, and the second is by applying Gaussian process machine learning methods to find the probability distribution of a hidden function. In conclusion, the proposed data-driven prediction methods could pave a new way to predict the unit interaction of dislocations with various GBs, which could show much higher accuracy compared to pre-existing geometric criteria.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Solutions Are the Problem: Ordered Two-Dimensional Covalent Organic Framework Films by Chemical Vapor Deposition

Covalent organic frameworks (COFs) are a promising class of crystalline polymer networks that are useful due to their high porosity, versatile functionality, and tunable architecture. Conventional solution-based methods of producing COFs are marred by slow reactions that produce powders that are difficult to process into adaptable form factors for functional applications, and there is a need for facile and fast synthesis techniques for making crystalline and ordered covalent organic framework (COF) thin films. In this work, we report a chemical vapor deposition (CVD) approach utilizing co-evaporation of two monomers onto a heated substrate to produce highly crystalline, defect-free COF films and coatings with hydrazone, imine, and ketoenamine COF linkages. Further, this all-in-one synthesis technique produces highly crystalline, 40 nm–1 μm-thick COF films on Si/SiO2 substrates in less than 30 min. Crystallinity and alignment were proven by using a combination of grazing-incidence wide-angle X-ray scattering (GIWAXS) and transmission electron microscopy (TEM), and successful conversion of the monomers to produce the target COF was supported by Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and UV–vis measurements. Additionally, we used atomic force microscopy (AFM) to investigate the growth mechanisms of these films, showing the coalescence of triangular crystallites into a smooth film. To show the wide applicability and scope of the CVD process, we also prepared crystalline ordered COF films with imine and ketoenamine linkages. These films show potential as high-quality size exclusion membranes, catalytic platforms, and organic transistors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Dark energy survey year 3 results: High-precision measurement and modeling of galaxy-galaxy lensing

We present and characterize the galaxy-galaxy lensing signal measured using the first three years of data from the Dark Energy Survey (DES Y3) covering 4132 deg 2 . These galaxy-galaxy measurements are used in the DES Y3 3 × 2 pt cosmological analysis, which combines weak lensing and galaxy clustering information. We use two lens samples: a magnitude-limited sample and the redmagic sample, which span the redshift range ∼ 0.2 – 1 with 10.7 and 2.6 M galaxies, respectively. For the source catalog, we use the metacalibration shape sample, consisting of ≃ 100 M galaxies separated into four tomographic bins. Our galaxy-galaxy lensing estimator is the mean tangential shear, for which we obtain a total SNR of ∼ 148 for maglim ( ∼ 120 for redmagic), and ∼ 67 ( ∼ 55 ) after applying the scale cuts of 6 Mpc / h . Thus we reach percent-level statistical precision, which requires that our modeling and systematic-error control be of comparable accuracy. The tangential shear model used in the 3 × 2 pt cosmological analysis includes lens magnification, a five-parameter intrinsic alignment model, marginalization over a point mass to remove information from small scales and a linear galaxy bias model validated with higher-order terms. We explore the impact of these choices on the tangential shear observable and study the significance of effects not included in our model, such as reduced shear, source magnification, and source clustering. We also test the robustness of our measurements to various observational and systematics effects, such as the impact of observing conditions, lens-source clustering, random-point subtraction, scale-dependent metacalibration responses, point spread function residuals, and B modes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Electrical and thermal characteristics of LaB6 thermionic hollow cathodes operating in He, D2, Ar, and Xe at 4–200 Pa and 0.25–5 A cm−2

We report characteristics of lanthanum hexaboride (LaB6) cathodes for conditions relevant to a high-voltage, high-current switching device. LaB6 is selected because of its relatively low work function, stability in the presence of many common gas impurities, and a low evaporation rate (long life) at operating temperatures. We have investigated the effect of cathode geometry, gas type, pressure, and current on a LaB6 hollow cathode in a diode configuration with an opposing planar anode. The gas is stagnant at a relatively low pressure of 4–200 Pa (0.03–1.5 Torr) because of device high-voltage standoff requirements. In all cases, the plasma voltage decreases to a plateau value with increasing current where a practical device would operate. The plateau voltage aligns with the fill gas excitation or ionization voltage, depending on conditions. At high current, the cathode temperature exceeds that required for thermionic emission to supply the total current, a behavior that is understood by accounting for back-diffusing bulk electrons.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

CRADA Number NFE-19-07847 with MantaPoole Technologies (CRADA Final Report)

As the world becomes more automated, smart and autonomous systems that utilize advanced 3D machine vision are becoming ever more prevalent. To function autonomously, these smart machines need to be able to identify the “what” and “where” of their application by capturing color and depth information. The color information provides the needed contrast information to identify the “what” of a scene while the depth information allows the smart machine to identify “where” an object in a 3D environment is. To capture both the color and depth information using only a single sensor, MantaPoole Technologies (MPT) is developing a new type of 3D light field camera. The approach leverages MPT’s active micro-optics technology which when aligned and placed over an imaging detector are designed to act as an advanced optical filter that controls the direction of light that reaches the underlying pixels.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Quantifying atmospheric carbon removal at pulp and paper mills: a life cycle assessment across system boundaries

The pulp and paper industry is a promising yet underexplored platform for large-scale carbon dioxide removal (CDR) due to its use of biogenic feedstocks and production of concentrated CO 2 emissions from point sources. This study presents the first comprehensive life cycle assessment (LCA) of retrofitting an amine-based carbon capture and storage (CCS) system into a representative virgin kraft pulp and paper mill in the Southeastern U.S. We evaluate carbon removal across five system configurations, applying both static and dynamic LCA methods under multiple functional units: CO 2 captured, biomass input, and paper output. Results show that CCS retrofits can convert a conventional mill from a net emitter into a net carbon sink, with total removal efficiencies from 17% to 92% (metric tonnes of CO 2 removed per metric tonne of CO 2 available for removal under selected boundary conditions). When carbon removal is normalized to the quantity of biogenic CO 2 captured—a narrow, gate-to-gate system boundary that considers only CCS facility emissions—removal efficiencies reached as high as 92%. The use of such narrow boundaries aligns with precedents in traditional LCA methodology, where gate-to-gate assessments are commonly applied to isolate process-level performance and allocate emissions accordingly, providing a consistent basis for comparison across technologies. Under broader cradle-to-grave boundaries—which begin tracking carbon at the point of its physical removal from the atmosphere via photosynthesis in the forest, and extend to include upstream forest operations, mill-wide emissions, and downstream product decomposition—efficiencies declined, ranging from 17% to 46% under static assumptions and dropping to 12% when accounting for dynamic biogenic carbon fluxes over time. These results underscore how system boundary definitions influence reported outcomes, while also illustrating the complementary roles of narrow and broad perspectives for different decision-making contexts.

09 BIOMASS FUELS↗