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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Parametric Analysis of Entry Vehicles for Giant Planet Missions

The Planetary Science Decadal Survey has identified Uranus and Saturn as high priori-ty destinations for a flagship and New Frontier missions respectively in the decade 2023-2032. The pro-posed presentation will focus on the entry and descent aspects of the entry vehicles design, considered as part of Giant Planet probe mission concepts, and associated trades for viable trajectory options. Giant Planet Entry Vehicle Parametric Study: Launch vehicle capabilities are evolving and provide an opportunity to increase instrumented probe dimensions. To assess the impact of larger aeroshell designs, a parametric study was conducted to understand the impact on aerothermal environments, TPS options, and TPS mass over a range of 1.0m to 2.0m aeroshell diameters for Uranus and Saturn probe concept missions. The 45° sphere-cone geometry is a legacy configuration that has demonstrated static stability and been used successfully in missions to Venus (Pioneer-Venus) and Jupiter (Galileo). A nose radius of 0.4 m was considered primarily to reduce the heat flux at the stagnation point compared to the smaller radii used in the Venus and Jupiter missions. Representative inertial velocities are chosen from a prior NASA Ames study. Viable entry trajectories to meet concept mission and science objectives were developed using the tool POST2. The newly developed thermal protection material called HEEET (Heatshield for Extreme Entry Environment Technology) was considered in the study. This material, which is at a technology readiness level (TRL) of 6, is highly customizable and available in two varieties: (i) a dual-layer version consisting of recession layer on top of an insulative layer, and (ii) a single-layer version consisting of the insulative layer alone, termed 3- dimensional Mid-Density Carbon Phenolic (3MDCP). Both options were considered for the forward heatshield (the sphere-cone part) in the pre-sent study.

Thermal Protection System↗
DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Integration of CeO 2 -Based Memristor with Vertically Aligned Nanocomposite Thin Film: Enabling Selective Conductive Filament Formation for High-Performance Electronic Synapses

The CeO 2 -based memristor has attracted significant attention due to its intrinsic resistive switching (RS) properties, large on/off ratio, and great plasticity, making it a promising candidate for artificial synapses. However, significant challenges such as high power consumption and poor device reliability hinder its broad application in neuromorphic microchips. To tackle these issues, in this work, we design a novel bilayer (BL) memristor by integrating a CeO 2 -based memristor with a Co-CeO 2 vertically aligned nanocomposite (VAN) layer and compare it with the single layer (SL) memristor. Preliminary electrical testing reveals that the BL memristor offers a reduced set/reset voltage (~67% lower), a higher on/off ratio (~5 × 10 2 ), enhanced device reliability, and improved device-to-device variation compared to the SL memristor. Insight from COMSOL simulation, coupled with microstructural analysis, provides a comprehensive elucidation on how the VAN layer facilitates the selective conductive filament (CF) formation. Subsequently, the plasticity of the BL memristor is evaluated through long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-time-dependent plasticity (STDP). The spiking neural network (SNN) built upon the BL memristor achieves remarkable accuracy (~94%) after only 12 iterations, underscoring its potential for high-performance neural networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Uranus Probe Entry and Descent Mission Concept

Introduction: Uranus was identified as the third highest priority flagship mission in the 2012-2022 Planetary Science Decadal Survey. This latest concept study was requested by the Decadal Survey panel to determine NASA’s planetary science priorities from 2022-2032. This study focused on the probe’s entry and descent aspects and associated trades for viable trajectory options. Uranus Mission and Descent Probe: The proposed Uranus Orbiter and Probe (UOP) Flagship mission will investigate Uranus and its surrounding moons using an orbiting spacecraft with a Uranus descent probe. Unlike previous studies the probe release will occur after orbit insertion allowing sufficient separation of critical events during the orbit insertion burn. The probe will be released at an altitude that allows one hour of in situ atmospheric readings that will be relayed to the orbiter. Afterwards the orbiter will transition to the moon tour phase of the mission. The configuration chosen for the entry aeroshell was a 45° sphere-cone. This shape has been used in the past in the Pioneer Venus Galileo missions. However, the nose radius considered in the present study differed from the values used in either of the previous configurations, primarily to reduce the heat flux at the stagnation point. A two-step approach was used in the development of flight trajectories for the chosen configuration. In the first step, the trajectory code POST2 was used to screen the thousands of entry states provided by interplanetary trajectory simulations, which were terminated at an altitude of 2000 km from the reference surface (1 bar) of Uranus. The screening criteria were: (i) optimization of the communication geometry between the entry probe and orbiter to ensure at least 1 hour of science measurements, (ii) peak stagnation point pressures to be less than six bar, (iii) peak heat fluxes to be less than 5 kW/cm2; the latter two constraints being the limits of ground-test capabilities of the arc jets at NASA Ames Research Center. The entry team investigated two trajectories that met the criteria above, a shallow entry (high heat load ~44 kJ/cm2) and a steeper entry (high heat rate ~1950 W/cm2). In the second step, the two bounding candidate entry states from the POST2 screening process were used in developing flight trajectories using TRAJ coupled with FIAT (a materials thermal response and sizing code) and a margins policy to determine a margined uniform thickness (hence mass) of the forward heatshield material based on the aerothermal environments at the stagnation point. Since the combination of TRAJ and FIAT size the TPS based on stagnation point environments only, flow field computations using DPLR were necessary to determine turbulent aerothermal environments on the conical flank, and the augmentation of these environments due to surface roughness. The environments at select locations on the forward heatshield were then used to size the thermal protection material, with the largest thickness value then used to estimate the mass. The newly developed woven thermal protection material –HEEET (Heatshield for Extreme Entry Environments Technology) –was considered for the forward heatshield and PICA (Phenolic-Impregnated Carbon Ablator) was considered for the backshell. These NASA-developed materials are at TRL 6 and TRL 9, respectively. Furthermore, two options were considered for the HEEET material: (i) a dual-layer option with a denser recession layer on top and an insulative layer underneath it, and (ii) a single layer option consisting of the insulative layer alone. Results: It is clear that probe entry states are feasible and the selected TPS options are able to perform in the predicted aerothermal environments thus enabling the mission to meet of the descent probe portion of this flagship mission

Entry Descent and Landing↗
NASA NTRS2023

Breaking Wave Field Statistics With A Multi-Layer Model

The statistics of breaking wave fields are characterised within a novel multi-layer framework, which generalises the single-layer Saint-Venant system into a multi-layer and non-hydrostatic formulation of the Navier–Stokes equations. We simulate an ensemble of phase-resolved surface wave fields in physical space, where strong nonlinearities, including directional wave breaking and the subsequent highly rotational flow motion, are modelled, without surface overturning. We extract the kinematics of wave breaking by identifying breaking fronts and their speed, for freely evolving wave fields initialised with typical wind wave spectra. The Λ(c) distribution, defined as the length of breaking fronts (per unit area) moving with speed c to c + dc following Phillips (J. Fluid Mech., vol. 156, 1985, pp. 505–531), is reported for a broad range of conditions. We recover the Λ(c) ∝ c−6 scaling without wind forcing for sufficiently steep wave fields. A scaling of Λ(c) based solely on the root-mean-square slope and peak wave phase speed is shown to describe the modelled breaking distributions well. The modelled breaking distributions are in good agreement with field measurements and the proposed scaling can be applied successfully to the observational data sets. The present work paves the way for simulations of the turbulent upper ocean directly coupled to a realistic breaking wave dynamics, including Langmuir turbulence, and other sub-mesoscale processes.

Jiarong Wu↗
DOE OSTI2025

Resonance ultrasound prediction of residual stress within a hybrid layer for additively manufactured samples

Hybrid additive manufacturing (AM) involves secondary processes or energy sources to alter specified locations within the build volume. Each hybrid step can refine the grain size, increase dislocation density, or modify residual stresses. Typically, the changes in mechanical properties are not confined within a single layer but have a compounding effect on preceding layers. Existing methods of measuring AM residual stress are limited in terms of their sensitivity, or they are destructive measurements. We propose using resonant ultrasound spectroscopy (RUS) to measure the residual stress in hybrid-AM components noninvasively, based on changes to the resonances, compared to a stress-free component. In this paper, we use finite element models to simulate residual stress in hybrid-AM components and to examine the sensitivity of RUS measurements in terms of frequency shifts and mode shapes with respect to single hybrid layers. Then, the RUS results are used to predict stress for a layer at a known location with unknown stress. Here, the approach highlights the capabilities of RUS to address an AM characterization challenge.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Dimensional crossover and band topology evolution in ultrathin semimetallic NiTe 2 films

Nickel ditelluride (NiTe 2 ), a recently discovered Type-II Dirac semimetal with topological Dirac fermions near the Fermi energy, is expected to exhibit strong thickness-mediated electronic tunability and intrinsic two-gap superconductivity in the single-layer limit. Realizing such intriguing phenomena requires the fabrication of ultrathin NiTe 2 films and an understanding of the underlying physics that is still under debate. By conducting experimental band mappings of ultrathin films prepared with molecular beam epitaxy, we reveal spectroscopic evidence for the dimensionality crossover of single-crystalline ultrathin NiTe 2 films as a function of film thickness. As the film thickness increases from one to five layers, the gap in the conical topological surface states closes. Comparisons of experimental to first-principles results also highlight difficulties in fabricating atomically smooth single-layer NiTe 2 films. Our results not only provide further impetus for studying emergent phenomena in NiTe 2 but also underscore the limitations of fabricating NiTe 2 films for device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structural and electronic switching of a single crystal 2D metal-organic framework prepared by chemical vapor deposition

The incorporation of metal-organic frameworks into advanced devices remains a desirable goal, but progress is hindered by difficulties in preparing large crystalline metal-organic framework films with suitable electronic performance. We demonstrate the direct growth of large-area, high quality, and phase pure single metal-organic framework crystals through chemical vapor deposition of a dimolybdenum paddlewheel precursor, Mo 2 (INA) 4 . These exceptionally uniform, high quality crystals cover areas up to 8600 µm 2 and can be grown down to thicknesses of 30 nm. Moreover, scanning tunneling microscopy indicates that the Mo 2 (INA) 4 clusters assemble into a two-dimensional, single-layer framework. Devices are readily fabricated from single vapor-phase grown crystals and exhibit reversible 8-fold changes in conductivity upon illumination at modest powers. Moreover, we identify vapor-induced single crystal transitions that are reversible and responsible for 30-fold changes in conductivity of the metal-organic framework as monitored by in situ device measurements. Gas-phase methods, including chemical vapor deposition, show broader promise for the preparation of high-quality molecular frameworks, and may enable their integration into devices, including detectors and actuators.

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

Direct Visualization of Surface Spin-Flip Transition in MnBi 4 Te 7

Here, we report direct visualization of spin-flip transition of the surface layer in antiferromagnet MnBi 4 Te 7 , a natural superlattice of alternating MnBi 2 Te 4 and Bi 2 Te 3 layers, using cryogenic magnetic force microscopy (MFM). The observation of magnetic contrast across domain walls and step edges confirms that the antiferromagnetic order persists to the surface layers. The magnetic field dependence of the MFM images reveals that the surface magnetic layer undergoes a first-order spin-flip transition at a magnetic field that is lower than the bulk transition, in excellent agreement with a revised Mills model. Our analysis suggests no reduction of the order parameter in the surface magnetic layer, implying robust ferromagnetism in the single-layer limit. The direct visualization of surface spin-flip transition not only opens up exploration of surface metamagnetic transitions in layered antiferromagnets, but also provides experimental support for realizing quantized transport in ultrathin films of MnBi 4 Te 7 and other natural superlattice topological magnets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

On-Demand Designing of Cathode Internal Surface Architecture for Dramatic Enhancement of SOFC Performance and Durability

This project is aimed to design and modify the internal surfaces of porous composite cathode from currently commercially viable Solid Oxide Fuel Cells (SOFCs), using additive manufacturing process of Atomic Layer Deposition (ALD). The material systems being investigated are commercial composite electrodes complex three-dimensional topographies. In term of the chemistry of the ALD layer applied on the internal surface of the porous cathode, this project has employed commercially relevant electrolyte, electrocatalyst and noble metal materials set. Such materials are fully compatible with the commercial fuel cells, and this project has developed special nanostructure on the surface of the commercial composite cathodes. The formation of the designed nanoarchitecture on the surface of SOFC cathode has been achieved through precise control of ALD parameters and their effect on overall cell performance and resultant electrochemical reaction mechanism of cathodes has been investigated through full cell electrochemical performance testing and nanostructure characterization by transmission electron microscopy (TEM). Under the support of this award, following has been achieved: (1). For cathode materials in solid oxide fuel cells (SOFCs), such as perovskite mixed conductor La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-x (LSCF), cation surface segregation and consequently losing conductivity and active sites for the oxygen reduction reaction (ORR) are problematic. To mitigate the cation segregation and enhance SOFC durability, further decorating the internal backbone surface using the desired electrocatalysts could be a promoting approach. Commonly, the cation segregation such as Sr is very volatile, so the effective surface decoration is ideally conformal. Nevertheless, the conformal surface coating would inevitably alter the ORR pathways that initially take place on the surface of the backbone. To reveal the impact of the conformal coating on both the catalytic activity and the conductivity of the cathode, the unary electrocatalyst of Pt or CoO x , was applied to the LSCF/SDC composite electrode of inherently functional SOFCs, respectively. Both ALD coating layers evolve strong interaction with the LSCF composite cathode. Upon operations, the Pt coating layer remains conformal on LSCF grain surfaces but turns into discrete particles on SDC grain surfaces. Meanwhile, CoO x conformal coating grows to be the discrete nanograins on both the LSCF and SDC grains. ALD coating of the cathode alone reduces the ohmic resistance up to 28 % for the entire cells. The increased conductivity induced by the ALD coating of Pt or CoO x is ascribed to different mechanisms. For the inherent functional SOFCs, the present study presents a novel and feasible approach to apply a conformal, dense coating layer on the surface of a mixed conductor, simultaneously increasing the conductivity and durability of the SOFC cathode. (2). High resistance of the oxygen electrode still significantly hinders the state-of-the-art Solid Oxide Fuel Cells (SOFCs). In particular, for an oxygen electrode consisting of mixed electronic and ionic conductors, such as perovskite lanthanum strontium cobalt ferrite (LSCF), it deteriorates due to its low chemical stability of the grain surface. Such degradation is often associated with the segregation of cations. To prevent the cation surface segregation and its resultant perovskite phase decomposition, we demonstrate a conformal ultra-thin (7-10 nm) surface heterogeneous coating layer consisting of subjacent discrete Pt nanoparticles capped with a superjacent fully dense conformal CoO x layer. The performance studies indicate the ALD coating reduces the cell series resistance by up to 40 %. The conformal CoO x layer consists of randomly orientated but single-layered nanograins, with high-density intergranular and surface grain boundaries serving as the electrochemical reaction sites and facilitating mass transport. The conformal coating layer appears to have successfully suppressed the Sr outward diffusion and confined the Sr enriched layer to a ~ 2 nm interface perovskite phase between the coating layer and the LSCF grain surface. Moreover, this ultra-thin Sr enriched perovskite layer presumably possesses high oxygen vacancy and high ionic conductivity and further imposes tensile strain to the LSCF grain surfaces. With the combination of a conformal CoO x nanoionics, Sr enriched layer, and its strained interface, the ALD coating induced surface layer is estimated to have a conductivity of ~ 1.27x10 4 S/cm, which is over two orders magnitude of that from LSCF at 750 ºC.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1984

Clouds above the Martin Limb: Viking observations

Whenever Viking Orbiter images included the limb of Mars, they recorded one or more layers of clouds above the limb. The height above the limb and the brightness (reflectivity) of these clouds were determined in a selected group of these images. Normalized individual brightness profiles of three separate traverses across the limb of each image are shown. The most notable finding is that some of these clouds can be very high. Many reach heights of over 60 km, and several are over 70 km above the limb. Statistically, the reflectivity of the clouds increases with phase angle. Reflectivity and height both appear to vary with season, but the selected images spanned only one Martian year, so the role of seasons cannot be isolated. Limb clouds in red-filter images tend to be brighter than violet-filter images, but both season and phase appear to be more dominant factors. Due to the limited sample available, the possible influences of latitude and longitude are less clear. The layering of these clouds ranges from a single layer to five or more layers. Reflectivity gradients range from smooth and gentle to steep and irregular.

Martin, L. J.↗
NASA NTRS2004

NASA Tech Briefs, September 2004

Topics covered include: Brazing SiC/SiC Composites to Metals; Composite-Material Tanks with Chemically Resistant Liners; Thermally Conductive Metal-Tube/Carbon-Composite Joints; Improved BN Coatings on SiC Fibers in SiC Matrices; Iterative Demodulation and Decoding of Non-Square QAM; Measuring Radiation Patterns of Reconfigurable Patch Antennas on Wafers; Low-Cutoff, High-Pass Digital Filtering of Neural Signals; Further Improvement in 3DGRAPE; Ground Support Software for Spaceborne Instrumentation; MER SPICE Interface; Simulating Operation of a Planetary Rover; Analyzing Contents of a Computer Cache; Discrepancy Reporting Management System; Silicone-Rubber Microvalves Actuated by Paraffin; Hydraulic Apparatus for Mechanical Testing of Nuts; Heat Control via Torque Control in Friction Stir Welding; Manufacturing High-Quality Carbon Nanotubes at Lower Cost; Setup for Visual Observation of Carbon-Nanotube Arc Process; Solution Preserves Nucleic Acids in Body-Fluid Specimens; Oligodeoxynucleotide Probes for Detecting Intact Cells; Microwave-Spectral Signatures Would Reveal Concealed Objects; Digital Averaging Phasemeter for Heterodyne Interferometry; Optoelectronic Instrument Monitors pH in a Culture Medium; Imaging of gamma-Irradiated Regions of a Crystal; Photodiode-Based, Passive Ultraviolet Dosimeters; Discrete Wavelength-Locked External Cavity Laser; Flexible Shields for Protecting Spacecraft Against Debris; Part 2 of a Computational Study of a Drop-Laden Mixing Layer; Controllable Curved Mirrors Made from Single-Layer EAP Films; and Demonstration of a Pyrotechnic Bolt-Retractor System.

Source record↗
DOE OSTI2025

Measurement and analysis of the Doppler broadened energy spectra of gamma radiation originating from the annihilation of positrons incident on clean and adsorbate-covered surfaces

We present measurements and theoretical modeling demonstrating the capability of coincidence Doppler broadened (CDB) annihilation gamma spectroscopy to provide element-specific information from the topmost atomic layer of surfaces. Our measurements show that the energy spectra of Doppler-shifted annihilation gamma photons emitted following the annihilation of positrons from the topmost atomic layers of clean and adsorbate covered surfaces of gold (Au), silver (Ag) and copper (Cu) differ significantly. The shape of the Doppler-broadened gamma spectrum, as analyzed using ratio curves, indicates that the elemental composition of the surface can still be identified despite contributions from positronium annihilation and a significant reduction in core electron annihilation. We estimate the chemical composition of the various probed surfaces by modeling the ratios of the measured Doppler spectra with respect to the Doppler spectra from a clean Cu surface using a linear combination of calculated ratio curves. The fitting of the experimental ratio curves was used to obtain an estimate of the elemental composition of Cu surfaces with sulfur segregation, oxygen adsorption, a thin film of Selenium (Se), and a single layer of graphene (SLG). A similar analysis was performed on the Ag surface with environmental adsorbates, the same surface after argon ion sputtering, as well as a sputter cleaned Au surface. The surface compositions obtained from the analysis of the CDB data were compared to the compositions obtained using positron annihilation induced Auger electron spectroscopy (PAES). Our results show that CDB can detect, identify, and quantify, sub-monolayer adsorbates and a single atomic layer deposited on metal substrates.

Lotfimarangloo, Sima [Univ. of Texas, Arlington, T↗
NASA NTRSDate not supplied

Ice-Over-Water Cloud Properties in an Artificial Neural Network Approach

Clouds are a crucial component of the atmospheric energy system, particularly the radiative balance within, above, and below the troposphere. The vertical distribution of cloud mass and phase determines layer heating rates, the loss of radiation to space, and the amount of radiative heating at the surface. Thus, it is important to know how clouds are distributed both vertically and horizontally at all times of day. Satellite remote sensing is the only approach available to monitor clouds day and night around the globe. In this paper several artificial neural network (ANN) algorithms, employing several Aqua MODIS infrared channels, profiles of relative humidity and temperature from GMAO numerical weather analyses, and the retrieved total cloud visible optical depth, are trained to detect multilayer ice-over-water cloud systems and to retrieve some of their properties as identified by a year of 2008 Aqua MODIS data matched with CloudSat and CALIPSO (CC) cloud profiles. The CC lidar and radar profiles provide the vertical structure that serves as output truth for the multilayer algorithm. The neural networks were trained using one year (2008) of cloud top height data from the CC dataset, with correlation around 0.94 (0.95) and MAE as low as 0.82 (0.81) km for nonpolar regions during the day (night). Applying the trained ANN to independent year 2009 MODIS data resulted in a combined ML and single layer hit rate of 86.4% (85.1%) for nonpolar regions during the day (night). Since the ANN is trained using near-nadir MODIS pixels, infrared radiance corrections were developed as a function of view zenith angle from MODIS and applied to off-nadir pixels when processing MODIS swath data. The multilayer amount derived with the ANN is relatively invariant with increasing view zenith angle compared to the multilayer amount without the corrections.

MODIS↗
NASA NTRSDate not supplied

Ice-Over-Water Cloud Identification in an Artificial Neural Network Approach

An artificial neural network (ANN) algorithm, employing several Aqua MODIS channels, the retrieved cloud phase and total cloud visible optical depth, and temperature and humidity vertical profiles is trained to detect multilayer (ML) ice-over-water cloud systems identified by matched 2008 CloudSat and CALIPSO (CC) data. The trained MLANN was applied to 2009 MODIS data resulting in combined ML and single layer detection accuracies of 87% (89%) and 86% (89%) for snow-free (snow-covered) regions during the day and night, respectively. When corrected for the viewing-zenith-angle dependence of each parameter, the ML fraction detected is relatively invariant across the swath. Compared to the CC ML variability, the MLANN is robust seasonally and interannually, and produces similar distribution patterns over the globe, except in the polar regions. Additional research is needed to conclusively evaluate the VZA dependence and further improve the MLANN accuracy. This approach should greatly improve the monitoring of cloud vertical structure using operational passive sensors.

MODIS↗
DOE OSTI2022

Multi-layer anodes for high-current applications

In this work, we present a combination of experiments and modeling of a two-layer anode structure designed by EnPower Inc. for high-energy and fast-charge capabilities. The anode consists of lower porosity near the current collector and higher porosity near the separator with comparable active materials in both regions. A pseudo-two-dimensional electrochemical–thermal model was designed to represent the performance of this electrode. Simulations, consistent with experiments, show superior ion transport and lithiation in the multi-layer anode (MLA) compared to a conventional single-layer anode (SLA). Surprisingly, this improved transport in MLA manifests as enhanced cathode performance during high-rate discharge and this, in turn, provides higher energy density for MLA. Similarly, during fast charge, less irreversible lithium is deposited due to this improved transport in MLA, and hence MLA exhibits less capacity fade compared to SLA. Polarization analysis demonstrates marginally lower cumulative overpotential for MLA in different case studies; however, MLA cells maintain a significantly higher capacity in the same conditions and have more than double the cycle life. Further, this means despite the apparently limited polarization benefit provided by MLA; the MLA structure can more reliably be employed in cell designs. Additional design changes are also analyzed by means of the model.

25 ENERGY STORAGE↗
DOE OSTI2026

Robustness of the smartpixels classifier for different simulated sensor geometries and non-ideal detector conditions

Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel detector in high-rate online event selection, such as the ATLAS or CMS first-level trigger systems. This data reduction can be accomplished with a neural network (NN) in the readout chip bonded with the sensor that recognizes and rejects tracks with low transverse momentum (p T ) based on the geometrical shape of the charge deposition (“cluster”). To design viable detectors for deployment, the dependence of the NN as a function of the sensor geometry, external magnetic field, irradiation, and noise must be understood. In this paper, we present first studies of the efficiency and data reduction for planar pixel sensors exploring these parameters. For the CMS HL-LHC sensor geometry, we obtain a signal efficiency of (91.9 ± 0.7)% and a data reduction of (29.7 ± 1.0)%. A smaller sensor pitch in the bending direction improves the p T discrimination, but a larger pitch can be partially compensated with detector thickness. Any accumulated radiation damage also changes the cluster shape, reducing the signal efficiency compared to the baseline by approximately 30–60% in absolute terms, but nearly all of the performance can be recovered through retraining of the network and updating the weights. Finally, the impact of noise was investigated, and retraining the network on noise-injected datasets was found to maintain performance within 6% of the baseline network trained and evaluated on noiseless data. •ASIC-compatible track-momentum classifier is robust in realistic detector conditions.•About 90% signal efficiency and 30% data reduction per layer for CMS HL-LHC geometry.•Single-layer signal efficiency increases for smaller pixel pitch or thicker sensors.•Performance with noise or after radiation damage mostly recovered by retraining.

Shekar, Danush [Illinois U., Chicago] (ORCID:00000↗
DOE OSTI2021

Application-driven synthesis and characterization of hexagonal boron nitride deposited on metals and carbon nanotubes

Hexagonal boron nitride (h-BN) is unique among two-dimensional materials, with a large band gap (~6 eV) and high in-plane thermal conductivity (>400 W m -1 K -1 ), second only to diamond among electrical insulators. Many studies to date have relied on exfoliated h-BN, however, for large-scale applications the material must be synthesized by methods such as chemical vapor deposition (CVD). Here, we first investigate single-layer h-BN synthesized by CVD on single crystal platinum (Pt), comparing these films with h-BN deposited on more commonly used polycrystalline Pt and Cu. The h-BN film grown on single crystal Pt has the lowest surface roughness and best spatial homogeneity, and our electrochemical transfer process allows the Pt to be reused with no measurable degradation. Additionally, we also demonstrate direct capping of carbon nanotubes (CNTs) with as-grown h-BN, but we find that the direct growth partly degrades the CNT electrical conductivity. On the other hand, we show that transferred monolayer h-BN can serve as an ultrathin barrier which protects MoS2 from damage at high temperatures and discuss other applications that take advantage of the conformal h-BN deposition.

2D materials↗