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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Operator Relaxation and the Optimal Depth of Classical Shadows

Classical shadows are a powerful method for learning many properties of quantum states in a sample-efficient manner, by making use of randomized measurements. Here we study the sample complexity of learning the expectation value of Pauli operators via “shallow shadows,” a recently proposed version of classical shadows in which the randomization step is effected by a local unitary circuit of variable depth t. Here we show that the shadow norm (the quantity controlling the sample complexity) is expressed in terms of properties of the Heisenberg time evolution of operators under the randomizing (“twirling”) circuit—namely the evolution of the weight distribution characterizing the number of sites on which an operator acts nontrivially. For spatially contiguous Pauli operators of weight k, this entails a competition between two processes: operator spreading (whereby the support of an operator grows over time, increasing its weight) and operator relaxation (whereby the bulk of the operator develops an equilibrium density of identity operators, decreasing its weight). From this simple picture we derive (i) an upper bound on the shadow norm which, for depth t~log⁡(k), guarantees an exponential gain in sample complexity over the t=0 protocol in any spatial dimension, and (ii) quantitative results in one dimension within a mean-field approximation, including a universal subleading correction to the optimal depth, found to be in excellent agreement with infinite matrix product state numerical simulations. Our Letter connects fundamental ideas in quantum many-body dynamics to applications in quantum information science, and paves the way to highly optimized protocols for learning different properties of quantum states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2018

WFIRST CGI Integral Field Spectrograph Performance and Post-Processing in the OS6 Observing Scenario

The WFIRST coronagraph instrument (CGI) will have an integral field spectrograph (IFS) backend to disperse the entire field of view at once and obtain spatially-resolved, low-resolution spectra of the speckles and science scene. The IFS will be key to understanding the spectral nature of the speckles, obtain science spectra of planets and disks, and will be used for broadband wavefront control. In order to characterize, predict, and optimize the performance of the instrument, we present a detailed model of the IFS in the context of the new OS6 observing scenario. The simulation includes spatial, spectral, and temporal variations of the speckle field on the IFS detector plane, which allows us to explore several post-processing methods and assess what gains can be expected. The simulator includes the latest models of the detector behavior when operating in photon-counting mode.

WFIRST coronagraph instrument (CGI)↗
DOE OSTI2023

Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs

Fully distributed, integrated surface–subsurface hydrological models (ISSHMs) have seen renewed interest due to availability of better software, high performance computing facilities, and high-resolution, spatially extensive data products. ISSHMs are valuable as tools for advancing system understanding as they can resolve multiple processes defined on the plot scale including three-dimensional interaction of surface water and groundwater. Here, we evaluated the performance of an ISSHM, the Advanced Terrestrial Simulator (ATS), on seven diverse catchments across the continental US using widely available data products to define model inputs without calibration. We compare the ATS-simulated streamflow and evapotranspiration with gauge observations and MODIS-derived evapotranspiration, respectively. Using the Kling-Gupta Efficiency (KGE) as metric, ATS with default data products performed reasonably well at 6 of 7 catchments for streamflow. However, in one of those 6 catchments ATS had poor performance on baseflow and ATS’s overall performance was thus judged to be inadequate despite the acceptable KGE. ATS performance for evapotranspiration was good in all 7 catchments using default data products. In the two catchments where ATS streamflow performance using default data products was not acceptable, the performance was significantly improved by using local information on subsurface properties below the soil. We also compare the model-simulated streamflow and evapotranspiration with the Sacramento soil moisture accounting (SAC-SMA) model, a semi-distributed model that was calibrated on a catchment-by-catchment basis. Uncalibrated ATS performance is comparable to the calibrated SAC-SMA model in terms of streamflow while ATS performance is similar to or better (much better in certain catchments) in reproducing MODIS-derived evapotranspiration. Reasonably good performance of ATS without catchment-specific calibration provides new confidence in the ISSHM class of models and community data products as tools for advancing understanding of watershed function in a changing environment.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

The Goddard Profiling Algorithm (GPROF): Description and Current Applications

Atmospheric scientists use different methods for interpreting satellite data. In the early days of satellite meteorology, the analysis of cloud pictures from satellites was primarily subjective. As computer technology improved, satellite pictures could be processed digitally, and mathematical algorithms were developed and applied to the digital images in different wavelength bands to extract information about the atmosphere in an objective way. The kind of mathematical algorithm one applies to satellite data may depend on the complexity of the physical processes that lead to the observed image, and how much information is contained in the satellite images both spatially and at different wavelengths. Imagery from satellite-borne passive microwave radiometers has limited horizontal resolution, and the observed microwave radiances are the result of complex physical processes that are not easily modeled. For this reason, a type of algorithm called a Bayesian estimation method is utilized to interpret passive microwave imagery in an objective, yet computationally efficient manner.

Olson, William S.↗
NASA NTRS2023

Marin County Wildfires Ii: Improving Fire Suppression Modeling to Inform Fire Prevention and Suppression Decisions in Marin County, Ca

A future of increased wildfires requires greater integration of spatial analysis and local knowledge of emergency responders. We examine the application of a Potential Operational Delineations (PODs) framework for strategic pre-fire planning in Marin County. PODs are spatial units for wildfire management that combine predictive modeling and local firefighter expertise to identify potential control locations as unit boundaries and assess the difficulty of suppression within units. Additionally, this project explores the integration of road networks and social vulnerability to assess environmental justice in evacuation safety. This project constitutes a novel application of the PODs framework as it integrates expertise from Marin County senior firefighters with a Fireline Location Model (FLM) to achieve POD definition and uses a Suppression Difficulty Score (SDS) to rank each POD. The FLM uses network analysis and hydrologic modeling to identify key roads and ridgelines as boundaries and combines them with expert knowledge, in the form of workshops, to construct PODs. Once identified, PODs are classified using SDS, which includes processed inputs such as LiDAR-derived aboveground biomass, ECOSTRESS Evaporative Stress Index, land use cover type from Sentinel-2 Imagery, and a digital elevation model. Environmental justice for evacuation safety incorporated three key road metrics such as connectivity, travel area, and exit capacity, the Social Vulnerability Index from the Center for Disease Control, and cell coverage to determine a final Evacuation Difficulty Score. Results indicate a strong link between road networks as primary POD boundaries, with ridgelines and waterways as secondary and tertiary locations. Specifically, we find 78.5% of expertise-identified POD boundaries align with FLM-determined boundaries. More validation is needed to support this process; however, initial results signal a feasible framework to integrate expertise and spatial analysis in local level strategic fire planning

Wildfire modeling↗
NASA NTRS1972

Physical electronics and surface physics

Current analytical and experimental investigations of the influence of surface processes on the rate of permeation of gases through solids are summarized. A brief description is presented of an analytical model that provides an approximate prediction of the dependence of the permeation rate on the rates of surface processes relative to the rate of the diffusion process. Results of experiments indicate that the desorption process of hydrogen from various materials is influenced by surface impurities to the degree that substantial changes in the spatial distribution of the desorbed molecules are observed when the impurities are varied. Except possibly for H-Nb, the permeation measurements indicate that the desorption process is not influenced to the degree that detectable changes in the permeation rates are observed when the impurities and temperatures are varied. On the basis of analysis, it is unlikely that the desorption process will be the rate-controlling step under the conditions of the present experiments.

Stickney, R. E.↗
NASA NTRS2006

Observed Scaling Behavior of Snow and Soil Moisture Signatures from the CLPX and NAFE Campaigns

Seasonal snow and soil moisture (SM) are important elements of the Earth's water cycle, acting as storage elements and by modulating water and energy exchanges between the land surface and the atmosphere and space. Accurate estimates of snow water equivalent (SWE) and SM also play an important role in weather and climate models over a range of scales. There are many advantages for the retrieval of snow parameters using passive microwave sensors. However, the current scale mismatch between many high-resolution applications and available satellite snow sensor footprints (approx.25km) presents challenges. There is also a scale mismatch for planned L-band soil moisture satellite measurements with their large (50-100 km) passive microwave footprints. Combined radiometer-radar schemes are being explored as possible aids, but this is still an area of ongoing research. Retrieval accuracies are limited in part by complex, and potentially nonlinear relationships between SWE or SM and microwave emission signatures. We will use airborne and satellite observations from two field campaigns to examine the scaling behavior of brightness temperatures and SWE and SM across scales from 100 meters to 10's of kilometers. The results will provide examples of the appropriate spatial sampling scales for new sensors, and guidance for downscaling schemes. The two campaigns are NASA's Cold Land Processes Experiment- 1 (CLPX- 1 : Colorado, 2002 & 2003), and the Australian National Airborne Field Experiment (NAFE, Nov, 2005). The analyses will also illustrate the roles and spatial scales of the underlying phenomena (e.g., vegetation, topography) that control subpixel heterogeneity.

Kim, Edward J.↗
DOE OSTI2021

Computational Tools for Additive Manufacture of Tailored Microstructure and Properties

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Currently most additive builds use a single set of process parameters (e.g. laser power and scan speed) which results in a part with a homogenous microstructure that provides a singular performance level. To move beyond this state, Raytheon Technologies Research Center worked to create a set of computational tools to track material evolution through each step of the additive process. Computational fluid dynamics and phase field models for microstructure evolution as a function of processing parameters, and crystal plasticity models fully coupling microstructure and mechanical properties for performance predictions were leveraged to establish a connection between additive parameters and the final microstructure. This framework was utilized to tailor spatially-varying mechanical properties in a part by appropriately controlling the microstructure evolution during the additive process. Specifically, a turbine blade was 3D printed from nickel superalloy IN718 using laser powder bed fusion with coarse grains in the airfoil section which experiences the highest temperatures and is creep limited while finer grains were printed in the root of the blade which experience higher stresses but at lower temperatures and is therefore fatigue limited. The benefit of being able to intentionally insert coarse grains in the high temperature region of the blade was showcased with a microstructure sensitive creep model that indicates longer creep life for coarser grains.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Electrolyte Design for Fast‐Charging Lithium‐Based Batteries

Fast charging is essential for the widespread adoption of lithium (Li)-ion batteries, but it is fundamentally limited by sluggish interfacial kinetics, Li plating, and electrolyte instability at high current densities. Over the past decade, electrolyte engineering has emerged as a key strategy to address these challenges. This review summarizes the development of fast-charging electrolytes over the past ten years and outlines a design framework. Electrolyte formulations are first deconstructed into their main components—solvents, salts, and functional additives—and representative strategies for tuning solvation structure and interphase chemistry are discussed to suppress Li plating and improve interfacial kinetics. The discussion then extends to advanced electrolyte systems, particularly localized high-concentration electrolytes (LHCEs), and their compatibility with different anode chemistries. Advanced characterization techniques are also summarized and categorized based on destructiveness, spatial and temporal resolution, quantitative analysis, and the chemical species or processes probed across multiple length scales. Recent progress in AI-enabled electrolyte discovery and battery management system (BMS) strategies for optimized fast-charging protocols is further highlighted. Finally, perspectives are presented on translating electrolyte innovations from academic research to practical applications, with emphasis on cell format, realistic operating conditions, and manufacturability.

25 ENERGY STORAGE↗
DOE OSTI2020

A Surface‐Strained and Geometry‐Tailored Nanoreactor that Promotes Ammonia Electrosynthesis

Abstract A surface‐strained and geometry‐optimized TiO 2 nanoreactor enhances the performance of electrocatalytic nitrogen fixation. The nanotubular confinement allows spatial regulation of the mass transport of nitrogen during the NRR process and offers an enlarged surface area, thus boosting the ammonia production with high selectivity. Both experimental and theoretical evidence support strained Ti 3+ sites, demonstrating a more favorable pathway for the N 2 activation and selective NH 3 production with a faster kinetic rate than the pristine TiO 2 . The TiO 2 ‐based nanoreactor with surface and bulk structure tailoring delivered an NH 3 yield rate up to 5.50 μg h −1 cm −2 (16.67 μg h −1 mg cat −1 ) and high faradaic efficiency of 26 % under ambient aqueous conditions. Our findings highlight the concept of lattice strain and geometry modified nanoreactors, which will have broad implications in the renewable energy catalysis and electrosynthesis of valuable products.

Li, Panpan↗
DOE OSTI2020

A Surface-Strained and Geometry-Tailored Nanoreactor that Promotes Ammonia Electrosynthesis

A surface-strained and geometry-optimized TiO 2 nanoreactor enhances the performance of electrocatalytic nitrogen fixation. The nanotubular confinement allows spatial regulation of the mass transport of nitrogen during the NRR process and offers an enlarged surface area, thus boosting the ammonia production with high selectivity. Both experimental and theoretical evidence support strained Ti 3+ sites, demonstrating a more favorable pathway for the N 2 activation and selective NH 3 production with a faster kinetic rate than the pristine TiO 2 . The TiO 2 -based nanoreactor with surface and bulk structure tailoring delivered an NH 3 yield rate up to 5.50 μg h -1 cm -2 (16.67 μg h -1 mg cat -1 ) and high faradaic efficiency of 26 % under ambient aqueous conditions. Furthermore, our findings highlight the concept of lattice strain and geometry modified nanoreactors, which will have broad implications in the renewable energy catalysis and electrosynthesis of valuable products.

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

Controlling Protein Enrichment in Lipid Sponge Phase Droplets using SNAP–Tag Bioconjugation

All cells use organized lipid compartments to facilitate specific biological functions. Membrane-bound organelles create defined spatial environments that favor unique chemical reactions while isolating incompatible biological processes. Despite the fundamental role of cellular organelles, there is a scarcity of methods for preparing functional artificial lipid-based compartments. In this work, we demonstrate a robust bioconjugation system for sequestering proteins into zwitterionic lipid sponge phase droplets. Incorporation of benzylguanine (BG)-modified phospholipids that form stable covalent linkages with an O 6 -methylguanine DNA methyltransferase (SNAP-tag) fusion protein enables programmable control of protein capture. We show that this methodology can be used to anchor hydrophilic proteins at the lipid-aqueous interface, concentrating them within an accessible but protected chemical environment. SNAP-tag technology enables the integration of proteins that regulate complex biological functions in lipid sponge phase droplets, and should facilitate the development of advanced lipid-based artificial organelles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Small Molecule Inhibitor-Modulated Al 2 O 3 Atomic Layer Deposition on Monolayer MoS 2 for Controlled Nucleation

The integration of ultrathin dielectrics on two-dimensional (2D) semiconductors is essential for advancing beyond-Si electronics. However, the intrinsic inertness of van der Waals 2D basal planes remains a primary bottleneck to achieving uniform dielectric nucleation and growth. Here, in this study, we introduce a small molecule inhibitor (SMI)-modulated thermal atomic layer deposition (ALD) strategy, exemplified by aluminum oxide (Al 2 O 3 ) ALD on monolayer molybdenum disulfide (1L MoS 2 ) with acetic acid (HAc) SMI. The ABC-type sequence comprises HAc inhibitor (A), trimethylaluminum (TMA) precursor (B), and deionized H 2 O coreactant (C). In situ quartz crystal microbalance (QCM) studies reveal robust HAc adsorption on Al 2 O 3 and suppression of subsequent oxide growth on HAc-passivated surfaces. When applied to 1L MoS 2 , this inhibitory pathway enables HAc to selectively passivate nascent Al 2 O 3 nuclei formed on the MoS 2 surface, limiting their three-dimensional (3D) island coarsening and redirecting precursor adsorption toward the uncovered basal plane. Consequently, nearly continuous ultrathin (∼1.5 nm) Al 2 O 3 films are achieved on 1L MoS 2 with markedly improved uniformity compared to standard Al 2 O 3 ALD using TMA and H 2 O, as validated by atomic force microscopy (AFM), cross-sectional scanning transmission electron microscopy (STEM), and energy-dispersive X-ray spectroscopy (EDS). Density functional theory (DFT) calculations further provide atomistic insight into HAc-modulated Al 2 O 3 nucleation, corroborating the energetic preference of HAc for Al 2 O 3 over MoS 2 and attenuated TMA adsorption on HAc-passivated surfaces. Spatially resolved Raman spectroscopy also confirms that the HAc-modulated process preserves the structural integrity of 1L MoS 2 , with only minimal strain and doping perturbations observed after dielectric deposition. This SMI-modulated approach offers a broadly applicable framework for controlling ALD nucleation across various inhibitors, ALD chemistries, and 2D materials, opening opportunities for reliable dielectric integration in next-generation nanoelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fate of Internal Waves on a Shallow Shelf

Internal waves strongly influence the physical and chemical environment of coastal ecosystems worldwide. We report novel observations from a distributed temperature sensing (DTS) system that tracked the transformation of internal waves from the shelf break to the surf zone over a narrow shelf slope region in the South China Sea. The spatially continuous view of temperature fields provides a perspective of physical processes commonly available only in laboratory settings or numerical models, including internal wave reflection off a natural slope, shoreward transport of dense fluid within trapped cores, and observations of internal rundown (near-bed, offshore-directed jets of water preceding a breaking internal wave). Analysis shows that the fate of internal waves on this shelf—whether transmitted into shallow waters or reflected back offshore—is mediated by local water column density structure and background currents set by the previous shoaling internal waves, highlighting the importance of wave-wave interactions in nearshore internal wave dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland

Abstract Quantification of methane (CH 4 ) gas emission from peat is critical to understand CH 4 budget from natural wetlands under a climate warming scenario. Previous studies have focused on prediction and mapping of CH 4 emission flux using process‐based models, while application of statistical‐empirical models for upscaling spatially sparse in situ measurements is scarce. In this study, we developed an empirical remote sensing upscaling approach to estimate CH 4 emission flux in the Everglades using limited in situ point‐based CH 4 emission flux measurements and Landsat data during 2013–2018. We spatially and temporally linked in situ data with Landsat surface reflectance based on temporally composite data sets and developed an object‐based machine learning framework to model and map CH 4 emission flux. An ensemble analysis of two machine learning models, k ‐Nearest Neighbor ( k ‐NN) and Support Vector Machine (SVM), shows that the upscaling approach is promising for predicting CH 4 emission flux with a R 2 of 0.65 and 0.87 based on a fivefold cross‐validation for a dry season and wet season estimation, respectively. We generated emission flux map products that successfully revealed the spatial and temporal heterogeneity of CH 4 emission within the dominant freshwater marsh ecosystem in the Everglades. We conclude that Landsat is promising for upscaling and monitoring CH 4 emission flux and reducing the uncertainty in emission estimates from wetlands.

Zhang, Caiyun↗
DOE OSTI2023

Permafrost microbial communities and functional genes are structured by latitudinal and soil geochemical gradients

Permafrost underlies approximately one quarter of Northern Hemisphere terrestrial surfaces and contains 25–50% of the global soil carbon (C) pool. Permafrost soils and the C stocks within are vulnerable to ongoing and future projected climate warming. The biogeography of microbial communities inhabiting permafrost has not been examined beyond a small number of sites focused on local-scale variation. Permafrost is different from other soils. Perennially frozen conditions in permafrost dictate that microbial communities do not turn over quickly, thus possibly providing strong linkages to past environments. Thus, the factors structuring the composition and function of microbial communities may differ from patterns observed in other terrestrial environments. Here, we analyzed 133 permafrost metagenomes from North America, Europe, and Asia. Permafrost biodiversity and taxonomic distribution varied in relation to pH, latitude and soil depth. The distribution of genes differed by latitude, soil depth, age, and pH. Genes that were the most highly variable across all sites were associated with energy metabolism and C-assimilation. Specifically, methanogenesis, fermentation, nitrate reduction, and replenishment of citric acid cycle intermediates. This suggests that adaptations to energy acquisition and substrate availability are among some of the strongest selective pressures shaping permafrost microbial communities. The spatial variation in metabolic potential has primed communities for specific biogeochemical processes as soils thaw due to climate change, which could cause regional- to global- scale variation in C and nitrogen processing and greenhouse gas emissions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Controlled patterning of crystalline domains by frontal polymerization

Materials with hierarchical architectures that combine soft and hard material domains with coalesced interfaces possess superior properties compared with their homogeneous counterparts. These architectures in synthetic materials have been achieved through deterministic manufacturing strategies such as 3D printing, which require an a priori design and active intervention throughout the process to achieve architectures spanning multiple length scales. Here we harness frontal polymerization spin mode dynamics to autonomously fabricate patterned crystalline domains in poly(cyclooctadiene) with multiscale organization. This rapid, dissipative processing method leads to the formation of amorphous and semi-crystalline domains emerging from the internal interfaces generated between the solid polymer and the propagating cure front. The size, spacing and arrangement of the domains are controlled by the interplay between the reaction kinetics, thermochemistry and boundary conditions. Small perturbations in the fabrication conditions reproducibly lead to remarkable changes in the patterned microstructure and the resulting strength, elastic modulus and toughness of the polymer. Furthermore, this ability to control mechanical properties and performance solely through the initial conditions and the mode of front propagation represents a marked advancement in the design and manufacturing of advanced multiscale materials. Drawing inspiration from biological systems in which structural complexity develops through dissipative reaction–diffusion processes, this study explores a transformative synthetic manufacturing strategy aimed at harnessing the principles underpinning morphogenic growth, unlocking new avenues for advanced materials design and fabrication. Synthetic coupled reaction-transport processes offer a versatile yet relatively underexplored method to manipulate the spatial attributes of synthetic materials10. Here we introduce an innovative manufacturing approach based on frontal ring-opening metathesis polymerization (FROMP) that draws parallels with morphogenic growth and development, enabling the formation of patterned microstructures within polymeric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A functional microbiome catalogue crowdsourced from North American rivers

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires knowledge of the spatial drivers of river microbiomes. However, understanding of the core microbial processes governing river biogeochemistry is hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we used a community science effort to accelerate the sampling, sequencing and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). GROWdb profiles the identity, distribution, function and expression of microbial genomes across river surface waters covering 90% of United States watersheds. Specifically, GROWdb encompasses microbial lineages from 27 phyla, including novel members from 10 families and 128 genera, and defines the core river microbiome at the genome level. GROWdb analyses coupled to extensive geospatial information reveals local and regional drivers of microbial community structuring, while also presenting foundational hypotheses about ecosystem function. Building on the previously conceived River Continuum Concept, we layer on microbial functional trait expression, which suggests that the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures, so that it can be widely accessed across disciplines for watershed predictive modelling and microbiome-based management practices.

59 BASIC BIOLOGICAL SCIENCES↗