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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles: REMOTE SENSING OF THERMODYNAMIC PROFILES

A review of remote sensing technology for lower tropospheric thermodynamic (TD) profiling is presented with focus on high accuracy and high temporal-vertical resolution. The contributions of these instruments to the understanding of the Earth system are assessed with respect to radiative transfer, land surface-atmosphere feedback, convection initiation, and data assimilation. We demonstrate that for progress in weather and climate research, TD profilers are essential. These observational systems must resolve gradients of humidity and temperature in the stable or unstable atmospheric surface layer close to the ground, in the mixed layer, in the interfacial layer—usually characterized by an inversion—and the lower troposphere. A thorough analysis of the current observing systems is performed revealing significant gaps that must be addressed to fulfill existing needs. We analyze whether current and future passive and active remote sensing systems can close these gaps. A methodological analysis and demonstration of measurement capabilities with respect to bias and precision is executed both for passive and active remote sensing including passive infrared and microwave spectroscopy, the global navigation satellite system, as well as water vapor and temperature Raman lidar and water vapor differential absorption lidar. Whereas passive remote sensing systems are already mature with respect to operational applications, active remote sensing systems require further engineering to become operational in networks. However, active remote sensing systems provide a smaller bias as well as higher temporal and vertical resolutions. For a suitable mesoscale network design, TD profiler system developments should be intensified and dedicated observing system simulation experiments should be performed.

58 GEOSCIENCES↗
DOE OSTI2015

Comparative study of 0° X-cut and Y + 36°-cut lithium niobate high-voltage sensing

A comparison study between Y + 36° and 0° X-cut lithium niobate (LiNbO{sub 3}) was performed to evaluate the influence of crystal cut on the acoustic propagation to realize a piezoelectric high-voltage sensor. The acoustic time-of-flight for each crystal cut was measured when applying direct current (DC), alternating current (AC), and pulsed voltages. Results show that the voltage-induced shift in the acoustic wave propagation time scaled quadratically with voltage for DC and AC voltages applied to X-cut crystals. For the Y + 36° crystal, the voltage-induced shift scales linearly with DC voltages and quadratically with AC voltages. When applying 5 μs voltage pulses to both crystals, the voltage-induced shift scaled linearly with voltage. For the Y + 36° cut, the voltage-induced shift from applying DC voltages ranged from 10 to 54 ps and 35 to 778 ps for AC voltages at 640 V over the frequency range of 100 Hz–100 kHz. Using the same conditions as the Y + 36° cut, the 0° X-cut crystal sensed a shift of 10–273 ps for DC voltages and 189–813 ps for AC voltage application. For 5 μs voltage pulses, the 0° X-cut crystal sensed a voltage induced shift of 0.250–2 ns and the Y + 36°-cut crystal sensed a time shift of 0.115–1.6 ns. This suggests a frequency sensitive response to voltage where the influence of the crystal cut was not a significant contributor under DC, AC, or pulsed voltage conditions. The measured DC data were compared to a 1-D impedance matrix model where the predicted incremental length changed as a function of voltage. When the voltage source error was eliminated through physical modeling from the uncertainty budget, the combined uncertainty of the sensor (within a 95% confidence interval) decreased to 0.0033% using a Y + 36°-cut crystal and 0.0032% using an X-cut crystal for all the voltage conditions used in this experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Magnetic Field Sensing via Acoustic Sensing Fiber with Metglas® 2605SC Cladding Wires

Magnetic field sensing has the potential to become necessary as a critical tool for long-term subsurface geophysical monitoring. The success of distributed fiber optic sensing for geophysical characterization provides a template for the development of next generation downhole magnetic sensors. In this study, Sentek Instrument’s picoDAS is coupled with a multi-material single mode optical fiber with Metglas® 2605SC cladding wire inclusions for magnetic field detection. The response of acoustic sensing fibers with one and two Metglas® 2605SC cladding wires was evaluated upon exposure to lateral AC magnetic fields. An improved response was demonstrated for a sensing fiber with in-cladding wire following thermal magnetic annealing (~400 °C) under a constant static transverse magnetic field (~200 μT). A minimal detectable magnetic field of ~500 nT was confirmed for a sensing fiber with two 10 μm cladding wires. The successful demonstration of a magnetic field sensing fiber with Metglas® cladding wires fabricated via traditional draw processes sets the stage for distributed measurements and joint inversion as a compliment to distributed fiber optic acoustic sensors.

Dejneka, Zach (ORCID:0000000179415708)↗
DOE OSTI2024

Magnetic Field Sensing via Multi-Material Acoustic Sensing Optical Fibers with Magnetostrictive Cladding Inclusions

In this conducted research, optical fiber sensors are used to measure low strength alternating magnetic fields. Various fiber sensor configurations are tested and investigated to demonstrate sensing capabilities at different field magnitudes and frequencies. Distributed acoustic sensing fibers (DAS) have been largely studied and documented across a variety of applications and sensing systems. This research uses the DAS technology in tandem with magnetostrictive materials to create a distributed multi-material optical fiber magnetic sensor. Magnetic sensing has high demand across different fields and often runs into challenges of extreme environments including high temperature, corrosion, and areas with poor accessibility. The robust and distributed nature of optical fiber sensors which can be cheaply produced for long lengths is an attractive option over other single point magnetic sensors. In down hole applications specifically, having a distributed sensor able to be deployed easily and over long distances for magnetic sensing would be a large improvement to bulkier traditional magnetometers. In the conducted study, different magnetostrictive materials are implemented in distributed optical fiber sensors to analyze and compare the effective sensitivity and potential commercial viability. Nickel, galfenol alloy, and MetGlas alloy inclusions are drawn into fused silica optical fibers with Bragg gratings inscribed later on for DAS capability. Each was investigated for its response to varying AC magnetic fields to determine relative sensitivity and resolution for distributed magnetic field sensing.

Dejneka, Zach↗
DOE OSTI2021

Crystal structure of a far-red–sensing cyanobacteriochrome reveals an atypical bilin conformation and spectral tuning mechanism

Significance Phytochromes are well-known far-red-light sensors found in plants that trigger adaptive responses to facilitate competition for light capture with neighboring plants. Red- and far-red sensing are also critical for cyanobacteria living in the far-red–enriched shade of plants. This work reports the crystal structure of a far-red–sensing cyanobacteriochrome, a distant cyanobacterial relative of phytochrome. These studies shed insights into the molecular basis of far-red-sensing by phycobilin-based photoreceptors. Owing to the deep tissue penetration of far-red light, far-red–sensing cyanobacteriochromes are promising protein scaffolds for developing genetically encoded photoswitches, optoacoustic contrast agents, and fluorescent probes for in situ imaging and optogenetic applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Metal Oxide Nanoarrays for Chemical Sensing: A Review of Fabrication Methods, Sensing Modes, and Their Inter-correlations

In recent years, engineered nanostructure assemblies such as nanowire arrays have attracted much research attention due to their unique chemical and functional characteristics collectively. The engineered nano-assemblies usually carry the characteristics distinct from bulk as a result of a size effect in their comprised elemental building blocks. The nanoscale size induced high surface-to-volume ratio is a fundamental attribute responsible for various chemical and physical properties required in various technologically important applications such as catalysts and sensors. This review article surveys the latest progress in engineered metal oxide nanostructure arrays, i.e., nanoarrays, for advanced chemical sensors' design and application. It starts with an overview of gaseous chemical sensors followed by surveys of various fabrication methods and routes for metal oxide nanoarrays. Different sensing modes and corresponding applications have been highlighted in the mixed gaseous chemical sensing, which provides new approaches and perspectives to meet the challenges of selective gas sensing, such as the cross-sensitivity and inter-correlation of multiple sensing signals.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Improving Land Surface Temperature Simulation in CoLM Over the Tibetan Plateau through Fractional Vegetation Cover Derived from a Remotely Sensed Clumping Index and Model-Simulated Leaf Area Index

Parameterizations of fractional vegetation cover (FVC) in land surface models have important effects on simulations of surface energy budget, especially in arid and semiarid regions. This study uses a FVC scheme in which FVC is derived from leaf area index and a remotely sensed clumping index. The performance of the new scheme (SMFVC) is evaluated against Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and in situ soil temperature observations, together with two other FVC schemes, a general FVC scheme (CTL) based on land cover map and a climatology-based FVC scheme (RSFVC) that uses long-term remotely sensed Normalized Difference Vegetation Index of MODIS. The three FVC schemes were implemented in the Common Land Model (CoLM) and applied in the Tibetan Plateau using the same forcing data and default parameters. Our results demonstrate that FVC schemes have significant influence on the CoLM performance: (1) the RSFVC and SMFVC schemes significantly reduce the LST biases found in CTL, particularly in grassland and during summer; (2) soil temperature evaluation by in situ observations from three networks on the Tibetan Plateau corroborates the LST results; and (3) the improvements are mainly related to representing temporal (seasonal) variability and subgrid heterogeneity of FVC, which improves surface albedo and surface energy balance. In other words, by including more vegetation characteristics, such as using a clumping index, land surface models may better simulate surface vegetation condition and further better represent the land surface energy budget over the Tibetan Plateau.

Li, Chengwei↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2015

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

Here, soil respiration (R s ), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual R s at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual R s estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zone soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Combined Plasmonic Au-nanoparticle and Conducting Metal Oxide High-temperature Optical Sensing with LSTO

<span style="color: rgba(0, 0, 0, 0.79); font-family: &quot;Source Sans Pro&quot;, source-sans-pro, museo_sans300, museo-sans, Arial, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">Fiber optic sensor technology offers several advantages for harsh-environment applications. However, the development of optical gas sensing layers that are stable under harsh environmental conditions is an ongoing research challenge. In this work, electronically conducting metal oxide lanthanum-doped strontium titanate (LSTO) films embedded with gold nanoparticles are examined as a sensing layer for application in reducing gas flows at high temperature (600–800 °C). A strong localized surface plasmon resonance (LSPR) based response to hydrogen is demonstrated in the visible region of the spectrum, while a Drude free electron-based response is observed in the near-IR. Characteristics of these responses are studied both on planar glass substrates and on silica fibers. Charge transfer between the oxide film and the gold nanoparticles is explored as a possible mechanism governing the Au LSPR response and is considered in terms of the corresponding properties of the conducting metal oxide-based matrix phase. Principal component analysis is applied to the combined plasmonic and free-carrier based response over a range of temperatures and hydrogen concentrations. It is demonstrated that the combined visible and near-IR response of these films provides improved versatility for multiwavelength interrogation, as well as improved discrimination of important process parameters (concentration and temperature) through application of multivariate analysis techniques.</span>

Wuenschell, Jeffrey K.↗
DOE OSTI2018

Changes in iron availability in Arabidopsis are rapidly sensed in the leaf vasculature and impaired sensing leads to opposite transcriptional programs in leaves and roots

The OLIGOPEPTIDE TRANSPORTER 3 (OPT3) has recently been identified as a component of the systemic network mediating iron (Fe) deficiency responses in Arabidopsis. Reduced expression of OPT3 induces an over accumulation of Fe in roots and leaves, due in part by an elevated expression of the IRON-REGULATED TRANSPORTER 1. Here we show however, that opt3 leaves display a transcriptional program consistent with an Fe overload, suggesting that Fe excess is properly sensed in opt3 leaves and that the OPT3-mediated shoot-to-root signaling is critical to prevent a systemic Fe overload. We also took advantage of the tissue-specific localization of OPT3, together with other Fe-responsive genes, to determine the timing and location of early transcriptional events during Fe limitation and resupply. Our results show that the leaf vasculature responds more rapidly than roots to both Fe deprivation and resupply, suggesting that the leaf vasculature is within the first tissues that sense and respond to changes in Fe availability. Our data highlight the importance of the leaf vasculature in Fe homeostasis by sensing changes in apoplastic levels of Fe coming through the xylem and relaying this information back to roots via the phloem to regulate Fe uptake at the root level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions

Here, compressive sensing is a powerful technique for recovering sparse solutions of underdetermined linear systems, which is often encountered in uncertainty quantification analysis of expensive and high-dimensional physical models. We perform numerical investigations employing several compressive sensing solvers that target the unconstrained LASSO formulation, with a focus on linear systems that arise in the construction of polynomial chaos expansions. With core solvers l1_ls, SpaRSA, CGIST, FPC_AS, and ADMM, we develop techniques to mitigate overfitting through an automated selection of regularization constant based on cross-validation, and a heuristic strategy to guide the stop-sampling decision. Practical recommendations on parameter settings for these techniques are provided and discussed. The overall method is applied to a series of numerical examples of increasing complexity, including large eddy simulations of supersonic turbulent jet-in-crossflow involving a 24-dimensional input. Through empirical phase-transition diagrams and convergence plots, we illustrate sparse recovery performance under structures induced by polynomial chaos, accuracy, and computational trade-offs between polynomial bases of different degrees, and practicability of conducting compressive sensing for a realistic, high-dimensional physical application. Across test cases studied in this paper, we find ADMM to have demonstrated empirical advantages through consistent lower errors and faster computational times.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Functionalized silica aerogels for gas-phase purification, sensing, and catalysis: A review

Silica aerogels have a rich history and a unique, fascinating gas-phase chemistry that has lent them to many diverse applications. This review starts with a brief discussion of the fundamental issues driving the movement of gases in silica aerogels and then proceeds to provide an overview of the work that has been done with respect to the purification of gases, sensing of individual gases, and uses of silica aerogels as catalysts for gas-phase reactions. Salient features of the research behind these different applications are presented, and, where appropriate, critical aspects that affect the practical use of the aerogels are noted. Specific sections under the gas-purification category focus on the removal of airborne nanoparticles, carbon dioxide, volatile organic compounds, sulfur gases and radioactive iodine from gas streams. The use of silica aerogels as sensors for humidity, oxygen, hydrocarbons, volatile acids and bases, various non-ammoniacal nitrogen gases, and viral particles is discussed. With respect to catalysis, the demonstrated use of silica aerogels as supports for oxidation, Fischer-Tropsch, alkane isomerization, and hydrogenation reactions is reviewed, along with a section on untested catalytic formulations involving silica aerogels. A short section focuses on recent developments in thermomolecular Knudsen compressor pumps using silica aerogel membranes. The review continues with an overview of the production methods, locations of manufacturing facilities globally, and a brief discussion of the economics before concluding with a few remarks about the present and future trends revealed by the work presented.

gas catalysis↗
DOE OSTI2018

Distinguishing one from many using super-resolution compressive sensing

We present that distinguishing whether a signal corresponds to a single source or a limited number of highly overlapping point spread functions (PSFs) is a ubiquitous problem across all imaging scales, whether detecting receptor-ligand interactions in cells or detecting binary stars. Super-resolution imaging based upon compressed sensing exploits the relative sparseness of the point sources to successfully resolve sources which may be separated by much less than the Rayleigh criterion. However, as a solution to an underdetermined system of linear equations, compressive sensing requires the imposition of constraints which may not always be valid. One typical constraint is that the PSF is known. However, the PSF of the actual optical system may reflect aberrations not present in the theoretical ideal optical system. Even when the optics are well characterized, the actual PSF may reflect factors such as non-uniform emission of the point source (e.g. fluorophore dipole emission). As such, the actual PSF may differ from the PSF used as a constraint. Similarly, multiple different regularization constraints have been suggested including the l 1 -norm, l 0 -norm, and generalized Gaussian Markov random fields (GGMRFs), each of which imposes a different constraint. Other important factors include the signal-to-noise ratio of the point sources and whether the point sources vary in intensity. In this work, we explore how these factors influence super-resolution image recovery robustness, determining the sensitivity and specificity. In conclusion, we determine an approach that is more robust to the types of PSF errors present in actual optical systems.

42 ENGINEERING↗
DOE OSTI2021

Quantum Sensing of Insulator‐to‐Metal Transitions in a Mott Insulator

Nitrogen vacancy (NV) centers, optically active atomic defects in diamond, have attracted tremendous interest for quantum sensing, network, and computing applications due to their excellent quantum coherence and remarkable versatility in a real, ambient environment. Taking advantage of these strengths, this paper reports on NV-based local sensing of the electrically driven insulator-to-metal transition (IMT) in a proximal Mott insulator. The resistive switching properties of both pristine and ion-irradiated VO 2 thin film devices are studied by performing optically detected NV electron spin resonance measurements. These measurements probe the local temperature and magnetic field in electrically biased VO 2 devices, which are in agreement with the global transport measurement results. In pristine devices, the electrically driven IMT proceeds through Joule heating up to the transition temperature while in ion-irradiated devices, the transition occurs nonthermally, well below the transition temperature. Here, the results provide direct evidence for nonthermal electrically induced IMT in a Mott insulator, highlighting the significant opportunities offered by NV quantum sensors in exploring nanoscale thermal and electrical behaviors in Mott materials.

McLaughlin, Nathan J.↗