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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Bio-Templated Chiral Zeolitic Imidazolate Framework for Enantioselective Chemoresistive Sensing

Chiral metal–organic frameworks (MOFs) have gained rising attention as ordered nanoporous materials for enantiomer separations, chiral catalysis, and sensing. Among those, chiral MOFs are generally obtained through complex synthetic routes by using a limited choice of reactive chiral organic precursors as the primary linkers or auxiliary ligands. Here, we report a template-controlled synthesis of chiral MOFs from achiral precursors grown on chiral nematic cellulose-derived nanostructured bio-templates. We demonstrate that chiral MOFs, specifically, zeolitic imidazolate framework (ZIF), unc-[Zn(2-MeIm) 2 , 2-MeIm=2-methylimidazole], can be grown from regular precursors within nanoporous organized chiral nematic nanocelluloses via directed assembly on twisted bundles of cellulose nanocrystals. The template-grown chiral ZIF possesses tetragonal crystal structure with chiral space group of P4 1 , which is different from traditional cubic crystal structure of I-43 m for freely grown conventional ZIF-8. The uniaxially compressed dimensions of the unit cell of templated ZIF and crystalline dimensions are signatures of this structure. We observe that the templated chiral ZIF can facilitate the enantiotropic sensing. It shows enantioselective recognition and chiral sensing abilities with a low limit of detection of 39 μM and the corresponding limit of chiral detection of 300 μM for representative chiral amino acid, D- and L- alanine.

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

Revealing the Chemical State of Palladium in Operating In 2 O 3 Gas Sensors: Metallic Pd Enhances Sensing Response and Intermetallic In x Pd y Compound Blocks It

The sensing response of metal oxides activated with noble metal nanoparticles is significantly influenced by changes to the chemical state of corresponding elements under operating conditions. Here, a PdO/rh-In 2 O3 consisting of PdO nanoparticles loaded onto rhombohedral In 2 O 3 was studied as a gas sensor for H 2 gas (100–40000 ppm in an oxygen-free atmosphere) in the temperature range of 25–450 °C. The phase composition and chemical state of elements were examined by resistance measurements combined with synchrotron-based in situ X-ray diffraction and ex situ X-ray photoelectron spectroscopy. As found, PdO/rh-In 2 O 3 undergoes a series of structural and chemical transformations during operation: from PdO to Pd/PdH x and finally to the intermetallic In x Pd y phase. The maximal sensing response (R N2 /R H2 ) of ~5 · 10 7 towards 40000 ppm (4 vol %) H 2 at 70 °C is correlated with the formation of PdH 0.706 /Pd. The In x Pd y intermetallic compounds formed around 250 °C significantly decrease the sensing response.

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

Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum

Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data (thermal, RGB, and multispectral) derived from an unmanned aerial vehicle (UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination (R 2 ) ranging from 0.40 to 0.53 under water-stressed environment and 0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yield-based drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data. Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance.

09 BIOMASS FUELS↗
DOE OSTI2025

Remotely Sensed High‐Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society

This paper reviews the current state of high‐resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high‐resolution remote sensing using visible, near‐infrared, thermal‐infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio‐geo‐physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state‐of‐the‐science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open‐access, high‐resolution (3–30 m), sub‐daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data‐limited regions of the world, especially the Global South for socio‐economic development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Nuclear spin assisted magnetic field angle sensing

Abstract Quantum sensing exploits the strong sensitivity of quantum systems to measure small external signals. The nitrogen-vacancy (NV) center in diamond is one of the most promising platforms for real-world quantum sensing applications, predominantly used as a magnetometer. However, its magnetic field sensitivity vanishes when a bias magnetic field acts perpendicular to the NV axis. Here, we introduce a different sensing strategy assisted by the nitrogen nuclear spin that uses the entanglement between the electron and nuclear spins to restore the magnetic field sensitivity. This, in turn, allows us to detect small changes in the magnetic field angle relative to the NV axis. Furthermore, based on the same underlying principle, we show that the NV coupling strength to magnetic noise, and hence its coherence time, exhibits a strong asymmetric angle dependence. This allows us to uncover the directional properties of the local magnetic environment and to realize maximal decoupling from anisotropic noise.

Physics↗
DOE OSTI2022

Real-time bioelectronic sensing of environmental contaminants

Real-time chemical sensing is crucial for applications in environmental and health monitoring. Biosensors can detect a variety of molecules through genetic circuits that use these chemicals to trigger the synthesis of a coloured protein, thereby producing an optical signal. However, the process of protein expression limits the speed of this sensing to approximately half an hour, and optical signals are often difficult to detect in situ. Here we combine synthetic biology and materials engineering to develop biosensors that produce electrical readouts and have detection times of minutes. Here we programmed Escherichia coli to produce an electrical current in response to specific chemicals using a modular, eight-component, synthetic electron transport chain. As designed, this strain produced current following exposure to thiosulfate, an anion that causes microbial blooms, within 2 min. This amperometric sensor was then modified to detect an endocrine disruptor. The incorporation of a protein switch into the synthetic pathway and encapsulation of the bacteria with conductive nanomaterials enabled the detection of the endocrine disruptor in urban waterway samples within 3 min. Our results provide design rules to sense various chemicals with mass-transport-limited detection times and a new platform for miniature, low-power bioelectronic sensors that safeguard ecological and human health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

In situ melt pool measurements for laser powder bed fusion using multi sensing and correlation analysis

Laser powder bed fusion is a promising technology for local deposition and microstructure control, but it suffers from defects such as delamination and porosity due to the lack of understanding of melt pool dynamics. To study the fundamental behavior of the melt pool, both geometric and thermal sensing with high spatial and temporal resolutions are necessary. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the evolution of the melt pool shape, keyhole, vapor plume, and thermal evolution in Ti–6Al–4V and 410 stainless steel spot melt cases. Aside from presenting the sensing capability, this paper develops an effective algorithm for high-speed X-ray imaging data to identify melt pool geometries accurately. Preprocessing methods are also implemented for the IR data to estimate the emissivity value and extrapolate the saturated pixels. Quantifications on boundary velocities, melt pool dimensions, thermal gradients, and cooling rates are performed, enabling future comprehensive melt pool dynamics and microstructure analysis. The study discovers a strong correlation between the thermal and X-ray data, demonstrating the feasibility of using relatively cheap IR cameras to predict features that currently can only be captured using costly synchrotron X-ray imaging. Such correlation can be used for future thermal-based melt pool control and model validation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Bio-inspired gas sensing: boosting performance with sensor optimization guided by “machine learning”

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

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

Barium ion sensing with IPG K + molecular probes

Fluorophores covalently bound to azacrown ether ionophores can be assembled into sensitive turn-on chemosensors. The size specificity and electron-rich nature of the ionophore's binding domain contribute to both selectivity and strong turn-on fluorescence sensing by various mechanisms when properly constructed. Aza-18-crown-6 ethers are quite selective for binding to K + and Ba 2+ , yet the more electron-withdrawing dicationic nature of barium imposes a larger electronic effect on turn-on fluorescent sensors. Barium chemosensors can be important for measuring soluble Ba 2+ in drinking water and have gained recent attention for their potential to enhance the detection of rare events in xenon decay. Here we quantify the capability of three chemosensors, marketed for biologically useful K + sensing, as effective probes for Ba 2+ ions. Here, we present measurements from bulk spectrofluorometry to characterize the system in aqueous solutions and demonstrate the usefulness of these species for low-background single-ion fluorescence microscopy, revealing new candidates for Ba 2+ sensing.

Miller, R. L. [Department of Chemistry and Biochem↗
DOE OSTI2025

Quantum sensing of paramagnetic analytes by nanodiamonds in levitated microdroplets and aqueous solutions

Nanodiamonds (ND) hosting negatively charged nitrogen-vacancy (NV-) color centers have received attention for applications in magnetic field, electric field, chemical, and bio-sensing. The versatility of these probes is their excellent room-temperature optical and spin properties, along with their small size, functionalized surfaces and resistance to bleaching, making them ideal as nanoscopic sensors in picoliter volumes (e.g. single cells, but also microcompartments and aerosols). For quantitative ND-NV- sensing of paramagnetic analytes in such contexts, however, there remains an incomplete understanding of how factors related to the aqueous phase environment control detection efficiency. To address this, optically detected magnetic resonance (ODMR) is measured in bulk macroscale solutions and single levitated microdroplets as a function of Gd+3 concentration (340 nM to 1.5 mM), nanodiamond size, pH, competitor ions, and ligands. The ODMR response to [Gd+3] is found to be nonlinear, and pH, ND and sample volume dependent; indicating the detection of Gd+3 requires efficient adsorption of the analyte to the diamond surface. Langmuir adsorption isotherms embedded in a quantitative photophysical model links the ODMR response to adsorption thermodynamics of Gd+3. The equilibrium constant for Gd+3 adsorption to a carboxylated ND surface is determined to be (1 ± 0.5) x 105 M-1 corresponding to a free energy of adsorption of (-28 ± 1) kJ mol-1. These results provide general insight into how complex aqueous and microscale environments impact nanodiamond based quantum sensing modalities, and portend their application as quantitative chemical sensors in microenvironments.

Brown, Emily K↗
DOE OSTI2022

High-resolution ultra-low field magnetic resonance imaging with a high-sensitivity sensing coil

We present high-resolution magnetic resonance imaging (MRI) at ultra-low field (ULF) with a proton Larmor frequency of around 120 kHz. The key element is a specially designed high-sensitivity sensing coil in the shape of a solenoid with a few millimeter gap between windings to decrease the proximity effect and, hence, increase the coil’s quality ([Formula: see text]) factor and sensitivity. External noise is strongly suppressed by enclosing the sensing coil in a copper cylindrical shield, large enough not to negatively affect the coil’s [Formula: see text] factor and sensitivity, measured to be 217 and 0.47 fT/Hz[Formula: see text], respectively. To enhance small polarization of proton spins at ULF, a strong pulsed 0.1 T prepolarization field is applied, making the signal-to-noise ratio (SNR) of ULF MRI sufficient for high-quality imaging in a short time. We demonstrate ULF MRI of a copper sulfate solution phantom with a resolution of [Formula: see text] and SNR of 10. The acquisition time is 6.3 min without averaging. The sensing coil size in the current realization can accommodate imaging objects of 9 cm in size, sufficient for hand, and it can be further increased for human head imaging in the future. Since the in-plane resolution of [Formula: see text] is typical in anatomical medical imaging, this ULF MRI method can be an alternative low-cost, rapid, portable method for anatomical medical imaging of the human body or animals. This ULF MRI method can supplement other MRI methods, especially when such methods are restricted due to high cost, portability requirement, imaging artifacts, and other factors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

High efficiency radio frequency antennas for amplifier free quantum sensing applications

Radio frequency (RF) signals are frequently used in emerging quantum applications due to their spin state manipulation capability. Efficient coupling of RF signals into a particular quantum system requires the utilization of carefully designed and fabricated antennas. Nitrogen vacancy (NV) defects in diamond are commonly utilized platforms in quantum sensing experiments with the optically detected magnetic resonance (ODMR) method, where an RF antenna is an essential element. We report on the design and fabrication of high efficiency coplanar RF antennas for quantum sensing applications. Single and double ring coplanar RF antennas were designed with -37 dB experimental return loss at 2.87 GHz, the zero-field splitting frequency of the negatively charged NV defect in diamond. The efficiency of both antennas was demonstrated in magnetic field sensing experiments with NV color centers in diamond. In conclusion, an RF amplifier was not needed, and the 0 dB output of a standard RF signal generator was adequate to run the ODMR experiments due to the high efficiency of the RF antennas.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Development of X-ray Wavefront Sensing Techniques for Adaptive Optics Control at the Advanced Photon Source

The planning and construction of new and upgraded high-brightness X-ray synchrotron and free electron laser sources, such as the Advanced Photon Source upgrade project (APS-U), are driving numerous opportunities to advance X-ray science and technologies. At the same time, an increasing number of highly diverse beamline experiments demand wavefront-preserving adaptive X-ray optics with both high precision and flexibility. At the APS, significant effort has been devoted to developing next-generation adaptive and corrective optics combined with state-of-the-art at-wavelength wavefront sensing techniques and an intelligent feedback control system for the automation and self-alignment of beamline optical systems. This article reviews recent achievements in these areas at the APS. Further, these include the development of in-situ wavefront sensing, the application in active feedback control of ultra-precision deformable mirrors, and the exploration of non-invasive wavefront sensing techniques for adaptive optics and beamline diagnostics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A review of carbon monitoring in wet carbon systems using remote sensing

Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integrating crop growth models with remote sensing for predicting biomass yield of sorghum

Abstract Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programmes to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (i) augment field phenotyping data and derive hard-to-measure phenotypes and (ii) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote-sensing measurements combined with manual phenotyping in West Lafayette, IN, in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, TX, between 2001 and 2018. These trials demonstrated that (i) maximum plant height, final dry biomass and radiation use efficiency (RUE) of photoperiod-sensitive and -insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass production in longer growing environments and (iii) the parameterized and validated models perform well in above-ground biomass simulations across years and locations. Crop growth models that integrate remote-sensing data offer an efficient approach to parameterize larger plant breeding populations.

Yang, Kai-Wei↗
DOE OSTI2024

High-angular-momentum Rydberg states in a room-temperature vapor cell for dc electric-field sensing

We prepare and analyze Rydberg states with orbital quantum numbers ℓ ≤ 6 using three-optical-photon electromagnetically induced transparency (EIT) and radio frequency (rf) dressing, and employ the high- ℓ states in electric-field sensing. Rubidium-85 atoms in a room-temperature vapor cell are first promoted into the 25 F 5 / 2 state via Rydberg-EIT with three infrared laser beams. Two rf dressing fields then (near-)resonantly couple the 25 F , 25 H ( ℓ = 5 ) , and 25 I ( ℓ = 6 ) Rydberg states. The dependence of the rf-dressed Rydberg-state level structure on rf powers, rf and laser frequencies is characterized using EIT. Furthermore, we discuss the principles of dc-electric-field sensing using high- ℓ Rydberg states and experimentally demonstrate the method using test electric fields of ≲ 50 V/m induced via photo-illumination of the vapor-cell wall. We measure the highly nonlinear dependence of the dc-electric-field strength on the power of the photo-illumination laser. Numerical calculations, which reproduce our experimental observations well, elucidate the underlying physics. Our paper is relevant to high-precision spectroscopy of high- ℓ Rydberg states, Rydberg-atom-based electric-field sensing, and plasma electric-field diagnostics. Published by the American Physical Society 2024

Duspayev, Alisher (ORCID:0000000153225762)↗
DOE OSTI2026

Quantum Sensing of Displacements with Stabilized Gottesman-Kitaev-Preskill States

We demonstrate how recent protocols developed for the stabilization of Gottesman-Kitaev-Preskill states can be used for the estimation of two-quadrature displacement sensing, with sensitivities approaching the multivariate quantum Cramer-Rao bound. Thanks to the stabilization, this sensor is backaction evading and can function continuously without reset, making it well suited for the detection of itinerant signals. Additionally, we provide numerical simulations showing that the protocol can unconditionally surpass the Gaussian limit of displacement sensing with prior information, even in the presence of realistic noise. Our work shows how reservoir engineering in bosonic systems can be leveraged for quantum metrology, with potential applications in force sensing, waveform estimation, and quantum channel learning.

Labarca, Lautaro [Univ. of Sherbrooke, QC (Canada)↗
DOE OSTI2025

Unsupervised anomaly clustering via offset alignment in multivariate grid sensing data

Modern industries increasingly rely on multi-sensor technologies to acquire complex, high-dimensional data streams, enabling advanced monitoring and control systems. One critical application is online anomaly detection in electrical smart grids, where multivariate and multimodal sensing technologies play a vital role. However, detecting anomalies in such time-series data is challenging due to their inherent temporal dependencies and stochastic behavior. Traditional approaches based on supervised and semi-supervised learning methods depend on labeled datasets, which are often unavailable in real-world scenarios. While unsupervised methods have emerged as promising alternatives, these methods are highly susceptible to noise and outliers commonly present in sensing applications. Furthermore, deep learning-based anomaly detection methods, despite their performance, are often criticized for their black-box nature, limiting their applicability in safety-critical and online environments where interpretability and explainability are paramount. In this work, we propose an unsupervised anomaly clustering method leveraging a cyclic alignment-based offset detection algorithm for multivariate time-series signals. The proposed method is applied to multivariate data collected from vibrational, voltage, and magnetic field sensors deployed in a local grid substation. Our results demonstrate the robustness of the algorithm in accurately clustering various anomalies/events across different sensing modalities. Additionally, we compare the effectiveness of the proposed approach against a simple pattern-based anomaly detection method, which performs well for univariate data but fails to generalize to multivariate and multimodal time-series data.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗