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NASA NTRSDate not supplied

Flight Test Configuration of the Sensor Payload and the Ground Nodes in Distributed Sensing Frameworks

The realization of the Urban Air Mobility (UAM) vision, entailing the deployment of high-density autonomous flights over urban areas, necessitates methodologies transcending contemporary airborne sensing techniques. An instrumental innovation in this realm is the advent of distributed sensing. In this paradigm, environmental sensors function as active agents, engaging in the triad of sensing, processing, and communication either amongst themselves, with ground stations, or both. This concerted effort creates a dynamic and responsive "smart space," facilitating real-time autonomous control. NASA Ames Research Center is actively engaged in a series of comprehensive indoor and outdoor flight tests to garner requisite data and insights essential for the actualization of this ambitious vision. This paper delineates the holistic configuration of the flight payload and the distributed ground nodes pivotal to the NASA flight test campaign. Furthermore, it expounds upon the intricacies of the sensor node communication framework. The narrative extends to provide a detailed overview of the strategic placement of distributed sensors and a comprehensive account of the varied indoor and outdoor flight tests orchestrated in pursuit of UAM objectives.

urban air mobility↗

NASA NTRSDate not supplied

NASA and External Quantum Sensing Capability Assessment for NASA Space-Based Science Measurements

The Subject Matter Experts in the Sensors & Instrumentation Quantum Sensing Community of Practice (CoP) requested an independent technical assessment of the agency's capabilities in QS to understand NASA's internal needs and competencies related to QS and compare agency capabilities with those available externally including industry, academia, and other government agencies. The outcomes of the assessment will help the agency in establishing appropriate strategies and investments to develop and maintain the state-of-the-art sensing competence and capabilities required to meet the agency’s future needs. NASA Engineering and Safety Center Review Board approved the assessment request and assigned NASA Technical Fellow to lead and conduct the assessment by engaging NASA Centers, NASA HQ and an independent, non-commercial, and highly credible Quantum Sensing Experts from Academia, Department of Defense, other Government Agencies. The assessment started in October 2021 and final report was completed in October 2023.

External Quantum Sensing↗

NASA NTRS2024

Applications of Remote Sensing for Land Use Planning Scenarios with Suitability Analysis

In regions undergoing rapid urbanization, such as West Africa, land use planning (LUP) is vital to accommodate growing population and manage natural resources. Suitability analysis modeling is a widely used tool in LUP to determine the extent to which a land area is suitable for a designated purpose, but there is a gap in the integration of remote sensing time series data into land use decisions. The goal of this study was to incorporate remote sensing time series information with suitability analyses to inform LUP decisions in urban areas. In the study area of Kumasi, Ghana, land cover trends and land surface temperature (LST) from 2000 to 2019 were used to understand climate change trends. Suitability analyses determined the fitness of land areas for predetermined uses. These background processes informed a genetic algorithm to project plausible futures for three land use scenarios. One scenario represented current land use planning practices for addressing population growth, another scenario prioritized minimizing climate change impacts while also accommodating population growth, and the final scenario focused on both of these climate and population goals in addition to high density urban development. Each of these scenarios was successful in achieving population accommodation and respective climate change mitigation goals. The results for these scenarios provide insight into plausible land use distributions in 2050 based on different planning approaches. The genetic algorithm was able to effectively develop results for each scenario through the integration of remotely sensed trends and suitability models, providing a novel approach to land use decision-making.

remote sensing time series↗

NASA NTRS2024

Proceedings of the Quantum Sensing Workshop, September 2022

To understand the NASA needs for quantum-sensing technologies and the capabilities developed internal and external to NASA, a workshop was held on September 27 through 29, 2022, in Newport News, VA. This workshop brought together senior leadership within NASA, technical experts within the quantum sensing community, NASA scientists and potential end-users of quantum-sensing technologies, and external stakeholders. This document contains the papers and presentations given at the Workshop.

Quantum Sensing↗

NASA NTRS2023

Unifying radiative transfer models in computer graphics and remote sensing, Part I: A Survey

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 1 of this work, we examine the evolution of radiative transfer models in computer graphics and highlight recent advancements that have the potential to push forward models in remote sensing beyond their current periphery of realism.

Radiative transfer↗

DOE OSTI2021

Universal Approach to Rapid Amplified Plasmonic Sensing Using Helix Defect Phase Transition Polymers

Abstract Permittivity sensing is a critically important analytical tool for bioscience, environmental, and industrial applications. The response time for commonly used plasmonic permittivity sensors is fundamentally set by the reaction kinetics of chemically adsorbed analytes. In this work, the proposal is to overcome this limit by combining plasmonic sensors with phase transition materials possessing a rapid amplified electrostatic response such as quadrupole moment induced molecular helix reversal. As a proof‐of‐concept, rapid sensing of CO 2 on a phase transition polytetrafluoroethylene substrate and amplification of permittivity response in plasmonic Fabry–Perot sensor is shown. The demonstrated universal approach holds promise for a wide range of applications in fast, real time sensing and monitoring of biological and environmental processes.

Shugayev, Roman↗

DOE OSTI2023

Sorption‐Induced Fiber Optic Plasmonic Gas Sensing via Small Grazing Angle of Incidence

Abstract Sensing technologies based on plasmonic nanomaterials are of interest for various chemical, biological, environmental, and medical applications. In this work, an incorporation strategy of colloidal plasmonic nanoparticles (pNPs) in microporous polymer for realizing distinct sorption‐induced plasmonic sensing is reported. This approach is demonstrated by introducing tin‐doped indium oxide pNPs into a polymer of intrinsic microporosity (PIM‐1). The composite film (pNPs‐polymer) provides distinct and tunable optical features on the fiber optic (FO) platform that can be used as a signal transducer for gas sensing (e.g., CO 2 ) under atmospheric conditions. The resulting pNPs‐polymer composite demonstrates high sensitivity response on FO in the evanescent field configuration, provided by the dramatic response of modes above the total‐internal‐reflection angle. Furthermore, by varying the pNPs content in the polymer matrix, the optical behavior of the pNPs‐polymer composite film can be tuned to affect the operational wavelength by over several hundred nanometers and the sensitivity of the sensor in the near‐infrared range. It is also shown that the pNPs‐polymer composite film exhibits remarkable stability over a period of more than 10 months by mitigating the physical aging issue of the polymer.

Kim, Ki‐Joong↗

DOE OSTI2022

A Bioinspired Artificial Injury Response System Based on a Robust Polymer Memristor to Mimic a Sense of Pain, Sign of Injury, and Healing

Abstract Flexible electronic skin with features that include sensing, processing, and responding to stimuli have transformed human–robot interactions. However, more advanced capabilities, such as human‐like self‐protection modalities with a sense of pain, sign of injury, and healing, are more challenging. Herein, a novel, flexible, and robust diffusive memristor based on a copolymer of chlorotrifluoroethylene and vinylidene fluoride (FK‐800) as an artificial nociceptor (pain sensor) is reported. Devices composed of Ag/FK‐800/Pt have outstanding switching endurance >10 6 cycles, orders of magnitude higher than any other two‐terminal polymer/organic memristors in literature (typically 10 2 –10 3 cycles). In situ conductive atomic force microscopy is employed to dynamically switch individual filaments, which demonstrates that conductive filaments correlate with polymer grain boundaries and FK‐800 has superior morphological stability under repeated switching cycles. It is hypothesized that the high thermal stability and high elasticity of FK‐800 contribute to the stability under local Joule heating associated with electrical switching. To mimic biological nociceptors, four signature nociceptive characteristics are demonstrated: threshold triggering, no adaptation, relaxation, and sensitization. Lastly, by integrating a triboelectric generator (artificial mechanoreceptor), memristor (artificial nociceptor), and light emitting diode (artificial bruise), the first bioinspired injury response system capable of sensing pain, showing signs of injury, and healing, is demonstrated.

36 MATERIALS SCIENCE↗

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↗