SEARCH · Search NASA

Results for “Sensor development”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,045 records · Page 58

All sources NASA NTRS DOE OSTI

DOE OSTI2025

TEAMER – Field Demonstration of MarineSitu’s Marine Energy Monitoring (Abstract)

In order to effectively monitor for marine life around marine energy devices and thus minimize the risk of collision, multiple sensors working in coordination and augmented with around-the-clock automated monitoring algorithms need to be installed in challenging high-energy tidal and wave environments. Such systems are often too expensive for widespread adoption, or lack sufficient sensors or smarts to enable around-the-clock, real-time monitoring without human involvement. MarineSitu has been working to tackle this problem by developing a low-cost, combined sonar and stereo camera sensor array with connected real-time AI-based algorithms for automatically detecting marine life in these marine energy suitable environments. In this TEAMER project with Pacific Northwest National Lab (PNNL), MarineSitu will be testing this novel sensor system for the first time in the high-energy tidal channel environment at PNNL’s Marine and Coastal Research Lab. Throughout this deployment, MarineSitu will be monitoring their system and running analytics on the sensor’s data in real-time. Meanwhile, PNNL Data Scientists and Ocean Engineers, will be evaluating the system’s effectiveness and ease of use both as a tool for plug-and-play environmental monitoring and novel environmental monitoring research. In doing so, the team will improve MarineSitu’s system and software, produce insightful data products, and develop novel visualizations and AI algorithms for combining and analyzing the data produced by systems like MarineSitu’s.

16 TIDAL AND WAVE POWER↗

DOE OSTI2016

Model-Based Sensor Placement for Component Condition Monitoring and Fault Diagnosis in Fossil Energy Systems

Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and fault diagnosis in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desired for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate fault severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for fault diagnosis at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level fault diagnosis and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Development and Evaluation of Chip-Enabled Raised Pavement Markers for Lane Line Detection

Increased energy consumption from autonomous vehicle (AV) sensors and computational load as well as upfront costs of sensors are barriers to broad AV adoption. For high quality and reliable perception of the driving environment, incoming data from multiple sensors need to be fused together using advanced computational algorithms, which requires a high compute load. As an alternative, infrastructure-based sensors can be designed to facilitate perception and sensing by supporting vehicle-to-infrastructure (V2I) information exchange. This work presents the initial development and evaluation of a novel energy efficient infrastructure-based sensor. The sensor, a chip-enabled raised pavement marker (CERPM), is capable of wireless communications to exchange environment information with AVs. As a test case, the developed CERPM is applied in real-world driving to perform lane line and drivable region detection for an AV. It is shown that CERPMs can be utilized to effectively detect the lane line and drivable region, which can improve perception while reducing the compute load.

Sharma, Sachin↗

DOE OSTI2023

Fact Sheet - Long-length Scintillating Fibers for Radiation Detection

As the world looks to underground geological repositories for storing nuclear waste, technologies to safeguard the material are needed. An example of a project that developed a new technology is Tripwire. Tripwire proposes a multi-sensor system approach for geological spent nuclear fuel repositories that relies of radiation, vibration, and electromagnetic detection. Tripwire was developed by the Applied Radiation Measurements and Systems (ARMS) group at INL and sponsored by the National Nuclear Security Administration (NNSA). The ARMS group performs research and development, testing and evaluation, operational support, and training focused on applied ionizing radiation detection and measurement. Application areas include nuclear engineering for advanced reactors and fuel cycle operations, nuclear nonproliferation, nuclear counterproliferation, nuclear forensics, and arms control and disarmament.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Engineered Glucose Oxidase‐Carbon Nanotube Conjugates for Tissue‐Translatable Glucose Nanosensors

Abstract Continuous and non‐invasive glucose monitoring and imaging is important for disease diagnosis, treatment, and management. However, glucose monitoring remains a technical challenge owing to the dearth of tissue‐transparent glucose sensors. In this study, we present the development of near‐infrared fluorescent single‐walled carbon nanotube (SWCNT) based nanosensors directly functionalized with glucose oxidase (GOx) capable of immediate and reversible glucose imaging in biological fluids and tissues. We prepared GOx‐SWCNT nanosensors by facile sonication of SWCNT with GOx in a manner that—surprisingly—does not compromise the ability of GOx to detect glucose. Importantly, we find by using denatured GOx that the fluorescence modulation of GOx‐SWCNT is not associated with the catalytic oxidation of glucose but rather triggered by glucose‐GOx binding. Leveraging the unique response mechanism of GOx‐SWCNT nanosensors, we developed catalytically inactive apo‐GOx‐SWCNT that enables both sensitive and reversible glucose imaging, exhibiting a ΔF/F 0 of up to 40 % within 1 s of exposure to glucose without consuming the glucose analyte. We finally demonstrate the potential applicability of apo‐GOx‐SWCNT in biomedical applications by glucose quantification in human plasma and glucose imaging in mouse brain slices.

Chemistry↗

DOE OSTI2026

Robotics for HVAC applications: A critical review and future perspectives

Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.

AI↗

DOE OSTI2025

Underwater thermomagnetic generator for remote marine thermal energy harvesting and sensing

Thermomagnetic generators offer a promising approach for sustainable power generation in remote marine environments. Here, this study presents the design, thermal modeling, and experimental validation of a passively driven underwater thermomagnetic generator developed for powering ocean observation and monitoring sensors. The generator was evaluated under varying working fluids, thermal boundary conditions, and extended operation to assess real-world applicability. Two fluids, deionized water and silicone-based Thermal C5, were tested under simulated shallow- and deep-ocean conditions. Deionized water outperformed Thermal C5, especially in colder environments (~5°C), achieving a peak output of 2.7 mW due to larger temperature gradients and enhanced convective-evaporative heat transfer. Long-duration tests revealed a transient evaporation-condensation cycle that temporarily reduced rotor immersion and performance before stabilizing. The generator powered commercial marine sensors for over 6 h without external batteries, demonstrating the viability of compact, passively cooled thermomagnetic systems for autonomous, off-grid marine sensing.

13 HYDRO ENERGY↗

DOE OSTI2026

Electron/hadron separation with the TRD based on GaAs pixel detectors

Transition Radiation Detectors (TRDs) are widely used for particle identification in both high-energy physics and astroparticle physics. They are typically equipped with gaseous detectors. The main limitation of these types of detectors is that TR photons and ionization losses cannot be decoupled, which significantly reduces the particle separation power. Recent advancements in the development of pixel detectors based on GaAs sensors offer a unique opportunity to effectively detect TR photons and separate them from ionization losses. Such detectors represent novel devices that combine precise tracking capabilities with particle identification (PID) properties. The present work is dedicated to an experimental study of particle identification properties of the TRD prototype based on 500 μ m-thick GaAs sensor bonded to a Timepix3 chip. Studies were performed at the CERN SPS and it was shown that at a particle momentum of 20 GeV/c, the probability of misidentifying a hadron as an electron is below 10 −2 for an electron detection efficiency of 98%–99%, and it is 1 . 6⋅10 −4 for electron detection efficiency of 90%. This performance surpasses the electron/hadron rejection power of all known TRDs by an order of magnitude for the same detector length.

GaAs detectors↗

DOE OSTI2020

Design, characterization and indoor validation of the optical soiling detector “DUSST”

Nowadays, photovoltaic (PV) technology has reached a high level of maturity in terms of module efficiency and cost competitiveness in comparison with other energy technologies. As PV has achieved high levels of deployment, the development of devices that can help to reduce PV operation and maintenance costs has become a priority. Soiling can be cause of significant losses in certain PV plants and its detection has become essential to ensure a correct mitigation. For this reason, accurate and low-cost monitoring devices are needed. While soiling stations have been traditionally employed to measure the impact of soiling, their high cost and maintenance have led to the development of innovative low-cost optical sensors, such as the device presented in this work and named “DUSST” (Detector Unit for Soiling Spectral Transmittance). The thermal characterization of DUSST’s components and the methodology used to predict soiling transmittance losses are presented in this study. The results demonstrate that the losses can be predicted with an error lower than 1.4%. The method has been verified with an experimental campaign with naturally soiled coupons exposed outdoors in Jaén, Spain.

14 SOLAR ENERGY↗

DOE OSTI2022

Molecular qubits based on photogenerated spin-correlated radical pairs for quantum sensing

Photogenerated spin-correlated radical pairs (SCRPs) in electron donor–bridge–acceptor (D–B–A) molecules can act as molecular qubits and inherently spin qubit pairs. SCRPs can take singlet and triplet spin states, comprising the quantum superposition state. Their synthetic accessibility and well-defined structures, together with their ability to be prepared in an initially pure, entangled spin state and optical addressability, make them one of the promising avenues for advancing quantum information science. Coherence between two spin states and spin selective electron transfer reactions form the foundation of using SCRPs as qubits for sensing. We can exploit the unique sensitivity of the spin dynamics of SCRPs to external magnetic fields for sensing applications including resolution-enhanced imaging, magnetometers, and magnetic switch. Molecular quantum sensors, if realized, can provide new technological developments beyond what is possible with classical counterparts. While the community of spin chemistry has actively investigated magnetic field effects on chemical reactions via SCRPs for several decades, we have not yet fully exploited the synthetic tunability of molecular systems to our advantage. This review offers an introduction to the photogenerated SCRPs-based molecular qubits for quantum sensing, aiming to lay the foundation for researchers new to the field and provide a basic reference for researchers active in the field. We focus on the basic principles necessary to construct molecular qubits based on SCRPs and the examples in quantum sensing explored to date from the perspective of the experimentalist.

Mani, Tomoyasu (ORCID:0000000241255195)↗

DOE OSTI2026

Rolling Root Mean Square Based Multimodal Anomaly Detection for Real Time Monitoring of Smart Grid

Reliable real-time monitoring is valuable for maintaining the operational integrity of modern electrical smart grids. Deployment of heterogeneous sensing technologies in substations has enabled high-resolution, multichannel waveform monitoring, but also introduces challenges for anomaly detection due to noise, baseline drift, and modality-dependent signal characteristics. In this work, we present a computationally efficient unsupervised method for multimodal event detection based on Rolling Root Mean Square based Event Detection (RRMSED). The method is developed using in-house, field deployed sensors collecting data at a utility substation. The sensing system comprises voltage and current sensors, triaxial accelerometers, and magnetometers, collectively capturing electrical, vibrational, and magnetic waveform measurements at high temporal resolution. RRMSED operates by extracting rolling RMS energy features and their first-order temporal differences from consecutive waveform segments for each channel and then applying channel-specific statistical thresholds learned from historical data. A persistence-based exceedance logic is employed to robustly identify transient events while suppressing impulsive noise, and to provide precise temporal localization with high resolution. The framework is designed for continuous server-side operation and can be deployed in real time without requiring complex models. Experiments on simulated waveform data with known ground truth demonstrate low false positive (FP) and false negative (FN) rates. Application to real substation data shows RRMSED to identify events that are not captured by conventional monitoring indicators including fast transient detection algorithm currently deployed in the system. These results indicate that rolling RMS based features provide an effective and practical basis for real-time multimodal event detection in smart-grid substations.

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

DOE OSTI2022

Large anomalous Nernst effect and nodal plane in an iron-based kagome ferromagnet

Anomalous Nernst effect (ANE), converting a heat flow to transverse electric voltage, originates from the Berry phase of electronic wave function near the Fermi energy E F . Thus, the ANE provides a sensitive probe to detect a topological state that produces large Berry curvature. In addition, a magnet that exhibits a large ANE using low-cost and safe elements will be useful to develop a novel energy harvesting technology. Here, we report our observation of a high ANE exceeding 3 microvolts per kelvin above room temperature in the kagome ferromagnet Fe 3 Sn with the Curie temperature of 760 kelvin. Our theoretical analysis clarifies that a “nodal plane” produces a flat hexagonal frame with strongly enhanced Berry curvature, resulting in the large ANE. Our discovery of the large ANE in Fe 3 Sn opens the path for the previously unexplored functionality of flat degenerate electronic states and for developing flexible film thermopile and heat current sensors.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Mechanical Stability Study for Integrable Optics Test Accelerator at Fermilab

The Integrable Optics Test Accelerator (IOTA) is proposed for operation at Fermilab. The goal of IOTA is to create practical nonlinear accelerator focusing systems with a large frequency spread and stable particle motion. The IOTA is a 40 m circumference, 150 MeV (e-), 2.5 MeV (p⁺) diagnostic test ring. A heavy low frequency steel floor girder is proposed as the primary tier for IOTA device component support. Two design lengths; (8) 4 m and (2) 2.8 m long girders with identical cross section completely encompass the ring. This study focuses on the 4 m length girder and the development of a working prototype. Hydrostatic Level Sensor (HLS), temperature, metrology and fast motion measurements characterize the anticipated mechanical stability of the IOTA ring.

43 PARTICLE ACCELERATORS↗

DOE OSTI2018

Nucleation and Crystal Growth in the Formation of Hierarchical Three-Dimensional Nanoarchitecture

This project is to obtain fundamental understandings of the operation of the Ostwald-Lussac (OL) Law and the oriented attachment (OA) mechanism in nucleation and growth of TiO2 nanorods (NR) via surface-reaction-limited pulsed chemical vapor deposition (SPCVD) process. Three-dimensional (3D) NW networks are a unique type of mesoporous architecture that offers extraordinary surface area density and superior transport properties of electrons, photons, and phonons. It is exceptionally promising for advancing the design and application of functional materials for photovoltaic devices, catalysts beds, hydrogen storage systems, sensors, and battery electrodes. Our group has developed the SPCVD technique by mimicking the mechanism of atomic layer deposition (ALD), which effectively decoupled the crystal growth from precursor concentration while retaining anisotropic 1D growth. For the first time, this technique realized a 3D NW architecture with ultrahigh density and achieved ~4-5 times enhancement on photo-conversion efficiency. Through the support of our current DOE award, we revealed the governing role of the OL Law in the nucleation stage of SPCVD. The formation of NR morphology in SPCVD was identified following the OA mechanism. We also discovered a unique vapor-phase Kirkendall effect in the evolution of tubular or core-shell NR structures. These understandings opened many new opportunities in designing 3D NW architectures with improved properties or new functionalities. Specifically, our accomplishments from this project include five aspects: (1) Observation of the Ostwald-Lussac Law in high-temperature ALD. (2) Observation of vapor-solid Kirkendall effect in ZnO-to-TiO2 nanostructure conversion. (3) Development of highly-efficient capillary photoelectrochemical (PEC) solar-fuel generation. (4) Development of efficient and stable electrochemical protections for black silicon PEC electrodes. (5) Development of doped polymers with tunable electrical properties. This project brings a new level of transformative knowledge on nucleation and crystal growth in the SPCVD NR growth processes. Specifically, quantification of the activation energy landscape guided by the OL law will allow us to establish a critical knowledge base of nucleation kinetics for SPCVD synthesis of NR branches on different material surfaces. Studying the OA kinetics will establish a transformative knowledge base to support this new crystal growth mechanism that can be applied to many functional material systems. This research will pave the road toward a capable and versatile synthesis technology for creating 3D hierarchical mesoscale structures.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Optimizing Energy Efficiency of Plants: Quantitative Analysis of Carbon Flux

The efficiency in harvesting light energy is essential for both crop and feedstock productivity. Key targets for optimization of energy efficiency include energy capture, conversion, translocation and storage. Surprisingly little is known about the mechanisms of carbon allocation and the communication between source and sink. We surmise that regulatory networks that coordinate carbon partitioning and allocation might be control points and thus suitable for optimization by engineering. At a first step towards identifying such regulatory networks, our lab identified key players in missing steps of allocation, specifically cellular sugar uptake (SUT family, founding members identified by my team) and cellular efflux carriers (SWEET superfamily, founding members identified by my team). In plants, both classes of transporters are encoded by multigene families. We show that specific subsets of SWEETs serve critical roles in cellular sucrose efflux in the leaf for phloem loading, in seeds for seed filling, as well as in nectar section and vacuolar storage; similarly SUTs play critical roles in cellular uptake during phloem loading and seed filling. Access to the plant SWEETs provided the clues needed for identifying novel bacterial and human sugar transporters. Moreover, we found that pathogens hijack SWEETs to access the plants energy resources. In contrast to SUTs, which are predominantly proton symporters, SWEETs appear to function as facilitators, and thus must be tightly regulated to prevent spillover of carbon into cell walls. One of the key goals of this project was to identify such carriers, characterize the role of key members in carbon allocation. To decrypt the biological circuits we need novel diagnostic tools that, analogous to oscilloscopes in electrical engineering of circuit boards, allow us to quantify fluxes; here by monitoring sugars. Such probes can provide crucial information on systems properties such as metabolic impedance. To measure steady state metabolite levels with minimal invasive methods, and to develop impedance measurements in vivo, we constructed and optimized a set of probes, namely Förster Resonance Energy Transfer (FRET) nanosensors for glucose and sucrose. The probes monitor the concentration of a specific metabolite with high temporal resolution, and, since encoded genetically, can be targeted to specific cells or even specific cellular compartments and thus obtain measurements with cellular and subcellular resolution. We also developed a new platform for monitoring such sensors in vivo, the RootChip.

09 BIOMASS FUELS↗

DOE OSTI2019

Regional Algal Feedstock Testebed (RAFT) Final Report

The RAFT project completed detailed characterization of 12 twelve potential production strains and extensively tested 3 the three best strains in long term seasonal cultivation trials. A total of x 272 cultivation experiments were completed at the three outdoor testbed locations as part of long term cultivation trials conducted over a three year period. The cultivation experiments tested various harvest strategies, crop protection strategies, and seasonal effects along with strain rotation strategies. A sensor, that continuously monitors cell density, was developed and a patent application was submitted. Models were develop and validated for predicting strain performance, determining biomass productivity, evaluating cultivation system design and operations, and evaluating large scale production scenarios. A detailed data base was established for the long term cultivation trials with discrete data from the x 272 testbed cultivation experiments. This data base will be maintained at the University of Arizona https://raft.arizona.edu/cultivation-data/ for public access and continued academic evaluations.

09 BIOMASS FUELS↗

DOE OSTI2021

Augmented Monitoring and Condition Assessment Program (AMCAP) - Proof-of-Principle (POP) Mockup for Non-Aluminum Spent Nuclear Fuel Container In-Situ Examinations

A disciplined engineering approach is being followed to develop an engineered system of tooling and sensors, characterization techniques, and deployment subsystems for in-situ inspection of the several container types used for the storage of non-aluminum spent nuclear fuel (NASNF) in L Basin under the Augmented Monitoring and Condition Assessment Program (AMCAP). Inspecting the containers to provide information on their structural condition helps ensure the safe handling and storage of the NASNF containers pending final disposition. This report describes the work performed in the initial two phases of this developmental work, namely the bench scale and proof-of-principle (POP) scale, which focus on sensor selection and the tooling design and fabrication for two remote non-destructive examination (NDE) methods. These methods include visual testing (VT) for a visual examination of the container surfaces and ultrasonic testing (UT) for a n examination to characterize container wall material thickness and flaws.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗