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All sources NASA NTRS DOE OSTI

DOE OSTI2026

Community Resilience Through Rapid Restoration Leveraging Distributed Energy Resources (DERs) and Low-Cost Sensors

Equitable and automated bottoms-up power restoration following an extreme event will be demonstrated at a site in Puerto Rico. To do so, the team will develop enhanced grid situational awareness techniques integrating behind-the-meter (BTM) distributed energy resources (DER) discovery, impedance sweeping based outage boundary detection, and feasible restoration path identification algorithms. Resilience metric will be developed and incorporated along with situational awareness information in a distributed Model Predictive Control (MPC)-based restoration optimization algorithm to control and mobilize grid assets. These algorithms will be validated through power hardware-in-the-loop experiments and ultimately, a site demonstration to show that outage recovery time and total recovered load could be improved by >20% over the baseline.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Ionic-Based Electrochemical Gas Sensors for Low-Cost, High-Sensitivity SO2 Detection

Sulfur dioxide (SO2) is a toxic gas associated with adverse health and environmental effects that necessitate reliable monitoring techniques. Here, we report the development of an all-solid-state electrochemical sensor utilizing a lithium borate (Li3BO3) solid electrolyte capable of subppm of SO2 detection. While subppm of SO2 sensing has been previously demonstrated in other solid-state electrolyte systems─such as stabilized zirconia, natrium super ionic conductors (NASICON) under mixed-potential conditions─here we establish Li3BO3 as an alternative solid electrolyte enabling equilibrium potentiometric sensing in an all-solid architecture. This sensor demonstrates a detection limit of at least 0.25 ppm, surpassing the human-olfactory threshold and meeting the rigorous requirements for industrial and personal monitoring applications. The sensing mechanism relies on the formation of Li2SO4 on the electrode surface, as evidenced by multimodal characterization techniques, including Raman spectroscopy, scanning electron microscopy (SEM), and scanning transmission electron microscopy (STEM). The strong linear correlation between the open-circuit potential (OCV) and the logarithm of SO2 concentration between 0.25 and 2 ppm indicates that the response is Nernstian in nature.

Lagunas, Francisco (ORCID:000000026377683X)↗

DOE OSTI2026

Evaluation of a catalytically aided thermal regeneration method for quartz filter-based black carbon sensing

Black carbon (BC)–a strong indicator of diesel particulate matter and other sources of incomplete carbonaceous fuel combustion–is an important air pollutant that affects public health, yet low-cost sensors capable of long-term, autonomous BC monitoring remain underdeveloped. We report on the development and evaluation of a novel BC prototype sensor that integrates soot collection on a quartz filter, in-situ optical transmission measurement, and thermal filter regeneration. To enable regeneration at lower temperatures, we evaluated the catalytic effects of various alkali metal salts pre-applied to the filter. Among these, cesium carbonate (Cs 2 CO 3 ) exhibited the strongest catalytic activity, lowering the temperature required for complete BC removal by up to 190 °C and reducing energy consumption by more than 75% compared to that required for untreated filters. The catalytic effect persisted through 10 BC collection–regeneration cycles. These findings demonstrate the potential of catalytically aided thermal regeneration in BC sensors and suggest a pathway toward energy-efficient and reduced maintenance air quality monitoring suitable for distributed BC monitoring networks.

Tang, Xiaochen [Lawrence Berkeley National Laborat↗

DOE OSTI2026

Ten questions concerning low-cost indoor air quality sensors: Perspectives from research and practice

Low-cost indoor air quality (IAQ) sensors are increasingly being used in homes and commercial and public buildings, driven by growing concerns about the impact of air on health, cognitive performance, and occupant wellbeing. These sensors offer a potentially transformative opportunity to increase spatial and temporal coverage of IAQ monitoring at a fraction of the cost of conventional reference instruments. However, their widespread use raises questions around accuracy, calibration, placement, data handling and interpretation, and integration into existing standards and workflows. This paper presents ten critical questions concerning the use of low-cost IAQ sensors in buildings, drawing on the latest empirical research, field deployments, and emerging practice. It discusses potential frameworks for deployment and evaluation, examines current sensor capabilities for measuring common pollutants, identifies methodological gaps in validation and uncertainty quantification, and outlines the extent to which existing IAQ standards can accommodate sensor-based evidence. The paper also explores how monitoring needs and deployment models vary by building type, the potential of real-time IAQ data to support building operations, and the ethical and legal implications of widespread sensor use. While significant challenges remain in ensuring data quality and building stakeholder trust, new applications are emerging through open data initiatives and advances in analytics and visualization. As the technology, science, and standards co-evolve, low-cost IAQ sensors are poised to become integral to routine building operation, building science, and environmental health research.

Parkinson, Thomas↗

DOE OSTI2024

Laser-induced graphene gas sensors for environmental monitoring

Artemesia tridentatais a foundational plant taxon in western North America and an important medicinal plant threatened by climate change. Low-cost fabrication of sensors is critical for developing large-area sensor networks for understanding and monitoring a range of environmental conditions. However, the availability of materials and manufacturing processes is still in the early stages, limiting the capacity to develop cost-effective sensors at a large scale. In this study, we demonstrate the fabrication of low-cost flexible sensors using laser-induced graphene (LIG); a graphitic material synthesized using a 450-nm wavelength bench top laser patterned onto polyimide substrates. We demonstrate the effect of the intensity and focus of the incident beam on the morphology and electrical properties of the synthesized material. Raman analyses of the synthesized LIG show a defect-rich graphene with a crystallite size in the tens of nanometers. This shows that the high level of disorder within the LIG structure, along with the porous nature of the material provide a good surface for gas adsorption. The initial characterization of the material has shown an analyte response represented by a change in resistance of up to 5% in the presence of volatile organic compounds (VOCs) that are emitted and detected byArtemisiaspecies. Bend testing up to 100 cycles provides evidence that these sensors will remain resilient when deployed across the landscapes to assess VOC signaling in plant communities. The versatile low-cost laser writing technique highlights the promise of low-cost and scalable fabrication of LIG sensors for gas sensor monitoring.

Chemistry↗

DOE OSTI2026

Hydrogen Detection via a Low-Cost Prototype Fiber-Optic Sensing System

The developed low-cost prototype sensing system successfully detects hydrogen concentrations across a wide range, demonstrating immediate feasibility for commercial deployment. Utilizing fiber-optic technology, the system delivers stable, reproducible performance from parts-per-million levels up to 100% pure H2 environments. Environmental and chemical cross-sensitivity evaluations confirm robust selectivity against methane and temperature changes, establishing its readiness for near-term commercialization.

fiber-optic H2 sensor↗

DOE OSTI2025

Assessing Indoor versus Outdoor PM 2.5 Concentrations during the 2025 Los Angeles Fires Using the PurpleAir Sensor Network

In January 2025, a series of fast-moving wildland-urban-interface (WUI) fires swept through the Los Angeles (LA) metropolitan area, causing severe air pollution. While the impacts of WUI fires on outdoor air quality have been extensively studied, indoor exposure remains less understood, despite most people sheltering indoors during WUI fires. Here, this study investigates the spatial and temporal patterns of indoor and outdoor PM 2.5 concentrations across the South Coast Air Basin, with a focus on LA County during the LA fires. Using high-resolution data from co-located indoor and outdoor PurpleAir (PA) sensors, we analyze hourly PM 2.5 levels and indoor/outdoor ratios. Outdoor PM 2.5 concentrations spiked sharply during the fires, reaching unhealthy levels exceeding 130 μg/m 3 , compared to the mean concentration (12 μg/m 3 ) during non-fire hours. Indoor concentrations also increased, though to a lesser extent, peaking around 60 μg/m 3 compared to a mean of 7 μg/m 3 during non-fire hours. This reflects the partial shielding that indoor environments provide from outdoor air pollution. The mean (0.42) and median (0.29) indoor/outdoor PM 2.5 ratios during LA fire hours were lower than the mean (0.93) and median (0.66) ratios during non-fire hours. Indoor/outdoor PM 2.5 ratios across sensors showed a wide distribution, reflecting differences in building characteristics and occupant behavior, such as indoor activities and the use of air purifiers. These findings emphasize the need for guidance and interventions to reduce indoor PM 2.5 exposure and protect public health during extreme WUI fire events.

2025 Los Angeles Fires↗

DOE OSTI2025

High sensitivity pressure and temperature quantum sensing in pentacene-doped p-terphenyl single crystals

Quantum sensors’ responsiveness to their physical environment enables detection of variables such as temperature (T), pressure (P), and strain. We present a molecular platform for PT sensing using para-terphenyl crystals doped with pentacene (PDP), leveraging optically detected magnetic resonance (ODMR) of photoexcited triplet electron spins. We observe maximal frequency variations of df/dP=1.8 MHz/bar from 0-8 bar and df/dT=247 kHz/K from 79–330 K, over 1200 times and threefold greater, respectively, than those seen with nitrogen-vacancy centers in diamond and > 85-fold greater pressure sensitivity over the previous record. Density functional theory calculations indicate picometer-level PT-induced molecular orbital shifts are measurable via ODMR. PDP offers additional advantages including high sensor doping levels, narrow ODMR linewidths, high contrast, and low-cost single crystal growth. Overall, this work reports low-cost, optically-interrogated PT sensors and lays the foundation for increased versatility of quantum sensors through synthetic molecular design.

Magnetic properties and materials↗

DOE OSTI2026

Spectrometer-free quantitative vapor sensing and classification via spatiotemporal imaging of porous silicon metasurfaces

Metasurfaces offer a compact platform for optical vapor sensing, but their practical deployment has been limited by weak evanescent light–matter interactions and reliance on spectrally resolved instrumentation. Here, we report porous silicon (pSi) metasurfaces for spectrometer-free quantitative detection of volatile organic compounds (VOCs) with strongly enhanced light–matter interaction. The engineered porosity increases sensitivity by >100× relative to non-porous dielectric metasurfaces, enabling limits of detection of 1.65 ppm for methanol and 9.1 ppm for ethanol across a broad dynamic range (<10 ppm to >103 ppm). Imaging-based readout provides a lightweight, spectrometer-free pathway for real-time quantitative sensing. Beyond quantitative detection, the mesoporous architecture introduces adsorption–desorption kinetics as an additional information channel. Analysis of the resulting spatiotemporal signatures enables kinetic fingerprinting without reliance on infrared spectral features or surface functionalization, and a lightweight machine-learning classifier differentiates acetone, methanol, ethanol, and isopropanol with 91.6% accuracy. These results establish porous metasurfaces as spatiotemporal sensing elements that couple quantitative vapor detection with kinetic fingerprinting through real-time dynamical responses, enabling low-cost, high-performance optical sensors.

Dash, Tomoshree [Clemson University]↗

DOE OSTI2026

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗

DOE OSTI2026

Comparison of CNN-Based Image Classification Approaches for Implementation of Low-Cost Multispectral Arcing Detection

Camera-based sensing has benefited in recent years from developments in machine learning data processing methods, as well as improved data collection options such as Unmanned Aerial Vehicles (UAV) mounted sensors. However, cost considerations, both for the initial purchase of sensors as well as updates, maintenance, or potential replacement if damaged, can limit adoption of more expensive sensing options for some applications. To evaluate more affordable options with less expensive, more available, and more easily replaceable hardware, we examine the use of machine learning-based image classification with custom datasets, utilizing deep learning based-image classification and the use of ensemble models for sensor fusion. Utilizing the same models for each camera to reduce technical overhead, we showed that for a very representative training dataset, camera-based detection can be successful for detection of electrical arcing. We also use multiple validation datasets, based on conditions expected to be of varying difficulty, to evaluate custom data. These results show that ensemble models of different data sources can mitigate risks from gaps in training data, though the system will be less redundant for those cases unless other precautions are taken. We found that with good quality custom datasets, data fusion models can be utilized without specialization in design to the specific cameras utilized, allowing for less specialized, more accessible equipment to be utilized as multispectral camera components. This approach can provide an alternative to expensive sensing equipment for applications in which lower-cost or more easily replaceable sensing equipment is desirable.

convolutional neural networks↗

DOE OSTI2026

A Modular Accelerator Robotics Framework for AD Robotics

Accelerator tunnels, such as the ones at Fermilab, remain highly radioactive after beam shutoff due to induced radiation from the beam. This residual radiation creates a hazardous environment for manual inspection and repair of beamline components. To minimize worker radiation dose and reduce beam downtime, the AD Robotics Initiative previously built a fleet of low-cost custom mobile robots. However, the custom Python sockets server-client architecture lacked standardization, causing development delays and complicating the integration of new sensors and actuators. Here, we developed a modular system using ROS2 and Docker to standardize the teleoperation and control interfaces. This system was validated by implementing a teleoperation controller with real-time, low-latency, and high-definition video feedback. The aim of this framework is for a new feature or even a robot to be integrated into the system simply by documenting the hardware configuration. Current integration of LiDAR, Odometry, and Depth Cameras provides the foundation for Simultaneous Localization and Mapping (SLAM) tasks. Finally, future work involves integration into the accelerator control system and the attachment of a 6 degree-of-freedom robotic arm for telemanipulation.

Rayyan Khan, M. [Fermilab; Rensselaer Poly.; Unlis↗

DOE OSTI2026

Modular Accelerator Robotics Framework Implementation For Accelerator Inspection

Rayyan Khana, M. [Unlisted, US, IL] (ORCID:0009000↗

DOE OSTI2025

Versatile & Intelligent Biodetection via Environmental Sensing (VIBES)

Reactive health monitoring strategies during events like the COVID-19 pandemic highlighted the need for predictive, threat-agnostic diagnostics that can detect both known diseases and novel chemical or biological threats. To address this, we investigated an optical biosensor as a breath volatile organic compound (VOC) analyzer, aiming to emulate biological olfaction. We assembled and validated the device with thin film metal coated substrate-based sensors. We immobilized small biological recognition elements on the substrates and delivered controlled concentrations of target VOCs. The sensor was irradiated with a visible laser and the sensor signal was recorded. We characterized the laser performance and tested 3 recognition elements for 2 VOCs with varying concentrations (1-100 ppm). We also evaluated enhancement of the signal using nanostructures on the metal film in comparison with planar film substrate. We demonstrated detecting ethanol reliably at concentrations as low as ~2 ppm along with preliminary detection of acetone (<100 ppm). We also found several unexpected factors that influence the sensor behavior that should be addressed to further refine the device’s performance. The nanostructures were, as expected, found to amplify the sensor signals. These findings demonstrate the feasibility of the optical bio-sensing modality for breath VOC monitoring at physiologically relevant levels. This positions LLNL to develop a low-cost, scalable, broad-spectrum health monitoring capability aligned with the Early Detection thrust of the Bioresilience Mission Focus Area and attract external funding.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Ecobuoys for Scalable Oceanography

An approach to scalable surface-drifting buoys is needed to enable the high spatial and temporal resolution of oceanographic data that the science and meteorological communities are asking for. With the number of active buoys predicted to increase by a factor of 100 or more, the impact on the environment becomes even more important. Here, we present a pathway to a scalable and sustainable generation of buoys. We identify the main criteria to be used when developing such buoys to be low cost, with reliable data and neutral or even positive environmental impact. For each buoy subsystem—hull, electronics, energy generation and storage, sensors, and communication system—cutting-edge technological solutions are presented, many of them from emerging research in marine or other disciplines. We then assess the potential solutions against the design criteria and plot a path toward small, environmentally friendly, low-cost, and low-power buoys.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Direct ink writing of shear exfoliated two-dimensional nanomaterial- elastomeric multifunctional nanocomposite

Direct ink writing (DIW) of polymer nanocomposites with high loadings of two-dimensional (2D) nanofillers (graphene and hexagonal boron nitride (hBN)) is challenging because of potential clogging, use of hazardous solvents, and agglomeration. Here, in this work, a shear exfoliation and sieving method to prepare DIW ink with high loading of nanofillers produced from low-cost bulk layered materials such as graphite and bulk hBN powder for successful DIW printing without the use of any solvents, binders, or plasticizers. The single-step exfoliation technique resulted in a composite with substantial layer reduction along the c-axis, as confirmed by SEM, TEM, XRD, and Raman analysis. Incorporating exfoliated graphene (40 wt%) increased viscosity by ∼6 orders of magnitude due to enhanced particle–matrix interactions, leading to pronounced yield stress behavior and a yield stress of approximately 1598 Pa, which enabled excellent shape retention during extrusion. Using the DIW technique, porous structures such as desalination membranes, self-sensing bone scaffolds, thermal management coating, and serpentine strain sensors were fabricated. When tested in a direct contact membrane distillation setup, the fabricated membrane demonstrated a promising permeate flux of 21.85 Lm −2 h −1 and a salt rejection of 74.3 %. The fabricated serpentine sensor exhibited stable signal variations under cyclic tensile loading, with a working range of 0–200 % strain and a maximum gauge factor of 43,735. A cell culture test using the printed bone scaffold demonstrated promising cell attachment and proliferation. The DIW printed hBN nanocomposite exhibited reversible shape change under heat, demonstrating potential 4D printing capability and efficient thermal management when exposed to high heat or flame.

Desalination↗

DOE OSTI2026

Light-tight Skipper-CCDs for X-Ray Detection in Space

Skipper Charge-Coupled Devices (skipper-CCDs) are pixelated silicon detectors with deep sub-electron resolution. Their radiation hardness and capability to reconstruct energy deposits with unprecedented precision make them a promising technology for space-based X-ray astronomy. In this scenario, optical and near-infrared photons may saturate the sensor, distorting the reconstructed signal. We present a light-tight shield for skipper-CCDs to suppress optical backgrounds while preserving X-ray detection efficiency. We deposited thin aluminum layers on the CCD surface using an e-beam evaporator and evaluated their blinding performance across wavelengths from 650 to 1000 nm using a monochromator, as well as the X-ray transmission using an 55 Fe source. We find that 50 and 100 nm layers provide >99.6% light suppression, with no efficiency loss for 5.9 and 6.4 keV X-rays. In addition, we used Geant4 simulations to extend these results to a broader energy range and quantify the efficiency loss for different aluminum thicknesses. Results show that thin aluminum coatings are an effective, low-cost solution for optical suppression in skipper-CCDs intended for X-ray detection and space instrumentation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Optimized Illuminance Operation-A Light-Driven Dilution Strategy to Improve Microalgae Biomass Productivity

Periodic dilution is a necessary operation to avoid light limitation due to self-shading as the culture grows dense. However, environmental conditions are constantly changing in outdoor cultivation systems, making it difficult to determine the optimal dilution rate. To address this challenge, this study evaluated a dilution approach based on light penetration to optimize illuminance (OptiLum) in the culture. Biomass concentration was controlled via sensor-feedback directed dilution to ensure that light reaching the bottom of the culture was maintained above the compensation intensity. Under replicated outdoor pond conditions, by keeping the entire culture within a net-positive photosynthetic zone, the OptiLum operation improved the biomass productivities of two top-performing strains, Picochlorum celeri and Tetraselmis striata, by 95 % and 86 %, respectively, compared to conventional semi-continuous batch cultivation. The dilution rate varied daily and was dynamically adjusted based on the light status within the culture, which is concurrently influenced by weather, culture density, and growth rate. The techno-economic analysis showed that the OptiLum operation could reduce biomass production cost by as much as 24 % and 33 % for P. celeri and T. striata, respectively, assuming a low-cost dewatering approach with initial gravity settling of biomass can be realized for both strains. However, a more costly two-stage dewatering strategy, comprising only membranes and centrifuges, may be necessary for non-settling strains, such as P. celeri, which would alternatively increase production costs by 23 % for the OptiLum case. The results demonstrated that the proposed OptiLum operation is a promising approach to improve biomass productivity and lower production cost via weather-responsive and self-adjusting dilution.

09 BIOMASS FUELS↗