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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Social Evolution and Cheating in Plant Pathogens

Plant pathogens are a critical component of the microbiome that exist as populations undergoing ecological and evolutionary processes within their host. Many aspects of virulence rely on social interactions mediated through multiple forms of public goods, including quorum-sensing signals, exoenzymes, and effectors. Virulence and disease progression involve life-history decisions that have social implications with large effects on both host and microbe fitness, such as the timing of key transitions. Considering the molecular basis of sequential stages of plant–pathogen interactions highlights many opportunities for pathogens to cheat, and there is evidence for ample variation in virulence. Case studies reveal systems where cheating has been demonstrated and others where it is likely occurring. Harnessing the social interactions of pathogens, along with leveraging novel sensing and -omics technologies to understand microbial fitness in the field, will enable us to better manage plant microbiomes in the interest of plant health.

Plant Sciences↗
DOE OSTI2026

CMOS-fabricated ultraviolet light modulator using low-loss alumina piezo-optomechanical photonics

Ultra-violet (UV) and near-UV wavelengths are necessary for many important optical transitions for quantum technologies and various sensing mechanisms for biological and chemical detection. However, all well-known photonic platforms have excessively high losses in the UV, which has prevented photonic integrated circuits (PICs) being used to address these and other important application spaces. Photonic waveguides using low-loss alumina cores and silicon dioxide cladding have emerged as a promising solution because of alumina’s large optical bandgap and the high quality of films enabled by atomic layer deposition. These properties allow passive, low-loss waveguide operation down to at least 266 nm using multimode widths. However, to the best of our knowledge, active alumina PICs have only been realized using thermo-optic tuning, which precludes switching speeds shorter than approximately 100 microseconds, high circuit densities, and cryogenically compatible operation. Here, we introduce a CMOS-fabricated, piezo-optomechanical PIC platform using alumina waveguides and piezoelectric aluminum nitride strain actuators. The platform allows for sub-microsecond switching times, high circuit densities, and cryogenic operation which is desired in many UV PIC applications. We demonstrate a high-performance, reconfigurable optical filter operating at wavelengths as low as 320 nm. The filter has a 6-nanosecond switching time, a loaded linewidth of 3.3 GHz, tuning rate of 28 V/linewidth, and a hold power of less than 20 nW at one linewidth detuning. This work establishes the foundations for a new class of CMOS-fabricated, rapidly reconfigurable, and low-power UV photonic circuits compatible with wavelengths as low as 225 nm.

Castillo, Zachary A. [Sandia National Laboratories↗
DOE OSTI2025

All-optical control of charge-trapping defects in rare-earth doped oxides

Abstract Charge-trapping defects in crystalline solids play important roles in applications ranging from microelectronics, optical storage, sensing and quantum technologies. On one hand, depleting trapped charges in the host matrix reduces charge noise and enhances coherence of solid-state quantum emitters. On the other hand, stable charge traps can enable high-density optical storage systems. Here we report all-optical control of charge-trapping defects via optical charge trapping (OCT) spectroscopy of a rare-earth ion doped oxide (Y 2 O 3 ). Charge trapping is realized by low intensity optical excitation in the 200–375 nm range. Charge detrapping or depletion is carried out by optically stimulated luminescence (OSL) under 532 nm stimulation. Using a Pr-doped Y 2 O 3 polycrystalline ceramic host matrix, we observe charging pathways via the inter-band optical absorption of Y 2 O 3 and via the 4f-5d transitions of Pr 3+ . We demonstrate effective control of the density of trapped charges within the Y 2 O 3 matrix at ambient environment. These results point to a viable method for controlling the local charge environment in rare-earth doped crystals via all-optical means, and pave the way for further development of efficient optical storage technologies with ultrahigh storage capacity, as well as for the localized control of quantum coherence in rare-earth doped solids.

França, Leonardo V. S. [Pritzker School of Molecul↗
DOE OSTI2020

Specifying Calibration of Environmental Sensors

The emergence of the Internet of Things is resulting in an increased ability of devices and systems to share data and is generating increasing interest in integrating sensors into a variety of devices deployed in the built environment. The value of such data is a function of how the data can be used. Data-producing devices and systems that enable valuable use-cases in turn can be seen as more valuable. Lighting systems are particularly interesting platforms for integrated sensors. Both indoor and outdoor lighting devices are becoming more connected, and their location is often ideally suited for hosting environmental sensors that can characterize the properties of indoor or outdoor spaces in ways that support a wide variety of use cases, from improving air quality to supporting fault diagnostics and prediction. The value of environmental-sensor-driven use-cases and the lighting systems that house them is dependent to some degree on sensor accuracy. Environmental sensors utilize a wide variety of sensing techniques or technologies and have varying accuracy. More-accurate, laboratory-grade products or reference standards are often used to characterize, refine, calibrate, adjust, and monitor devices that are deployed, or are intended to be deployed, in physical spaces of interest. Sensors or reference standards need to be calibrated periodically to ensure that their use yields accurate measurements. Calibration needs, however, vary in sophistication, based on user and use-case requirements. This paper provides guidance for evaluating the performance of environmental sensors so as to ensure that they meet user or use-case needs. It describes best practices that have been developed for a) calibrating sensors to ensure some known level of accuracy, and b) determining whether calibration-laboratory accreditation meets user or use-case needs. Excerpts from laboratory scopes of accreditation are shared to reveal the diversity of terminology and format among them. In an effort to aid those who currently have sensors calibrated or who have new or changing needs for sensor calibration, rationale is provided for why a specification might be used to request calibration services that meet specific needs. Commercially available calibration-service providers that are accredited for environmental-sensor calibration are compared and contrasted, and a specification template that might be used for requesting this calibration is presented. The specification template should be tailored to meet each user’s needs. To illustrate, an example set of environmental-sensor test conditions (reflecting the planned usage of the device to be calibrated) is used to develop a customized calibration specification, and commercially available service providers are assessed in terms of their qualifications for calibration to that particular implementation of the specification template.

42 ENGINEERING↗
DOE OSTI2024

Artificial-Intelligence Aided Design and Synthesis of Novel Layered 2D Multi-Principal Element Materials for Energy Storage (Final Report)

This DOE-EPSCoR project aimed to predict, synthesize, and characterize novel layered two-dimensional (2D) high-entropy materials (HEMs). These 2D-HEMs, composed of multiple principal elements in nearly equal concentrations, are distinct from traditional 2D materials (typically containing two or three elements) and conventional alloys (dominated by a single primary element with minor secondary additions). Their unique structural and compositional features enable significant lattice strain accommodation, resulting in enhanced electrode performance and potential applications in catalysis, hydrogen storage, sensing, quantum information technologies, and flexible electronics. The research focused on addressing four fundamental questions: (i) What combinations of elements can form stable and synthesizable 2D-HEMs? (ii) What mechanisms drive the stability and synthesizability of crystalline single-phase 2D-HEMs? (iii) How do local chemical disorder and defects influence the macroscopic electronic and mechanical properties? and (iv) What charge storage mechanisms are active in selectively synthesized 2D-HEMs for battery and supercapacitor electrode applications? To achieve these goals, the project employed an integrated theory-experiment approach, incorporating high-throughput first-principles calculations, theoretical modeling, data mining, experimental synthesis, and advanced characterization techniques. The advanced computing resources and state-of-the-art experimental characterization facilities at Oak Ridge National Laboratory (ORNL) were leveraged through collaboration. Beyond scientific advancements, the project contributed to workforce development. Two postdoctoral researchers and three graduate students at the University of Maine were trained through co-advising by ORNL scientists and collaborative interactions, strengthening their expertise in cutting-edge materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimization-Based Resiliency Verification in Microgrids via Maximal Adversarial Set Characterization

Critical energy infrastructures are increasingly relying on advanced sensing and control technologies for efficient and optimal utilization of flexible energy resources. Algorithmic procedures are needed to ensure that such systems are designed to be resilient to a wide range of cyber-physical adversarial events. This paper provides a robust optimization framework to quantify the largest adversarial perturbation that a system can accommodate without violating pre-specified resiliency metrics. We formulate the maximal adversarial set characterization as a bi-level optimization problem which is solved via Lagrangian relaxations.We illustrate the proposed algorithm on an islanded microgrid example: a modified IEEE 123-node feeder with distributed energy resources. Simulations are carried out to characterize the tolerable adversarial perturbations for varying levels of available flexibility (energy reserves).

Nazir, Mohammad Nawaf↗
DOE OSTI2024

Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Hydrogen Detection Strategies to Support H2@SCALE - The NREL Sensor Laboratory

Hydrogen represents a major pathway to decarbonize and stabilize the national and international energy industry and select manufacturing markets. To facilitate the development of hydrogen markets, the US Department of Energy initiated H2@Scale to bring together stakeholders to advance affordable hydrogen production, transport, storage, and utilization to increase revenue opportunities in multiple energy sectors. One major impediment to hydrogen implementation is cost. To expedite the use of hydrogen in energy and other markets, the United States announced in 2021 the Hydrogen Shot, which seeks to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). As the cost of hydrogen drops, new applications will emerge that will require unique configurations of existing equipment and infrastructure, and eventually lead to advances in the generation and utilization of hydrogen. As the hydrogen economy expands, sensors and detection methods will need to adapt to changing infrastructure demands to address the primary targets of health & safety, emissions monitoring, and process control. The NREL Sensor Laboratory is playing a pivotal role in advancing the use of hydrogen sensors and detection methodologies in each of these categories to support DOE's mission for safe and efficient utilization in emerging markets. Health & safety monitors are required to ensure that operators and facilities can react to unintended hydrogen releases, either as GH2, LH2, or as a constituent of blends (e.g., natural gas or ammonia). Current detection methodologies focus on safety applications to detect near its lower flammable limit (4 vol %), and typically include point sensors in applications such as fixed or mobile detectors (e.g., personal gas monitors). Methodologies amenable for area detection include acoustic, emerging optical imaging methods, and flame detectors. Comparable detection strategies can be utilized for emissions monitoring and quantization, however few methods can simultaneously cover both low (emissions) and high (health & safety) levels. Deployment of emission level detectors will be required to 1) reduce product loss through small but potentially significant leaks from an environmental or cost perspective, 2) reduce downtime of high demand systems by early identification of eminent system failures (leaks through pump or compressor seals indicative of impending failure), and 3) address potential emission monitoring requirements that may be set by regulating bodies. The first two points should be adopted by industry to reduce the cost-of-goods-sold. The third main category for hydrogen detection relates to process control and may be advantageous for many existing applications. Two main applications are emerging. For example, the purity requirements for hydrogen that is dispensed from refueling systems for hydrogen fuel cell electric vehicles (FCEV) is rigorously regulated by the Standard SAE J2719, which prescribes maximum allowable levels of multiple impurities in the hydrogen fuel and must be verified by a regulatory body. Hydrogen contaminant detectors (HCD) integrated to the fueling station can assure this compliance. HCDs must be able operate in 100% H2 backgrounds and be able to distinguish between multiple contaminants at low ppm to low ppb levels. Secondly, as a strategy to decarbonize the natural gas grid, there are proposals to blend hydrogen with natural gas. This blending will affect transport applications (pipeline infrastructure), stationary combustion systems (turbines), and consumer and commercial appliances. In the short-term, hydrogen levels up to 20% are proposed. Variations in the hydrogen level can have dramatic impact on the combustion process and on the potential response of safety sensors. These mixtures may be regulated so that the concentration at a delivery point must be monitored with high precision. However, routine maintenance may introduce background gases such as ambient air (with water) or maintenance gases (introduced with welding processes or adhesive outgassing.) Therefore, the detection methodology must be robust enough to recover or respond to various contaminants. Several reviews can be found in literature addressing sensing and detection technologies, including their limitations and applications. However, for most applications, limitations can be alleviated by combining various detection techniques either through system integration or implementation of machine learning methods (artificial intelligence). In this presentation, we will discuss several applications, highlight their current approach for hydrogen detection, and suggest detection strategies to supplement their limitations.

ENERGY STORAGE,HYDROGEN↗
DOE OSTI2024

SVM-Based Synchronized Fault Detection for 100% Renewable Microgrids: Preprint

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.

100% microgrid↗
DOE OSTI2021

Quantum coherence confined

Quantum coherence refers to the ability of a quantum state to maintain its entanglement and superposition in the face of interactions and the effects of thermalization. It is indispensable for quantum technologies — be they in sensing, transduction or computing — permitting them, in principle, to outperform their classical counterparts. To realize the promise of these technologies, one key challenge is to identify quantum states within systems that are quantum coherent. Now, writing in Nature Physics, Wen Lin Tan and collaborators report that they have used a system of trapped rare-earth-ion arrays to demonstrate a class of disorder-free quantum states that have enhanced coherence and experience anomalous patterns of thermalization1. Here, the pivotal notion behind their observations is the confinement of fundamental excitations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Revealing complex subsurface dynamics with continuous seismic monitoring: Observations using distributed acoustic sensing and surface orbital vibrators during hydraulic fracturing

Understanding hydraulic fracturing is crucial to improving the stimulation of unconventional reservoirs and increasing fluid production. This study develops a novel seismic monitoring technology using distributed acoustic sensing (DAS) and surface orbital vibrators (SOV) to capture fracture seismic response and mechanical properties at high temporal intervals. We analyze continuous time-lapse vertical seismic profiling (VSP) data acquired every hour during the first nine days of treatment of an unconventional reservoir in the Austin Chalk/Eagle Field Laboratory. The VSP data contain clear seismic signals scattered from the activated fractures. The spatiotemporal changes of the fracture reflectivity revealed by the SOV/DAS data correlate well with the observations of fracture locations inferred from low-frequency DAS data. These results capture the fracture opening and closure processes, as well as highlight potential prestage activations of the fractures due to hydraulic connectivity with preexisting fracture systems. Therefore, analysis of the presented data set provides a unique opportunity to understand fracture initiation and subsequent evolution, not only in the context of unconventional resources but also in enhanced geothermal systems.

Correa, Julia↗
DOE OSTI2023

Scalable manufacturing of quantum light emitters in silicon under rapid thermal annealing

Quantum light sources play a fundamental role in quantum technologies ranging from quantum networking to quantum sensing and computation. The development of these technologies requires scalable platforms, and the recent discovery of quantum light sources in silicon represents an exciting and promising prospect for scalability. The usual process for creating color centers in silicon involves carbon implantation into silicon, followed by rapid thermal annealing. However, the dependence of critical optical properties, such as the inhomogeneous broadening, the density, and the signal-to-background ratio, on centers implantation steps is poorly understood. We investigate the role of rapid thermal annealing on the dynamic of the formation of single color centers in silicon. We find that the density and the inhomogeneous broadening greatly depend on the annealing time. We attribute the observations to nanoscale thermal processes occurring around single centers and leading to local strain fluctuations. Our experimental observation is supported by theoretical modeling based on first principles calculations. The results indicate that annealing is currently the main step limiting the scalable manufacturing of color centers in silicon.

42 ENGINEERING↗
DOE OSTI2024

Mid-IR UAV-based sensing platform with deep learning to Identify and Quantify Gaseous Emission in Gas Flares

This report details the development and evaluation of a Mid-Infrared (Mid-IR) Unmanned Aerial Vehicle (UAV)-based sensing platform integrated with deep learning algorithms for the identification and quantification of gaseous emissions in gas flares. The project, spearheaded by Omega Optics, Inc., aimed to address environmental monitoring challenges by leveraging advanced photonic technologies and autonomous UAV operations. The research focused on designing, optimizing, and fabricating photonic crystal waveguides and grating couplers to enhance the sensitivity and accuracy of gas detection. A comprehensive drone-based system was developed, featuring a miniaturized sensor, GPS module, and microcontroller communication network for real-time gas concentration monitoring. The system's adaptive sampling algorithm, implemented using the Robot Operating System (ROS), enables autonomous detection and localization of gas emission sources. Preliminary results demonstrate the platform's capability to detect and monitor gas emissions with high precision, cost-effectiveness, and scalability. Future work will expand upon this foundation by introducing 3D wind model-based learning for dynamic environmental conditions and further enhancing the user interface and data processing algorithms to support broader environmental monitoring applications. Overall, this project represents a significant step forward in UAV-based environmental sensing technologies, offering robust solutions for detecting and mitigating the impacts of gaseous emissions on public health and safety.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

SERS and the scientific career of Richard P. Van Duyne (1945–2019)

This article describes the scientific career of our dear friend Richard P. Van Duyne. Rick was the discoverer of surface-enhanced Raman spectroscopy (SERS), and over the period from the mid-1970s until his death in 2019, he served as both the intellectual leader of the field, and the technical genius who pushed the field forward through his numerous inventions and discoveries. Rick's work went through many phases, and even involved a significant period of inactivity that Rick was able to break out of, but especially after 2000, there was a period of incredible creativity that ultimately led to a detailed understanding of what the SERS enhancement factor is all about and also led to fabrication techniques for making optimum SERS substrates such that applications of SERS to a wide variety of real-world problems in sensing became possible. In addition, Rick's SERS work spawned numerous related technologies, including index of refraction sensing based on localized surface plasmon resonance (LSPR) measurements, and the extension of SERS to its tip-enhanced cousin TERS, to electrochemical versions of SERS and TERS, and to ultrafast nonlinear techniques. The practical applications he pursued included diagnostics for diseases such as cancer, hand-held glucose sensing, and SERS studies related to art conservation. Furthermore, Rick's legacy will probably be best known through the numerous students, postdocs, and collaborators that passed through his lab and who are now active researchers in areas related to SERS at universities and industrial labs around the world.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Review: PFAS Adsorption, Sensing, and Remediation with Engineered Nanoporous Materials

Per- and polyfluoroalkyl substances (PFAS) are among the most abundant environmental contaminant species. They are widespread due to uncontrolled industrial and commercial use and have been linked to health risks such as cancer. With rising global concerns about the public health effects of PFAS, there is an incentive to develop strategies for reliable monitoring and effective PFAS removal, particularly in drinking water. Traditional PFAS sensing techniques are inefficient due to long measurement times, high labor input, high costs, and limitations to ex situ analysis. Current commercially available sorbents for PFAS removal generally lack the ability to capture PFAS components rapidly and quantitatively. Furthermore, existing sorbents are notably inefficient in removing the more toxic, smaller PFAS chemical chains. Large-scale applications of these methods tend to be costly and resource-intensive. Pacific Northwest National Laboratory has developed unique strategies for PFAS sensing and removal with capture probe technology that has an affinity for fluorocarbons, including PFAS. For both sensing and removal, the customizable capture probes can target specific PFAS compounds since they used a metal-organic framework (MOF)-based technology that can be fine-tuned and molecularly tailored. This tailoring of the materials enables high PFAS sensitivity, selectivity, and faster uptake. PNNL’s capture probe materials have been developed into a sensing technique based on the interactions of the capture probes with PFAS at the molecular level and further transduced that to a quantifiable electrochemical response. The materials have been integrated into a sensor platform developed by New Jersey Institute of Technology (NJIT). Tunable capture probes with a range of detection sensitivities allow for faster and more sensitive PFAS detection limits as low as what appears to be 0.5 ng/L (compared to the U.S. Environmental Protection Agency health advisory level of 70 ng/L). Customization of the capture probe results in improved sorption capacities (fast kinetics and high capacities) compared to commercial granular activated carbons. This research applied various experimental and modeling tools to improve understanding the molecular level interactions between the sorbents towards PFAS adsorption properties.

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

Voltage regulation in distribution grids: A survey

Environmental and sustainability concerns have caused a recent surge in the penetration of distributed energy resources into the power grid. This may lead to voltage violations in the distribution systems making voltage regulation more relevant than ever. Owing to this and rapid advancements in sensing, communication, and computation technologies, the literature on voltage control techniques is growing at a rapid pace in distribution networks. In particular, there is a paradigm shift from traditional offline centralized approaches to distributed ones leveraging increased and varied types of actuators, real-time sensing, fast and efficient computations, and an overall distributed situational awareness. This paper reviews state-of-the-art voltage control algorithms, summarizes the underlying methods, and classifies their coordination mechanisms into local, centralized, distributed, and decentralized. The underlying solution methodologies are further classified into two categories, open-loop and feedback-based. Two specific example workflows are provided to illustrate these solutions for voltage regulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

High Fluence Active Irradiation and Combined Effects Testing of Sapphire Optical Fiber Distributed Temperature Sensors

Investigate the in-pile performance of sapphire optical fiber temperature sensors and to develop clad sapphire optical fibers for in-pile instrumentation. Evaluate the distributed sensing performance of the sensors through optical backscatter reflectometry under combined radiation and temperature effects, and high fluence.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗