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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Designed 2D protein crystals as dynamic molecular gatekeepers for a solid-state device

The sensitivity and responsiveness of living cells to environmental changes are enabled by dynamic protein structures, inspiring efforts to construct artificial supramolecular protein assemblies. However, despite their sophisticated structures, designed protein assemblies have yet to be incorporated into macroscale devices for real-life applications. We report a 2D crystalline protein assembly of C98/E57/E66 L-rhamnulose-1-phosphate aldolase ( CEE RhuA) that selectively blocks or passes molecular species when exposed to a chemical trigger. CEE RhuA crystals are engineered via cobalt(II) coordination bonds to undergo a coherent conformational change from a closed state (pore dimensions <1 nm) to an ajar state (pore dimensions ~4 nm) when exposed to an HCN(g) trigger. When layered onto a mesoporous silicon (pSi) photonic crystal optical sensor configured to detect HCN (g) , the 2D CEE RhuA crystal layer effectively blocks interferents that would otherwise result in a false positive signal. The 2D CEE RhuA crystal layer opens in selective response to low-ppm levels of HCN (g) , allowing analyte penetration into the pSi sensor layer for detection. These findings illustrate that designed protein assemblies can function as dynamic components of solid-state devices in non-aqueous environments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Rapid discovery and evolution of nanosensors containing fluorogenic amino acids

Binding-activated optical sensors are powerful tools for imaging, diagnostics, and biomolecular sensing. However, biosensor discovery is slow and requires tedious steps in rational design, screening, and characterization. Here we report on a platform that streamlines biosensor discovery and unlocks directed nanosensor evolution through genetically encodable fluorogenic amino acids (FgAAs). Building on the classical knowledge-based semisynthetic approach, we engineer ~15 kDa nanosensors that recognize specific proteins, peptides, and small molecules with up to 100-fold fluorescence increases and subsecond kinetics, allowing real-time and wash-free target sensing and live-cell bioimaging. An optimized genetic code expansion chemistry with FgAAs further enables rapid (~3 h) ribosomal nanosensor discovery via the cell-free translation of hundreds of candidates in parallel and directed nanosensor evolution with improved variant-specific sensitivities (up to ~250-fold) for SARS-CoV-2 antigens. Altogether, this platform could accelerate the discovery of fluorogenic nanosensors and pave the way to modify proteins with other non-standard functionalities for diverse applications.

Biosensors↗
DOE OSTI2023

Design, construction, and validation of an in-situ groundwater trace element analyzer with applications in carbon storage

Abstract It is estimated that carbon emissions should reach net-zero by 2050 to meet important climate targets. Carbon capture is likely necessary to reach these targets, requiring a long-term storage solution such as geological carbon sequestration. However, as with any subsurface activity, leakage can occur, potentially impacting groundwater quality near the storage site. Rapid detection is essential to mitigate damage to this resource. Since CO 2 will acidify groundwater, the concentrations of acid soluble minerals and associated cations will increase. Thus, an in-situ, real-time element analysis system based on laser-induced breakdown spectroscopy (LIBS) is under development to monitor these elements. The system splits the traditional LIBS system into a miniature, all-optical sensor head built around a passively Q-switch laser fiber coupled to a control unit. Previous work has validated the LIBS technique for use at high pressure as well as the split system design. In this work, a fieldable prototype sensor is developed and tested in an onsite monitoring well where trace elements concentrations (approx. 0–3 ppm) were tracked over 20 days. These concentrations varied in response to local rainfall, diluting with increased rain, demonstrating the ability of a LIBS-based sensor to track trace elements under real-world conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The ion-gas-neutral interactions with surfaces-2 (IGNIS-2) facility for the study of plasma–material interactions

The Ion-Gas-Neutral Interactions with Surfaces-2 (IGNIS-2) surface science facility has been designed at the Pennsylvania State University with the specific purpose of enabling experiments to study plasma–material interactions. This in situ surface modification and characterization facility consists of multiple reconfigurable substations that are connected through a central transfer chamber. This fully connected vacuum system ensures that the physical and chemical properties of samples are not altered between surface modification and analysis. The modification techniques in IGNIS-2 include a low-energy (<300 eV), high-flux (up to 1016 cm−2 s−1) broad-beam ion source, a liquid metal dropper, a lithium injection system, an RF sputter source, and an evaporator. Its characterization techniques include charged particle-based techniques, such as low-energy ion scattering (enabled by two <5 keV ion sources) and x-ray photoelectron spectroscopy, and photon and light-based techniques, such as x-ray fluorescence, multi-beam optical stress sensors, and optical cameras. All of these techniques can be utilized up to mTorr pressures, allowing both in situ and in operando studies to be conducted. Results are presented on lithium wetting experiments of argon-irradiated tungsten-based composites, surface stress measurements of tungsten films during deuterium ion irradiation, and temperature-programmed desorption of deuterium-irradiated graphite to demonstrate the in situ capabilities of this new facility.

Instruments & Instrumentation↗
DOE OSTI2022

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Observing fish interactions with marine energy turbines using acoustic cameras

Abstract Marine current energy converters such as tidal and riverine turbines have the potential to provide reliable, clean power. The risk of collision of fishes with marine energy turbines is not yet well understood, in part due to the challenges associated with observing fish at turbine sites. Turbidity and light availability can limit the effectiveness of optical sensors like video cameras, motivating the use of acoustic cameras for this task. However, challenges persist in collecting and interpreting data acquired from acoustic cameras. Given the limited number of turbine deployments to date, it is prudent to draw on the application of acoustic cameras to monitor fish in other scenarios. This article synthesizes their use for other fisheries applications to inform best practices and set realistic expectations for the results of acoustic camera monitoring at turbine sites. We discuss six key tasks performed with acoustic cameras: detecting objects, identifying objects as fish, counting fish, measuring fish, classifying fish taxonomically and analysing fish behavior. Specific challenges to monitoring fish at turbine sites are discussed. This article is intended to serve as a reference for researchers, regulators and marine energy developers on effective use of acoustic cameras to monitor fish at turbine sites. The studies detailed in this article provide evidence that, in some scenarios, acoustic cameras can be used to inform the risk of fish collision with marine energy turbines but doing so requires careful study design and data processing.

Cotter, Emma↗
DOE OSTI2023

Increased range and contrast in fog with circularly polarized imaging

Fogs, low lying clouds, and other highly scattering environments pose a challenge for many commercial and national security sensing systems. Current autonomous systems rely on optical sensors for navigation whose performance is degraded by highly scattering environments. In our previous simulation work, we have shown that polarized light can penetrate through a scattering environment such as fog. We have demonstrated that circularly polarized light maintains its initial polarization state better than linearly polarized light, even through large numbers of scattering events and thus ranges. This has recently been experimentally verified by other researchers. In this work, we present the design, construction, and testing of active polarization imagers at short-wave infrared and visible wavelengths. We explore multiple polarimetric configurations for the imagers, focusing on linear and circular polarization states. The polarized imagers were tested at the Sandia National Laboratories Fog Chamber under realistic fog conditions. Here, we show that active circular polarization imagers can increase range and contrast in fog better than linear polarization imagers. We show that when imaging typical road sign and safety retro-reflective films, circularly polarized imaging has enhanced contrast throughout most fog densities/ranges compared to linearly polarized imaging and can penetrate over 15 to 25 m into the fog beyond the range limit of linearly polarized imaging, with a strong dependence on the interaction of the polarization state with the target materials.

47 OTHER INSTRUMENTATION↗
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 OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
DOE OSTI2024

Reduction of Iron (III) Oxide in Microwaves Toward Gasification Studies

The purpose of this study is to reduce hematite (Fe2O3) using hydrogen (H2), carbon monoxide (CO), and methane (CH4) gases using microwave irradiation. Conventional temperature programmed reduction of iron phase transformation under H2, CO, and CH4 agree with the literature. Furthermore, the activation energy for the iron phase transformation in a conventional reactor was in the order of H2>CO>CH4, suggesting that CH4 was the best reductant to initiate the reduction at a much lower temperature than CO, and CO was significantly better than H2 atmosphere. Due to the uneven temperature distribution, the apparent activation energy of microwave reduction under H2 was approximately 1/4th when compared to the conventional reduction in H2. X-ray diffraction (XRD) analysis showed a mixed oxide phase in microwave reduction as opposed to the clear phase transformation in conventional studies. To combat the temperature measurement issues in microwave studies, fiber optic sensors and Forward Looking InfraRed (FLIR) sensors were used to acquire the actual temperature distribution in the catalyst bed. Therefore, a reasonably accurate activation energy estimation was achieved in the catalyst bed. Combining these with the characterization and quantification of the different iron phases in microwave reduction will provide a blueprint for future microwave-assisted gasification studies.

Aireddy, Divakar Reddy↗
DOE OSTI2025

Main Shaft Bending Moment Analysis for a Gamesa G97 2-MW Wind Turbine

In a collaborative project between Siemens Gamesa Renewable Energy and the National Renewable Energy Laboratory (NREL), a cost-effective method to measure dynamic main shaft torque and the influence of non-torque loads was applied to the gearbox of the Gamesa G97 2-megawatt (MW) wind turbine at the NREL Flatirons Campus and verified through a field test campaign that took place in 2023. The method involved the installation of fiber-optic sensors on the ring gear of the gearbox. Additionally, traditional strain gauges and a rotary encoder were installed on the main shaft to measure mechanical torque and orthogonal bending moments. We compare the measured total main shaft bending moments from the field test campaign to those predicted by an updated OpenFAST model of the G97 wind turbine for similar environmental conditions. We also assessed the sensitivity of the predicted bending moment to varying levels of turbulence intensity, shear exponent, and yaw error.

17 WIND ENERGY↗
DOE OSTI2025

Abstract for CRADA between NETL and GlycoSurf, Inc.

The National Energy Technology Laboratory (NETL) and GlycoSurf, Inc. (Participant) will collaborate in the development of novel luminescent sensing materials for rare earth elements using chemically modified surfactants. Rare earth elements are economically critical metals that are used in many technologies relevant to both energy and national defense. Slow and expensive characterization methods for rare earth element analysis are a major pain point for domestic production of these metals; the development of low-cost optical sensing materials and platforms can significantly reduce the time and financial costs associated with rare earth element prospecting and process monitoring. NETL has extensive experience developing inexpensive and compact optical sensors for critical metals such as rare earths. GlycoSurf, Inc. has commercialized high performance surfactants for the selective extraction of rare earth elements in complex environments such as acid mine drainage. By modifying these surfactants with fluorescent functional groups, trace concentrations of rare earths may be detected through a process called “photosensitization,” where the sensing material induces element-specific emission bands that enable different rare earth elements to be detected and distinguished. This project will enable the development of sensing materials capable of selectively detecting trace quantities of valuable rare earths in challenging conditions, including high ionic strength, highly acidic matrices.

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

SATS Enhanced Capabilities and Demonstration of Improved Ramp Rates for In-Cell Testing

This report presents the development of the second-generation Severe Accident Test Station, referred to as SATS 2.0. SATS 2.0 represents a major advancement in addressing critical research needs for the United States nuclear industry, particularly in relation to fuel fragmentation, relocation, and dispersal (FFRD) concerns associated with the nuclear industry’s goal to extend fuel burnup beyond a peak rod average of 62 GWd/tU. The central objective of SATS 2.0 was the deployment a new furnace capable of achieving heating rates up to 100°C/s. This achievement addresses critical capability gaps, enabling assessment of prioritized research objectives established by the Electric Power Research Institute’s (EPRI) Collaborative Research on Advanced Fuel Technologies (CRAFT) working group. These research objectives are directly related to high-burnup loss-of-coolant accident (LOCA) conditions and the effects of prolonged exposure to elevated temperatures, both of which are crucial to enhancing nuclear safety and efficiency. The SATS 2.0 system builds upon prior experience with the original SATS system, which focused on evaluating accident-tolerant fuel (ATF) cladding concepts during accident conditions. However, the new system not only expands upon prior core capabilities by achieving higher heating rates with a better furnace but also plans to incorporate novel auxiliary systems to create a versatile platform for addressing current research needs. For example, a system was developed for the quantification and characterization of fission gas released during high-temperature transients. Additionally, in situ measurement capabilities were developed, such as digital image correlation which enabled the real-time capture of strain related to balloon and burst events and fiber-optic sensors that allowed for high-fidelity characterization of temperature gradients. Demonstration tests of the new 12-lamp furnace achieved heating rates up to 120°C/s. Notably, SATS 2.0 demonstrated its capacity to simulate LOCA burst tests at different pressures, yielding burst data that align with historical empirical models. Moreover, the system exhibited its capability to simulate complex conditions observed in anticipated operational occurrences, while effectively mitigating temperature overshoots. These accomplishments mark significant progress toward overcoming FFRD challenges and advancing the United States nuclear industry's safety basis and technical capabilities for extended burnup.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Quantum Enhanced Fiber Sensing for Oil and Gas Applications (Final Technical Report)

The major goal of the project was to develop a quantum sensing approach compatible with current infrastructure in the oil and gas industry that could provide a quantum enhancement, with a focus towards pipeline leak detection. The proposed approach to achieving this goal consisted of the use of quantum states of light to enhance the sensitivity of fiber optic sensors beyond the classical limit. To address the limitations of existing commercial leak detection, the project focused on the study of two technologies: distributed acoustic sensors (DAS) and fiber Bragg grating (FBG)-based sensors. The initial phase of the project centered around the identification and mitigation of dominant sources of classical noise to enable operation at the classical shot noise limit (SNL). The second phase focused on the use of quantum resources to obtain sensitivities beyond the classical SNL. The final phase sought to combine quantum noise reduction techniques with advanced dynamical classical noise cancelation approaches.

02 PETROLEUM↗
DOE OSTI2020

Using Low-Cost Measurement Systems to Investigate Air Quality: A Case Study in Palapye, Botswana

Exposure to particulate air pollution is a major cause of mortality and morbidity worldwide. In developing countries, the combustion of solid fuels is widely used as a source of energy, and this process can produce exposure to harmful levels of particulate matter with diameters smaller than 2.5 microns (PM 2.5 ). However, as countries develop, solid fuel may be replaced by centralized coal combustion, and vehicles burning diesel and gasoline may become common, changing the concentration and composition of PM 2.5 , which ultimately changes the population health effects. Therefore, there is a continuous need for in-situ monitoring of air pollution in developing nations, both to estimate human exposure and to monitor changes in air quality. In this study, we present measurements from a 5-week field experiment in Palapye, Botswana. We used a low-cost, highly portable instrument package to measure surface-based aerosol optical depth (AOD), real-time surface PM 2.5 concentrations using a third-party optical sensor, and time-integrated PM 2.5 concentration and composition by collecting PM 2.5 onto Teflon filters. Furthermore, we employed other low-cost measurements of real-time black carbon and time-integrated ammonia to help interpret the observed PM 2.5 composition and concentration information during the field experiment. We found that the average PM 2.5 concentration (9.5 μg∙m –3 ) was below the World Health Organization (WHO) annual limit, and this concentration closely agrees with estimates from the Global Burden of Disease (GBD) report estimates for this region. Sulfate aerosol and carbonaceous aerosol, likely from coal combustion and biomass burning, respectively, were the main contributors to PM 2.5 by mass (33% and 27% of total PM 2.5 mass, respectively). While these observed concentrations were on average below WHO guidelines, we found that the measurement site experienced higher concentrations of aerosol during first half our measurement period (14.5 μg∙m –3 ), which is classified as "moderately unhealthy" according to the WHO standard.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Photoluminescence Induced in Mineral Oil by Ionizing Radiation

We have studied photoluminescence (PL) properties of vacuum pump mineral oil as a function of the type and intensity of ionizing radiation exposure. The mineral oil has a simple aliphatic structure, which possesses no chromophore in the traditional sense. Unexpected PL in the mineral oil has characteristic features such as variation of the emission peak wavelength depending on the excitation wavelength and intensity dramatically increasing with radiation dose. The observed behavior can be understood in the framework of a previously suggested model introducing aggregation-induced PL and the formation of conjugated clusters comprising nontraditional chromophores. Our findings can be used for the development of optical sensors for real-time monitoring of oil utilized in vacuum pumps in nuclear fusion reactors.

Bliznyuk, Valery N.↗