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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Combined Nitrogen and Phosphorous Recovery via Electrochemical Technology Integration into Municipal Wastewater Treatment Plants

This project developed electroN-P, an electrochemical technology for recovering nitrogen and phosphorus as a fertilizer product from anaerobic digester centrate, targeting an advancement from TRL 4 to 6. The reactor was scaled 220× from a 250 mL batch cell to a continuous 4-channel system treating 55 L of wastewater, recovering >80% of phosphorus. Energy consumption under constant-voltage operation was lower than the embedded energy of conventional fertilizers, while constant-current operation produced a cost-competitive product with a smaller reactor footprint. TEA and LCA outcomes were highly sensitive to the magnesium source; alternative magnesium salt configurations projected reduced costs and embedded energy compared to sacrificial rods, but at the expense of significantly longer operation times. The recovered fertilizer performed comparably to diammonium phosphate and triple superphosphate in soil and plant trials. Integrating the technology into a whole-plant model reduced aeration energy by nearly 50%, lowering the levelized cost of water treatment from $0.668/m 3 to $0.648/m 3 . Results support targeting commercialization at smaller (≤1 MGD) facilities with high-strength digester streams. Further investigation into the transport and corrosion kinetics governing sacrificial anode wear is recommended to support continuous, longer-term operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Knowledge Graph of RB-Tnseq Data from Fitness Browser (KP-DP1)

Motivation: Predicting microbial gene fitness across environmental conditions remains a central challenge for predictive phenomics and autonomous experimentation. Fitness assays generate large volumes of genotype–phenotype measurements difficult to integrate with experimental metadata and biological function in a form that supports mechanistic reasoning. Knowledge graphs offer a semantic framework for unifying modalities and enabling context-aware inference. Results: We build GIMME (Graph Inference for Microbial Metabolism Exploration), a semantically grounded knowledge graph that unifies gene fitness measurements spanning 10 Pseudomonas species with experimental metadata and biological context. Media are decomposed into chemical components and experiments carry structured links to natural-language descriptions. The resulting graph supports two inference modes: (1) symbolic graph traversal to surface candidate gene–environment and gene–chemical associations, and (2) learned inference using heterogeneous graph neural networks that propagate information across neighborhoods. We formulate link regression over (gene, media, experiment) triplets, combining learned gene embeddings with pretrained LLM sourced text embeddings of node descriptions to predict gene fitness. We then augment a baseline MLP with an auxiliary message-passing encoder (GraphSAGE/GAT) that propagates information over gene–protein–function and media–chemical subgraphs, and fuse the two pathways with a gated residual connection. This approach produces strong agreement with held-out fitness measurements (GraphSAGE Pearson r 0.74) while also highlighting inference challenges in extreme-fitness regimes. We aggregate GAT edge-attention weights by relation type and layer to estimate which biological and environmental relations most influence fitness predictions. Conclusion: This work explores using knowledge graphs as “context graphs” for microbial phenotype prediction. They provide a rich substrate which enables explainable retrieval of supporting evidence, and provides a natural bridge to autonomous workflows that prioritize the next experiment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women

Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the semantic relationships in the text document although it has been widely applied in text mining. BERTopic, created in 2022, leveraged advances in deep learning and can capture the contextual relationships between words. In this work, we integrated Artificial Intelligence (AI) modules to LDA and BERTopic and provided a comprehensive comparison on the analysis of prescription opioid-related cardiovascular risks in women. Opioid use can increase the risk of cardiovascular problems in women such as arrhythmia, hypotension etc. 1,837 abstracts were retrieved and downloaded from PubMed as of April 2024 using three Medical Subject Headings (MeSH) words: “opioid,” “cardiovascular,” and “women.” Machine Learning of Language Toolkit (MALLET) was employed for the implementation of LDA. BioBERT was used for document embedding in BERTopic. Eighteen was selected as the optimal topic number for MALLET and 23 for BERTopic. ChatGPT-4-Turbo was integrated to interpret and compare the results. The short descriptions created by ChatGPT for each topic from LDA and BERTopic were highly correlated, and the performance accuracies of LDA and BERTopic were similar as determined by expert manual reviews of the abstracts grouped by their predominant topics. The results of the t-SNE (t-distributed Stochastic Neighbor Embedding) plots showed that the clusters created from BERTopic were more compact and well-separated, representing improved coherence and distinctiveness between the topics. Our findings indicated that AI algorithms could augment both traditional and contemporary topic modeling techniques. In addition, BERTopic has the connection port for ChatGPT-4-Turbo or other large language models in its algorithm for automatic interpretation, while with LDA interpretation must be manually, and needs special procedures for data pre-processing and stop words exclusion. Therefore, while LDA remains valuable for large-scale text analysis with resource constraints, AI-assisted BERTopic offers significant advantages in providing the enhanced interpretability and the improved semantic coherence for extracting valuable insights from textual data.

Research & Experimental Medicine↗
DOE OSTI2024

A Hermetic Package Technique for Multi-Functional Fiber Sensors through Pressure Boundary of Energy Systems Based on Glass Sealants

This paper presents a hermitic fiber sensor packaging technique that enables fiber sensors to be embedded in energy systems for performing multi-parameter measurements in high-temperature and strong radiation environments. A high-temperature stable Intrinsic Fabry–Perot interferometer (IFPI) array, inscribed by a femtosecond laser direct writing scheme, is used to measure both temperature and pressure induced strain changes. To address the large disparity in thermo-expansion coefficients (TECs) between silica fibers and metal parts, glass sealants with TEC between silica optical fibers and metals were used to hermetically seal optical fiber sensors inside stainless steel metal tubes. The hermetically sealed package is validated for helium leakages between 1 MPa and 10 MPa using a helium leak detector. An IFPI sensor embedded in glass sealant was used to measure pressure. The paper demonstrates an effective technique to deploy fiber sensors to perform multi-parameter measurements in a wide range of energy systems that utilize high temperatures and strong radiation environments to achieve efficient energy production.

Optics↗
DOE OSTI2024

Experimental Investigation of Low-Frequency Distributed Acoustic Sensor Responses to Two Parallel Propagating Fractures

Low-frequency distributed acoustic sensing (LF-DAS) is a diagnostic tool for hydraulic fracture propagation with far-field monitoring using fiber optic sensors. LF-DAS senses strain rate variation caused by stress field change due to fracture propagation. Fiber optic sensors are installed in the monitoring wells in the vicinity of a fractured well. From the strain responses, fracture propagation can be evaluated. To understand subsurface conditions with multiple propagating fractures, a laboratory-scale hydraulic fracture experiment was performed simulating the LF-DAS response to fracture propagation with embedded distributed optical fiber strain sensors under these conditions. The experiment was performed using a transparent cube of epoxy with two parallel radial initial flaws centered in the cube. Fluid was injected into the sample to generate fractures along the initial flaws. The experiment used distributed high-definition fiber optic strain sensors with tight spatial resolutions. The sensors were embedded at two different locations on opposite sides of the initial flaws, serving as observation/monitoring locations. We also employed finite element modeling to numerically solve the linear elastic equations of equilibrium continuity and stress–strain relationships. The measured strains from the experiment were compared to simulation results from the finite element model. The experimentally derived strain and strain-rate waterfall plots from this study show the responses to both fractures propagating, while the fracture at the lower position took most of the fluid during the experiment. Interestingly, a fracture first began propagating from the upper flaw of the two flaws, but once the lower fracture was initiated, it grew much faster than the upper fracture. Both fibers were intercepted by the lower fracture, further verifying the strain signature as a fracture is approaching and intersecting an offset fiber.

Chemistry↗
DOE OSTI2024

Experimental Investigation of Steel-Borne Acoustic Pulses for Fault Pinpointing in Pipe-Type Cable Systems: A Scaled-Down Model Approach

Pipe-type cable systems, including high-pressure fluid-filled (HPFF) and high-pressure gas-filled cables, are widely used for underground high-voltage transmission. These systems consist of insulated conductor cables within steel pipes, filled with pressurized fluids or gases for insulation and cooling. Despite their reliability, faults can occur due to insulation degradation, thermal expansion, and environmental factors. As many circuits exceed their 40-year design life, efficient fault localization becomes crucial. Fault location involves prelocation and pinpointing. Therefore, a novel pinpointing approach for pipe-type cable systems is proposed, utilizing accelerometers mounted on a steel pipe to capture fault-induced acoustic signals and employing the time difference of arrival method to accurately pinpoint the location of the fault. The experimental investigations utilized a scaled-down HPFF pipe-type cable system setup, featuring a carbon steel pipe, high-frequency accelerometers, and both mechanical and capacitive discharge methods for generating acoustic pulses. The tests evaluated the propagation velocity, attenuation, and pinpointing accuracy with the pipe in various embedment conditions. The experimental results demonstrated accurate fault pinpointing in the centimeter range, even when the pipe was fully embedded, with the acoustic pulse velocities aligning closely with the theoretical values. These experimental investigation findings highlight the potential of this novel acoustic pinpointing technique to improve fault localization in underground systems, enhance grid reliability, and reduce outage duration. Further research is recommended to validate this approach in full-scale systems.

Moutassem, Zaki↗
DOE OSTI2024

Runaway Eccentricity Growth: A Pathway for Binary Black Hole Mergers in AGN Disks

Binary black holes (BBHs) embedded within the accretion disks that fuel active galactic nuclei (AGN) are promising progenitors for the source of gravitational wave (GW) events detected by LIGO/VIRGO. Several recent studies have shown that when these binaries form, they are likely to be highly eccentric and retrograde. However, many uncertainties remain concerning the orbital evolution of these binaries as they either inspiral toward merger or disassociate. Previous hydrodynamical simulations exploring their orbital evolution have been predominantly two-dimensional or have been restricted to binaries on nearly circular orbits. We present the first high-resolution, three-dimensional local shearing-box simulations of both prograde and retrograde eccentric BBHs embedded in AGN disks. We find that retrograde binaries shrink several times faster than their prograde counterparts and exhibit significant orbital eccentricity growth, the rate of which monotonically increases with binary eccentricity. Our results suggest that retrograde binaries may experience runaway orbital eccentricity growth, which may bring them close enough together at pericenter for GW emission to drive them to coalescence. Although their eccentricity is damped, prograde binaries shrink much faster than their orbital eccentricity decays, suggesting they should remain modestly eccentric as they contract toward merger. Finally, binary precession driven by the AGN disk may dominate over precession induced by the supermassive black hole depending on the binary accretion rate and its location in the AGN disk, which can subdue the evection resonance and von Ziepel–Lidov–Kozai cycles.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

The IDEA detector concept for FCC-ee

A detector concept, named IDEA, optimized for the physics and running conditions at the FCC-ee is presented. After discussing the expected running conditions and the main physics drivers, a detailed description of the individual sub-detectors is given. These include: a very light tracking system with a powerful vertex detector inside a large drift chamber surrounded by a silicon wrapper, a high resolution dual readout crystal electromagnetic calorimeter, an HTS based superconducting solenoid, a dual readout fiber calorimeter and three layers of muon chambers embedded in the magnet flux return yoke. Some examples of the expected detector performance, based on fast and full simulation, are also given.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Shake-up and shake-off spectra in the electron capture decay of atomic $^7$Be

The most stringent laboratory-based experimental limits on the existence of sub-MeV sterile neutrinos are currently set by decay spectroscopy of radioactive $^7$Be embedded into superconducting sensors. The systematic uncertainties are dominated by the modeling of the electron shake-up and shake-off spectra that are not based on state-of-the-art atomic theory and do not include electron correlations or relativistic effects. We have used the multiconfiguration Dirac-Fock formalism to obtain correlated wavefunctions ab initio and compute all single and double shake processes in the electron capture decay of atomic $^7$Be. The simulations can explain some but not all of the observed spectral features, likely because the wave functions are modified by the Ta sensor material that the $^7$Be is embedded into. The new models also show that the L/K electron capture ratio of $^7$Be in Ta has previously been slightly underestimated revising the previous value of 0.070(7) to a new value of 0.0756(20).

Atomic Physics (physics.atom-ph)↗
DOE OSTI2024

Experimentation on Finned-Tube Microchannel Heat Exchanger Incorporating Phase Change Material and R-410A

Integrating latent heat thermal energy storage, particularly phase change materials (PCM), in building heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems, offers substantial benefits for peak load shifting and energy efficiency. While numerous studies explore PCM-embedded heat exchangers (PCM-HX) using single-phase heat transfer fluids, there needs to be more investigation regarding PCM-HX utilizing two-phase refrigerant flow. This gap is even more pronounced for microchannel heat exchanger (MCHX) configurations. In this study, an MCHX-based PCM-HX was built and tested to analyze and evaluate the performance of a PCM-HX operating with a two-phase refrigerant as the working fluid. A commercially available MCHX condenser unit was embedded with 2.6 kg of RT35 PCM and tested using R-410A as the working fluid, using an in-house test loop to control the refrigerant inlet conditions precisely. Thin and minimally invasive T-type thermocouples traced the temperature evolution along MCHX channels and tube banks. Additional larger T-type thermocouple probes at the refrigerant inlet and outlet measured the average power of the PCM-HX during condensing tests, ranging from 0.7 kW to 1.6 kW based on mass flow rates varying from 3.5 g/s to 8 g/s. This experimental study on MCHX-based PCM-HX with a two-phase refrigerant has the potential to offer practical design guidelines for the direct integration of PCM-TES in HVAC&R systems without the use of a secondary loop, while caution is advised to avoid a substantial increase in the refrigerant charge amount.

42 ENGINEERING↗
DOE OSTI2024

Formal Methods for Provably Secure Software and Firmware

This project addresses a gap observed in verifying the programming in embedded devices used in international arms control: namely verifying that embedded programming in an arms control device does exactly what it is supposed to do, no more and no less, every time without fail, and without disclosing unauthorized information accidentally or intentionally. In critical military, aerospace, and industrial safety systems this problem is sometimes addressed using formal methods (FM). This multi-year project seeks to identify formal methods toolsets useable in arms control regimes, with emphasis on applicability, ease of use, long term availability, and support.

formal methods, Arms Control Verification↗
DOE OSTI2024

Fiber reinforced aerogel composites

Fiber reinforced aerogel composites, including a transparent composite material that contains an aerogel and fibers embedded into the aerogel and/or bonded to one or more surfaces of the aerogel, and composites that contain an aerogel tile and an assemblage of fibers embedded into the aerogel tile or bonded to the aerogel tile that are useful as Cherenkov radiators for the detection and identification of subatomic particles. Also, methods of making and using the composites.

Pegg, Ian Louis↗
DOE OSTI2024

Privacy-Preserving Control of Partitioned Energy Resources

Distributed energy resources are an increasingly important part of the electric grid. We examine the problem of partitioning a distributed energy resource among many users while providing privacy to them. In this model, clients can send requests to a server, the server can verify that the requests are valid and aggregate them, but it cannot see the actual values in the requests. Without privacy, each user is forced to reveal their daily schedule or energy use. Energy resources add a novel challenge that prior systems do not address: they require verifying limits on private power (a rate over time) and energy (a sum) values. Furthermore, the cryptographic mechanisms must run on embedded energy control systems. We describe Weft, a novel cryptographic system that verifies both power (rate) and energy (integral) constraints on private client values and aggregates them. The key insight behind the approach is to rely on additively homomorphic secret shares, which allows servers to compute sums from rates. We present 3 cryptographic proof systems with different system trade-off for embedded systems: bit-splitting proofs minimize memory use, sorting proofs minimize computation, and commitment proofs minimize network communication. Using bit-splitting proofs, it takes an IoT client using a CortexM microcontroller 4 minutes of compute time to privately control its share of an energy resource for a day at 20s granularity.

Laufer, Evan↗
DOE OSTI2024

Copper and bismuth-based sorbent characterization in simulated iodine off-gas streams

The effective capture of volatile radioiodine, a fission product present in used nuclear fuel (UNF), is of paramount importance for development of used fuel reprocessing schemes to prevent release of radioiodine during unit operations and to meet regulatory standards for air emissions. A well-studied method for iodine capture in off-gas streams is the use of silver-functionalized zeolite phases (AgZ), which exploit chemisorption of I to Ag. Advances into other Ag-functionalized materials, including aerogels and metal organic frameworks (MOFs), are underway [1]. Additional metals with the capability to chemisorb iodine, including Cu, Bi, and Sn, [1] are being evaluated as alternatives to Ag for potential applicability to iodine management in off-gas systems. The design of novel functionalized sorbents with Cu and Bi, including composites with metal particles embedded in PAN substrates [2] and composites with metal sulfides embedded in PAN [3], is an ongoing area of study for improved iodine capture. Previously reported work on novel PAN-based metal sorbents has provided the synthesis, characterization, and iodine capture efficiency of this new class of sorbent. Specifically, the metal sulfide PAN composites are found to be easy to produce and reproduce, as well as having a high iodine loading potential under static conditions [3]. Due to the favorable testing previously performed with metal sulfide PAN composites, further testing into the performance of these composites under gas streams containing I2(g) in combination with NO2(g) and H2O(g) is needed. Humid streams of NO2(g) may arise from dissolver off-gas streams, when used fuel is dissolved in HNO3(aq) [4]. NO2(g) has been found to reduce AgZ sorption capacity for I because of oxidation of Ag, the chemisorbing agent, to Ag2O [5]. It follows that performance evaluation of novel sorbents under highly oxidizing conditions such as NO2(g) streams is critical. Therefore, the objective of this study is to determine the effect of flowing NO2(g) and H2O(g) streams on iodine sorption capacity and sorbent performance. This work utilizes custom-built gas handling capabilities for sorbent exposure along with solid-state characterization techniques to assess the physical and chemical properties of sorbents before and after exposure.

copper, bismuth, iodine capture, iodine sorbent, p↗
DOE OSTI2025

Fusing Edge Computing with Transport Security by Leveraging the Controller Area Network Transport Security Tracking and Reporting (C-STAR) Unit

Rapid advances in embedded system complexity and capability provides exciting opportunities for transportation security deployment. Manufacturers and developers of these embedded systems continue to provide lower cost and more powerful solutions that can be leveraged by researchers and engineers. Furthermore, deploying these devices at the “edge” of the Internet-of-Things (IoT) infrastructure provides opportunities for highly capable applications in transport security. In an edge computation architecture, the device is co-located at the source of the data in the larger IoT structure – this provides computational capability at the location directly where the data is collected. For shipment transport security, this provides a direct compute node for digestion of data and mitigation actions in real-time. In our application, the vehicle provides a significant amount of this data that can be processed in real-time via the Controller Area Network Transport Security Tracking and Reporting (C-STAR) edge device. Utilization of a computational node located on the vehicle, such as the C-STAR, capitalizes on previously discussed opportunities of edge architectures. In this paper, we will discuss this security solution’s usability, current deployments, and scalability to further applications in transport security. First, we will cover the supported vehicle platforms that can leverage the C-STAR technology. This will be particularly relevant to medium- and heavy-duty vehicles transporting high-risk shipments. Second, we will speak to current deployments of the C-STAR that are ongoing. Finally, we will discuss additional areas for expansion such as maturing the onboard algorithms through continuing collaborations.

Cook, Adian [ORNL] (ORCID:0000000160825395)↗
DOE OSTI2025

SHF: Symmetrical Hierarchical Forest with Pretrained Vision Transformer Encoder for High-Resolution Medical Segmentation

This paper presents a novel approach to addressing the long-sequence problem in high-resolution medical images for Vision Transformers (ViTs). Using smaller patches as tokens can enhance ViT performance, but quadratically increases computation and memory requirements. Therefore, the common practice for applying ViTs to high-resolution images is either to: (a) employ complex sub-quadratic attention schemes or (b) use large to medium-sized patches and rely on additional mechanisms within the model to capture the spatial hierarchy of details. We propose Symmetrical Hierarchical Forest (SHF), a lightweight approach that adaptively patches the input image to increase token information density and encode hierarchical spatial structures into the input embedding. We then apply a reverse depatching scheme to the output embeddings of the transformer encoder, eliminating the need for convolution-based decoders. Unlike previous methods that modify attention mechanisms or use a complex hierarchy of interacting models, SHF can be retrofitted to any ViT model to allow it to learn the hierarchical structure of details in high-resolution images without requiring architectural changes. Experimental results demonstrate significant gains in computational efficiency and performance: on the PAIP WSI dataset, we achieved a 3∼32×speedup or a 2.95%∼7.03% increase in accuracy (measured by Dice score) at a 64K2 resolution with the same computational budget, compared to state-of-the-art production models. On the 3D medical datasets BTCV and KiTS, training was 6×faster, with accuracy gains of 6.93% and 5.9%, respectively, compared to models without SHF.

Zhang, Enzhi [Hokkaido University, Japan]↗
DOE OSTI2026

Artificial Intelligence Thermostat to Detect Faults

Residential air conditioners and heat pumps often experience faults due to inadequate maintenance, which can severely reduce efficiency or even cause system failure. Common issues include dirty or clogged air filters and refrigerant leaks. These problems degrade performance and increase energy use and operating costs. This study presents a smart thermostat with embedded artificial intelligence to detect such faults and alert homeowners when maintenance is needed. The thermostat uses low-cost measurements—including return-air temperature, relative humidity, supply-air temperature, outdoor-air temperature, and condenser subcooling—to identify abnormal operations. Because different faults produce distinct response patterns, tailored algorithms are developed to recognize characteristic fault signatures. The investigation is built on a detailed co-simulation platform that couples EnergyPlus with the DOE/ORNL Heat Pump Design Model (HPDM). EnergyPlus represents the building’s dynamic environment, while HPDM is a high-fidelity, hardware-based model that can simulate fault-free performance as well as a wide range of faults, including gradual degradation such as minor refrigerant leakage. This platform provides a virtual training and testing environment that helps distinguish fault-induced behavior from normal operation and supports development of robust diagnostic algorithms. Using this framework, a Dynamic Bayesian Network was developed to identify two common faults—gradual refrigerant charge loss and indoor airflow blockage—and the AI-embedded thermostat was verified through annual building simulations.

Shen, Bo [ORNL] (ORCID:0000000336600393)↗
DOE OSTI2025

Multimaterial 3D Printing in Activating Bath Enables In Situ Polymerization of Thermosets with Intricate Geometries and Diverse Elastic Behaviors

Polydicyclopentadiene, p(DCPD), is a high‐performance thermoset valued for its exceptional toughness, strength, and stiffness. When copolymerized with 1,5‐cyclooctadiene (COD), its mechanical properties can be tuned from glassy to rubbery at room temperature. While frontal polymerization enables a rapid and energy‐efficient route to 3D print DCPD‐based materials, challenges such as ink shelf life and gravitational distortion, especially in direct ink writing of soft COD‐rich formulations, must be considered. Here, a complementary chemical strategy is presented, embedded 3D printing, that enables localized in situ polymerization of printed DCPD/COD inks within a reactive support matrix. The matrix provides both physical support and a reservoir of chemical activator, which diffuses into the ink, activates a latent bis(N‐heterocyclic carbene) Ru precatalyst, and initiates ring‐opening metathesis polymerization. Curing begins at the ink–matrix interface and propagates inward via diffusion, stabilizing the interface and preventing capillary‐driven deformation regardless of the matrix yield stress. This approach eliminates the need for cold storage, external curing, or photoinitiation, significantly expanding the processing window. Using this method, diverse thermosetting and elastomeric architectures are fabricated with features as small as 5 µm and aspect ratios of 100, including interlinked chains, shallow spherical shells exhibiting snap‐through buckling, and hair‐like fin arrays inaccessible through traditional techniques.

chemical activation↗