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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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537 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Project Development of an Electrochemical Denitration and Caustic Generation System for HLW Pretreatment at Hanford - 26350

An engineering-scale electrochemical processing skid is proposed to perform the denitration of Hanford tank waste, which would help to mitigate a key process concern with the direct feed processing of the Hanford Tank Waste Treatment and Immobilization Plant (WTP). The reduction of nitrates and organic compounds in the waste feed will directly reduce hazardous NOx and ammonia gases generated during the vitrification process, which in turn will aid in addressing potential regulatory and safety challenges associated with processing large volumes of tank waste. This paper highlights the past legacy work, project layout, accomplishments from Phase 1 and research and development envisioned for Phase 2. An innovative electrochemical denitration and caustic generation (EDCGe) process was demonstrated for the pretreatment of tank waste at the Savannah River Site (SRS) in the early 2000s. The denitration electrolyzer, off-gas abatement system, and caustic generator electrolyzer are being developed with the intent that the denitration electrolyzer will convert nitrate and nitrite anions to nitrogen gas while also yielding other gaseous byproducts, which may include N2O, NH3, VOCs, and H2. The gaseous byproducts will be managed via a tandem off-gas catalyst-bed treatment system. The caustic generation electrolyzer will recycle NaOH from the feed to produce a clean caustic stream for use within the batching tanks at Hanford, aiding in the preparation of waste for WTP. The reduction in hazardous emissions and improved waste treatment processes provides a robust solution for nuclear waste management, contributing to environmental safety and regulatory compliance. The EDCGe technology is being adapted, modified, and updated for the preparation of the Direct Feed-High Level Waste (DF-HLW) flowsheet at Hanford. Phase 1 demonstrated a bench-scale proof-of-concept for reactions involving the denitration electrolyzer and gas phase abatement of ammonia. The electrochemical technology is drawing on the scientific outcomes that were reported in the legacy work. The results from Phase 1 demonstrated the viability of the EDCGe system in reducing the nitrogen species of simple non-radioactive waste simulants. Commercially available alloys used as electrode materials and membranes are being studied for the denitration and caustic generation electrolyzers. The continuation of this project holds promise for broader applications, such as energy-efficient ammonia production, and contributes significant advancements in nuclear waste management. Additional material discovery has been investigated into ceramic Na super ion conductive (NaSICON) materials and off-gas abatement catalyst discovery. NaSICON is of interest for selective transport of Na within the electrolyzers to make a clean caustic stream. Future integration and optimization efforts, informed by Phase 1 results and ongoing research, will continue to drive advancements in nuclear waste management technology. The technology developed for the EDCGe treatment of tank waste will also have broader potential to inform other fields, such as energy-efficient ammonia production, as well as ammonia abatement catalysis through the lessons learned in electrochemical nitrate reduction. The applications and benefits of this research extend beyond Hanford and the Savannah River Site, supported by a collaborative team of scientists and engineers from national labs, academia, and industry, ensuring a comprehensive approach to solving complex waste treatment challenges. The team is leveraging advanced electrochemical technologies, machine learning, novel catalysts tailored for gaseous nitrogen species, and cutting-edge reactor systems to enhance the process efficiency and effectiveness of the denitration process.

Rodene, Dylan [Savannah River National Laboratory ↗
DOE OSTI2026

RFID-Enabled Tamper-Indicating Seal

Tamper-indicating seals protect sensitive materials, valuable equipment, and critical shipments. Most conventional seals must be checked through manual visual inspection and do not provide a digital record of their condition. Some attempts to remove, alter, or bypass a seal may also leave little visible damage, making tampering difficult to identify during routine inspections. Los Alamos National Laboratory has developed a smart tamper-indicating seal that converts even subtle tampering into a persistent digital record. The seal communicates its identity and status wirelessly through standard radio-frequency identification (RFID) readers, giving organizations a faster, more reliable way to verify that an asset has remained secure.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Cr plasma-material-interaction in PISCES-RF: D thermal release, retention, and erosion

Pure chromium (Cr) targets were exposed to high-flux deuterium (D) plasmas in the Pisces-RF linear plasma device, and measurements of D retention, release, and erosion were subsequently performed. Post-exposure D retention was quantified using temperature-programmed desorption on Cr targets irradiated by 50 eV ions over a broad range of exposure temperatures (423–873 K) and ion fluences (3 × 10 24 – 3 × 10 26 m −2 ). The retained D inventory was observed to decrease rapidly with increased exposure temperature, from approximately ∼7 × 10 20 m −2 at ∼ 420 K, to then saturate near ∼ 10 20 m −2 for exposure temperatures above ∼550 K. Separately, at fixed exposure temperature (∼450 K), D retention was found to have only a weak dependence on increasing ion fluence. Lastly, the erosion of Cr in D plasma was investigated for ion impact energies in the range 40 ≤ E i ≤ 250 eV. Erosion was inferred using optical emission spectroscopy (OES) from the ratio of emission lines (Cr I (425.4 nm)/D I (656.1 nm)) measured close to the target. Conversion of the OES yield data to net erosion yield was made with singular ion energy target mass-loss measurements. These net erosion yield data were then further corrected to obtain gross erosion yield by accounting for a re-deposition factor, computed using a simple model. The gross erosion yield is found to be 2–4 times lower than predicted by SDTrimSP, consistent with that typically observed for light-ion sputtering under high-flux plasma conditions.

Chromium↗
DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗
DOE OSTI2025

Integration and Optimization of Loads, and Renewables for Grid-Interactive Efficient Buildings

Deploying advanced building control strategies in small- and medium-sized commercial buildings (SMBs) is critical but constrained by two major challenges: (1) the lack of sensor and control infrastructure (SCI) and (2) the significant engineering effort required for implementation and configuration. While these barriers limit large-scale adoption in SMBs, they remain underexplored in the literature, which has largely emphasized feasibility over scalability.

25 ENERGY STORAGE↗
DOE OSTI2026

Generation of 3.3-mJ, 2.45-µm, sub-2-cycle laser pulses via hollow-core fiber pulse compression

We demonstrate nonlinear compression of mid-infrared pulses from a Cr:ZnSe chirped-pulse amplifier using a gas-filled stretched hollow-core fiber followed by bulk-material compression. Starting from 90 fs, 2.45 µm pulses with 5.3 mJ energy, spectral broadening in the gas-filled capillary combined with optimized dispersion management enables compression to 15 fs, less than two optical cycles at 2.45 µm, with 3.3 mJ pulse energy, corresponding to a peak power of approximately 0.12 TW. The simplicity of the approach, based on a single hollow-core fiber stage and bulk dispersion compensation, makes it scalable to higher energies and establishes a robust route to mid-infrared drivers for high harmonic generation and attosecond applications.

Britton, Mathew [SLAC National Accelerator Laborat↗
DOE OSTI2026

How efficiently can AI recognize Wireless Devices?

This poster presents a hardware benchmarking methodology for a 3-layer CNN waveform classifier deployed using ONNX Runtime on an NVIDIA Jetson AGX Orin. The dataset consist of 9 signal types, -30 to +30 dB SNR with 5dB increments. Benchmarking on the Jetson AGX Orin gave an accuracy of 91.9% and GPU throughput of 107,120 predictions/sec (23× faster than CPU). The Jetson GPU reached approximately 27M samples/sec with stable performance but fell below the 40 MHz rate needed for real-time radio feeds. Sustained testing of 5 minutes confirmed stable performance with no memory leaks, establishing a reproducible benchmarking baseline for future edge-deployment optimization.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Multi-Resolution UAV Path Replanning for Inspection of Tailings Dams

Autonomous inspection of large and complex structures with a commercial unmanned aerial vehicle (UAV) is a challenging problem that has been addressed in recent years. In this paper, we address the global motion planning problem of creating autonomous inspection missions for UAVs considering photogrammetry constraints. We focus on the inspection of large tailings dams, which are dam structures used to store waste byproducts of mining. Our method uses a prior sparse point cloud of the dam to generate a voxel grid, where paths satisfying photogrammetry constraints are tested for collisions. We then apply the A* algorithm as a local planner to avoid obstacles within the global mission. Moreover, we address the problem of changing routes online by using octree-based multi-resolution grids for efficient and fast pathfinding. Our results, obtained using tridimensional maps of an actual coal mine tailings dam, show that using octrees for multi-resolution motion planning is faster than using a fixed voxel grid in online missions while inspecting large structures.

42 ENGINEERING↗
DOE OSTI2026

Open quantum system approach to inclusive jet production in heavy-ion collisions

We derive a factorization formula for inclusive jet production in heavy-ion collisions using the tools of Effective Field Theory (EFT). We show how physics at widely separated scales in this process can be systematically separated by matching to EFTs at successively lower virtualities. Owing to a strong scale separation, we recover a vacuum-like DGLAP evolution above the jet scale, while the additional low-energy scales induced by the medium effectively probe the internal structure of the jet. As a result, the cross section can be written as a series with an increasing number of subjets characterized by perturbative matching coefficients each of which is convolved with a distinct function. These functions encode broadening, medium-induced radiations as well as quantum interference such as the Landau-Pomeranchuk-Migdal effect and color coherence dynamics to all orders in perturbation theory. As a first application of this EFT framework, we investigate the case of an unresolved jet and show how the cross section can be factorized and fully separate the jet dynamics from the universal physics of the medium. To compare to the existing literature, we explicitly compute the medium jet function at next-to-leading order in the coupling and leading order in medium opacity.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Alumina–Titania Nanolaminate Condensers for Hot Programmable Catalysis

Nanolaminates composed of thin alternating layers of Al2O 3 and TiO 2 (ATO) were engineered by using atomic layer deposition as the dielectric material for a Pt-on-carbon catalytic condenser. Investigation assessed synthesis parameters including the deposition temperature, Al 2 O 3 and TiO 2 layer thicknesses, total number of layers, and a capping Al 2 O 3 layer on the maximum charge accumulation in the Pt catalyst. The highest capacitance ATO configuration demonstrated a specific capacitance of ∼1200 nF/cm 2 with working voltages of ±5 V, enabling the storage of 4 × 10 13 electrons or holes per cm 2 at room temperature. The ATO devices exhibited enhanced capacitance at elevated temperatures of up to 400 °C, suggesting the suitability of these materials for high-temperature applications. Adsorption of carbon monoxide on the Pt/C-ATO device characterized by grazing incidence infrared spectroscopy showed changes in the surface binding energy of 13.1 ± 0.8 kJ/mol for an applied external voltage bias of ±1 V.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Ion Transport in Concentrated Crosslinked Solid Polymer Electrolytes

Crosslinking polymers is a common approach to create mechanically stable solid materials such as polymer electrolytes for lithium batteries. In conventional liquid electrolytes, the solvent molecules move freely to accommodate the field-induced motion of ions. However, in crosslinked polymer electrolytes, the rearrangement of polymer segments is constrained by the deformation limits of the network. Herein, we develop a new transport model that accounts for both the formation of concentration gradients and the elasticity of the electrolyte. The elasticity is incorporated by adding an additional term related to the entropy of crosslinked strands to the electrochemical potential of the salt. The resulting Crosslink Model contains two adjustable parameters: $\mathcal{N}$, the average number of monomers in a strand, and λ crit , the maximum strain the network can sustain. These solid-like constraints produce singularities in the governing transport equations, fundamentally altering the concentration profiles. Plateaus in salt concentrations emerge near the electrodes, and network elasticity introduces a strain overpotential. When compared to a Baseline Model ($\mathcal{N}$ → ∞, equivalent to concentrated solution theory), which predicts steepest gradients near the electrodes, both models yield similar current–voltage relationships. Model predictions are compared to electrochemical data for a poly(ethylene oxide)-based crosslinked polymer electrolyte.

Patel, Vivaan [University of California, Berkeley,↗
DOE OSTI2026

Nonequilibrium dynamics of two-level systems directly after cryogenic alternating bias

Two-level systems (TLSs) are a dominant decoherence mechanism in superconducting qubits, microwave kinetic inductance detectors, and quantum dots. TLSs are tunneling states commonly found in amorphous materials and interfaces, which interact electromechanically, leading to energy loss. Fluctuations in the frequency of the TLSs are a significant problem for qubit operation and force recalibration every few hours. In this study, we probe the effect of alternating bias at cryogenic temperatures on TLSs in a purpose-built LC oscillator. When an in situ alternating bias is applied, the steady-state spectra of the TLSs disappear. Spectroscopy reveals transient behavior, in which the TLS frequency fluctuates on the order of minutes. Thermal cycling above 10 K reverses these effects, restoring the TLS spectrum to its original state. Importantly, the intrinsic loss tangent of the LC oscillator remains unchanged before and after the application of the alternating bias. We propose that the disappearance of the steady-state spectrum is caused by nonequilibrium energy buildup from strain in the oxide film introduced by the pulsed voltage-bias sequence. Understanding this nonequilibrium energy could shed light on TLS fluctuations and reduce the calibration requirements for qubits.

general physics↗
DOE OSTI2026

Distributed quantum approximate optimization algorithm on a quantum-centric supercomputing architecture

Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than are necessary to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1000-bit problem), achieving a high solution quality and short time-to-solution, outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA’s applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.

Kim, Seongmin [ORNL] (ORCID:0000000159063004)↗
DOE OSTI2026

Gas permeation properties of amorphous zeolitic imidazolate framework membranes made by atomic/molecular layer deposition

Amorphous metal-organic framework (MOF) membranes are desirable because they may retain some of the molecular sieving properties of their crystalline counterparts while being free of grain boundary defects, which often hinder the consistent achievement of high membrane performance. However, current methods, like melting and compression, for fabricating amorphous MOF membranes involve multi-step processes that require the formation of a crystalline membrane first, that is then amorphized, and therefore, could be challenging to scale. Here, we utilize atomic/molecular layer deposition (ALD/MLD) of diethylzinc (DEZ) and 2-methylimidazole (2mIm) to directly synthesize ultrathin amorphous zeolitic imidazolate framework (aZIF) deposits on γ-alumina-coated α-alumina supports. As the number of ALD/MLD cycles increased from 10 to 300, gas permeances decreased while ideal selectivities increased. Mixture separation factors for C3H6/C3H8, CO2/N2, and H2/C3H8 as high as 4, 37, and 194, respectively, were obtained. At 200 °C and 2.5 bar equimolar feed of H2 and C3H8, a H2/C3H8 mixture separation factor of 185 is obtained with an H2 permeance of ca. 4.15x10-8 mol/m2-s-Pa (124 GPU). Additionally, the membrane achieves a CO2/N2 mixture separation factor of 37 at 25 °C and 1 bar with a CO2 permeance of ca. 3.96x10-8 mol/m2-s-Pa (118 GPU). Considering the vast array of compositionally distinct aZIFs that can be potentially deposited by this approach, an enormously large parameter space for membrane design is emerging to be explored.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Observation of deuteron and antideuteron formation from resonance-decay nucleons

High-energy hadronic collisions generate environments characterized by temperatures above 100 MeV (refs.), about 100,000 times hotter than the centre of the Sun. At present, it is therefore unclear how light (anti)nuclei with mass number A of a few units, such as the deuteron, 3 He or 4 He, each bound by only a few MeV, can emerge from these collisions. Here, the ALICE Collaboration reports that deuteron–pion momentum correlations in proton–proton (pp) collisions provide model-independent evidence that about 90% of the observed (anti)deuterons are produced in nuclear reactions following the decay of short-lived resonances, such as the Δ(1232). These findings, obtained by the ALICE Collaboration at the Large Hadron Collider, resolve a gap in our understanding of nucleosynthesis in ultrarelativistic hadronic collisions. Apart from offering insights on how (anti)nuclei are formed in hadronic collisions, the results can be used in the modelling of the production of light and heavy nuclei in cosmic rays and dark-matter decays.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

The effect of gamma ray irradiation on few layered MoSe2: A material for nuclear and space applications

In recent years, emerging two-dimensional (2D) materials, such as molybdenum diselenide (MoSe2), have been at the center of attention for many researchers. This is due to their unique and fascinating physicochemical properties that make them attractive in space and defense applications that include shielding harsh irradiation environments. In this study, we examined the effects of gamma (γ) rays at various doses on the structural, chemical, and optical properties of MoSe2 layers. After the samples were exposed to intense gamma radiation (from a 60Co source) with various exposure times to vary the total accumulated dosage (up to 100 kGy), Raman and photoluminescence spectroscopies were used to study and probe radiation-induced changes to the samples. When compared to pristine materials, very few changes in optical properties were typically observed, indicating good robustness with little sensitivity, even at relatively high doses of gamma radiation. The imaging using scanning electron microscopy revealed a number of nano-hillocks that were connected to substrate alterations. X-ray photoelectron spectroscopies revealed that Mo’s binding energies remained the same, but Se’s binding energies blueshifted. We associated this shift with the decrease in Se vacancies that occurred after irradiation as a result of Mo atoms creating adatoms next to Se atoms. When compared to pristine materials, very few changes in optical, chemical, and structural properties were typically observed. These findings highlight the inherent resilience of MoSe2 in hostile radioactive conditions, which spurs additional research into their optical, electrical, and structural characteristics as well as exploration for potential space, energy, and defense applications.

Materials Science↗
DOE OSTI2026

Predicting Atomistic Transitions with Transformers

Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.

36 MATERIALS SCIENCE↗