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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Sorbent Regeneration via Radiofrequency-Assisted Dielectric Heating for Direct Air Capture of CO 2

Direct air capture (DAC) of CO 2 is a negative emission technology that utilizes sorbents requiring a regeneration step for repeated ab(/d)sorption–desorption cycles. Here we report an unconventional approach to desorb captured CO 2 from a porous sorbent using radiofrequency (RF) irradiation for dielectric heating and targeted energy transfer, thus enabling modularity and promoting renewable energy input. Regeneration of composites of functional ionic liquid and metal–organic framework with high CO 2 capacity and enhanced transport is demonstrated under conditions relevant to DAC. RF-assisted dielectric heating at the megahertz frequency range shows effective absorption of electromagnetic energy and consequently rapid release of the captured CO 2 .

Ionic liquids↗
DOE OSTI2024

Tuning Surface, Phase, and Magnetization of Superparamagnetic Magnetite by Ionic Liquids: Single-Step Microwave-Assisted Synthesis

Achieving colloidal and chemical stability in ferrofluids by surface modification requires multiple steps, including purification, ex situ modification steps, and operation at high temperatures. In this study, a single-step microwave-assisted methodology is developed for iron oxide nanoparticle (IONP) synthesis utilizing a series of imidazolium-based ionic liquids (ILs) with chloride, bis(trifluoromethylsulfonyl)imide, and pyrrolide anions as the reaction media, thus eliminating the use of volatile organics while enabling rapid synthesis at 80 °C as well as in situ surface functionalization. The characterized surface functionality, hydrodynamic particle size, magnetization, and colloidal stability of the IONPs demonstrate a strong dependence on the IL structure, ion coordination strength, reactivity, and hydrophilicity. The IONPs present primarily a magnetite (Fe 3 O 4 ) phase with superparamagnetism with the highest saturation magnetization at 81 and 73 emu/g at 10 and 300 K, respectively. Depending on the IL coating, magnetization and exchange anisotropy decrease by 20 and 2–3 emu/g (at 35 wt % IL), respectively, but still represent the highest magnetization achieved for coated IONPs by a coprecipitation method. Further, the surface-functionalized superparamagnetic magnetite nanoparticles show good dispersibility and colloidal stability in water and dimethyl sulfoxide at 0.1 mg/mL concentration over the examined 3 month period. This study reports on the intermolecular and chemical interactions between the particle surface and the ILs under synthetic conditions as they relate to the magnetic and thermal properties of the resulting IONPs that are well suited for a variety of applications, including separation and catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Amine-Assisted Ligand-Exchange Method to Enhance Photovoltaic Parameters in FAPbI3 Nanocrystal Solar Cells

Perovskite colloidal nanocrystals (PeNCs) have exceptional optoelectronic properties and phase stability, making them promising for photovoltaic applications. However, insulating ligands on PeNC surfaces limit the current density and reduce the power conversion efficiency (PCE) in PeNC solar cells (SCs). This study introduces an amine-assisted ligand-exchange (ALE) strategy using 3-phenyl-1-propylamine (3P1P) to effectively remove long ligands from PeNC films. ALE reduced long-chain ligand density without increasing the number of defect states and therefore reduced the exciton-binding energy of FAPbI3 NC films. These changes facilitated exciton dissociation and charge transport in FAPbI3 PeNC SCs. The facilitation of exciton dissociation was due to the increased magnetic dipole interaction between excitons after the ALE process. The use of ALE achieved FAPbI3 PeNC SCs that had an improved short-circuit current density of 17.98 mA/cm2 and a PCE of 15.56% with improved stability after the treatment and negligible hysteresis. This work provides new insight into engineering PeNC films.

amines↗
DOE OSTI2025

Plasma-Assisted Surface Nitridation of Proton Intercalatable WO 3 for Efficient Electrocatalytic Ammonia Synthesis

Electrocatalytic nitrogen reduction (eNRR) offers a green pathway for the production of NH 3 from N 2 and H 2 O under ambient conditions. Transition metal oxynitrides (TMO x N y ) are among the most promising catalysts but face challenges in achieving a high yield and faradaic efficiency (FE). This work develops a hybrid WO x N y /WO 3 catalyst with a unique heterogeneous interfacial complexion (HIC) structure. This design enables in situ generation and delivery of highly active hydrogen atoms (H*) in acidic electrolytes, promoting nitrogen hydrogenation and the formation of nitrogen vacancies (Nv) on the WO x N y surface. This significantly enhances the selectivity of eNRR for NH 3 synthesis while suppressing the hydrogen evolution reaction (HER). A simple two-step fabrication process─microwave hydrothermal growth followed by plasma-assisted surface nitridation─was developed to fabricate the designed catalyst electrode, achieving an NH 3 yield of 3.2 × 10 –10 mol·cm –2 ·s –1 with 40.1% FE, outperforming most TMN/TMO x N y electrocatalysts. Multiple control experiments confirm that the eNRR follows an HIC-enhanced Mars–van Krevelen (MvK) mechanism.

Catalysts↗
DOE OSTI2024

Enhancing Resource Recovery through Electro-Assisted Regeneration of an Ammonia-Selective Cation Exchange Resin

Ammonia-selective adsorbents can manage reactive nitrogen in the environment and promote a circular nutrient economy. Weak acid cation exchangers loaded with zinc exhibit high ammonia selectivity but face two implementation barriers: the stability of the zinc–carboxylate bond in complex wastewaters and energy- and logistics-intensive adsorbent regeneration with acidic solutions. In this paper, we examined the stability of zinc–carboxylate bonds in varying solutions (pure ammonium solution, synthetic urine, and real urine) and during electro-assisted regeneration. For electrochemical regeneration, both electrolyte concentration and current density influenced the trade-off between ammonia regeneration and zinc elution. Using 10 mM K 2 SO 4 anolyte at a 0.08 mA/cm 2 current density, we achieved 4% zinc elution and 61% ammonia regeneration. In contrast, using 100 mM K 2 SO 4 at 4.96 mA/cm 2 improved the regeneration efficiency to 97% but eluted 60% of zinc. We found that electrolyte concentration was the key factor influencing the regeneration efficiency of the NH 3 -selective adsorbents. Due to prevalent zinc elution, we designed an in situ procedure for reforming the zinc–carboxylate bond and achieved similar adsorption densities between pre- and post-regenerated resins, thus enabling multiple cycle resin use. Ultimately, this study advances understanding of ammonia-selective resins that can facilitate high-purity, selective, and durable recovery of nutrients from waste streams.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Sustainable Aviation Fuel from High-Strength Wastewater via Membrane-Assisted Volatile Fatty Acid Production: Experimental Evaluation, Techno-economic, and Life-Cycle Analyses

To reduce emissions from combustion of fossil fuels, sustainable aviation fuels (SAFs) have the potential to decarbonize the aviation sector. Redirecting wastes from conventional waste management practices and using them as cost-effective feedstocks for low-carbon fuels can reduce emissions from both waste disposal and fuel combustion. One approach is to upgrade wet wastes to SAF precursors, such as volatile fatty acids (VFAs). Here, in this study, novel membrane-assisted arrested methanogenesis was developed to convert high-strength wastewater to VFAs. Based on experimental results of VFA production, techno-economic and life-cycle analyses were conducted to estimate the potential economic and environmental benefits of SAF production from high-strength wastewater via VFAs. By evaluating three proposed scenarios for VFA production, a minimum production cost of VFA is achieved at $\$$0.60/kg VFA at a wastewater flow rate of 1100 MT/d. For the corresponding VFA-derived SAF, the estimated minimum fuel selling price is $\$$4.64/gasoline gallon equivalent. The life-cycle analysis shows that up to a 71% reduction in greenhouse gas emissions can be achieved relative to its fossil-counterpart along with lower water and fossil-fuel consumption.

09 BIOMASS FUELS↗
DOE OSTI2025

Revealing Solvent-Assisted Li+ Transport in the Solid Electrolyte Interphase operando

The performance of energy-dense lithium metal batteries is critically influenced by the properties of the solid electrolyte interphase (SEI). Yet, progress in understanding this layer has been limited by the lack of accurate operando characterization because the SEI evolves dynamically during cycling. Here, we apply dynamic electrochemical impedance spectroscopy (dEIS) to resolve the real-time evolution of the SEI on lithium metal in ether-based electrolytes with varying degrees of fluorination. We find that faster stabilization of the compact SEI resistance correlates with improved passivation and higher Coulombic efficiency. Unexpectedly, compact SEI resistance correlates directly with Li+ solvation energy, revealing that weaker Li+ solvation increases not only bulk but also interphase resistance. These findings challenge the conventional view of the SEI as a purely solid-phase conductor and instead support a solvent-assisted Li+ transport mechanism within the compact SEI. This framework emphasizes the need to balance SEI ionic conductivity with the Li+ solvation environment to maximize lithium metal battery performance.

Sacci, Robert [ORNL] (ORCID:0000000200735221)↗
DOE OSTI2024

Plasma-assisted manipulation of vanadia nanoclusters for efficient selective catalytic reduction of NO x

Supported nanoclusters (SNCs) with distinct geometric and electronic structures have garnered significant attention in the field of heterogeneous catalysis. However, their directed synthesis remains a challenge due to limited efficient approaches. This study presents a plasma-assisted treatment strategy to achieve supported metal oxide nanoclusters from a rapid transformation of monomeric dispersed metal oxides. As a case study, oligomeric vanadia-dominated surface sites were derived from the classic supported V 2 O 5 -WO 3 /TiO 2 (VWT) catalyst and showed nearly an order of magnitude increase in turnover frequency (TOF) value via an H 2 -plasma treatment for selective catalytic reduction of NO with NH 3 . Such oligomeric surface VO x sites were not only successfully observed and firstly distinguished from WO x and TiO 2 by advanced electron microscopy, but also facilitated the generation of surface amide and nitrates intermediates that enable barrier-less steps in the SCR reaction as observed by modulation excitation spectroscopy technologies and predicted DFT calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Universal salt-assisted assembly of MXene from suspension on polymer substrates

Abstract Two-dimensional carbides and nitrides, known as MXenes, are promising for water-processable coatings due to their excellent electrical, thermal, and optical properties. However, depositing hydrophilic MXene nanosheets onto inert or hydrophobic polymer surfaces requires plasma treatment or chemical modification. This study demonstrates a universal salt-assisted assembly method that produces ultra-thin, uniform MXene coatings with exceptional mechanical stability and washability on various polymers, including high-performance polymers for extreme temperatures. The salt in the Ti 3 C 2 T x colloidal suspension reduces surface charges, enabling electrostatically hydrophobized MXene deposition on polymers. A library of salts was used to optimize assembly kinetics and coating morphology. A 170 nm MXene coating can reduce radiation temperature by ~200 °C on a 300 °C PEEK substrate, while the coating on Kevlar fabric provides comfort in extreme conditions, including outer space and polar regions.

Science & Technology - Other Topics↗
DOE OSTI2025

Quantum annealing-assisted lattice optimization

High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging. In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation. By applying our algorithm to the NbMoTaW alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA. We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations. Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions

Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.

Wang, Xin↗
DOE OSTI2025

Synthesis of a series of rare-earth-based multi-anion chalcogenide iodides RE 3 Si 2 Se x S 8− x I (RE = La, Ce, Pr, and Nd) using the flux-assisted boron–chalcogen mixture method

Single crystals and polycrystalline powders of rare earth mixed chalcogenide iodides La 3 Si 2 Se 1.21 S 6.79 I, Ce 3 Si 2 Se 1.39 S 6.61 I, Pr 3 Si 2 Se 1.22 S 6.78 I, and Nd 3 Si 2 Se 1.18 S 6.82 I were prepared using the reactive flux-assisted boron–chalcogen mixture (BCM) method at 850 °C. All compounds crystallized in the monoclinic crystal system, space group C2/c (space group number 15). The series adopts the La 3 Si 2 O 8 Cl structure type, containing isolated SiQ 4 tetrahedra connected by REQ 8 (RE = La, Ce, Pr and Nd) polyhedra; this arrangement creates tunnels that are filled by I atoms. The partial substitution of S by Se was carried out to modulate the optical properties. Phase pure samples and uniform solid solutions were obtained for all compositions as determined using powder X-ray diffraction patterns. Polycrystalline powders were used for physical property measurements, including magnetic susceptibility and UV-Vis diffuse reflectance. The solid-state UV-Vis data for the polycrystalline La 3 Si 2 Se 1.21 S 6.79 I, Ce 3 Si 2 Se 1.39 S 6.61 I, and Pr 3 Si 2 Se 1.22 S 6.78 I samples revealed band gaps of E g = 2.5(1), 2.2(1), and 2.3(1) eV, typical of semiconductors. Magnetic measurements indicated that Ce 3 Si 2 Se 1.39 S 6.61 I and Nd 3 Si 2 Se 1.18 S 6.82 I exhibit paramagnetic behavior with slightly negative Weiss constants θ = −25 and −38. The photoluminescence spectrum of Ce 3 Si 2 Se 1.39 S 6.61 I exhibits a broad emission band around ∼493 nm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Rapid 235 U/ 238 U determination by matrix assisted ionization–time-of-flight mass spectrometry

Matrix-assisted ionization (MAI) of inorganic analytes is a nascent research domain that holds promise for rapid, potentially facility-deployable analytical applications. Here, we present results of MAI uranium isotopic analysis ( 235 U/ 238 U) obtained on the timescale of minutes utilizing simple sample preparation and an ambient ionization time-of-flight mass spectrometer (ToF MS). Experimental MAI-ToF MS characterization of uranium Certified Reference Materials (CRMs) was used to establish method calibration and validate quantitative 235 U/ 238 U determination spanning depleted, natural, and low-enriched uranium isotopic compositions. Secondary standard analyses with total uranium mass loadings of 5–500 ng per analysis yield accurate calibrated 235 U/ 238 U results and relative uncertainties of 4.7–17.2% (approx. ±95% confidence level), with weighted-mean uncertainties approaching 1.5%. This method permits accurate determination of uranium isotopic composition in a sample with uranium content as low as 200 pg for equal atom 235 U: 238 U. Instrument detection limits constrain the minimum uranium mass required to identify the presence of highly enriched uranium (HEU ≥20% 235 U) as only 500 pg using the method presented here. MAI-ToF MS quantitation of relatively extreme isotope ratios ( 235 U/ 238 U ≤ 0.01) is limited by detection of minor 235 U (LoD 100 pg 235 U/analysis ≈ 10 ng total U/analysis), and subsequent method optimization is anticipated to further reduce these limits. These findings underscore the potential of MAI-ToF MS for isotopic characterization of uranium and other inorganic species for both basic and applied science.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Machine learning-assisted profiling of a kinked ladder polymer structure using scattering

Ladder polymers consisting of fused rings in the backbone have very limited conformational freedom, which results in very different properties from traditional linear polymers. However, accurately determining their size and chain conformations from solution scattering remains a challenge. Their chain conformations of kinked ladder polymers are largely governed by the structures and relative orientations or configurations of the repeat units, unlike conventional polymer chains whose bending angles between repeat units follow a unimodal Gaussian distribution. Meanwhile, traditional scattering models for polymer chains do not account for these unique structural features. This work introduces a novel approach that integrates machine learning with Monte Carlo simulations to construct a model that can describe the geometry of a type of kinked CANAL ladder polymers. We first develop a Monte Carlo simulation model for sampling the configuration space of CANAL ladder polymers, where each repeat unit is modeled as a biaxial segment. Then, we establish a machine learning-assisted scattering analysis framework based on Gaussian Process Regression. Finally, we conduct small-angle neutron scattering experiments on a CANAL ladder polymer solution to apply our approach. Our method uncovers structural features of such ladder polymers that conventional methods fail to capture.

Ding, Lijie [Oak Ridge National Laboratory (ORNL),↗