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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 181 records · Page 10

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↗

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 ↗

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),↗

Iron-assisted growth of anisotropic ZnO nanostructures

Anisotropic nanostructures offer a promising pathway to modulate structure-function relationships of materials. However, the correlation between growth direction of high-quality anisotropic nanostructures, the synthesis conditions and mechanisms controlling their growth, and their magnetic and optical properties remain underexplored. In this study, we developed an iron-assisted anisotropic growth method to form zinc oxide nanostructures on the O-polar (0001̄) surface, resulting in two distinct ZnO-based nanostructures: hand-shaped nanostructures and truncated hexagonal nanopyramids. In contrast to most reports of anisotropic nanostructure synthesis, which primarily focus on morphology control through ligand-ligand interactions, the current study probes the effects of doping on anisotropic growth, and how doping, along with ligand-ligand interactions and facet-specific ligand binding, control nanostructure morphology. The reaction mechanisms leading to formation of these novel structures were thoroughly probed by systematically manipulating synthesis parameters. A two-step formation mechanism was identified: first, a hexagonal platform forms through an initial homogeneous nucleation process, followed by secondary heterogeneous nucleation, which results in metastable secondary nanostructures growing on the oxygen-rich template. Optical and magnetic properties of these Fe/ZnO nanostructures were characterized. Our findings provide a new strategy that uses a magnetic element as a dopant to build new nanostructures of ZnO with controllable size and shape growing on an oxygen-rich crystal plane. These materials could have applications in novel technologies where both optoelectronic and magnetic properties are of interest.

Xuan, Zhengxi↗

High-efficiency frost and ice control via sensing-assisted nanovibrational slippery surfaces

Frost and ice accretions on surfaces pose persistent challenges across numerous industrial, residential and transportation systems. While various removal strategies exist, they often suffer from limited effectiveness or high energy consumption, such as frosting delay, ice crack generation, and Joule heating. Here, in this work, we report a novel integrated approach combining vibrational quasi-liquid surface (QLS) and capacitive sensing for efficient condensate, frost, and ice management. Compared to Joule heating, our approach does not rely on complete melting and evaporation for removal, resulting in 68% and 95% energy savings for frost and ice removal, respectively. Our QLS coating significantly reduces surface retention forces, achieving 91% and 87% less residual mass compared to hydrophilic surfaces for frost and ice removal through surface nanovibration, respectively. The integrated capacitive sensor provides real-time detection of different phase states, enabling on-demand removal in precise timeframes. This sensor-assisted approach showed 3.8 times lower energy consumption compared to conventional Joule heating for defrosting. This synergistic integration of surface engineering, nanovibration, and intelligent sensing represents a significant advancement in phase change processes, offering an energy-efficient solution for frost and ice mitigation in energy-intensive systems.

Shen, Yuchen [Univ. of Texas at Dallas, Richardson↗

Chloride modification of vacuum-assisted blade-coated perovskite solar cells and mini-modules in ambient environment

Scalable deposition of perovskite thin films under ambient conditions remains a key challenge for the commercialization of perovskite photovoltaics, primarily due to the difficulty of controlling crystallization kinetics and suppressing non-radiative recombination during film formation. In this work, we present an effective strategy to modulate crystallization kinetics and non-radiative recombination in vacuum-quenched blade-coated perovskite films processed in air using chloride-based additives. By systematically comparing PbCl 2 and 3-chloropropylamine hydrochloride (Cl-PACl), we elucidate how different chloride incorporation pathways influence crystallization behavior and optoelectronic properties. In situ transmission and in situ photoluminescence measurements reveal that PbCl 2 acts as a transient additive that slows intermediate/perovskite phase formation while suppressing non-radiative recombination, leading to improved film quality. In contrast, Cl-PACl introduces additional trap states that deteriorate optoelectronic performance. As a result, PbCl 2 -modified perovskite solar cells achieve a champion power conversion efficiency (PCE) of 24.2% and remarkable operational stability, retaining over 90% of their initial performance after 800 hours of continuous illumination. Furthermore, we successfully scaled the process, fabricating a perovskite mini-module that delivered a champion efficiency of 18.0%. This study underscores the critical importance of coupling vacuum-assisted blade coating with transient additive engineering for achieving high-performance, stable, and scalable perovskite photovoltaics processed in ambient air.

14 SOLAR ENERGY↗

Rare‐Earth Reduction in High‐Speed PM‐Assisted Synchronous Reluctance Motors Using Hybrid Magnet Strategies and Design Optimisation for 800 V EVs

The trend towards electrifying transportation systems has stimulated research endeavours aimed at developing electric machines that are not only high speed and efficient but also low‐cost and compact. To support this transition, the US Department of Energy (DOE) has established ambitious targets of $7/kW cost and 12 kW/L power density for the electric drivetrains. This paper attempts to meet these targets by proposing high‐speed permanent magnet assisted synchronous reluctance machine (PMASynRM) topologies enabled by hybrid magnet strategies, combining rare‐earth (RE) magnets with low‐cost, RE‐free alternatives. Modified 2‐layer and 3‐layer U‐shaped rotor configurations, featuring structural reinforcements to withstand high mechanical stress at elevated speeds, are developed. A multi‐objective design optimisation framework is employed to optimise the rotor design, targeting reductions in RE magnet volume, magnet demagnetisation risk and torque ripple while achieving the desired electromagnetic performance. A comprehensive analysis of the optimised rotor designs, compared against a 16,000 rpm, 800 V baseline IPM motor, shows over 70% RE reduction, demagnetisation risk below 3.5% and torque ripple under 10%. Detailed analyses of performance trends across various magnet combinations and rotor configurations highlight the viability of hybrid magnet PMASynRM designs as cost‐effective, robust and energy‐efficient solutions for next‐generation EVs.

33 ADVANCED PROPULSION SYSTEMS↗

Toward machine-learning-assisted PW-class high-repetition-rate experiments with solid targets

We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Recovery of Scandium from Bauxite Residue Using Sulfation Roasting-Assisted Water Leaching Process

Here, this study presents a method for recovering scandium from Indonesian bauxite residue (red mud) through a sulfation roasting-assisted water leaching process. Ammonium sulfate, (NH 4 ) 2 SO 4 , was chosen as the sulfate source for its lower melting point, less corrosivity, decreased toxicity, and the potential for ammonia recovery. A modified mixing system was introduced to improve the distribution of (NH 4 ) 2 SO 4 on red mud particles by combining them in water. A series of experiments were conducted to optimize the sulfation roasting process by examining the effects of the (NH 4 ) 2 SO 4 /red mud ratio, temperature, and roasting time. Subsequently, water leaching was optimized while considering the effect of leaching time and liquid-to-solid (L/S) ratio. The leaching of scandium from the roasted red mud showed rapid kinetics, even when using only water. Under the roasting conditions of 1/1 (g/g) (NH 4 ) 2 SO 4 /red mud ratio, 700°C, 60 min roasting time, and the leaching conditions of 65°C, 60 min, and 10 mL/g (L/S), over 90% of scandium was recovered, along with 64.4% Al leaching and below 8% Fe leaching. These results were further corroborated through comprehensive analyses such as x-ray diffraction (XRD) and scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS). This study offers insights into an alternative, more environmentally friendly method for Sc recovery from red mud, promoting the sustainable use of hazardous waste like red mud.

ammonium sulfate↗

Dipolar focusing in laser-assisted positronium formation

Abstract We consider positronium formation in collisions of positrons with excited hydrogen atoms H(n) in an infrared laser field theoretically. This process is assisted by the dipolar focusing effect: a positron moving in a superposition of a laser field and the dipolar field can approach the atomic target even if its trajectory starts with a very large impact parameter, leading to a significant enhancement of the Ps formation cross section. The classical trajectory Monte Carlo method, which is justified for n ⩾ 3 , allows efficient calculation of this enhancement. A similar effect can occur in collisions of positrons with other atoms in excited states, which can lead to improvements in the efficiency of positronium formation.

Optics↗

Laser-assisted radiative recombination in a cold hydrogen plasma

Abstract We study the process of laser-assisted radiative recombination of an electron with a proton in a cold hydrogen plasma employing the semiclassical Kramers’ approach which involves calculation of classical trajectories in combined laser and Coulomb fields and the use of the correspondence principle. Due to the Coulomb focusing effect, recombination is the most effective when the initial electron momentum is parallel to the laser polarization. Orders of magnitude enhancement of the cross section, as compared to the laser-free case, is observed in this case. With increasing angle between the electron momentum and polarization, the recombination cross section drops. However, even after averaging over Maxwellian velocity distribution we obtain a substantial enhancement of the recombination rate constant, as compared to the zero-field case. For the field intensities in the range 30–350 MW cm −2 , the enhancement occurs in the region of the radiation wavelength from 5 to 20 µ m and for the plasma temperature from 20 to 300 K.

Fabrikant, I. I. (ORCID:0000000293849454)↗

First result of boronization assisted by the ICWC on EAST with full metal wall

Boron (B), a low-Z (atomic number) material, has been widely utilized in wall conditioning to improve plasma performance in fusion devices. In 2023, boronization was successfully conducted on EAST featuring an ITER-like tungsten divertor and fully metallic first wall. The process employed predischarge coating with carborane (C 2 B 10 H 12 ) as the working material, assisted by ion cyclotron wall conditioning (ICWC). After one time 12 g boronization, it was found the thickness of B film was approximately 120 nm. Post-boronization observations indicated that substantial hydrogen (H) release during initial plasma discharges compared with the consumed W/B wall, attributed to H co-deposition during the ICWC-boronization processing, which led to uncontrollable divertor neutral pressure and plasma density. The H/(H + D) ratio demonstrated a gradual reduction from ∼85% to 30% over more than 1850 s of deuterium plasma, with a cumulative injected energy of 2325 MJ. The B coating significantly enhanced the stored energy in plasma and improved confinement performance. The stored energy in plasma showed an increase of about 20%, primarily due to a reduction in impurity radiation, including oxygen (O) and heavy impurities such as tungsten (W), iron (Fe), and copper (Cu). The effective ion charge (Z eff ) decreased from 2.3 to 2.0. Following ICWC-boronization, the line-integrated radiation profile decreased by nearly 35% in the plasma core, plasma density and electron temperature exhibited an increase of ∼7% and 12% due to enhanced wall fueling and reduced impurity radiation. The lifetime of boronization, as evaluated by the line emissions from boron and other impurity radiation, was about 1700 s of deuterium plasma, with a cumulative injected energy of 2125 MJ on EAST. These findings provide significant insights for evaluating ICWC-boronization applicability in ITER with full W wall structure.

EAST↗

A modular GUI-based program for genetic algorithm-based feedback-assisted wavefront shaping

Abstract We have developed a modular graphical user interface (GUI)-based program for use in genetic algorithm-based feedback-assisted wavefront shaping. The program uses a class-based structure to separate out the universal modules (e.g. GUI, multithreading, optimization algorithms) and hardware-specific modules (e.g. code for different SLMs and cameras). This modular design makes the program easily adaptable to a wide range of lab equipment, while providing easy access to a GUI, multithreading, and three optimization algorithms (phase-stepping, simple genetic, and microgenetic).

97 MATHEMATICS AND COMPUTING↗

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗

Cavity-assisted magnetization switching in a quantum spin-phonon chain

Néel order switching in antiferromagnets has typically required intense optical driving, leading to substantial heating and limited efficiency. By placing antiferromagnets inside a terahertz-driven optical cavity, we propose a multi-particle mechanism for Néel order switching that benefits from reduced heating. Our analysis reveals that phonons are indispensable to this mechanism. A driven cavity mode couples to a spin-phonon chain, with all excitations dissipating energy through external baths. Mean-field analysis shows that cavity photons induce sublattice spin-density imbalance—an intrinsic symmetry-breaking effect absent without the cavity. Contrary to known (1–10 V/nm) laser fields required to switch the Néel order, our mechanism enables switching at remarkably low laser fields (1–5 V/μm), selectively targeting low-energy and perpendicular magnon modes. By virtue of the suppressed heating, the switching remains highly tunable through laser fluence, damping, and photon loss, establishing a low-dissipation route toward cavity-assisted opto-spintronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Dissipation-assisted steady-state entanglement engineering based on electron transfer models

Here, we propose a series of dissipation-assisted entanglement generation protocols that can be implemented on a trapped-ion quantum simulator. Our approach builds on the single-site molecular electron transfer (ET) model recently realized in experiment [So et al., Sci. Adv. 10, eads8011 (2024)]. This model leverages spin-dependent boson displacement and dissipation controlled by sympathetic cooling. We show that, when coupled to external degrees of freedom, the ET model can be used as a dissipative quantum control mechanism, enabling the precise tailoring of both spin and boson steady states of a target subsystem. We derive simplified analytical formalisms that offer intuitive insights into the dissipative dynamics. Using realistic interactions in a trapped-ion system, we develop a protocol for generating 𝑁-qubit and 𝑁-boson 𝑊 states. Additionally, we generalize this protocol to realize generic 𝑁-qubit Dicke states with tunable excitation numbers. Finally, we outline a realistic experimental setup to implement our schemes in the presence of noise sources.

chemical charge transfer↗