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1,734 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

High-resolution modeling of indoor radon exposure with uncertainty quantification in Utah

Indoor radon accounts for 37% of population-level exposure to ionizing radiation in the United States. However, radon metrics are typically reported at coarse spatial scales, potentially obscuring meaningful local variation. We developed a high-resolution modeling framework to estimate indoor radon concentrations across Utah while explicitly quantifying predictive uncertainty. A total of 19,497 residential radon measurements collected between 2006 and 2017 were combined with environmental and housing characteristics and analyzed using a geospatial neural network that accommodates spatial dependence and nonlinear associations. Predictions were generated on a uniform hexagonal grid at 0.73 km2 resolution (H3 level 8). Out-of-sample predictions aggregated to the H3 level 8 grid showed good agreement with observed concentrations (Pearson r=0.64), while household-level predictions exhibited more moderate agreement (r=0.45). The model produced well-calibrated uncertainty estimates, with 24.1% of held-out observations exceeding the predicted 75th-percentile threshold. Maps of predicted radon concentrations and the probability of exceeding the U.S. EPA action level of 148 Bq/m3 (4 pCi/L) revealed substantial fine-scale spatial heterogeneity that was not apparent in conventional coarse-resolution summaries, with greater local variability observed in densely monitored urban counties than in sparsely sampled regions. High-resolution radon models that explicitly quantify uncertainty provide a useful framework for characterizing the spatial distribution of indoor radon and identifying areas of elevated exceedance risk. These findings highlight the value of fine-scale monitoring data and uncertainty-aware modeling approaches for radon exposure assessment, environmental risk characterization, and radon-related health research.

Wu, Yunhan [ORNL] (ORCID:0000000178842994)↗
DOE OSTI2023

Hybrid Data‐Driven Discovery of High‐Performance Silver Selenide‐Based Thermoelectric Composites

Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data-driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe-based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe-based materials prepared using a simple high-throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m −1 K −2 , which is a 75% improvement from the baseline composite (nominal composition of Ag 2 Se 1 ). In conclusion, the success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Role of Asparagine as a Gatekeeper Residue in the Selective Binding of Rare Earth Elements by Lanthanide‐Binding Peptides

Abstract Lanthanide‐binding tag (LBT) peptides selectively complex lanthanide cations (Ln 3+ ) in their binding pockets and are promising for lanthanide separation. However, designing LBTs that selectively target specific Ln 3+ cations remains a challenge due to limited molecular‐level understanding and control of interactions within the lanthanide‐binding pocket. In this study, we reveal that the N5 asparagine residue acts as a gatekeeper in the binding pocket, resulting in a 100‐fold selectivity for smaller Lu 3+ over larger La 3+ cations. Nuclear magnetic resonance spectroscopy and molecular dynamics simulations show that the N5 residue weakly binds to the larger La 3+ cation, permitting H 2 O molecules inside the pocket. For the smaller Lu 3+ cations, the N5 residue forms an inter‐arm hydrogen bond with the E14 glutamic acid residue, locking the Lu 3+ cation in the pocket and preventing H 2 O infiltration. Mutating the N5 asparagine to a D5 aspartic acid prevents such a hydrogen bond, eliminating the gatekeeping mechanism and precipitously reducing selectivity. The resulting binding affinity to Ln 3+ cations is non‐monotonic but generally increases with cation size. These results suggest a molecular design paradigm: the reduced affinity for larger lanthanides is due to open pocket conformations, while the selectivity of smaller Ln 3+ cations over larger ones is due to the gatekeeping hydrogen bond.

Chemistry↗
DOE OSTI2025

Specific Bacterial Taxa and Their Metabolite, DHPS, May Be Linked to Gut Dyshomeostasis in Patients with Alzheimer’s Disease, Parkinson’s Disease, and Amyotrophic Lateral Sclerosis

Background: Neurodegenerative diseases (NDDs) are multifactorial disorders frequently associated with gut dysbiosis, oxidative stress, and inflammation; however, the pathophysiological mechanisms remain poorly understood. Methods: Using untargeted mass spectrometry-based metabolomics and 16S sequencing of human stool, we investigated bacterial and metabolic dyshomeostasis in the gut microbiome associated with early disease stages across three NDDs—amyotrophic lateral sclerosis (ALS), Alzheimer’s disease (AD), Parkinson’s disease (PD)—and healthy controls (HC). Results: We discovered a previously unrecognized link between a microbial-derived metabolite with an unknown role in human physiology, 2,3-dihydroxypropane-1-sulfonate (DHPS), and gut dysbiosis in NDDs. DHPS was downregulated in AD, ALS, and PD, while bacteria involved in DHPS metabolism, Eubacterium and Desulfovibrio, were increased in all disease cohorts. Additionally, select taxa within the Clostridia class had strong negative correlations to DHPS, suggesting a potential role in DHPS metabolism. A catabolic product of DHPS is hydrogen sulfide, and when in excess, it is known to promote inflammation, oxidative stress, mitochondrial damage, and gut dysbiosis, known hallmarks of NDDs. Conclusions: These findings suggest that cryptic sulfur metabolism via DHPS is a potential missing link in our current understanding of gut dysbiosis associated with NDD onset and progression. As this was a hypothesis generating study, more work is needed to elucidate the role of DHPS in gut dysbiosis and neurodegenerative diseases.

Nutrition & Dietetics↗
DOE OSTI2026

Collapse of magnetized white dwarfs as site of heavy-element formation and kilonova signal

We present the first end-to-end calculation connecting the accretion-induced collapse (AIC) of a magnetized, rapidly rotating white dwarf to observable kilonova signatures, combining two-dimensional (2D) general-relativistic neutrino-magnetohydrodynamic simulations, followed by radiation hydrodynamics with in-situ nuclear network and 2D Monte Carlo radiative transfer with spatially resolved heating rates. Unlike all previous unmagnetized AIC models – which predicted proton-rich, $^{56}$Ni-dominated ejecta – strong magnetic fields eject ${\approx }\, 0.2\, \mathrm{ M}_\odot$ of neutron-rich material ($\langle Y_e \rangle \sim 0.24$) on dynamical time-scales, before neutrino irradiation can raise the electron fraction, enabling strong r-process nucleosynthesis up to and beyond the third peak. The resulting kilonova is lanthanide-rich ($X_{\rm lan} \approx 8~{{\ \rm per\ cent}}$) and dominated by near-infrared emission. We compute synthetic light curves in the Large Synoptic Survey Telescope and J ames Webb Space Telescope bands and find striking agreement, without parameter tuning, between the observations of AT 2023vfi/GRB 230307A and our broadband light curves for polar viewing angles. These results establish magnetized AIC as a viable channel for heavy r-process element production and a compelling progenitor candidate for long-duration gamma-ray bursts with kilonova signatures.

MHD↗
DOE OSTI2026

Adsorption of hydroxamic acid ligands for improved extraction of rare earth elements from monazite ores

Efficient separation of rare earth element (REE) ores via froth flotation requires the development of novel ligands with enhanced adsorption capacity and selectivity. To realize these advances, understanding the mechanisms underlying interactions between the ligand and mineral surfaces is essential. This study systematically evaluates the adsorption behavior of alkyl- and aromatic alkyl-substituted hydroxamic acid ligands on monazite surfaces using complementary spectroscopic techniques, including UV–visible (UV–vis) spectroscopy, Raman spectroscopy, infrared spectroscopy, and vibrational sum frequency generation (SFG) spectroscopy, together with the ab initio molecular dynamics (AIMD) simulations. Among the studied ligands, octanohydroxamic acid (OHA) and 4-ethoxy-N,2-dihydroxybenzamide (EDHBA) exhibit high adsorption capacity under basic pH (8–10) by forming multilayers on the surface. OHA has a higher equilibrium adsorption capacity compared to EDHBA, but it forms a less stable multilayer susceptible to disruption in the presence of interfering ions. AIMD results show that OHA adopts a single stable chelating geometry, while EDHBA exhibits multiple binding modes involving distinct interactions with La surface atoms and phosphate-bound oxygens, resulting in more complex adsorption kinetics. The variations in surface binding and intermolecular interactions observed between alkyl and aromatic molecules influence the differences in adsorption kinetics, equilibrium adsorption capacities on the mineral surface, and their flotation performance. This work provides valuable insight into the adsorption mechanism of ligands at mineral interfaces, which is crucial for guiding the design of new ligands with enhanced separation performance.

Zhou, Muchu [ORNL] (ORCID:0000000182650215)↗
DOE OSTI2023

Host galaxies of ultra-strong Mg ii absorbers at z ∼ 0.7

ABSTRACT We report spectroscopic identification of the host galaxies of 18 ultra-strong Mg ii systems (USMg ii) at 0.6 ≤ z ≤ 0.8. We created the largest sample by merging these with 20 host galaxies from our previous survey within 0.4 ≤ z ≤ 0.6. Using this sample, we confirm that the measured impact parameters ($\rm 6.3\leqslant D[kpc] \leqslant 120$ with a median of 19 kpc) are much larger than expected, and the USMg ii host galaxies do not follow the canonical $\rm {\it W}_{2796}-{\it D}$ anticorrelation. We show that the presence and significance of this anticorrelation may depend on the sample selection. The $\rm {\it W}_{2796}-{\it D}$ anticorrelation seen for the general Mg ii absorbers show a mild evolution at low $\rm W_{2796}$ end over the redshift range 0.4 ≤ z ≤ 1.5 with an increase of the impact parameters. Compared to the host galaxies of normal Mg ii absorbers, USMg ii host galaxies are brighter and more massive for a given impact parameter. While the USMg ii systems preferentially pick star-forming galaxies, they exhibit slightly lower ongoing star-forming rates compared to main sequence galaxies with the same stellar mass, suggesting a transition from star-forming to quiescent states. For a limiting magnitude of mr < 23.6, at least 29 per cent of the USMg ii host galaxies are isolated, and the width of the Mg ii absorption in these cases may originate from gas flows (infall/outflow) in isolated haloes of massive star forming but not starbursting galaxies. We associate more than one galaxy with the absorber in $\ge 21~{{\ \rm per\ cent}}$ cases, where interactions may cause wide velocity spread.

Astronomy & Astrophysics↗
DOE OSTI2025

Manganese‐Based Spinel Cathodes: A Promising Frontier for Solid‐State Lithium‐Ion Batteries

Recently, all-solid-state lithium-ion batteries (ASSLIBs), which exhibit improved safety and enhanced energy density compared to conventional commercialized lithium-ion batteries (LIBs), thereby have garnered extensive research interest. Among the promising cathode candidates, Mn-based spinel cathodes LiMn 2 O 4 (LMO) and LiNi 0.5 Mn 1.5 O 4 (LNMO), with the unique characteristics of low cost, structural stability, and 3D Li-ion diffusion channels, have demonstrated excellent performance in LIBs and presented great potential in ASSLIBs applications. However, several challenges, including structural degradations, poor interfacial contact, large interfacial resistance, and Mn-dissolution/diffusion during the electrochemical cycling, hinder their practical applications and commercialization in the ASSLIBs. Particularly, the high-voltage LNMO cathodes suffer from the challenge of electrochemical incompatibility with most of the solid-state electrolytes (SSEs). Herein, the spinel structure, the electrochemical behavior, and the structural degradation of the LMO/LNMO are explored. The characteristics and recent progress of the mitigating strategies to the challenges of various SSEs, including polymer-, oxide-, composite-, sulfide-, halide-, and LiPON-based SSEs, are introduced when paired with LMO/LNMO. Finally, the directions for future research to advance Mn-based spinel cathodes and fulfill the requirements of the next-generation ASSLIBs are also discussed.

Dou, Yu [Concordia University, Montreal, QC (Canad↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗
DOE OSTI2026

Joint scheduling of energy, fast and primary frequency response reserves in integrated transmission–distribution networks

Inverter-based distributed energy resources (DERs) connected to distribution networks (DNs) can provide fast frequency support, but their reserve deliverability depends on feeder constraints and differs from synchronous primary frequency response (PFR). Existing transmission–distribution coordination studies usually treat reserve generically or neglect feeder-level feasibility, while frequency-security scheduling studies rarely represent distribution feeders explicitly. This paper develops a bi-level day-ahead scheduling framework for integrated transmission–distribution networks that jointly clears energy, transmission-side PFR, and distribution-side fast frequency response (FFR) under exogenous hourly inertia and largest-loss inputs from an external unit commitment (UC) schedule. The transmission problem is modeled with DC-optimal power flow (OPF) and closed-form second-order cone (SOC) frequency-security constraints, whereas each DN is represented by a reserve-aware branch-flow AC-OPF so that scheduled fast reserves remain deliverable during activation. The bi-level problem is reformulated through Karush–Kuhn–Tucker (KKT) conditions into a mixed-integer SOC program, and a penalty term is used to tighten the distribution-network relaxation. In the reduced test system, lower exogenous inertia increased the required primary response from 179.64 MW to 191.08 MW, distribution-side fast response reduced total frequency-response procurement by up to 4.9%, and neglecting distribution constraints overstated the combined distribution-side energy and reserve award by up to 18%. In the expanded study, the largest case was solved in 2.02 s with a 0.00% optimality gap. Time-domain simulations kept the frequency nadir above 59.0 Hz in all tested hours. These results demonstrate the value of fast-response modeling and distribution-feasible reserve delivery in coordinated market clearing.

Noh, Seung-Gil↗
DOE OSTI2026

Anode-less Solid-State Li–S Batteries Enabled by Fe-Stabilized Polysulfides

Anode-less solid-state lithium-sulfur batteries (SSLSBs) with lithium sulfide (Li 2 S) as the cathode promise a high energy density and ease of manufacturing. However, Li 2 S is plagued by poor conductivity, sluggish activation kinetics, and a poor cycle life. Here, in this study, we report an FeCl 3 -activated Li 2 S (FLS) cathode with solid-state polysulfide intermediates generated through a redox reaction between FeCl 3 and Li 2 S. This strategy is shown to boost the electrical conductivity of Li 2 S by 7 orders of magnitude and lower the activation barrier. During cycling, Fe plays a significant role in stabilizing the highly active polysulfide species, contributing to the exceptional electrochemical performance. The FLS cathode achieves 80% capacity retention over 500 cycles with >99% Li 2 S utilization. Furthermore, a Li-metal-free (anode-less) full cell retained over 80% of its initial capacity after 240 cycles. This work underscores the promise of leveraging Fe-stabilized polysulfides in enabling high-energy, long-lasting, solid-state Li-S batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Electrochemical Behavior of Cerium at an Indium Tin-Doped Oxide Electrode in Acidic Media

The redox behavior and speciation of cerium at mesoporous thin films composed of nanoparticles of indium tin-doped oxide (nITO) electrodes were characterized in pH 4.8, 0.1 M acetate buffer and both 0.1 and 1 M HNO 3 using electrochemical techniques and X-ray photoelectron spectroscopy. Anodic deposition of ceria species from Ce(III) to the nITO electrode was achieved under all solvent conditions via spontaneous condensation of electrochemically generated ceric hydroxide species. In 1 M nitric acid, the rate of CeO 2 dissolution is on the same order as CeO 2 deposition, resulting in negligible amounts of CeO 2 electrodeposited at the nITO surface. The cathodic stripping of CeO 2 from the nITO substrate deposited in 0.1 M nitric acid or pH 4.8 acetate buffer follows a 2-step process where Ce(IV)-oxide is initially reduced to an unstable Ce(III)-oxide species that rapidly undergoes acid catalyzed dissolution to yield soluble Ce(III) (aq) . These findings provide a foundation for the pH and anodic potential controlled deposition of CeO 2 thin films to ITO substrates, which can aid in the development of materials composed of ceria. As a result, they can also be used to infer likely analogous actinide redox behavior and speciation at these electrodes.

Cerium↗
DOE OSTI2026

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Thermally unstable roosts influence winter torpor patterns in a threatened bat species

Abstract Many hibernating bats in thermally stable, subterranean roosts have experienced precipitous declines from white-nose syndrome (WNS). However, some WNS-affected species also use thermally unstable roosts during winter that may impact their torpor patterns and WNS susceptibility. From November to March 2017–19, we used temperature-sensitive transmitters to document winter torpor patterns of tricolored bats (Perimyotis subflavus) using thermally unstable roosts in the upper Coastal Plain of South Carolina. Daily mean roost temperature was 12.9 ± 4.9°C SD in bridges and 11.0 ± 4.6°C in accessible cavities with daily fluctuations of 4.8 ± 2°C in bridges and 4.0 ± 1.9°C in accessible cavities and maximum fluctuations of 13.8 and 10.5°C, respectively. Mean torpor bout duration was 2.7 ± 2.8 days and was negatively related to ambient temperature and positively related to precipitation. Bats maintained non-random arousal patterns focused near dusk and were active on 33.6% of tracked days. Fifty-one percent of arousals contained passive rewarming. Normothermic bout duration, general activity and activity away from the roost were positively related to ambient temperature, and activity away from the roost was negatively related to barometric pressure. Our results suggest ambient weather conditions influence winter torpor patterns of tricolored bats using thermally unstable roosts. Short torpor bout durations and potential nighttime foraging during winter by tricolored bats in thermally unstable roosts contrasts with behaviors of tricolored bats in thermally stable roosts. Therefore, tricolored bat using thermally unstable roosts may be less susceptible to WNS. More broadly, these results highlight the importance of understanding the effect of roost thermal stability on winter torpor patterns and the physiological flexibility of broadly distributed hibernating species.

Biodiversity & Conservation↗
DOE OSTI2026

Enhanced Catalytic Dechlorination of Polyvinyl Chloride (PVC) and H2 Production Enabled by Synergistic Gaδ+/Ga0 Active Sites in Liquid Metal Particles

Polyvinyl chloride (PVC) is ubiquitous yet challenging to recycle due to its tendency to thermally decompose above 250 °C, releasing toxic, corrosive chlorinated compounds, and its inability to melt. Here, we report a catalytic strategy for PVC upcycling at 160 °C using gallium liquid metal particles (Ga-LMP) featuring a dynamic Ga-GaOOH core–shell architecture. These catalysts enable concurrent dechlorination and hydrogen evolution, yielding up to 7% H2 (based on initial hydrogen atoms in PVC) along with a highly dechlorinated (>95%) carbonaceous solid and aqueous HCl. Mechanistic investigations combining X-ray photoelectron spectroscopy, infrared spectroscopy, solid-state NMR, inelastic neutron scattering, and ab initio molecular dynamics reveal a synergistic interplay between Gaδ+ sites in the GaOOH shell and metallic Ga0 in the core. Cationic Ga initiates C–Cl bond activation and HCl formation, while progressive reduction of the shell exposes Ga0 sites that promote C–H activation and H2 evolution. Control experiments with a Ga salt and bulk Ga liquid metal confirmed that neither oxidation state alone can achieve both transformations efficiently. This work establishes a dynamic dual-site paradigm for liquid metal catalysis, in which the in situ evolution and coexistence of oxidized and metallic species enable sequential and cooperative bond activation pathways. These findings provide a general design principle for novel liquid metal catalysts that target challenging polymer transformations under mild conditions.

Zingg, Benjamin [ORNL] (ORCID:0009000914530153)↗
DOE OSTI2026

NOx mediated formation of ε – UO 3 during thermal decomposition of uranyl nitrate hexahydrate under static air conditions

The thermal decomposition of uranyl nitrate hexahydrate in air at temperatures of 650 – 800 °C is expected to yield α – U 3 O 8 through intermediate UO 3 phases. Here, n this study, Raman spectroscopy complimented by powder X-ray diffraction revealed the unexpected formation of ε – UO 3 as an accompanying phase during thermal decomposition of UNH at 700 °C under static air conditions. Experiments demonstrated that ε – UO 3 does not form during the initial ramp up stage nor during the high temperature heating period; but instead forms during the cooling stage between 250 – 400 °C. Additional experiments revealed that UNH initially decomposes through an amorphous UO 3 intermediate prior to α – U 3 O 8 formation. Attempts to bypass the U 3 O 8 precursor in the formation of ε – UO 3 were unsuccessful, supporting the necessity of U 3 O 8 (α – U 3 O 8 in this study) in the formation of ε – UO 3 . The observed phase evolution is proposed to result from the NOx species generated during UNH thermal decomposition, which create localized oxidizing conditions within the furnace under static air conditions. Furthermore, a predominantly phase pure ε – UO 3 was synthesized under static air conditions through the addition of an extra plateau at 250 °C during the cooling step. These findings demonstrate the importance of gas-phase chemistry and cooling conditions in uranium oxide phase evolution and provide additional insight into ε – UO 3 formation pathways.

Epsilon uranium trioxide↗
DOE OSTI2026

Rinse-Free, Sodium-Efficient Synthesis of O3-Type Layered Oxide Materials Enabled by Acetate Precursors

Sodium-ion batteries (SIBs) are a sustainable alternative to lithium-ion systems for global electrification, with O3-type layered oxide cathodes offering high specific capacity and feasibility of scalable synthesis. Industrial co-precipitation synthesis of these cathodes typically uses transition metal sulfates, requiring extensive water rinsing to remove Na 2 SO 4 impurities, a process that consumes significant water and risks residual inactive phases if incomplete. Here, this work introduces a rinse-free, resource-efficient approach using metal acetate precursors. Residual sodium acetate in non-rinsed precursors decomposes during sintering to generate Na 2 CO 3 in situ, partially substituting an external sodium resource (e.g., NaOH and Na 2 CO 3 ) and reducing its consumption by ∼18–20%. Phase-pure O3-Na 1.0 Ni 1/3 Fe 1/3 Mn 1/3 O 2 (NFM111) cathodes synthesized via this method exhibit microstructure and electrochemical performance comparable to rinsed sulfate-derived counterparts, with initial capacities of 141 mAh g −1 at C/20 (7.5 mA g −1 ). By eliminating rinsing and minimizing sodium reagent use, this acetate-based route enhances sustainability and scalability of layered oxide production for SIBs.

acetate vs. sulfate↗
DOE OSTI2026

Vanishing dynamic strength measured in a transient high-pressure phase of tin

This work reports two independent experimental estimations of the dynamic strength of the body-centered tetragonal (bct) 𝛾 phase of tin, which occurs at pressures above about 9 GPa and transforms promptly back to the ambient 𝛽 phase upon pressure release. Measuring strength in such a transient high-pressure phase is challenging. One measurement used free-surface Richtmyer-Meshkov instabilities generated with gas gun impact. Another set of measurements used ramp-release loading in a pulsed-power facility. Strength estimations came from comparison to simulations using a comprehensive multiphase modeling framework that includes equation of state, shear moduli, and strength separately for each phase, and treats mixed-phase regions. Both experimental methods found the 𝛾-phase strength to be very low, within experimental uncertainty of zero. The existing literature on other materials, by contrast, almost universally reports higher strength in transient high-pressure phases compared with ambient phases. Recent Molecular-Dynamics simulations on tin in the literature showed almost zero deviatoric strength during a deformation-induced bct → bct transformation in which one of the 𝛾-phase 𝑎 axes “flips” to the 𝑐 axis. This reorientation currently provides the most plausible explanation for the low observed strength in 𝛾-phase tin.

36 MATERIALS SCIENCE↗