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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

The two radiative states of the Arctic atmosphere and their impacts on the surface energy budget of sea ice

The surface energy budget (SEB) is a central regulator of Arctic climate and sea ice evolution, yet its processes remain poorly constrained due to sparse observations and complex, coupled surface-atmosphere interactions. This study leverages year-long measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) to provide the most comprehensive assessment to date of the central Arctic SEB and its modulation by atmospheric variability. Ship- and ice-based observations from October 2019 to September 2020 were used to directly measure or tightly constrain each term of the SEB, leading to exceptional energetic closure with the seasonal snow and ice mass balance. The analysis reveals strong seasonal transitions in atmosphere-surface energy transfer that are modulated by the atmospheric state and constrained by the ability of the surface temperature to respond. Classification of the atmosphere into its two dominant radiative states—the semi-transparent (ST) and opaque (OP)—highlights the central role of synoptic-scale variability in clouds. The ST atmospheric state dominated the long winter ice growth season, with limited cloudiness supporting persistent surface radiative cooling and ice growth. The OP state, associated with liquid-containing or thick ice clouds, became dominant in spring, with the combination of increased solar heating and cloud surface longwave warming driving ice and snow melt. Eddy covariance versus bulk approaches for deriving surface turbulent heat fluxes provide vastly different perspectives on the role of turbulence in modulating the SEB. These results establish a high-quality benchmark dataset for Arctic SEB studies and demonstrate how the balance of atmospheric radiative states exerts a first-order control on the annual evolution of the sea ice. The findings have broad implications for advancing observing technologies, understanding Arctic amplification, improving climate models, and predicting future sea ice change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A copula-based rank histogram ensemble filter

Serial ensemble filters implement triangular probability transport maps to reduce high-dimensional inference problems to sequences of state-by-state univariate inference problems. The univariate inference problems are solved by sampling posterior probability densities obtained by combining constructed prior densities with observational likelihoods according to Bayes' rule. Many serial filters in the literature focus on representing the marginal posterior densities of each state. However, rigorously capturing the conditional dependencies between the different univariate inferences is crucial to correctly sampling multidimensional posteriors. This work proposes a new serial ensemble filter, called the copula rank histogram filter (CoRHF), that seeks to capture the conditional dependency structure between variables via empirical copula estimates; these estimates are used to rigorously implement the triangular (state-by-state univariate) Bayesian inference. The success of the CoRHF is demonstrated on two-dimensional examples and the Lorenz'63 problem. A practical extension to the high-dimensional setting is developed by localizing the empirical copula estimation, and is demonstrated on the Lorenz'96 problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM’s ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Improving thermodynamic nudging in the E3SM Atmosphere Model version 2 (EAMv2): strategy and hindcast skills on weather systems

Nudging techniques are commonly employed to constrain atmospheric simulations toward observed states, facilitating model evaluation and sensitivity studies. However, if applied improperly – particularly to thermodynamic variables such as temperature and humidity – nudging can distort physical processes and introduce spurious biases, undermining the credibility of the simulations. This study presents an improved nudging implementation that applies vertically modulated tendencies to reduce adverse impacts on model physics. The framework is tested in version 2 of the Energy Exascale Earth System Model (EAMv2) using a suite of hindcast simulations nudged toward ERA5 reanalysis. We systematically evaluate the individual and combined effects of nudging wind, temperature, and humidity fields on the model's ability to represent large-scale atmospheric states and high-impact weather systems. Results show that the revised strategy – particularly when nudging temperature and humidity at selected levels – enhances hindcast skill by improving agreement with ERA5 without degrading the hydrological cycle or precipitation processes. Additional improvements in surface temperature, outgoing longwave radiation, and precipitation biases are achieved through targeted nudging of land-surface variables. The proposed approach strengthens the representation of large-scale conditions relevant to tropical cyclones, atmospheric rivers, and extratropical cyclones in the low-resolution EAMv2. These findings demonstrate that carefully designed thermodynamic nudging, especially of temperature and humidity, improves the realism of constrained simulations and broadens the utility of nudged EAMv2 for atmospheric modeling, machine learning, and high-impact weather research.

Atmospheric river↗
DOE OSTI2025

NLR HPC Kestrel Jobs Data

Overview: Anonymized job-level records from the Kestrel HPC system at the National Laboratory of the Rockies (NLR). Each record represents a Slurm batch job with scheduling metadata, resource requests, utilization, energy estimates, and efficiency metrics. Sensitive fields (user, account, job name, submit line, working directory, submit script, and job type) are replaced with 7-character cryptographic hashes. System & Timeframe: Kestrel is located at the NLR campus. Standard compute nodes have 104 cores and 256 GB RAM; bigmem nodes have 2,000 GB. GPU nodes (gpu-h100 partition) use NVIDIA H100 GPUs. Data covers jobs submitted August 2023 through December 2025. Funding provided by the U.S. Department of Energy, EERE. Files: esif.hpc.kestrel.job-anon.zip — Anonymized job records (Hive-partitioned Parquet) datacard.md — Full dataset documentation ~11 million rows, 50 variables. Readable with PyArrow, pandas, DuckDB, Apache Spark, or any Parquet-compatible tool. Data Collection: Jobs collected via sacct with timezone-aware export (SLURM_TIME_FORMAT="%Y-%m-%dT%H:%M:%S%z"), loaded into PostgreSQL. Calculated columns updated via database triggers and batch functions. All timestamps use timestamptz and correctly handle DST transitions. Preprocessing: Anonymization of name, user, account, submit_line, work_dir, submit_script, and job_type via 7-char hex hashes Derived columns: queue_wait, cpu_eff, max/min/avg_mem_eff, energy estimates Simplified job state mapping (e.g., "CANCELLED by 132357" → "CANCELLED") Boolean flags: python_job, reframe_job Temporal decomposition: year, month, day, day_of_week, hour, minute from submit_time Shared node tracking: shared_job_count, nodes_shared, jobs_shared Key Variables: Scheduling: job_id, partition, state_simple, submit_time, start_time, end_time, queue_wait Resources: nodes_req/used, processors_req/used, memory_req, wallclock_req/used, gpus_requested Efficiency: cpu_eff, max/min/avg_mem_eff Energy: cpu_energy_tdp_estimated_max/used_watt_hours, consumed_energy_raw_joules, consumed_energy_raw_watt_hours Sharing: shared_job_count, nodes_shared, jobs_shared Partitions: short, standard, debug, gpu-h100 Job States: CANCELLED, COMPLETED, FAILED, PENDING, RUNNING QoS Levels: normal, high Important Notes: Timestamps include timezone offsets; DST transitions are handled correctly, though adding intervals across DST boundaries requires offset adjustment shared_job_count reflects physical node co-residency, not use of the shared partition Job step records and raw Slurm JSONB fields are excluded Do not attempt to re-identify individuals from hashed fields

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multiwavelength study of OT 081: broadband modelling of a transitional blazar

ABSTRACT OT 081 is a well-known, luminous blazar that is remarkably variable in many energy bands. We present the first broadband study of the source, which includes very high energy (VHE, $E\gt $ 100 GeV) $\gamma$-ray data taken by the MAGIC (Major Atmospheric Gamma-ray Imaging Cherenkov telescopes) and H.E.S.S. (High Energy Stereoscopic System) imaging Cherenkov telescopes. The discovery of VHE $\gamma$-ray emission happened during a high state of $\gamma$-ray activity in July 2016, observed by many instruments from radio to VHE $\gamma$-rays. We identify four states of activity of the source, one of which includes VHE $\gamma$-ray emission. Variability in the VHE domain is found on daily time-scales. The intrinsic VHE spectrum can be described by a power law with index $3.27\pm 0.44_{\rm stat}\pm 0.15_{\rm sys}$ (MAGIC) and $3.39\pm 0.58_{\rm stat}\pm 0.64_{\rm sys}$ (H.E.S.S.) in the energy range of 55–300 and 120–500 GeV, respectively. The broadband emission cannot be successfully reproduced by a simple one-zone synchrotron self-Compton model. Instead, an additional external Compton component is required. We test a lepto-hadronic model that reproduces the data set well and a proton-synchrotron-dominated model that requires an extreme proton luminosity. Emission models that are able to successfully represent the data place the emitting region well outside of the broad-line region to a location at which the radiative environment is dominated by the infrared thermal radiation field of the dusty torus. In the scenario described by this flaring activity, the source appears to be a flat spectrum radio quasar (FSRQ), in contrast with past categorizations. This suggests that the source can be considered to be a transitional blazar, intermediate between BL Lac and FSRQ objects.

Abe, H.↗
DOE OSTI2024

Medium-density amorphous ice unveils shear rate as a new dimension in water’s phase diagram

Recent experiments revealed a new amorphous ice phase, medium-density amorphous ice (MDA), formed by ball-milling ice I h at 77 K [Rosu-Finsen et al., Science 379, 474–478 (2023)]. MDA has density between that of low-density amorphous (LDA) and high-density amorphous (HDA) ices, adding to the complexity of water’s phase diagram, known for its glass polyamorphism and two-state thermodynamics. The nature of MDA and its relation to other amorphous ices and liquid water remain unsolved. Here, we use molecular simulations under controlled pressure and shear rate at 77 K to produce and investigate MDA. Here. we find that MDA formed at constant shear rate is a steady-state nonequilibrium shear-driven amorphous ice (SDA), that can be produced by shearing ice I h , LDA, or HDA. Our results suggest that MDA could be obtained by ball-milling water glasses without crystallization interference. Increasing the shear rate at ambient pressure produces SDAs with densities ranging from LDA to HDA, revealing shear rate as a new thermodynamic variable in the nonequilibrium phase diagram of water. Indeed, shearing provides access to amorphous states inaccessible by controlling pressure and temperature alone. SDAs produced with shearing rates as high as 10 6 s −1 sample the same region of the potential energy landscape than hyperquenched glasses with identical density, pressure, and temperature. Intriguingly, SDAs obtained by shearing at ~10 8 s −1 have density, enthalpy, and structure indistinguishable from those of water “instantaneously” quenched from room temperature to 77 K over 10 ps, making them good approximants for the “true glass” of ambient liquid water.

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

Polymorphism and Negative Linear Compressibility in Pyrazine-d4

Pyrazine (1,4-diazine) is a widely used linker in coordination polymers, molecular magnets, and metal–organic frameworks, yet its solid-state polymorphism is controlled by weak C–H···N interactions. Using variable-temperature and high-pressure neutron diffraction, a previously unrecognized low-temperature transition to phase IV is identified, showing that the form stabilized under pressure can also be accessed below ∼90 K at ambient pressure in the perdeuterated compound. The transition from phase III → IV involves a symmetry-breaking lattice distortion driven by the reorganization of C–H···N hydrogen bonds, accompanied by negative linear compressibility, which is primarily accommodated through the reduction in void-space within the structure. Structural analysis reveals that while the overall monoclinic framework of phase IV is preserved under both temperature and pressure routes, the evolution of hydrogen-bonded network is distinct depending on the pathway taken. These results establish pyrazine as a model molecular solid for studying pressure–temperature polymorphism and demonstrate how weak directional interactions can produce anomalous mechanical responses in organic crystals.

Ridley, Christopher J. [Oak Ridge National Laborat↗
DOE OSTI2024

Compound Flooding in Halmstad: Common Causes, Interannual Variability and the Effects of Climate Change

Compound flooding was investigated in Halmstad on the Swedish west coast. The highest sea level ever recorded in Sweden was recorded by the Halmstad tide gauge. Moreover, through the city runs Nissan, one of the largest hydropower-producing rivers in southern Sweden. Together these features make the city an interesting location to study compound flooding. Co-variability between different hazards are studied using a unique set of downscaled climate scenario projections produced using state-of-the-art regional atmosphere, ocean and hydrological models. River discharge and sea level annual maxima are found to be correlated with peak strength on interannual time scales. This co-variability is found to derive from a mutual dependence that these hazards have on the North Atlantic Oscillation (NAO). Precipitation also shares this dependence in winter months and acts as a driver, building up the fluvial river hazard, while potentially adding additional pluvial hazard. Trends are also assessed, and it was found that future changes in compound flooding will most likely be driven primarily by mean sea level rise and secondarily by increases in mean river discharge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Disparities in the air quality monitoring stations and PM₂.₅ in Chicago’s air quality landscape

Fine particulate matter (PM₂.₅) poses significant public and environmental health risks in urban areas. Chicago’s dense industry and traffic create variable air quality, yet monitoring is unevenly distributed, resulting in undersampling of air quality data in some city areas. This study applied a hybrid approach using GIS-based kernel density mapping, interpolation modeling (IDW, Spline, Kriging) of USEPA monitoring data, multi-scale temporal trend analyses (hourly to annual), and ESDA. Accordingly, the density surface showed that monitors are concentrated in the affluent north, northwest, and southwest sides of Chicago (up to ~ 0.07 stations per sq mile), while the south and southeast regions, with predominantly minority communities, have virtually no coverage. Overall, citywide coverage is minimal (~ 4–5 monitors total; ~0.02 per sq mile; ≈1 per 600,000 residents). Temporal analyses showed that the city’s mean annual PM₂.₅ (~ 10.8 µg/m³) exceeds USEPA/WHO standards (9 µg/m³), with summer means (~ 17.1 µg/m³) significantly higher than other seasons. Diurnally, a clear pattern was observed, with PM₂.₅ concentrations peaking overnight (00:00–03:00) and during the morning rush hours, and dipping during midday to late afternoon. Spatial distribution of PM₂.₅ identified hotspots near O’Hare Airport, the downtown Loop area, and south-side neighborhoods, contrasting with lower concentrations on the north side, revealing Chicago’s socioeconomic divides and resulting environmental inequities. The findings underscore the need for expanded monitoring and targeted interventions in under-monitored, high-pollution communities to advance equitable community health.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Water Column Respiration in the Yakima River Basin is Explained by Temperature, Nutrients and Suspended Solids

Understanding aquatic ecosystem metabolism involves the study of two key processes: carbon fixation via primary production and organic C mineralization as total ecosystem respiration (ERtot). In streams and rivers, ERtot includes respiration in the water column (ERwc) and in the sediments (ERsed). While literature surveys suggest that ERsed is often a dominant contributor to ERtot, recent studies indicate that the relative influence of sediment-associated processes versus water column processes can fluctuate along the river continuum. Still, a comprehensive understanding of the factors contributing to these shifts within basins and across stream orders is needed. Here we contribute to this need by measuring ERwc and collecting water samples across 47 sites in the Yakima River basin, Washington, USA. We found that ERwc rates varied throughout the basin during baseflow conditions, ranging from –7.38 to 0.36 g O2 m?3 d?1, and encompassed the range of ERwc literature values. Additionally, by comparing to ERtot estimates for rivers across the contiguous United States, we suggest that the contribution of ERwc rates to reach-scale ERtot rates across the Yakima River was likely highly variable, but we did not test this directly. We observed that temperature, nutrient concentrations (dissolved organic carbon, total dissolved nitrogen), and total suspended solids explained 41% of ERwc variability across the basin. Our findings highlight the potential relevance of water column processes in aquatic ecosystem metabolism, with the Yakima River basin serving as an environmentally diverse river network representative of the larger Columbia River basin that spans much of the Pacific Northwest region of the United States. Our results are generally congruent with previous work, suggesting that the observed variability and suite of associated environmental factors influencing ERwc are potentially transferable across basins.

Laan, Maggi M.↗
DOE OSTI2025

NLR HPC Eagle Jobs Data and Additional Energy Metrics

Overview: Anonymized job-level records from the Eagle high-performance computing (HPC) system at the National Laboratory of the Rockies (NLR). Each record represents a Slurm batch job with scheduling metadata, resource requests, resource utilization, CPU/GPU energy consumption, and efficiency metrics. Sensitive fields (user, account, job name) are replaced with cryptographic hashes. System & Timeframe: Eagle was a 2,000-node, 8-petaflop system operated at NLR from 2019–2024. Data covers the full operational lifetime of the system. Slurm data was processed nightly; timestamps are in Mountain Time. Funding provided by the U.S. Department of Energy, EERE. Files: esif.hpc.eagle.job-anon.zip — Core anonymized job records (Hive-partitioned Parquet) esif.hpc.eagle.job-anon-energy-metrics.zip — Same records with additional iLO and Ganglia energy metrics datacard.md — Full dataset documentation ~13.8 million rows, 62 variables. Readable with PyArrow, pandas, DuckDB, Apache Spark, or any Parquet-compatible tool. Data Collection: Jobs collected via sacct through a pipeline: Eagle Jobs API → Redpanda → StreamSets → HPCMON API → PostgreSQL. Node-level power from iLO (HP Integrated Lights-Out); GPU power from Ganglia monitoring, joined to jobs via node lists and time ranges. Preprocessing: Anonymization of name, user, and account fields via cryptographic hashing Derived columns: queue_wait, cpu_eff, max_mem_eff Simplified job state mapping (e.g., "CANCELLED BY 12345" → "CANCELLED") QoS accounting rules (buy-in, standby, or Slurm QoS value) CPU energy estimated from TDP (200W, Intel Xeon Gold 6154, 18 cores) Timezone-aware columns (_tz) sourced from LEX accounting database to correctly handle DST transitions Key Variables: Scheduling: job_id, partition, state_simple, submit_time_tz, start_time_tz, end_time_tz, queue_waitResources: nodes_req/used, processors_req/used, memory_req, wallclock_req/used, gpus_requested Efficiency: cpu_eff, max_mem_eff Energy: cpu_energy_tdp_estimated_max/used_watt_hours, node_energy_total_watt_hours (iLO), gpu0/1_energy_total_watt_hours (Ganglia) Partitions: bigmem, bigmem-8600, bigscratch, csc, dav, ddn, debug, gpu, haswell, long, mono, short, standard Job States: CANCELLED, COMPLETED, FAILED, NODE_FAIL, OUT_OF_MEMORY, PENDING, RUNNING, TIMEOUT QoS Levels: Unknown, normal, buy-in, debug, penalty, high, standby Important Notes: Non-_tz timestamp columns may be off by one hour across DST boundaries; use _tz columns for time difference calculations Energy fields are null for jobs without monitoring coverage Job step records and raw Slurm JSONB fields are excluded from this extract Do not attempt to re-identify individuals from hashed fields

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Statistical fracture behavior of doped UO 2 using a ball-on-ring equibiaxial flexure test method

Metal oxide dopants, such as titanium and chromium oxides, have garnered considerable attention for their potential to increase grain size (≥ 30 µm) in UO 2 fuel, purportedly enhancing fission gas retention during reactor operation. Fuel performance is significantly impacted by fuel fracture behavior, so it is important to understand the effects of enhanced grain size and dopant content on UO 2 fuel fracture. UO 2 pellets were doped with 0.1 wt% TiO 2 and 0.3 wt% Cr 2 O 3 to alter density and grain size. Inductively coupled plasma mass spectroscopy measured dopant levels pre- and post-sintering. X-ray diffraction revealed lattice changes and microstrain via Rietveld refinement. Field emission scanning electron microscopy determined grain sizes of approximately 30 µm for TiO 2 doping and 7 µm for Cr 2 O 3 doping. Transverse rupture strength tests were performed on over 30 samples per dataset to obtain characteristic strength and Weibull modulus. Results indicate no statistical difference in fracture strength between 0.1 wt% TiO 2 doped UO 2 and undoped UO 2 , while 0.3 wt% Cr 2 O 3 doped UO 2 exhibited a 20% decrease in fracture strength. Doped UO 2 samples also showed reduced Weibull modulus compared to undoped UO 2 , suggesting increased scatter in fracture strength. This study's findings suggest that titanium and chromium oxide doping in UO 2 , regardless of grain size, induce residual stresses, decreasing fracture strength and increasing variability in fracture behavior.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Internal Climate Variability Obscures Future Freezing Rain Changes Despite Global Warming Trend

Abstract Although numerous studies have projected changes in freezing rain under future climate conditions, the internal variability of freezing rain remains poorly quantified. Here, we introduce a framework utilizing a novel machine‐learning algorithm to diagnose freezing rain in reanalysis and climate model simulations. By employing multivariate quantile mapping, we decompose the projected freezing rain trend into contributions from changes in temperature, relative humidity, and precipitation, which helps separate the forced response from internal climate variability. Our finding reveals a notable decrease in freezing rain occurrence in most areas. Despite a substantial temperature increase, internal variability overshadows climate forcing across a large portion of the eastern United States until about 2050. This insight has implications for practitioners, suggesting that the observed freezing rain frequency climatology continues to provide a relevant baseline for decision‐making in the near term. However, longer‐term design and adaptation plans should consider the projected changes in these regions.

Zhuang, Haoyu (Richard) [Department of Atmospheric↗
DOE OSTI2024

Assessment of the errors of high-fidelity two-qubit gates in silicon quantum dots

Achieving high-fidelity entangling operations between qubits consistently is essential for the performance of multi-qubit systems. Solid-state platforms are particularly exposed to errors arising from materials-induced variability between qubits, which leads to performance inconsistencies. Here we study the errors in a spin qubit processor, tying them to their physical origins. We use this knowledge to demonstrate consistent and repeatable operation with above 99% fidelity of two-qubit gates in the technologically important silicon metal-oxide-semiconductor quantum dot platform. Analysis of the physical errors and fidelities in multiple devices over extended periods allows us to ensure that we capture the variation and the most common error types. Physical error sources include the slow nuclear and electrical noise on single qubits and contextual noise that depends on the applied control sequence. Furthermore, we investigate the impact of qubit design, feedback systems and robust gate design to inform the design of future scalable, high-fidelity control strategies. Our results highlight both the capabilities and challenges for the scaling-up of silicon spin-based qubits into full-scale quantum processors.

Quantum dots↗
DOE OSTI2025

Analysis of the SiMPL Method for Density-Based Topology Optimization

We present a rigorous convergence analysis of a new method for density-based topology optimization that provides pointwise bound-preserving design updates and faster convergence than other popular first-order topology optimization methods. Due to its strong bound preservation, the method is exceptionally robust, as demonstrated in numerous examples here and in the companion article [D. Kim et al., Struct. Multidiscip. Optim., 68 (2025), 74]. Furthermore, it is easy to implement with clear structure and analytical expressions for the updates. Our analysis covers two versions of the method, characterized by the employed line search strategies. We consider a modified Armijo backtracking line search and a Bregman backtracking line search. For both line search algorithms, our algorithm delivers a strict monotone decrease in the objective function and further intuitive convergence properties, e.g., strong and pointwise convergence of the density variables on the active sets, norm convergence to zero of the increments, convergence of the Lagrange multipliers, and more. In addition, the numerical experiments demonstrate apparent mesh-independent convergence of the algorithm. Here, we refer to the new algorithm as the SiMPL method (pronounced “simple”), which stands for Sigmoidal Mirror descent with a Projected Latent variable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Porous Interlayers that Getter Surface-Segregating Species for Improved Silver Wetting, Adhesion, and Electrical Contact on Stainless Steel SOFC Components

Here, porous nickel interlayers or porous platinum interlayers were shown to promote the wetting, spreading, and adhesion of silver on alumina-forming ferritic stainless steel (AFFSS) and chromia-forming ferritic stainless steel (CFFSS). These interlayers resulted in dense, crack-free AFFSS|Ag-Ni|CFFSS and AFFSS|Ag-Pt|CFFSS braze joints that, after 300 h in 650 °C air, displayed shear strengths up to 70 MPa similar to, or larger than, those of AFFSS|Ag-CuO|CFFSS or AFFSS|Ag|CFFSS joints subjected to identical treatment. Similarly, after exposure to 25 cycles of (50 switches between) 12 h of 650 °C air and 12 h of 650 °C 4%H 2 –96%N 2 , AFFSS|Ag-Ni|CFFSS and AFFSS|Ag-Pt|CFFSS braze joints displayed shear strengths significantly larger than those of AFFSS|Ag-CuO|CFFSS or AFFSS|Ag|CFFSS joints subjected to identical treatment. In addition, nickel and platinum were found to chemically getter surface-segregating steel constituents, particularly Al from the AFFSS. As a result, Ag-Ni and Ag-Pt electrical contact resistances on AFFSS and CFFSS substrates were several orders of magnitude lower than those of conventional Ag-CuO reactive air brazes or Heraeus C8710 Ag contact pastes. Together, these results suggest that Ag-Pt and especially Ag-Ni may be useful for the fabrication of durable joints, seals, and/or electrical contacts in electrical/electrochemical devices exposed to high temperatures and/or variable oxygen partial pressure environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Demographic drivers of gut microbiome diversity

Abstract The gut microbiome plays a central role in orchestrating metabolic, immune, and neurological functions essential for human health. While extensive research has explored the effects of diseases and pathological conditions on gut microbiome composition, the influence of demographic factors remains underexplored, limiting our understanding of microbiome variations in disease states. This study addresses this gap by investigating the impact of demographic variables, including age, sex, and geography, on gut microbiome diversity in healthy individuals. Using the American Gut Project’s extensive dataset and the QIIME2 bioinformatics pipeline, we conducted a comprehensive analysis of microbial profiles across diverse demographic groups. Our results revealed significant age-related shifts in microbial richness and composition, and geographic location strongly influenced phylogenetic diversity. In contrast, sex exhibited limited impact on microbial diversity within healthy BMI ranges. These findings highlight the critical role of demographic factors in shaping gut microbiome diversity, providing a foundational framework to better contextualize disease-related microbiome variations and advance personalized healthcare approaches.

Biotechnology & Applied Microbiology↗