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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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Schematic representation of the robotic beamline scientist system. The architecture integrates a sample database, ROS2 Action service, and Bluesky tooling to facilitate adaptive robotic sample management.
Aims. We investigate the metallicity, age, and orbital anatomy of the inner Milky Way, specifically focussing on the outer bar region. Methods. We integrated a sample of APOGEE DR16 inner Galaxy stars in a state of the art bar-bulge potential with a slow pattern speed and investigated the link between the resulting orbits and their [Fe/H] and ages. By superimposing the orbits, we built density, [Fe/H], and age maps of the inner Milky Way, which we divided further using the orbital parameters eccentricity, |Xmax|, and |Zmax|. Results. We find that at low heights from the Galactic plane, the Galactic bar gradually transitions into a radially thick, vertically thin, elongated inner ring with average solar [Fe/H]. This inner ring is mainly composed of stars with AstroNN ages between 4 and 9 Gyr with a peak in age between 6 and 8 Gyr, making the average age of the ring ~6 Gyr. The vertical thickness of the ring decreases markedly towards younger ages. We also find very large L4 Lagrange orbits that have average solar to super-solar metallicities and intermediate ages. Lastly, we confirm a clear X-shape in the [Fe/H] and density distributions at large Galactic heights. Conclusions. The orbital structure obtained for the APOGEE stars reveals that the Milky Way hosts an inner ring-like structure between the planar bar and corotation. This structure is on average metal rich, intermediately aged, and enhances the horizontal metallicity gradient along the bar’s major axis.
Accurate active galactic nucleus (AGN) identifications in large galaxy samples are crucial for the assessment of the role of AGN and AGN feedback in the co-evolution of galaxies and their central supermassive black holes. Emission-line flux-ratio diagnostics are commonly used to identify AGN in optical spectra. New large samples of integral field unit observations allow exploration of the role of aperture size in the classification process. In this paper, we present galaxy classifications for all 10010 galaxies observed within the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. We use Baldwin-Philips-Terlevich line flux-ratio diagnostics combined with an Hα equivalent threshold in 60 apertures of varying size for the classification, and provide the corresponding catalogs. MaNGA-selected AGN primarily lie below the main sequence of star-forming galaxies, and reside in massive galaxies with stellar masses of ~10 11 M ⊙ and a median Hα-derived star formation rate of ~1.44M ⊙ yr -1 . We find that the number of “fake” AGN increases significantly beyond selection apertures of >1.0 R eff because of increased contamination from diffuse ionized gas (DIG). A comparison with previous works shows that the treatment of the underlying stellar continuum and flux measurements can significantly impact galaxy classification. Our work provides the community with AGN catalogs and galaxy classifications for the full MaNGA survey.
The critical task of inferring anomalous cross-field transport coefficients is addressed in simulations of boundary plasmas with fluid models. A workflow for parameter inference in the UEDGE fluid code is developed using Bayesian optimization with parallelized sampling and integrated uncertainty quantification. In this workflow, transport coefficients are inferred by maximizing their posterior probability distribution, which is generally multidimensional and non-Gaussian. Uncertainty quantification is integrated throughout the optimization within the Bayesian framework that combines diagnostic uncertainties and model limitations. As a concrete example, we infer the anomalous electron thermal diffusivity $\chi_\perp$ from an interpretive 2D model describing electron heat transport in the conduction-limited region with radiative power loss. The workflow is first benchmarked against synthetic data and then tested on H-, L-, and I-mode discharges to match their midplane temperature and divertor heat flux profiles. We demonstrate that the workflow efficiently infers diffusivity and its associated uncertainty, generating 2D profiles that match 1D measurements. Future efforts will focus on incorporating more complicated fluid models and analyzing transport coefficients inferred from a large database of experimental results.
Quantifying changes in soil organic carbon (SOC) stocks and other soil properties is essential for understanding how soils will respond to land management practices and global change. Although they are widely used, comparisons of SOC stocks at fixed depth (FD) intervals are subject to errors when changes in bulk density or soil organic matter occur. The equivalent soil mass (ESM) method has been recommended in lieu of FD for assessing changes in SOC stocks in mineral soils, but ESM remains underutilized for SOC stocks and has rarely been used for other soil properties. In this paper, we draw attention to the limitations of the FD method and demonstrate the advantages of the ESM approach. We provide illustrations to show that the FD approach is susceptible to errors not only for quantifying SOC stocks but also for soil mass-based properties such as SOC mass percent, C:N mass ratio, and δ 13 C. Here we describe the ESM approach and show how it mitigates the FD method limitations. Using bulk density change simulations applied to an empirical dataset from bioenergy cropping systems, we show that the ESM method provides consistently lower errors than FD when quantifying changes in SOC stocks and other soil properties. To simplify the use of ESM, we detail how the method can be integrated into sampling schemes, and we provide an example R computer script that can perform ESM calculations on large datasets. We encourage future studies, whether temporal or comparative, to utilize sampling methods that are amenable to the ESM approach. Overall, we agree with previous recommendations that ESM should be the standard method for evaluating SOC stock changes in mineral soils, but we further suggest that ESM may also be preferred for comparisons of other soil properties including mass percentages, elemental mass ratios, and stable isotope composition.
Indoor air cleaning systems that incorporate CO 2 sorbent materials enable HVAC load shifting and efficiency improvements. This study developed a bench-scale experimental system to evaluate the performance of a sorbent under controlled operation conditions. A thermostatic holder containing 3.15 g sorbent was connected to a manifold that delivered CO 2 -enriched air at a known temperature and relative humidity (RH). The air stream was also enriched with 0.8-2.1 ppm formaldehyde. The CO 2 concentration was monitored in real-time upstream and downstream of the sorbent, and integrated formaldehyde samples were collected at different times using DNPH-coated silica cartridges. Sorbent regeneration was carried out by circulating clean air in countercurrent. Almost 200 loading/regeneration cycles were performed in the span of 17 months, from which 104 were carried out at reference test conditions defined by loading with air at 25°C, 38% RH, and 1000 ppm CO 2 , and regenerating with air at 80°C, 3% RH and 400 ppm CO 2 . The working capacity decreased slightly from 43-44 mg CO 2 per g sorbent to 39-40 mg per g over the 17 months. The capacity increased with lower loading temperature (in the range 15-35°C) and higher regeneration temperature, between 40 and 80°C. The CO 2 capacity was not sensitive to the moisture content in the range 6-9 g/m 3 , and decreased slightly when dry air was used. Loading isothermal breakthrough curves were fitted to three simple adsorption models, verifying that pseudo-first-order kinetics appropriately describes the adsorption process. The model predicted that equilibrium capacities decreased with increasing temperature from 15 to 35°C, while adsorption rate constants slightly increased. The formaldehyde adsorption efficiency was 80%-99% in different cycles, corresponding to an average capacity of 86 ± 36 µg/g. Formaldehyde was not quantitatively released during regeneration, but its accumulation on the sorbent did not affect CO 2 adsorption.
Measurements of $K_S^0$ and Λ 0 production in $t\bar{t}$ final states have been performed. They are based on a data sample with integrated luminosity of 4.6 fb -1 from proton–proton collisions at a centre-of-mass energy of 7 TeV, collected in 2011 with the ATLAS detector at the Large Hadron Collider. Neutral strange particles are separated into three classes, depending on whether they are contained in a jet, with or without a $b$-tag, or not associated with a selected jet. The aim is to look for differences in their main kinematic distributions. A comparison of data with several Monte Carlo simulations using different hadronisation and fragmentation schemes, colour reconnection models and different tunes for the underlying event has been made. The production of neutral strange particles in $t \bar{t}$ dileptonic events is found to be well described by current Monte Carlo models for $K_S^0$ and Λ 0 production within jets, but not for those produced outside jets.
There is a series of Python and bash scripts to parse genomics datasets used to evaluate the coevolution of gene families and the feature importance of gene families using an SVM classifier. - Cover analysis: takes a list of single-copy genes in a set of genomes, aligns and builds the gene trees to determine if two gene families have a signature of covariation with one another. It parses the files to run phykit cover script described here: https://jlsteenwyk.com/PhyKIT/usage/index.html - SVM-classifier: This Python script is an SVM-based genomic classifier designed for biological data analysis. It combines machine learning with feature selection to identify important genomic markers and classify biological samples. Core Functionality: The script uses Support Vector Machines from scikit-learn to classify genomic data, incorporating SelectKBest for automated feature selection and leave-one-out cross-validation for performance assessment. It operates in multiple modes: feature ranking, optimal combination discovery, and sample prediction. Primary Applications: Genomic sample classification and biomarker discovery Feature importance analysis in high-dimensional biological datasets Prediction of sample categories based on genomic profiles Research applications requiring robust classification of biological data Key Advantages: High-dimensional handling: SVMs excel with genomic data's typical high feature-to-sample ratios Integrated feature selection: Reduces noise and computational overhead while identifying key markers Probability estimation: Provides confidence scores essential for biological interpretation Validation robustness: Leave-one-out cross-validation ensures reliable performance metrics Operational flexibility: Multiple analysis modes support different research phases from exploration to prediction
Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. While tools for co-profiling single-cell genomes, transcriptomes, and epigenomes are available, accessing proteomes in parallel is more challenging. We developed nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells using RNA sequencing and mass spectrometry-based proteomics, respectively. nanoSPLITS can precisely quantify over 5000 genes, 2000 proteins, and 140 phosphopeptides per single cell and identify candidate cell markers from these modalities. By exploring Cdk1-mediated cell cycle arrest, we demonstrate how nanoSPLITS single-cell multiomics can provide comprehensive cellular characterization with insights into covarying protein/gene clusters, unique phosphorylation events, and mitotic pathways.
Goals: Realization of advanced predictive models that capture aspects of processing structure-properties-performance (PSPP) in ways that build on scale bridging, data science, and transformational physics insights from experiments; Based on a robust and seamless integration of sample manufacturing, characterization (pre- and post-mortem), theory and simulation, and novel experiments; Ultimately we want to be able to reliably predict performance, and control the functionality of materials in extreme environments.
Bayesian model evidence (BME) is a measure of the average fit of a model to observation data given all the parameter values that the model can assume. By accounting for the trade-off between goodness-of-fit and model complexity, BME is used for model selection and model averaging purposes. For strict Bayesian computation, the theoretically unbiased Monte Carlo based numerical estimators are preferred over semi-analytical solutions. This study examines five BME numerical estimators and asks how accurate estimation of the BME is important for penalizing model complexity. The limiting cases for numerical BME estimators are the prior sampling arithmetic mean estimator (AM) and the posterior sampling harmonic mean (HM) estimator, which are straightforward to implement, yet they result in underestimation and overestimation, respectively. We also consider the path sampling methods of thermodynamic integration (TI) and steppingstone sampling (SS) that sample multiple intermediate distributions that link the prior and the posterior. Although TI and SS are theoretically unbiased estimators, they could have a bias in practice arising from numerical implementation. For example, sampling errors of some intermediate distributions can introduce bias. We propose a variant of SS, namely the multiple one-steppingstone sampling (MOSS) that is less sensitive to sampling errors. We evaluate these five estimators using a groundwater transport model selection problem. SS and MOSS give the least biased BME estimation at an efficient computational cost. If the estimated BME has a bias that covariates with the true BME, this would not be a problem because we are interested in BME ratios and not their absolute values. On the contrary, the results show that BME estimation bias can be a function of model complexity. Thus, biased BME estimation results in inaccurate penalization of more complex models, which changes the model ranking. This was less observed with SS and MOSS as with the three other methods.
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This study integrates quantitative data on personal exposure to polycyclic aromatic hydrocarbons (PAHs) in 162 silicone wristbands with demographics, behavioral information, and housing characteristics to explore contributions to residential exposure in a superfund-adjacent community over the course of a year. Forty-six residents completed questionnaires and wore silicone wristbands as personal passive samplers for seven consecutive days on up to four separate occasions in alternating months between November 2022 and June 2023. It was hypothesized that individual behaviors and housing characteristics are sources of dependence and correlation between personal PAH exposures. 50 PAHs were detected at least once, 17 of which were alkylated PAHs. Exposure to PAHs of similar molecular weight was often correlated, notably between naphthalenes (2-rings) and higher molecular weight PAHs (3 or more rings). Generalized linear mixed models identified flooring type, participant age, and sampling month as important predictors of increased PAH exposure, and flooring type, and use of wood stoves or heavy machinery as predictors of increased naphthalene exposure relative to higher molecular weight PAHs. Individual chemical models based on concentration data and detection frequencies corroborated these findings across multiple PAHs. We demonstrate that personal exposure is not static and the degree of variability in personal exposure is individual. Hence, identification of influential exposure factors through repeated measures of chemical exposure and characterization of variability in personal exposure as performed in this study, is important in the development of exposure mitigation strategies.
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage in LACT is often a dominant source of severe artifacts in the reconstructed images, making it a challenging inverse problem. We present DOLCE, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLCE can form high-quality images from severely under-sampled data by integrating data-consistency updates with the sampling updates of a diffusion model, which is conditioned on the transformed limited-angle data. We show through extensive experimentation on several challenging real LACT datasets that, the same pre-trained DOLCE model achieves the SOTA performance on drastically different types of images. Additionally, we show that, unlike standard LACT reconstruction methods, DOLCE naturally enables the quantification of the reconstruction uncertainty by generating multiple samples consistent with the measured data.
The multi-scale Computed Tomography (CT) and core flow facility in the Geocharacterization Laboratory at NETL, Morgantown yields porosity, permeability, and fracture properties of rock core samples obtained from the subsurface while maintaining the integrity of the sample. Additionally, geophysical bulk rock properties are analyzed with the laboratory’s GeoTEK multi-sensor core logger in a comparable fashion to downhole methods. NETL researchers collaborate with stakeholders within the carbon storage, oil and gas, and critical minerals sectors. Since 2017, over 1.88 miles of core have been analyzed within the laboratory and all data is publicly available through the Technical Report Series (TRS) on the Energy Data eXchange (EDX). Additionally, the website, RokBase, was curated to extrapolate and visualize the high-resolution data from field operations. The characterization of the Lively Grove #1 (LG#1) well provides a case study into the full capabilities of the Geocharacterization Laboratory. During the comprehensive study of LG#1 ~1-2 mm in diameter, vertical to bedding, cylindrical structures were identified throughout the St. Peter Formation. These structures are pervasive throughout the St. Peter Formation at depth and are characterized as the trace fossil, Skolithos.
The biomechanical properties of cells and tissues play an important role in our fundamental understanding of the structures and functions of biological systems at both the cellular and subcellular levels. Recently, Brillouin microscopy, which offers a label-free spectroscopic means of assessing viscoelastic properties in vivo, has emerged as a powerful way to interrogate those properties on a microscopic level in living tissues. However, susceptibility to photodamage and photobleaching, particularly when high-intensity laser beams are used to induce Brillouin scattering, poses a significant challenge. This article introduces a transformative approach designed to mitigate photodamage in biological and biomedical studies, enabling nondestructive, label-free assessments of mechanical properties in live biological samples. By leveraging quantum-light-enhanced stimulated Brillouin scattering (SBS) imaging contrast, the signal-to-noise ratio is significantly elevated, thereby increasing sample viability and extending interrogation times without compromising the integrity of living samples. The tangible impact of this methodology is evidenced by a notable three-fold increase in sample viability observed after subjecting the samples to three hours of continuous squeezed-light illumination, surpassing the traditional coherent light-based approaches. The quantum-enhanced SBS imaging holds promise across diverse fields, such as cancer biology and neuroscience where preserving sample vitality is of paramount significance. By mitigating concerns regarding photodamage and photobleaching associated with high-intensity lasers, this technological breakthrough expands our horizons for exploring the mechanical properties of live biological systems, paving the way for an era of research and clinical applications.
Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics. To streamline cross-sectioning and lamella fabrication, we have implemented fully automated workflows that standardize laser ablation and PFIB milling sequences. These workflows incorporate pre-programmed protocols for material removal, alignment, and thinning, reducing user intervention and ensuring consistency across different sample types. Machine learning algorithms further enhance automation by predicting optimal milling strategies and adapting parameters based on material properties and sectioning requirements. This approach significantly improves throughput while maintaining the structural integrity of prepared samples for high-resolution imaging and analysis, including transmission electron microscopy. Beyond sample preparation, our automation platform enables the acquisition of large, high-resolution datasets through serial sectioning, image alignment, and 3D reconstruction. These automated routines facilitate multi-scale characterization, capturing structural and compositional details from the nanoscale to the device level. By reducing variability and increasing efficiency, our automated approach enhances defect analysis, failure diagnostics, and process optimization, accelerating advancements in materials research and device engineering.
The study by Jung et al. introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to enhance the efficiency of transition path sampling (TPS). By enabling on-the-fly estimation of the committor probability and simultaneously deriving a human-interpretable reaction coordinate, AIMMD offers a robust framework for elucidating the mechanistic pathways of complex molecular processes. Here, this commentary provides a discussion and critical analysis of the core AIMMD framework, explores its recent extensions, and offers an assessment of the method’s potential impact and limitations.