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DOE OSTI2024

Comparative analysis of nutrient concentrations in generalist and specialist tree species and soils, Manaus, Brazil

This dataset was collected near Manaus, Brazil, at ZF-2 site, inside the North-South transect plots from 20221011 to 20221020. Measurements were made on specialists and generalist tree species along topographic gradient (in upland high-clay content soils of plateaus and high sandy content and partially flooded soils of valleys). We selected nine species (with four replicates each, totaling 35 individuals) occurring in different topographic positions: three plateau specialists, three valley specialists, and three generalists, where leaf and trunk samples were collected from each individual, and soil samples for carbon and nutrient analysis and quantification. Three soil pits were opened around each sample tree, about one meter apart (total of 105 soil pits each 60-cm deep), where soil samples were collected at four depths: 0-5, 5-10, 10-30 and 30-50 cm. In each of the three pits around each tree, one single sample was taken at each depth and combined to obtain a composite sample per depth per individual tree (35 trees × 4 depths = 140 soil samples). The files “Plant_Nutrient_Concentrations_NS_Transect_Manaus.csv” and “Soil_Nutrient_Concentrations_NS_Transect_Manaus.csv” contain the nutrient concentration data from plant and soil material, respectively. Additionally, the file “Sample_Info.csv” contains details about each variable including units and data type. The file “Species_Info.csv” includes information about each sampled individual, such as species, family, diameter at the breast height (DBH), and more. The dataset is ready to be used in any programming language like python or R. This dataset was originally published on the NGEE Tropics Archive and is being mirrored on ESS-DIVE for long-term archival Acknowledgement: Funding for NGEE-Tropics data resources was provided by the U.S. Department of Energy Office of Science, Office of Biological and Environmental Research.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

ML-based Micro-CT SOFC Microstructure Models (from Kent 2026 Microstructural Augmentation paper)

Overview -------------------------- This repository contains datasets from the manuscript **"Enhanced Generalizability to Deep-Learning Quantification of 3D Microstructural Characteristics through Microstructurally Aware Augmentation of Scarce Data"** (*William F. Kent, Rochan Bajpai, Rachel C. Kurchin, William K. Epting, Harry W. Abernathy, Paul A. Salvador. Submitted 2026*). The methods are also described in the dissertation **Data Intensive Analysis of Solid Oxide Cell Microstructures** (*Doctoral dissertation, Carnegie Mellon University, 2025*). The datasets here are trained convolutional neural network (CNN) models for predicting key microstructural properties of solid oxide cell (SOC) electrodes from low-res, 2-channel 3D images, as well as some helpful code. The parameters for input images are provided in the paper. Sample data is provided in the file `Combined_anode_aug_dual_1k_examples` - that particular data was used to train `anode_all_aug.pth` and will work most accurately with that model. Please familiarize yourself with all caveats on accuracy and applicability, as detailed in the associated paper. Usage -------------------------- The basic usage is as follows, assuming `model_fn` is the path to the .pth file, and `X` is 2-channel input image(s) of the proper dimensions (either one image of shape `[2,12,24,24]`, or a batch of N input images of shape `[N,2,12,24,24]`): from CNN_inferencer import load_model_for_inference model = load_model_for_inference(model_fn) y_predicted = model(X) The model object automatically handles input scaling and output de-scaling based on the way the models were trained - in other words, pass in a 2-channel micro-CT image, and it will output microstructural property values in real units. ## Other model object attributes Note that model has useful attributes other than its forward pass model(X). * `model.output_descaler` - returns the output descaler object. Model does the de-scaling when generating inferences, but you may want to re-use this de-scaler on other values to e.g. compare predictions to ground truth from already-scaled training data. * `model.prop_names` - Gives the property names of the predicted y values, in order. Only exists if there's an output scaler as part of the model object, which there will be in the models provided here. ## Usage with sample data Here is a short script to use with the included sample data. from CNN_inferencer import display_predictions, load_model_for_inference, calculate_mape, parity_plot import h5py import numpy as np model_fn = 'anode_all_aug.pth' data_fn = 'Combined_anode_aug_dual_1k_examples.h5' N_samples = 200 figure_outdir = '.' model = load_model_for_inference(model_fn) with h5py.File(data_fn,'r') as f: XX = f['X'] #These are the 2-channel 3D images yy = f['y'] #These are the ground-truth microstructural properties, but they have been scaled for training - need to de-scale below N = XX.shape[0] #How many images total in the input data file #Run inferences on N_samples random samples from XX. #Run in a batch, much more efficient than one at a time. ii = np.random.choice(N,N_samples,replace=False) ii.sort() y_pred = model(XX[ii]) #Get the original/true (but normalized/scaled) values from the training dataset... #Because they were normalized, they are not in real units yet. So let's also de-scale them using model.output_scaler. y_true = model.output_scaler.transform(yy[ii]) #Let's display actual values for just 5 random ones for i in np.random.choice(N_samples,5,replace=False): display_predictions(y_true[i], y_pred[i], model.prop_names) #Make parity plots for each property (ground truth vs predicted values) #Also label each plot with the mean abs. percent error (MAPE) of the predicted values for i,key in enumerate(model.prop_names): mape = calculate_mape(y_true[:,i], y_pred[:,i]) parity_plot(y_true[:,i], y_pred[:,i], figure_outdir, key, extra_title=f' ({mape:.2f}% MAPE)')

3D microstructure↗

DOE OSTI2024

Constraining Cross Section and Beam Systematics for Future NOvA Sterile Neutrino Search

he NOvA (NuMI Off-axis $\nu_e$ Appearance) experiment measures neutrino oscillations in a nearly pure muon (anti)neutrino beam over a 810 km baseline. We search for sterile neutrino driven oscillations in both neutral current (NC) and charged current (CC) muon neutrino samples. A deficit of NC interactions at the Far Detector could occur since sterile neutrinos do not couple to the Z boson. A modulation of the muon neutrino disappearance probability is possible due to a new mass splitting. The NuMI beam line is capable of running in forward or reverse horn current modes, which creates a neutrino or antineutrino beam, respectively. Samples collected in forward horn current (FHC) or reverse horn current (RHC) modes are subject to beam optics uncertainties, in addition to cross section and hadron production uncertainties. We explore the ategories. To tackle reduction of cross section uncertainties, we study how splitting the neutral current (NC) samples can impact the systematic reduction and its implications on oscillation parameters, leveraging results from Monte Carlo simulations. Additionally, we look at the possibility of using a $\nu$-on-e scattering sample. The $\nu$-on-e scattering process has minimal cross section uncertainties compared to that of neutrino-nucleus interactions, which can help constrain the overall flux normalizations. Using both the ZHC sample, NC split sample, and $\nu$-on-e scattering sample jointly with FHC samples would reduce the focusing uncertainties and the hadron production uncertainties in the future sterile analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Femto-second Laser’s Enabling New Length Scale Fabrications for Rapid Post Irradiation Examination of Materials: Concluding LDRD Project Poster

Mechanical testing campaigns are required to qualify materials for advanced reactor conditions, yet economical and safety limitations restrict the number of standardized mechanical tests that can be performed. Reducing the size of the sample is one approach to addressing these challenges and to accelerating testing. Previous research has shown that smaller mechanical test samples produce higher yield and ultimate stress values compared to values measured from standard sample sizes: the “smaller is stronger” effect. Specimens used in accelerated material testing campaigns must reflect bulk material performance to enable engineering scale material property measurement. The objective of this research project was to determine if engineering scale mechanical behavior—the yield stress—could be measured with micro-tensile test samples smaller than traditional standard testing geometries. The relationship between yield stress and sample size was explored with two different nuclear-relevant structural materials: Zircaloy-4 and tungsten. Mechanical testing of both metals demonstrated decreasing yield stress values with increasing sample gauge size across three different sizes. Yield stress values from the largest gauge size, fabricated with a femto-second laser ablation system, approach bulk material yield stress values reported in published literature. Preliminary analysis of the tungsten samples indicates the yield stress value depends on the grain characteristics within the gauge section, in addition to the gauge size. Accompanying modeling efforts, including response surface generation and crystal plasticity approaches, further demonstrated that the size of the sample gauge section alone cannot explain the change in yield stress values.

36 MATERIALS SCIENCE↗

DOE OSTI2026

WHONDRS Surface Water and Sediment Geochemistry and Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon (v2)

This dataset supports a broader study developing conceptual models for river corridor critical zone processes across spatial scales and was generated in collaboration with the HJ Andrews River Corridor Critical Zone Workshop in 2025. The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen) from 48 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Some of the sites have been impacted by the Holiday Farm Fire and the Lookout Fire in 2020 and 2023, respectively. Related data were collected as part of the workshop and will be published separately in collaboration with other workshop attendees and available at http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. Related genomic data can be found on the National Center for Biotechnology Information (NCBI) under BioProject PRJNA1503030 (see critical details section below for more information). Additional related data collected in 2016 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3377027 and http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1-2019 (Ward et al., 2019). This data package was originally published in March 2026. It was updated in August 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos, (2) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data, (3) a data checks report, (4) a folder of sample data, (5) file-level metadata, (6) data dictionary, (7) field metadata, (8) readme, (9) international generic sample number (IGSN) mapping file; and (10) field protocol. The sample data subfolder contains surface water and sediment (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages, (2) total dissolved nitrogen data and averages, (3) methods codes, (4) FTICR-MS methods; and (5) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the CoreMS processed data and seven subfolders, thee containing .xml files for each sample type (sediment, surface water and blank samples), three containing the sediment CoreMS output files for each sample type (sediment, surface water and blank samples), and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, .json, .jpg, or .jpeg.

Biogeochemistry↗

DOE OSTI2024

Large-Volume Injection and Assessment of Reference Standards for n -Alkane δD and δ 13 C Analysis via Gas Chromatography Isotope Ratio Mass Spectrometry

Compound-specific stable isotope analysis of hydrogen (δD) and carbon (δ 13 C) in organic compounds is a valuable tool in biogeochemical research. A key limitation of this method is the relatively large amount of sample required to achieve desirable precision. We developed a large-volume (20 μL) injection method that allows for high throughput analysis of less concentrated samples and tested it for δ 13 C and δD measurements of n-alkanes. We also conducted a comparison of reference standards and assessed several methods to normalize and correct n-alkane δD and δ13C measurements. The mean precision of the δD method based on 233 environmental n-alkane samples (two to three replications per sample) is 4.0‰ (1σ, estimated from the weighted mean of the pooled unbiased standard deviations) and 0.46‰ (1σ) for δ 13 C from 37 environmental samples (two to three replications per sample). The evaluation of reference standards shows that the use of n-alkane standards with large offsets in δD values in adjacent n-alkane chains can lead to biases in measurement correction. The large-volume injection method shows good reproducibility of δ 13 C and δD measurements of n-alkanes and reduces the required sample concentration by about 80%. We propose that for δD measurements, a reference standard set should be used in which each reference standard has a limited range of δD values and no adjacent n-alkane chains, to minimize memory effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Temporal relationships among lunar crustal rocks

Temporal relationships among the three most common suites of lunar crustal rocks have been investigated by obtaining new high precision ages on Felsic/Alkali-suite Quartz monzodiorite Clast B from breccia 15405 and Magnesian-suite norite 78235/6/8/55/56 and comparing them to previously dated ferroan anorthosite sample 60025. The weighted average age of 4337.19 ± 0.49 Ma of 15405 Clast B is defined by zircon U-Pb and Pb-Pb ages as well as mineral isochron Sm-Nd and Nd-Nd ages. It is identical to the weighted average age for Apollo 17 norite 78235/6/8/55/56 of 4334.1 ± 3.5 Ma which is defined by Pb-Pb ages measured on baddeleyites in this investigation and less precise Pb-Pb and Sm-Nd ages reported in the literature. Both ages are ∼ 25 Ma younger than the weighted average of Sm-Nd and Pb-Pb ages reported in the literature on ferroan anorthosite 60025 of 4359.3 ± 2.3 Ma. The fact that ages of all three samples are defined by multiple U-Pb, Pb-Pb, Sm-Nd, and 142 Nd- 143 Nd chronometers provide confidence that they record the igneous crystallization history of the samples and do not represent disturbances or mixing lines with no temporal significance. Here, the extent to which these three ages represent broader scale magmatism is difficult to evaluate. Nevertheless, the age defined for 15405 Clast B, 78235/6/8/55/56, and 60025 are contemporaneous with the peak of ages observed in detrital zircons from the Apollo 12, 14, 15, and 17 landing sites (4340 ± 20 Ma), a Mg-suite Sm-Nd whole rock isochron defined by samples from Apollo 14, 15, 16, and 17 landing sites (4348 ± 25 Ma), and a Ferroan Anorthosite-suite Sm-Nd whole rock isochron defined by samples from the Apollo 15 and 16 landing sites (4354 ± 29 Ma). This implies that Ferroan Anorthosite-suite magmatism is temporally distinct and earlier than magmatism associated with the Mg-suite and the Felsic/Alkali-suite, as predicted by the lunar magma ocean model of lunar differentiation. The short 35 ± 10 Ma interval between primary ferroan anorthosite magmatism and secondary magmatism suggests that the lunar crust formed over a limited period of time. Although heat from decay of long-lived isotopes, large impacts, tidal heating associated with interactions between the Earth and Moon, and density driven overturn of the magma ocean have all been invoked to explain production of ancient secondary crustal magmatism, only tidal heating and cumulate overturn are consistent with the apparent short duration of secondary crustal magmatism and the great depth of crystallization implied for some Mg-suite samples. The initial ε 143 Nd values derived from the 15405 Clast B and 78238 Mg-suite norite isochrons, as well as a Mg-suite whole rock isochron are −0.23 ± 0.11, −0.27 ± 0.74, and −0.25 ± 0.09, respectively. They are identical within uncertainty indicating that Mg-suite and Felsic/Alkali-suite magmas were derived from materials that had the same time averaged Sm/Nd ratios since the formation of the solar system. This, combined with the contemporaneous nature of 15405 Clast B and 78235/6/8/55/56 Mg-suite norite, is consistent with evolution of both samples, and likely both magma suites, from a common source through closed system fractional crystallization or partial melting processes.

Chronology↗

DOE OSTI2024

A high-volume resonator for L-band DNP-NMR

DNP-NMR and EPR experiments that operate at or greater than L-band (i.e., ν 0 (e – ) = 1–2 GHz) are typically limited to maximum sample volumes of several hundred µL. These experiments rely on well-known resonator designs for DNP/EPR irradiation such as the loop-gap resonator and Alderman-Grant coil, where their maximum volumes limit further application to imaging experiments and high-throughput screening beyond L-band. Herein, we demonstrate a birdcage (BC) resonator design that can accommodate several mL of sample while operating around 1.5 GHz. The sample volume is maximized by using two identical BC resonators in a stacked configuration. Simulations are used to optimize the BC design and the performance is validated experimentally with liquid-state Overhauser-DNP-NMR experiments. This BC design exploits just the parasitic capacitance of conductive rings and features no fixed tuning capacitors. An enhancement of –77 is achieved on a 10 mM 4-Amino-TEMPO in H 2 O sample for a 5 mL sample volume. Finally, the associated sample heating is minimal due to the low-E-fields generated and the large sample mass with +3.4 K when driving 100 W for several seconds.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Optical vibrational spectroscopic signatures related to U 3 O 8 production processes

Uranium ore concentrates are materials found early within the nuclear fuel cycle and contain high concentrations of uranium in an easily transported form, making the concentrates a likely target for illegal diversion. These concentrates are typically converted to U 3 O 8 for further processing and, therefore, may lose specific physicochemical characteristics in determining the materials’ source and processing history. In this work, we explore the Raman spectra of eight oxide samples produced from various uranium ore concentrates and processing pathways to examine the presence of spectroscopic signatures relating to each sample's process history. Samples produced from amine extraction and dialkylphosphoric acid extraction processes show unique characteristics due to high concentrations of α-UO 3 , whereas samples calcinated from metallic diuranates do not form pure α-U 3 O 8 because of metallic ion inclusions. Pure α-U 3 O 8 oxide samples are obtained through calcination of ammonium diuranate, ammonium uranyl carbonate, and metastudtite intermediates. The Raman spectra of these oxide samples show close agreement with pristine α-U 3 O 8 spectra. However, deviations from the pristine spectra are observed in the 300–460 cm -1 spectral range. In conclusion, these deviations are unique identifying signatures that were likely created by lasting effects from the process history. Spectral center of mass calculations indicate grouping of samples based on processing history.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Improving understanding of NpO 2 and Np 2 O 5 through vibrational spectroscopy

Raman spectra of three NpO 2 samples and two samples produced from a modified direct denitration (MDD) process were collected. The spectral features of the NpO 2 samples were consistent and indicated only NpO 2 . The spectra of the MDD samples indicated the presence of NpO 2 and an additional phase attributed to the neptunium binary oxide Np 2 O 5 . These Raman spectra are the first reported of Np 2 O 5 , and the proportions of these neptunium oxide phases varied within the samples, suggesting significant sample inhomogeneity. Peaks in the Raman spectra of Np 2 O 5 at 569 and 782 cm –1 were tentatively assigned to concerted, symmetric stretches of the neptunyl cations. Finally, laser-induced heating of regions in the MDD samples that were rich in Np 2 O 5 showed spectral features that indicated conversion to NpO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Integrative Multi-PTM Proteomics Reveals Dynamic Global, Redox, Phosphorylation, and Acetylation Regulation in Cytokine-treated Pancreatic Beta Cells

Studying regulation of protein function at a systems level necessitates an understanding of the interplay among diverse post-translational modifications (PTMs). A variety of proteomics sample processing workflows are currently used to study specific PTMs but rarely characterize multiple types of PTMs from the same sample inputs. Method incompatibilities and laborious sample preparation steps complicate large-scale physiological investigations and can lead to variations in results. The single-pot, solid-phase-enhanced sample preparation (SP3) method for sample cleanup is compatible with different lysis buffers and amenable to automation, making it attractive for high-throughput multi-PTM profiling. Herein, we describe an integrative SP3 workflow for multiplexed quantification of protein abundance, cysteine thiol oxidation, phosphorylation, and acetylation. The broad applicability of this approach is demonstrated using cell and tissue samples, and its utility for studying interacting regulatory networks is highlighted in a time-course experiment of cytokine-treated ß-cells. We observed a swift response in global regulation of protein abundances consistent with rapid activation of JAK-STAT and NF-?B signaling pathways. Regulators of these pathways as well as proteins involved in their target processes displayed multi-PTM dynamics indicative of a complex cellular response stages: acute, adaptation, and chronic (prolonged stress). PARP14, a negative regulator of JAK-STAT, had multiple co-localized PTMs that may be involved in intraprotein regulatory crosstalk. Our workflow provides a high-throughput platform that can profile multi-PTMomes from the same sample set, which is valuable in unraveling the functional roles of PTMs and their co-regulation.

proteomics, PTM, automation, SP3, cysteine thiol o↗

DOE OSTI2024

Observation of tantalum deposition and growth on TiB2 and ZrB2 from PISCES-RF deuterium and helium plasma exposures

Deuterium and helium plasma exposures on bulk TiB2 and ZrB2 samples were performed using the PISCES-RF linear plasma device. 40 and 90 eV deuterium ion plasma exposures were performed at 240 and 800 °C sample temperatures, and 80 eV helium ion plasma exposures were performed at 800 °C sample temperatures. Following plasma exposures, it was discovered that two plasma conditions (90 eV deuterium and 80 eV helium at 800 °C) resulted in thick (>200 nm) tantalum-rich (>10 at%) surface features on the targets, presumably from tantalum sourced from a tantalum adapter mask or cap used as part of the target holder. This work aims to characterize these tantalum-rich features and examine the mechanisms of impurity deposition.Plasma-induced surface morphology of the tantalum-rich surface layers depends on plasma properties and target temperature and chemistry. Greater titanium sputtering compared to zirconium resulted in more distinct surface features in the TiB2 samples compared to the ZrB2 samples via increased, prompt deposition onto tantalum surface impurities. There is still uncertainty as to why thick tantalum deposition only occurred under some plasma exposure conditions but not others; it is likely due to tantalum sputtering by a combination of boron molecules from the targets and carbon-impurities in the tantalum mask or targets. Impurity driven surface features are a well-documented phenomena in samples exposed to plasma from linear plasma device facilities—this work confirms the occurrence of this and emphasizes the need for chemistry characterization of isolated post-mortem surface features in plasma-exposed samples.

Nuckols, Lauren↗

DOE OSTI2025

Optical 14 C Tracing for Biological and Pharmaceutical Applications Using Two-Color Cavity Ringdown Spectroscopy

Laser-based 14 C quantitation has been proposed as a more affordable, higher-throughput, table-top alternative to accelerator mass spectrometry (AMS). Here, we demonstrate the feasibility of a mid-IR 14 C detector based on two-color cavity ringdown spectroscopy (2C-CRDS) for low-level 14 C isotope tracing in biological studies. The 2C-CRDS technique quantifies the sample 14 C content by measuring the 14 CO 2 absorption signals from the combusted samples with mid-IR lasers. With 2C-CRDS, we previously demonstrated the most sensitive and accurate optical measurements of 14 CO 2 . The current detection sensitivity and quantitation accuracy of the instrument, at a few parts per quadrillion (where a quadrillion = 10 15 ) 14 C/C mole fraction, is competitive against AMS. Here, by applying the 2C-CRDS 14 C sensor to two applications relevant to 14 C-labeled biochemical analysis and pharmaceutical studies, we demonstrate sub-fCi level (where 1 fCi = 10 –15 Ci) quantitation of sample 14 C activity, with a minimum sample-size requirement of 3 mg of carbon. The current measurement throughput, ~25 min/sample, is largely limited by the sampling efficiency of the online combustion and CO 2 processing interface to the 2C-CRDS instrument. The possibility of a significantly improved measurement throughput of a few minutes per sample is suggested by the results of a flow-through 14 CO 2 sampling scheme. In conclusion, this improved measurement efficiency, combined with the relatively low cost and compact size of a 2C-CRDS sensor, could potentially revolutionize high-sensitivity 14 C tracing in biological, pharmaceutical, and clinical studies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

MHONGOOSE: A MeerKAT nearby galaxy H I survey

The MHONGOOSE (MeerKAT H IObservations of Nearby Galactic Objects: Observing Southern Emitters) survey maps the distribution and kinematics of the neutral atomic hydrogen (H I) gas in and around 30 nearby star-forming spiral and dwarf galaxies to extremely low H Icolumn densities. The H Icolumn density sensitivity (3σover 16 km s −1 ) ranges from ∼5 × 10 17 cm −2 at 90″ resolution to ∼4 × 10 19 cm −2 at the highest resolution of 7″. The H Imass sensitivity (3σover 50 km s −1 ) is ∼5.5 × 10 5 M ⊙ at a distance of 10 Mpc (the median distance of the sample galaxies). The velocity resolution of the data is 1.4 km s −1 . One of the main science goals of the survey is the detection of cold accreting gas in the outskirts of the sample galaxies. The sample was selected to cover a range in H Imasses from 10 7 M ⊙ to almost 10 11 M ⊙ in order to optimally sample possible accretion scenarios and environments. The distance to the sample galaxies ranges from 3 to 23 Mpc. In this paper, we present the sample selection, survey design, and observation and reduction procedures. We compared the integrated H Ifluxes based on the MeerKAT data with those derived from single-dish measurement and find good agreement, indicating that our MeerKAT observations are recovering all flux. We present H Imoment maps of the entire sample based on the first ten percent of the survey data, and find that a comparison of the zeroth- and second-moment values shows a clear separation in the physical properties of the H Ibetween areas with star formation and areas without related to the formation of a cold neutral medium. Finally, we give an overview of the H I-detected companion and satellite galaxies in the 30 fields, five of which have not previously been cataloged. We find a clear relation between the number of companion galaxies and the mass of the main target galaxy.

Astronomy & Astrophysics↗

DOE OSTI2025

TDCOSMO 2025: Cosmological constraints from strong lensing time delays

We present cosmological constraints from eight strongly lensed quasars (hereafter, the TDCOSMO-2025 sample). Building on previous work, our analysis incorporated new deflector stellar velocity dispersions measured from spectra obtained with the James Webb Space Telescope (JWST), the Keck Telescopes, and the Very Large Telescope (VLT), utilizing improved methods. We used integrated JWST stellar kinematics for five lenses, VLT-MUSE for 2, and resolved kinematics from Keck and JWST for RX J1131−1231. We also considered two samples of non-time-delay lenses: 11 from the Sloan Lens ACS (SLACS) sample with Keck-KCWI resolved kinematics; and four from the Strong Lenses in the Legacy Survey (SL2S) sample. We improved our analysis of line-of-sight effects, the surface brightness profile of the lens galaxies, and orbital anisotropy, and corrected for projection effects in the dynamics. Our uncertainties are maximally conservative by accounting for the mass-sheet degeneracy in the deflectors’ mass density profiles. The analysis was blinded to prevent experimenter bias. Our primary result is based on the TDCOSMO-2025 sample, in combination with Ωm constraints from the Pantheon+ Type Ia supernovae (SN) dataset. In the flat Λ cold dark matter (CDM), we find H0 = 71.6+3.9−3.3 km s−1 Mpc−1. The SLACS and SL2S samples are in excellent agreement with the TDCOSMO-2025 sample, improving the precision on H0 in flat ΛCDM to 4.6%. Using the Dark Energy Survey SN Year-5 dataset (DES-SN5YR) or DESI-DR2 baryonic acoustic oscillations (BAO) likelihoods instead of Pantheon+ yields very similar results. We also present constraints in the open ΛCDM, wCDM, w0waCDM, and wϕCDM cosmologies. The TDCOSMO H0 inference is robust and consistent across all presented cosmological models, and our cosmological constraints in them agree with those from the BAO and SN.Key words: cosmological parameters / cosmology: observations / dark energy / distance scale⋆⋆ Brinson Fellow.⋆⋆⋆ NHFP Einstein Fellow.

Birrer, Simon [SUNY, Stony Brook] (ORCID:000000033↗

DOE OSTI2024

NMDC Field Notes Phone Application (NMDC Field Notes) v1.0

This software is for an application, designed for phones and/or tablets, to assist microbiome researchers collecting samples in the field to also collect metadata in a standardized and systematic way. The type of samples covered would be e.g. soil sample from a forest, sediment sample from a lake, or water sample in the ocean. The app allows users to collect sample information and metadata in an easy and intuitive way, and connects to the NMDC submission portal (https://data.microbiomedata.org/submission/home) a website from where users can review the samples they collected and add more information if necessary.

Kalita, Patrick↗

DOE OSTI2025

Fusion Model for Metagenomics

This work highlights the use of an embeddings approach that can encode multiple features and create efficient contextualization of profiled metagenomes derived from microbiome samples using computer vision models and image representations of the abundance profiles. The model's embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples. The model's embeddings can be used to cluster existing samples based on multiple conditions and interpretations, and new embeddings can be quickly created for new samples and fitted to existing clusters to characterize them. This has practical applications for unknown, unlabeled microbiome samples.

Valdes, CamiloA [Lawrence Livermore National Labor↗

DOE OSTI2026

CHESS 2025: Leaf Area Index (LAI) for meadow, shrub, tree, and understory vegetation

This dataset contains Leaf Area Index (LAI) measurements made as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Data were collected in the Upper Gunnison Basin, Colorado, across three study domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). Field observations of LAI were collected within 72 hours of airborne data collection by the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. LAI measurements were collected using the LICOR LAI-2200C Plant Canopy Analyzer following protocols outlined in the instrument manual (LI-COR 2019). Sampling targeted four distinct vegetation types: meadows, shrubs, trees, and aspen forest understory. We have archived data separately by site type because different field methods were used for each. At meadow sites, measurements were made at the four corners of 1m x 1m plots, with the instrument moving inward toward the center of the plot. At shrub sites, we measured the canopies of individual shrubs. At tree sites, we made measurements within a 10m x 10m subplot centered around a focal tree, with 30 observations taken on a regular grid. At aspen understory sites, we measured overstory trees following the tree protocol and understory herbaceous vegetation following the meadow protocol. All measurements included above-canopy (A) and below-canopy (B) readings, with specific protocols for scattering correction measurements in direct-sun conditions. Data were processed using the R package `rlai` (Worsham 2025). This package includes functions to calculate LAI, gap fraction, apparent clumping factor (Ω), scattering correction, and other canopy metrics. Package contents: Full file descriptions appear in ‘flmd.csv’. Files named according to the convention ‘lai_*_summary_data_cleaned.csv’ contain summary values of LAI, apparent clumping factor (Ωapp), and scattering correction factors for each site. These are the analysis-ready products that most data users will work with. Files named ‘lai_*_metadata_cleaned.csv’ contain additional site-level observations made during field collection. We have also archived intermediate and supplementary data for users who wish to check our processing approach or apply alternative methods. ‘raw_lai_2200C.zip’ contains the raw files as read from the LI-COR instrument, with no processing applied, in TXT format. The zip archive contains subdirectories by site type, which are further subdivided by sampling area. Filenames correspond to the sampling site number. ‘intermediate_results.zip’ contains detailed output from the processing routines, in JSON format. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘scattering_correction_logs.zip’ contains logfiles from the implementation of Kobayashi et al.'s (2013) scattering correction algorithm. The logfiles report values of several parameters at each iteration of the algorithm, as the model converges toward a stable solution. They are intended for users who want to verify scattering correction performance. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘spot_checks.csv’ reports LAI and other values for a small number of files processed with LI-COR FV2200 software (LI-COR 2013) using the same control parameters as in our R-based approach. Additional metadata are provided in a data dictionary describing column names and definitions (dd.csv), and in a file-level metadata file (flmd.csv). All zip files can be expanded with common archive utilities. TXT, CSV, and JSON files can be ingested into R or Python computing environments or read in common text editor utilities. Geospatial information: Geospatial data for mapping measurement site locations are in the files CHESS_polygons_lai_UTM.geojson, CHESS_polygons_shrub_UTM.geojson, and CHESS_polygons_meadow_UTM.geojson in the companion geospatial package for the 2025 CHESS campaign, ‘CHESS 2025: Location data for field observations and sampling’ (Henderson et al., 2026). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. * Todorov and Worsham are co–first authors.

2018 NEON and 2025 CHESS Campaigns↗