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DOE OSTI2020

Atomic force microscopy: Emerging illuminated and operando techniques for solar fuel research

Integrated photoelectrochemical devices rely on the synergy between components to efficiently generate sustainable fuels from sunlight. The micro- and/or nanoscale characteristics of the components and their interfaces often control critical processes of the device, such as charge-carrier generation, electron and ion transport, surface potentials, and electrocatalysis. Understanding the spatial properties and structure–property relationships of these components can provide insight into designing scalable and efficient solar fuel components and systems. These processes can be probed ex situ or in situ with nanometer-scale spatial resolution using emerging scanning-probe techniques based on atomic force microscopy (AFM). In this Perspective, we summarize recent developments of AFM-based techniques relevant to solar fuel research. We review recent progress in AFM for (1) steady-state and dynamic light-induced surface photovoltage measurements; (2) nanoelectrical conductive measurements to resolve charge-carrier heterogeneity and junction energetics; (3) operando investigations of morphological changes, as well as surface electrochemical potentials, currents, and photovoltages in liquids. Opportunities for research covered here include: (1) control of ambient conditions for performing AFM measurements; (2) in situ visualization of corrosion and morphological evolution of electrodes; (3) operando AFM techniques to allow nanoscale mapping of local catalytic activities and photo-induced currents and potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Data from: Nitrification is a minor source of nitrous oxide (N2O) in an agricultural landscape and declines with increasing management intensity

The long-term contribution of nitrification to nitrous oxide (N2O) emissions from terrestrial ecosystems is poorly known and thus poorly constrained in biogeochemical models. Here, using Bayesian inference to couple 25 years of in situ N2O flux measurements with site-specific Michaelis-Menten kinetics of nitrification-derived N2O, we test the relative importance of nitrification-derived N2O across six cropped and unmanaged ecosystems along a management intensity gradient in the U.S. Midwest. We found that the maximum potential contribution from nitrification to in situ N2O fluxes was 13-17% in a conventionally fertilized annual cropping system, 27-42% in a low-input cover-cropped annual cropping system, and 52-63% in perennial systems including a late successional deciduous forest. Actual values are likely to be less than 10% of these values because of low N2O yields in cultured nitrifiers (typically 0.04 to 8% of NH3 oxidized) and competing sinks for available NH4+ in situ. Most nitrification-derived N2O was produced by ammonia oxidizing bacteria (AOB) rather than archaea (AOA), who appeared responsible for no more than 30% of nitrification-derived N2O production in all but one ecosystem. Although the proportion of nitrification-derived N2O production was lowest in annual cropping systems, these ecosystems nevertheless produced more nitrification-derived N2O (higher Vmax) than perennial and successional ecosystems. We conclude that nitrification is minor relative to other sources of N2O in all ecosystems examined.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Quantitative chemical assay of nanogram-level particulate matter using aerosol mass spectrometry: characterization of particles collected from uncrewed atmospheric measurement platforms

Abstract. Aerosol generation techniques have expanded the utility of aerosol mass spectrometry (AMS) for offline chemical analysis of airborne particles and droplets. However, standard aerosolization techniques require relatively large liquid volumes (e.g., several milliliters) and high sample masses that limit their utility. Here we report the development and characterization of a micronebulization AMS (MN-AMS) technique that requires as low as 10 µL of sample and can provide the quantification of the nanogram level of organic and inorganic substances via the usage of an isotopically labeled internal standard (34SO42-). Using standard solutions, the detection limits for this technique were determined at 0.19, 0.75, and 2.2 ng for sulfate, nitrate, and organics, respectively. The analytical recoveries for these species are 104 %, 87 %, and 94 %, respectively. This MN-AMS technique was applied successfully to analyze filter and impactor samples collected using miniature particulate matter (PM) samplers deployable on uncrewed atmospheric measurement platforms, such as uncrewed aerial systems (UASs) and tethered balloon systems (TBSs). Chemical composition of PM samples collected from a UAS field campaign conducted at the Department of Energy's (DOE) Southern Great Plains (SGP) observatory was characterized. The offline MN-AMS data compared well with the in situ PM composition measured by a co-located aerosol chemical speciation monitor (ACSM). In addition, the MN-AMS and ion chromatography (IC) agreed well for measurements of sulfate and nitrate concentrations in the PM extracts. This study demonstrates the utility of combining MN-AMS with uncrewed measurement platforms to provide quantitative measurements of ambient PM composition.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Geomorphic mapping and permafrost occurrence on the Koyukuk River floodplain near Huslia, Alaska

Permafrost occurrence measured in-situ and geomorphic maps drawn by hand using high-resolution satellite imagery for the Koyukuk River floodplain near Huslia, Alaska. The dataset was collected to characterize permafrost extent and rates of formation and degradation due to river processes and climate change. Permafrost occurrence and thickness of the active layer was measured using a 1 or 2 m long permafrost probe or through direct observation (coring, digging trench, exposed ice). Measurements were from June 27 - July 8, 2018 and September 26 - October 1, 2022 and are saved as a csv file. Geomorphic maps delineate floodplain landforms, the relative age of floodplain deposits, and prior paths of the river produced by connecting oxbow lakes. Maps are saved as georeferenced shapefiles that can be imported into QGIS or ArcGIS software.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Using electron energy-loss spectroscopy to measure nanoscale electronic and vibrational dynamics in a TEM

Electron energy-loss spectroscopy (EELS) can measure similar information to x-ray, UV–Vis, and IR spectroscopies but with atomic resolution and increased scattering cross-sections. Recent advances in electron monochromators have expanded EELS capabilities from chemical identification to the realms of synchrotron-level core-loss measurements and to low-loss, 10–100 meV excitations, such as phonons, excitons, and valence structures. EELS measurements are easily correlated with electron diffraction and atomic-scale real-space imaging in a transmission electron microscope (TEM) to provide detailed local pictures of quasiparticle and bonding states. This perspective provides an overview of existing high-resolution EELS (HR-EELS) capabilities while also motivating the powerful next step in the field—ultrafast EELS in a TEM. Ultrafast EELS aims to combine atomic-level, element-specific, and correlated temporal measurements to better understand spatially specific excited-state phenomena. Ultrafast EELS measurements also add to the abilities of steady-state HR-EELS by being able to image the electromagnetic field and use electrons to excite photon-forbidden and momentum-specific transitions. We discuss the technical challenges ultrafast HR-EELS currently faces, as well as how integration with in situ and cryo measurements could expand the technique to new systems of interest, especially molecular and biological samples.

Chemistry↗

DOE OSTI2025

Energy scale and resolution for anti-$k_t$ jets with radius parameters $R$ = 0.2 and 0.6 measured in proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

Jets with different radius parameters R are an important tool for probing quantum chromodynamics processes at different angular scales. Jets with small R = 0.2 are instrumental in measurements of the substructure of large-R jets resulting from collimated hadronic decays of energetic W, Z, and Higgs bosons, top quarks, and of potential new resonances. This paper presents measurements of the energy scale, resolution, and associated uncertainties of jets with radius parameters R = 0.2 and 0.6, obtained using the ATLAS detector. The results are based on 37 fb -1 of proton–proton collision data from the Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV. A new in situ method for measuring jet energy scale differences between data and Monte Carlo simulations is presented. The systematic uncertainties in the jet energy scale for central jets $(|\eta | < 1.2)$ typically vary from 1% to about 5% as a function of $|\eta |$ at very low transverse momentum, $p_T$ of around 20 GeV for both R = 0.2 and 0.6 jets. The relative energy resolution ranges from (35 ± 6)% at $p_T$ = 20 GeV to (6 ± 0.5)% at $p_T$ = 300 GeV for central R = 0.2 jets, and is found to be slightly worse for R + 0.6 jets. Finally, the effect of close-by hadronic activity on the jet energy scale is investigated and is found to be well modelled by the ATLAS Monte Carlo simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Calibration of the sub-aperture backscatter system on OMEGA EP

The sub-aperture backscatter (SABS) diagnostic on the OMEGA EP Laser System is a diagnostic that is used to measure the backscattered and sidescattered light during laser–plasma interaction experiments that are relevant to high-energy-density physics and inertial confinement fusion. The diagnostic collects stimulated Brillouin scattering (SBS) UV light at around 351 nm and stimulated Raman scattering (SRS) in the visible-light regime in the 420–720-nm-wavelength range and provides spectrally and temporally resolved information. Five 1-in. light collectors, composed of a lens, ground glass diffuser, and coupling into a 300-μm fiber, are positioned behind the last steering mirror on one of the four beamlines to catch a portion of the beam cross section (~1.5%) of the emission that is scattered into the beamline. The SRS light is collected in two light collectors, combined, and transported via graded index fibers to a streaked spectrometer. The SABS-SRS streak spectrometer has a temporal and spectral resolution of 100 ps and 1 nm, respectively. Three other light collectors collect, combine, and transport the SBS signal to a Hamamatsu high-voltage photodiode, where an oscilloscope digitizes the data, providing a time resolution of better than 1 ns. To obtain an absolute energy calibration of SRS measurements, light signals of known energy and wavelength were injected into the light collectors one at a time. The resulting counts on the streak camera charge-coupled device for SRS are then correlated with the incident fluence of scattered light at the light collector in order to allow a quantitative assessment of streak camera sensitivity to determine the energy of the scattered light during experiments. The measurements were performed in situ from the light collectors to the detectors. Other offline measurements provided the transmission of the optics between the target chamber center and the light collectors.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Antiproton background of the Mu2e experiment and the vertical misalignment of the Mu2e beamline

Mu2e experiment will search for the neutrinoless µ − → e − conversion in the field of an aluminum nucleus. The total expected background in Mu2e Run I is 0.11 ± 0.03 events, and the antiproton background, 0.01 ± 0.003(stat) ± 0.010(syst), is about 10% of the total. The report discusses the antiproton background of the Mu2e experiment. As it will be explained, the antiproton background is sensitive to the vertical misalignment of the Mu2e beamline. Therefore, it is important to measure the misalignment in situ. The report introduces a data-driven technique of measuring the beamline misalignment. The material is organized as follows. Section 2 describes the Mu2e experiment. Section 3 presents the antiproton background. Section 4 explains the alignment in the apparatus. Section 5 presents the vertical misalignment and a method to measure it from the data. Section 6 presents the initial simulations made and the discussion of the results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

SPRUCE Xylem Native Embolism and Leaf Traits of Picea mariana and Larix laricina, 2019

This data set contains measurements of native embolism in branch xylem and associated branch and leaf traits from Picea mariana (Black Spruce) and Larix laricina (Tamarack) from September-October 2019 at the SPRUCE experiment (Hanson et al. 2017). Data are presented in one comma-separated (*.csv) file. Native embolism, a measurement of in-situ embolism in the xylem tissue that blocks water movement, measurements were conducted at the end of the growing season on cut branches using the hydraulic pipette method (see Peters et al. 2023 for full method). In short, branch segments were connected to hydraulic apprentice and flow rates of perfusion liquid were measured using graduated pipettes and stopwatch. After initial conductance measurements, branch segments were flushed using vacuum infiltration and hydraulic conductance was remeasured to calculate the percent loss in conductance due to embolism (PLC). Three branch segments (distal, middle, and proximal) were measured from each branch representing different diameters size classes. This dataset also contains leaf area associated with each measured branch segment, calculate leaf mass per area (LMA), sapwood specific conductivity (Ks), leaf area specific conductivity (Kl) and the sapwood area to leaf area ratio (Huber value). Measurements were made on mature trees from all ten treatment enclosures. One branch from each of five trees per species were used from each enclosure where available. Some plots do not contain five individual Larix laricina, in which case all available trees were sampled. All measurements were conducted in late September/early October 2019, at the end of growing season but before Larix laricina needle senescence. There are 10 experimental plots at SPRUCE: five temperature treatments (+0, +2.25, +4.5, +6.75, +9°C) at ambient CO2, and the same five temperature treatments at elevated CO2 (+500 ppm). These data were collected after the treatments had been running in full for three years, meaning much of the material measured was grown under treatment conditions.

Huber value↗

DOE OSTI2025

Evaluation of Hanford 200 West Area Tank Farms (241-S/241-SX-/241-U tank farms) Physical Properties Data for Use in Development of West Area Tank Treatment (WATT) Processing

With the recent acceptance of West Area Tank Treatment disposition alternative for 200 West Area tanks at the Hanford Site by the State of Washington and the U.S. Department of Energy, a review was initiated to identify the physical properties data available in the literature for the Hanford 241-S, 241-SX, and 241-U tank farms. The literature reviewed indicated that there is a relatively small set of useful data on physical properties of 200 West Area tanks, and the data that do exist are biased around a narrow range of tank samples. Much of the testing between the 1990s and mid-2010s was intended to support either enhanced sludge washing or feed delivery to the Pretreatment Facility at the Hanford Waste Treatment and Immobilization Plant. As such, some physical properties of 200 West Area samples were measured under conditions that are no longer relevant. Because of the distinctly different nature of many past processes at the 200 West Area compared to the 200 East Area, insight from waste testing in the 200 East Area waste should be used with caution, as there may be significantly different qualities in the physical properties data between these two areas (both in situ and as measured in laboratory analyses). Based on this assessment, there is a need to collect additional physical property data to support planning for 200 West Area retrievals.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Characterizing Hygroscopic Films of Polyzwitterions in Electric Fields Using Neutron and X-ray Reflectometries: Electrostriction or Mass Loss?

Herein we study responses of thermally annealed ultrathin films deposited on silicon substrates and containing polyzwitterions to applied electric fields by using specular neutron reflectometry (NR). In particular, we applied 7 kV under vacuum at 150 °C on the films containing poly(1-(3-sulfonatopropyl)-2-vinylpyridinium) (P2VPPS) and its blends with either a deuterated ionic liquid (EMIMBF 4 - d 11 ), potassium bromide (KBr), or deuterated sodium polystyrenesulfonate (NaPSS-d 7 ). The voltage was applied over an air gap, and the in situ neutron reflectivity measurements allowed us to measure changes in the films. In all the cases, we measured decreases in thicknesses of the films, which varied up to ~8% depending on the added salt. Posteriori X-ray reflectivity (XRR) measurements on the same films at room temperature reveal that these films were highly hygroscopic, which led to the presence of water in these films. Analysis of the NR and the XRR revealed that the decrease in the thickness of the films in the neutron reflectivity experiments on heating resulted from the loss of water and the ionic liquid but not from electrostrictive effects. The in situ NR and posteriori XRR experiments revealed not only the hygroscopic nature of these films but also depth-resolved structural rearrangements due to the applied electric fields in the films containing electrolytes and polyelectrolytes. This work shows that a combination of NR and XRR can be used to distinguish between mass loss and electrostriction in films containing charged polymers such as polyzwitterions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Unveiling microstructure-property correlations in nuclear materials with high-energy synchrotron X-ray techniques

Understanding the microstructure-property correlation is critical for the performance evaluation of in-service materials and the development of advanced materials for nuclear reactor applications. Experimental studies are challenging for nuclear materials which are often hazardous. Recent developments in high-energy synchrotron X-ray (HEX) techniques offer the potential to address this challenge, by providing direct observations of internal responses to external stimuli in bulk-like materials, through in situ or 3D measurements. Here, in this review, developments in HEX techniques are introduced and recent applications in nuclear materials are presented. The results offer unprecedented insights into materials performance and provide unique input to computational models.

36 MATERIALS SCIENCE↗

DOE OSTI2025

In situ synchrotron x-ray studies of epitaxial SrCoO x films during ionic liquid gating

The manipulation of ions in complex oxide materials can be used to mimic brain-like plasticity through changes to the resistivity of a neuromorphic device. Advances in the design of more energy efficient devices require improved understanding of how ions migrate within a material and across its interface. We investigate the exchange of oxygen and hydrogen in a model SrCoO x epitaxial film—a material that transitions between a ferromagnetic metal and antiferromagnetic insulator depending on the oxygen concentration. Changes to the film during ionic liquid gating were measured by in situ synchrotron x-ray techniques as a function of time and gate voltage, examining the reversibility of the oxide over one complete gating cycle. We find that the out-of-plane lattice constant and oxygen vacancy concentration of SrCoO x are largely reversible although changes were observed in the ordered vacancy structure. Our results provide much needed insight into electrolyte-gated phase behavior in the transition metal oxides.

Li, Yan [Argonne National Laboratory (ANL), Argonn↗

DOE OSTI2020

Measurement of the gamma ray background in the Davis cavern at the Sanford Underground Research Facility

Deep underground environments are ideal for low background searches due to the attenuation of cosmic rays by passage through the earth. However, they are affected by backgrounds from γ -rays emitted by 40 K and the 238 U and 232 Th decay chains in the surrounding rock. The LUX-ZEPLIN (LZ) experiment will search for dark matter particle interactions with a liquid xenon TPC located within the Davis campus at the Sanford Underground Research Facility, Lead, South Dakota, at the 4850-foot level. In order to characterise the cavern background, in-situ γ -ray measurements were taken with a sodium iodide detector in various locations and with lead shielding. The integral count rates (0–3300 keV) varied from 596 Hz to 1355 Hz for unshielded measurements, corresponding to a total flux from the cavern walls of 1.9 ± 0.4 γ cm −2 s −1 . The resulting activity in the walls of the cavern can be characterised as 220 ± 60 Bq/kg of 40 K, 29 ± 15 Bq/kg of 238 U, and 13 ± 3 Bq/kg of 232 Th.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was 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.

EARTH SCIENCE > LAND SURFACE > SOILS↗

DOE OSTI2022

Cosmogenic nuclide and solute flux data from central Cuban rivers emphasize the importance of both physical and chemical mass loss from tropical landscapes

We use 25 new measurements of in situ produced cosmogenic 26 Al and 10 Be in river sand, paired with estimates of dissolved load flux in river water, to characterize the processes and pace of landscape change in central Cuba. Long-term erosion rates inferred from 10 Be concentrations in quartz extracted from central Cuban river sand range from 3.4–189 Mg km –2 yr –1 (mean 59, median 45). Dissolved loads (10–176 Mg km –2 yr –1 ; mean 92, median 97), calculated from stream solute concentrations and modeled runoff, exceed measured cosmogenic- 10 Be-derived erosion rates in 18 of 23 basins. This disparity mandates that in this environment landscape-scale mass loss is not fully represented by the cosmogenic nuclide measurements. The 26 Al / 10 Be ratios are lower than expected for steadystate exposure or erosion in 16 of 24 samples. Depressed 26 Al/ 10 Be ratios occur in many of the basins that have the greatest disparity between dissolved loads (high) and erosion rates inferred from cosmogenic nuclide concentrations (low). Depressed 26 Al/ 10 Be ratios are consistent with the presence of a deep, mixed, regolith layer providing extended storage times on slopes and/or burial and extended storage during fluvial transport. River water chemical analyses indicate that many basins with lower 26 Al/ 10 Be ratios and high 10 Be concentrations are underlain at least in part by evaporitic rocks that rapidly dissolve. Our data show that when assessing mass loss in humid tropical landscapes, accounting for the contribution of rock dissolution at depth is particularly important. In such warm, wet climates, mineral dissolution can occur many meters below the surface, beyond the penetration depth of most cosmic rays and thus the production of most cosmogenic nuclides. Our data suggest the importance of estimating solute fluxes and measuring paired cosmogenic nuclides to better understand the processes and rates of mass transfer at a basin scale.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was 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.

EARTH SCIENCE > LAND SURFACE > SOILS↗

DOE OSTI2022

Performance of a Novel In-Situ Converted Additive for High Voltage Li-ion Pouch Cells

In search for new classes of additives for high voltage NMC/graphite lithium-ion cells, the precursor additive bis(trimethylsilyl) malonate (bTMSM) is shown to be activated via a spontaneous reaction with LiPF 6 and LiBF 4 salts in carbonate-based electrolyte to form lithium tetrafluoro(malonato)phosphate (LiTFMP), and lithium difluoro(malonato)borate (LiDFMB), respectively. The reaction schemes and rates were studied via NMR spectroscopy and GCMS. The effects of LiTFMP and LiDFMB on high voltage electrochemical performance were then examined up to 4.5 V in Li[Ni 0.4 Mn 0.4 Co 0.16 ]O 2 (NMC442)/graphite and Li[Ni 0.6 Mn 0.4 Co 0.0 ]O 2 (NMC/640)/graphite pouch cells using aggressive voltage-hold cycling, long-term charge/discharge cycling, storage experiments, electrochemical impedance spectroscopy, and gas evolution measurements. While in situ converted additives suffer from gassing issues due to the presence of trimethylfluorosilane (TMSF) gas, a side product of the in situ reaction of bTMSM with LiPF 6 , the cycling and storage capability for the activated additives under study shows competitive performance and controlled impedance when compared to other well-known high voltage additives. Micro X-ray fluorescence spectroscopy (μXRF) confirmed that LiTFMP successfully minimizes the rate of transition metal deposition on the surface of graphite apparently by forming a protective agent at the cathode surface, hence allowing for improved cycling performance at high voltages.

25 ENERGY STORAGE↗