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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modeling Pb(II) Adsorption on Mineral Surfaces: Bridging Density Functional Theory and Experiment with Thermodynamic Insights

Despite decades of work on aqueous lead (Pb) adsorption on a-Fe2O3 (hematite) and a-Al2O3 (alumina), gaps between measurements and modeling obscure molecular-level understanding. Achieving well-matched geometries between theory and experimental for mineral-water interfaces is a hurdle, as surface functional group type and distribution must be accounted for in determining mechanisms. Additionally, computational methods that can describe the substrate are often not appropriate to capture aqueous effects. Progress requires focusing on well-studied and relevant systems, such as key facets (001),(012), and (110) of hematite and alumina, and ubiquitous contaminants such as aqueous Pb. In the past, bulk-parametrized bond-valence principles were used to rationalize Pb(II) adsorption trends. These approaches can break down at surfaces, where flexible bonding environments and adsorption-induced surface relaxations play a critical role. Here, we adapt and apply a density functional theory (DFT) and thermodynamics framework, integrating DFT-calculated energies with experimental data and electrochemical principles, to predict Pb(II) adsorption. Our model results capture trends across the full set of surfaces and predict that inner-sphere Pb(II) sorption on (001) alumina varies from unfavorable to weakly favorable across a range of pH conditions. This aligns with experiment insights that Pb(II) interacts at that surface through outer-sphere interactions. Extending to Fe(II) adsorption, we demonstrate a coverage-dependent site preference, potentially explaining disorder in overlayers grown by the oxidative adsorption of Fe(II) on hematite (001).

lead contamination↗
DOE OSTI2025

Montane Conifer, Aspen, Meadow, and Sagebrush Metagenome Resolved Genomes and Traits in East River Watershed, Colorado, USA

Climate change is driving vegetation shifts in mountain watersheds, with unknown impacts on biogeochemical cycles. We hypothesize that these shifts will reshape soil microbiomes and associated biogeochemical processes. As a part of Lawrence Berkeley National Laboratory (LBNL) Watershed Science Focus Area (SFA), we assessed microbiome and microbial functional trait differences between soils under conifer, aspen, forby meadows, and sagebrush across the East River Watershed, CO, controlling for elevation and aspect.Here we present metagenome assembled genomes (MAGs) for the bacterial and archaeal communities from soils 0-20cm in depth across three locations in the watershed—Headwaters, Upper Reaches, and Lower Reaches from August 3-11th 2016. Each location was further subdivided into two blocks, with one block on a west facing aspect, and two on the east aspect of the valley. Within blocks, two samples per vegetation type were taken (one at each depth). This resulted in 66 samples, which were sequenced at JGI and can be found under the Joint Genome Institute (JGI) Genomes Online Database (GOLD) sequencing project Gs0118068. Metagenomes were assembled through an inhouse pipeline (see methods), binned using four autobinners (concoct, maxbin2, metabat2, and vamb) and consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>75%) and contamination (<25%), and dereplicated at 95% ANI using drep. The dataset includes a zip file of 687 genomes (Vegtype_MAGS.zip), the accession numbers for the underlying metagenomes, a csv file with MAG quality metrics and taxonomy from Genome Taxonomy Database (GTDB) and National Center for Biotechnology Information (NCBI) taxonomic representative genome proteins (EastRiver_Vegtype_drep_genome_info.csv), and a file containing MAG quality metrics and taxonomy (gtdb_drep_bin_taxonomy.csv). The dataset additionally includes a sample metadata file (EastRiver_Vegtype_sample_metadata.csv), a metadata file used to register associated samples with IGSNs (International Generic Sample Numbers) (samples.csv), a Google KML file for the sampled locations (sample_collection_sites.kml), a location metadata file (locations.csv), a file-level metadata file (flmd.csv), and a data dictionary (dd.csv) file.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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Association of chronic fatigue syndrome with premature telomere attrition

Background: Chronic fatigue syndrome (CFS), also known as myalgic encephalomyelitis (ME), is a severely debilitating condition of unknown etiology. The symptoms and risk factors of ME/CFS share features of accelerated aging implicated in several diseases. Using telomere length as a marker, this study was performed to test the hypothesis that ME/CFS is associated with accelerated aging. Methods: Participant (n = 639) data came from the follow-up time point of the Georgia CFS surveillance study. Using the 1994 CFS Research Case Definition with questionnaire-based subscale thresholds for fatigue, function, and symptoms, participants were classified into four illness groups: CFS if all criteria were met (n = 64), CFS-X if CFS with exclusionary conditions (n = 77), ISF (insufficient symptoms/fatigue) if only some criteria were met regardless of exclusionary conditions (n = 302), and NF (non-fatigued) if no criteria and no exclusionary conditions (n = 196). Relative telomere length (T/S ratio) was measured using DNA from whole blood and real-time PCR. General linear models were used to estimate the association of illness groups or T/S ratio with demographics, biological measures and covariates with significance set at p < 0.05. Results: The mean T/S ratio differed significantly by illness group (p = 0.0017); the T/S ratios in CFS (0.90 ± 0.03) and ISF (0.94 ± 0.02) were each significantly lower than in NF (1.06 ± 0.04). Differences in T/S ratio by illness groups remained significant after adjustment for covariates of age, sex, body mass index, waist–hip ratio, post-exertional malaise and education attainment. Telomere length was shorter by 635, 254 and 424 base pairs in CFS, CFS-X and ISF, respectively, compared to NF. This shorter telomere length translates to roughly 10.1–20.5, 4.0–8.2 and 6.6–13.7 years of additional aging in CFS, CFS-X and ISF compared to NF respectively. Further, stratified analyses based on age and sex demonstrated that the association of ME/CFS with short telomeres is largely moderated by female subjects < 45 years old. Conclusions: This study found a significant association of ME/CFS with premature telomere attrition that is largely moderated by female subjects < 45 years old. Our results indicate that ME/CFS could be included in the list of conditions associated with accelerated aging. Further work is needed to evaluate the functional significance of accelerated aging in ME/CFS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Understanding the Oxygen Evolution Reaction Mechanism on CoO x using Operando Ambient-Pressure X-ray Photoelectron Spectroscopy

Photoelectrochemical water splitting is a promising approach for renewable production of hydrogen from solar energy and requires interfacing advanced water-splitting catalysts with semiconductors. Understanding the mechanism of function of such electrocatalysts at the atomic scale and under realistic working conditions is a challenging, yet important, task for advancing efficient and stable function. This is particularly true for the case of oxygen evolution catalysts and, here, we study a highly active Co 3 O 4 /Co(OH) 2 biphasic electrocatalyst on Si by means of operando ambient-pressure X-ray photoelectron spectroscopy performed at the solid/liquid electrified interface. Spectral simulation and multiplet fitting reveal that the catalyst undergoes chemical-structural transformations as a function of the applied anodic potential, with complete conversion of the Co(OH) 2 and partial conversion of the spinel Co 3 O 4 phases to CoO(OH) under precatalytic electrochemical conditions. Furthermore, we observe new spectral features in both Co 2p and O 1s core-level regions to emerge under oxygen evolution reaction conditions on CoO(OH). The operando photoelectron spectra support assignment of these newly observed features to highly active Co 4+ centers under catalytic conditions. Comparison of these results to those from a pure phase spinel Co 3 O 4 catalyst supports this interpretation and reveals that the presence of Co(OH) 2 enhances catalytic activity by promoting transformations to CoO(OH). The direct investigation of electrified interfaces presented in this work can be extended to different materials under realistic catalytic conditions, thereby providing a powerful tool for mechanism discovery and an enabling capability for catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Molecular Dynamics Modeling of PPTA Crystals in Aramid Fibers

In this work, molecular dynamics modeling is used to study the mechanical properties of PPTA crystallites, which are the fundamental microstructural building blocks of polymer aramid bers such as Kevlar. Particular focus is given to constant strain rate axial loading simulations of PPTA crystallites, which is motivated by the rate-dependent mechanical properties observed in some experiments with aramid bers. In order to accommodate the covalent bond rupture that occurs in loading a crystallite to failure, the reactive bond order force eld ReaxFF is employed to conduct the simulations. Two major topics are addressed: The rst is the general behavior of PPTA crystallites under strain rate loading. Constant strain rate loading simulations of crystalline PPTA reveal that the crystal failure strain increases with increasing strain rate, while the modulus is not a ected by the strain rate. Increasing temperature lowers both the modulus and the failure strain. The simulations also identify the C N bond connecting the aromatic rings as weakest primary bond along the backbone of the PPTA chain. The e ect of chain-end defects on PPTA micromechanics is explored, and it is found that the presence of a chain-end defect transfers load to the adjacent chains in the hydrogen-bonded sheet in which the defect resides, but does not in uence the behavior of any other chains in the crystal. Chain-end defects are found to lower the strength of the crystal when clustered together, inducing bond failure via stress concentrations arising from the load transfer to bonds in adjacent chains near the defect site. The second topic addressed is the nature of primary and secondary bond failure in crystalline PPTA. Failure of both types of bonds is found to be stochastic in nature and driven by thermal uctuations of the bonds within the crystal. A model is proposed which uses reliability theory to model bonds under constant strain rate loading as components with time-dependent failure rate functions. The model is shown to work well for predicting the onset of primary backbone bond failure, as well as the onset of secondary bond failure via chain slippage for the case of isolated non-interacting chain-end defects.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Comparative study of Minnesota functionals performance on ferroelectric BaTiO 3 and PbTiO 3

Density functional theory based simulations are the leading tool for the computational investigation of ferroelectrics and the parametrization of their classical potentials. However, the predictions often depend strongly on the exchange correlation functional. The most popular choices, LDA and GGA, tend to underestimate or overestimate some structural, electric, and energy properties. These impede development of highly accurate classical potentials that extend the reach of first-principles simulations to finite temperatures and realistic sizes. In this work, we investigate the performance of recently developed Minnesota exchange correlation functionals on prototypical ferroelectrics BaTiO 3 , and PbTiO 3 , in comparison with some popular ones. We find that there exists strong correlation between predictions for some properties (tetragonality, phase energy difference, and polarization) by different functionals. Along the correlation line, we find a range of functionals (including some from the Minnesota suite) whose predictions fall in between those of LDA and GGA and, therefore, offer an improvement. A way to relatively rank functional performance with respect to chosen benchmarks is proposed and applied to identify the top performers for BaTiO 3 and PbTiO 3 . Here, the performance is found to be material dependent. Therefore, we propose that the performance assessment carried out in this work is employed to "screen" functionals prior to their use on ferroelectrics. Quick and computationally inexpensive, it is likely to lead to improved descriptions, especially for classical potential parametrization. The Minnesota functional suite is found to be suitable for this task.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Understanding and Improving the Scale Dependence of Trigger Functions for Convective Parameterization Using Cloud-Resolving Model Data

As the resolution of global climate model increases, whether trigger functions in current convective parameterization schemes still work remains unknown. In this study, the scale dependence of undilute and dilute dCAPE, Bechtold, and heated condensation framework (HCF) triggers is evaluated using the cloud-resolving model (CRM) data. It is found that all these trigger functions are scale dependent, especially for dCAPE-type triggers, with skill scores dropping from ~0.6 at the lower resolutions (128, 64, and 32 km) to only ~0.1 at 4 km. The average convection frequency decreases from 14.1% at 128 km to 2.3% at 4 km in the CRM data, but it increases rapidly in the dCAPE-type triggers and is almost unchanged in the Bechtold and HCF triggers across resolutions, all leading to large overpredictions at higher resolutions. In the dCAPE-type triggers, the increased frequency is due to the increased rate of dCAPE greater than the threshold (65 J kg −1 h −1 ) at higher resolutions. The box-and-whisker plots show that the main body of dCAPE in the correct prediction and overprediction can be separated from each other in most resolutions. Moreover, the underprediction is found to be corresponding to the decaying phase of convection. Hence, two modifications are proposed to improve the scale dependence of the undilute dCAPE trigger: 1) increasing the dCAPE threshold and 2) considering convection history, which checks whether there is convection prior to the current time. With these modifications, the skill at 16 km, 8 km, and 4 km can be increased from 0.50, 0.27, and 0.15 to 0.70, 0.61, and 0.53, respectively.

Song, Fengfei↗
DOE OSTI2016

Assembly scaffold NifEN: A structural and functional homolog of the nitrogenase catalytic component

Significance This work provides direct evidence for the formation of an M-cluster on the assembly scaffold NifEN, establishing NifEN as the second known protein that houses a nitrogenase cofactor. A “half-on, half-off” scheme of cofactor biosynthesis can be proposed based on the outcome of this study, which suggests an asymmetric nature of the assembly sites in the seemingly equivalent αβ-halves of NifEN and a coordination of various biosynthetic events via a unique conformational switch on/off mechanism. The comparable substrate-reducing capabilities of NifEN and NifDK establish the former as a structural and functional homolog of the latter, providing a proof-of-concept for the feasibility of probing key catalytic features of NifDK via reconstruction of a NifDK equivalent on the basis of a “simplified” template, NifEN.

Fay, Aaron W.↗
DOE OSTI2016

Theoretical and Experimental Study on the Optoelectronic Properties of Nb 3 O 7 (OH) and Nb 2 O 5 Photoelectrodes

Nb 3 O 7 (OH) and Nb 2 O 5 nanostructures are promising alternative materials to conventionally used oxides, e.g. TiO 2 , in the field of photoelectrodes in dye-sensitized solar cells and photoelectrochemical cells. Despite this important future application, some of their central electronic properties such as the density of states, band gap, and dielectric function are not well understood. In this work, we present combined theoretical and experimental studies on Nb 3 O 7 (OH) and H-Nb 2 O 5 to elucidate their spectroscopic, electronic, and transport properties. The theoretical results were obtained within the framework of density functional theory based on the full potential linearized augmented plane wave method. In particular, we show that the position of the H atom in Nb 3 O 7 (OH) has an important effect on its electronic properties. To verify theoretical predictions, we measured electron energy-loss spectra (EELS) in the low loss region, as well as, the O-K and Nb-M 3 element-specific edges. These results are compared with corresponding theoretical EELS calculations and are discussed in detail. In addition, our calculations of thermoelectric conductivity show that Nb 3 O 7 (OH) has more suitable optoelectronic and transport properties for photochemical application than the calcined H-Nb 2 O 5 phase.

14 SOLAR ENERGY↗
DOE OSTI2016

Electronic control of H + current in a bioprotonic device with Gramicidin A and Alamethicin

In biological systems, intercellular communication is mediated by membrane proteins and ion channels that regulate traffic of ions and small molecules across cell membranes. A bioelectronic device with ion channels that control ionic flow across a supported lipid bilayer (SLB) should therefore be ideal for interfacing with biological systems. Here, we demonstrate a biotic–abiotic bioprotonic device with Pd contacts that regulates proton (H + ) flow across an SLB incorporating the ion channels Gramicidin A (gA) and Alamethicin (ALM). We model the device characteristics using the Goldman–Hodgkin–Katz (GHK) solution to the Nernst–Planck equation for transport across the membrane. We derive the permeability for an SLB integrating gA and ALM and demonstrate pH control as a function of applied voltage and membrane permeability. This work opens the door to integrating more complex H + channels at the Pd contact interface to produce responsive biotic–abiotic devices with increased functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Reconstruction of the interaction term between dark matter and dark energy using SNe Ia

We apply a parametric reconstruction method to a homogeneous, isotropic and spatially flat Friedmann-Robertson-Walker (FRW) cosmological model filled of a fluid of dark energy (DE) with constant equation of state (EOS) parameter interacting with dark matter (DM)\@. The reconstruction method is based on expansions of the general interaction term and the relevant cosmological variables in terms of Chebyshev polynomials which form a complete set orthonormal functions. This interaction term describes an exchange of energy flow between the DE and DM within dark sector. To show how the method works we do the reconstruction of the interaction function expanding it in terms of only the first six Chebyshev polynomials and obtain the best estimation for the coefficients of the expansion assuming three models: (a) a DE equation of the state parameter w = −1 (an interacting cosmological Λ), (b) a DE equation of the state parameter w = constant with a dark matter density parameter fixed, (c) a DE equation of the state parameter w = constant with a free constant dark matter density parameter to be estimated, and using the Union2 SNe Ia data set from ''The Supernova Cosmology Project'' (SCP) composed by 557 type Ia supernovae. In both cases, the preliminary reconstruction shows that in the best scenario there exist the possibility of a crossing of the noninteracting line Q = 0 in the recent past within the 1σ and 2σ errors from positive values at early times to negative values at late times. This means that, in this reconstruction, there is an energy transfer from DE to DM at early times and an energy transfer from DM to DE at late times. We conclude that this fact is an indication of the possible existence of a crossing behavior in a general interaction coupling between dark components.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Frequency-dependent dielectric function of semiconductors with application to physisorption

Furthermore, the dielectric function is one of the most important quantities that describes the electrical and optical properties of solids. Accurate modeling of the frequency-dependent dielectric function has great significance in the study of the long-range van der Waals (vdW) interaction for solids and adsorption. In this work we calculate the frequency-dependent dielectric functions of semiconductors and insulators using the GW method with and without exciton effects, as well as efficient semilocal density functional theory (DFT), and compare these calculations with a model frequency-dependent dielectric function. We find that for semiconductors with moderate band gaps, the model dielectric functions, GW values, and DFT calculations all agree well with each other. However, for insulators with strong exciton effects, the model dielectric functions have a better agreement with accurate GW values than the DFT calculations, particularly in high-frequency region. To understand this, we repeat the DFT calculations with scissors correction, by shifting the DFT Kohn-Sham energy levels to match the experimental band gap. We find that scissors correction only moderately improves the DFT dielectric function in the low-frequency region. Based on the dielectric functions calculated with different methods, we make a comparative study by applying these dielectric functions to calculate the vdW coefficients (C3 and C5) for adsorption of rare-gas atoms on a variety of surfaces. We find that the vdW coefficients obtained with the nearly free electron gas-based model dielectric function agree quite well with those obtained from the GW dielectric function, in particular for adsorption on semiconductors, leading to an overall error of less than 7% for C 3 and 5% for C 5 . This demonstrates the reliability of the model dielectric function for the study of physisorption.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Metallic Fuel Performance Code Requirements for the Versatile Test Reactor Project

Metallic nuclear fuels have been proposed for use in several emerging nuclear reactor designs, and a number of codes have been developed to model these fuels and assess their performance. Qualification of these metallic nuclear fuels will ultimately require monitored irradiation of lead test assemblies, but the use of fuel performance codes can reduce the uncertainty associated with these efforts by quantifying uncertainties and estimating margins to failure ahead of time. In this work, metallic fuel performance code requirements are defined for the Versatile Test Reactor (VTR) project using input from the Experimental Breeder Reactor II functional requirements, operational requirements, and design criteria. This work focuses on the thermomechanical responses and irradiation behaviors of metallic fuel elements that are representative of the proposed VTR driver fuel design concepts. As such, many of the code requirements and physics/modeling discussions in this work are expected to be broadly applicable to metallic fuel applications outside of the VTR project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

FUN-BioCROP model with litter decomposition parameters derived from the LIDET dataset

This repository contains the code and data necessary to run the FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms, and Protection) model with litter decomposition parameters derived from a modified Monte Carlo simulation that used the Long-term Intersite Decomposition Experiment Team dataset. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of Files: FUNBioCROP_LIDET Study.Rmd R code with FUN-BioCROP model that can be run with 10 different sets of parameters for litter decomposition (Baseline, LIDET, or eight other best parameter sets identified in the modified Monte Carlo simulation. CORPSE Functions_Bioenergy_V2.R Code with CORPSE model functions, called by FUNBioCROP_LIDET Study.Rmd Model Input Data: bulk.csv, bulk_till.csv, rhizo.csv, rhizo_till.csv, litter.csv Initial C and N (kg C or N/m2) pool values for each soil compartment, final values from spin up. All five files have the same columns: (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 Model Input Data: FluxTower_AvgSoilT.csv: Average daily soil temperature (oC) at 10 cm depth at University of Illinois Urbana-Champaign (UIUC) Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: FluxTower_AvgSoilVWC.csv: Average daily soil volumetric water content (VWC) at 10 cm depth at UIUC Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: input_CCS_LIDET Study.csv: This file has daily data to run FUN-BioCROP (Column - Description - Units): yr - calendar year - year doy - day of year (1 to 365) (no leap year) - day anpp - aboveground NPP (DayCent) - kg C/m2/day bnpp - belowground NPP (DayCent) - kg C/m2/day aglivc - live aboveground biomass carbon (DayCent) - kg C/m2 bglivcj - live juvenile fine root biomass carbon (DayCent) - kg C/m2 bglivcm - live mature fine root biomass carbon (DayCent) - kg C/m2 aglivn - live aboveground biomass nitrogen (DayCent) - kg N/m2 bglivnj - live juvenile fine root biomass nitrogen (DayCent) - kg N/m2 bglivnm - live mature fine root biomass nitrogen (DayCent) - kg N/m2 nyr - simulation year - year cult - indicates a cultivation event (0 or 1) crop - indicates a new crop (0 or 1) fert - indicates a fertilizer event (0 or 1) frst - indicates the first day of the growing season (0 or 1) harv - indicates a harvest event (0 or 1) last - indicates the end of the growing season (0 or 1) croptype - crop type (0=none; 1=alfalfa; 2=corn; 3=grass clover pasture; 4=soybean; 5=wheat) cropsrl - crop specific root length - mm/g root cultrhizmix - fraction of rhizosphere mixed with bulk soil during cultivation (0.0-1.0) - fraction cultlitmix - fraction of litter mixed with bulk soil during cultivation (0.0-1.0) - fraction harvremov - fraction of above ground biomass removed during harvest (0.0-1.0) - fraction fertamt - fertilization amount - g N/m2 lifehist - plant life history (0 = annual, 1 = perennial) froot_turnover_c - amount of C in fine root turnover - kg C/m2 froot_turnover_n - amount of N in fine root turnover - kg N/m2 agrd_turnover_c - amount of C in aboveground biomass turnover - kg C/m2 agrd_turnover_n - amount of N in aboveground biomass turnover - kg N/m2 leaf_litter_fastfrac - Fast decomposing fraction of leaf litter (0.0-1.0) - fraction root_litter_fastfrac - Fast decomposing fraction of root litter (0.0-1.0) - fraction root_diameter - root diameter - mm root_length - root length - mm root/m2 rhizo_frac - fraction of total soil volume that is rhizosphere (0.0 - 1.0) - fraction date - date in format YYYY-MM-DD Instructions: Save the model code ("FUN-BioCROP_LIDET Study.Rmd") and accompanything files (data streams and CORPSE function code) in the same folder. In model code "Chunk 3: Load CORPSE Data Streams" set the working directory (setwd) to the folder with the files saved in step #1. In "Chunk 5: Define LIDET parameter sets" select the litter decomposition parameter set to be used in the run, and comment out all other sets. If changing any parameter values, edit them in "Chunk 6: Load parameters." Run all chunks up to and including "Chunk 10: Prepare Data for Export." In "Chunk 11: Export Output Data" edit data frames for export and filenames, as necessary. "Chunk 12: Graph Total Soil C" makes a figure of C remaining over the model run period. Description of each model chunk (in file FUN-BioCROP_LIDET Study.Rmd): Chunk 1: Remove all functions, clear memory. Removes all functions from R environment, clears the memory. Chunk 2: Load Packages. Loads packages necessary to run the code. Chunk 3: Load CORPSE Data Streams. Sets the working directory and loads the data files necessary to run CORPSE. Chunk 4: Load CORPSE Functions. Loads the R script with CORPSE functions from the working directory, "CORPSE Functions_Bioenergy_V2.R". Chunk 5: Define LIDET parameter sets. Has ten different parameter sets for litter decomposition tested in this study: Baseline parameters, LIDET parameters, and the other 8 best performing parameter sets identified in the modified Monte Carlo. To run the model, all but one parameter set must be commented out. Chunk 6: Load Parameters. Loads all fixed parameters to run the model. Data frame with definitions of parameters is in the CORPSE function script "CORPSE Functions_Bioenergy_V2.R" Chunk 7: Prepare Data Streams. Takes data streams loaded in Chunk 3 and puts them in the format necessary to run the model. The model is coded to run at least two sites at a time, so if only one site is being run it must be run in duplicate. Individual data tables of daily values are created in this chunk from the input data file. Chunk 8: Set Initial Conditions. Creates data tables of soil C and N pools for each soil compartment (rhizo_till, rhizo, bulk_till, bulk, litter) and loads initial values into the data tables. Creates lists for each soil compartment to hold model output. Chunk 9: Load FUN Data and Set Up Matrices. Uses DayCent data to calculate FUN input data: root and leaf N demand, total N demand, plant CN, leaf N available for retranslocation, and litter production. Creates matrices for FUN model outputs. Chunk 10: Run Model. Runs the model. Chunk 11: Prepare Data for Export. Combines data from each day saved as lists into data frames for each soil compartment. Adds values from all soil compartments together to calculate total soil values, creates separate data frames for each soil C and N pool (e.g., protected slow C) for the total soil value. Adds different C and N pools together to calculate total soil C and N for all layers. Creates data frame of ratio of protected to unprotected SOC. Organizes FUN data for export. Chunk 12: Export Results. Exports CSV files of model results to the working directory. Chunk 13: Graph Total Soil C. Makes figure of C remaining over time. Related Links: Original FUN-BioCROP model: https://github.com/BrzostekEcologyLab/FUN-BioCROP LIDET dataset: https://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023

Juice, Stephanie↗
DOE OSTI2024

Multiple Functional Bonds Integrated Interphases for Long Cycle Sodium-Ion Batteries

Sodium-ion batteries (SIBs) have garnered significant interest as one of the most promising energy suppliers for power grid energy storage. However, the poor electrode/electrolyte interfacial stability leads to continual electrolyte decomposition and transition metal dissolution, resulting in rapid performance degradation of SIBs. In this work, we propose a strategy integrating multiple functional bonds to regulate electrode/electrolyte interphase by triple-coupling of succinonitrile (SN), sodium hexafluorophosphate (NaPF 6 ) and fluorinated ethylene carbonate (FEC). Theoretical calculation and experiment results show that the solvation structure of Na + and ClO 4 – is effectively reconfigured by the solvated FEC, SN and PF 6 – in PC-based carbonate electrolyte. The newly developed electrolyte demonstrates increased Na + -FEC coordination, weakened interaction of Na + -PC and participation of SN and PF 6 – anions in solvation, resulting in the formation of a conformal interfacial layer comprising of sodium oxynitrides (NaN x O y ), sodium fluoride (NaF) and phosphorus oxide compounds (NaP x O y ). Consequently, a 3 Ah pouch full cell of hard carbon//NaNi 1/3 Fe 1/3 Mn 1/3 O 2 exhibits an excellent capacity retention of 90.4 % after 1000 cycles. Detailed postmortem analysis of interface chemistry is further illustrated by multiple characterization methods. Finally, this study provides a new avenue for developing electrolyte formulations with multiple functional bonds integrated interphases to significantly improve the long-term cycling stability of SIBs.

25 ENERGY STORAGE↗
DOE OSTI2026

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. 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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Atomistic modeling of out-of-pile xenon diffusion by vacancy clusters in UO 2

We report that diffusion of Xe atoms in UO 2 fuel is important for nuclear fuel performance, and is enabled by interaction with U and O vacancies. Previous work using atomistic calculations based on density functional theory (DFT) and empirical potentials (EP) focused on the role of small vacancy clusters (Xe U2Oy , y = 0, 1, 2) for Xe transport, but the model predictions failed to reproduce the experimental observations by consistently underestimating Xe diffusivity. In this work, where we focused on out-of-pile conditions (i.e. in the absence of irradiation), we have explored two of the uncertainties associated with the model: the DFT methodology and the types of clusters considered. Additionally, we found that using the energy barriers obtained with GGA + U DFT allows to reconcile simulation results and experimental observations in the intrinsic regime (i.e. out-of-pile conditions). The Xe UO cluster is the preferred configuration at high temperature, while the mobile Xe U2O cluster takes over below 1720 K. The latter cluster is also the main contributor to Xe diffusion and good agreement is obtained with current models empirically fitted to experiments for diffusion at high temperature. A simple expression that captures most of Xe diffusivity is proposed, relying on Xe UO and Xe U2O clusters only. We also determined the out-of-pile concentration and diffusivity of extended clusters Xe U z O y , 3 ≤ x ≤ 9, and determined that these do not contribute significantly to the overall diffusivity under intrinsic conditions. However, we speculate that the Xe U 4 O 3 and Xe U 8 O 9 clusters, which have relatively low migration barrier, may play a role under irradiation conditions, because the radiation-induced enhanced vacancy concentration would rapidly increase the fraction of large Xe-vacancy clusters.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Comparison between Gaussian-type orbitals and plane wave ab initio density functional theory modeling of layer silicates: Talc [Mg{sub 3}Si{sub 4}O{sub 10}(OH){sub 2}] as model system

The quantum chemical characterization of solid state systems is conducted with many different approaches, among which the adoption of periodic boundary conditions to deal with three-dimensional infinite condensed systems. This method, coupled to the Density Functional Theory (DFT), has been proved successful in simulating a huge variety of solids. Only in relatively recent years this ab initio quantum-mechanic approach has been used for the investigation of layer silicate structures and minerals. In the present work, a systematic comparison of different DFT functionals (GGA-PBEsol and hybrid B3LYP) and basis sets (plane waves and all-electron Gaussian-type orbitals) on the geometry, energy, and phonon properties of a model layer silicate, talc [Mg{sub 3}Si{sub 4}O{sub 10}(OH){sub 2}], is presented. Long range dispersion is taken into account by DFT+D method. Results are in agreement with experimental data reported in literature, with minimal deviation given by the GTO/B3LYP-D* method regarding both axial lattice parameters and interaction energy and by PW/PBE-D for the unit-cell volume and angular values. All the considered methods adequately describe the experimental talc infrared spectrum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗