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VMn is beta Np-derived structured and crystallizes in the orthorhombic Cmmm space group. The structure is three-dimensional. V is bonded in a 8-coordinate geometry to four equivalent V and four equivalent Mn atoms. All V–V bond lengths are 2.53 Å. All V–Mn bond lengths are 2.45 Å. Mn is bonded in a 8-coordinate geometry to four equivalent V and four equivalent Mn atoms. All Mn–Mn bond lengths are 2.53 Å.
MnB2N3H17 is beta Np structured and crystallizes in the orthorhombic Pnma space group. The structure is zero-dimensional and consists of four MnB2N3H17 clusters. Mn2+ is bonded to three N3- and three H+0.76+ atoms to form MnH3N3 pentagonal pyramids that share a cornercorner with one BH4 tetrahedra and an edgeedge with one BH4 tetrahedra. All Mn–N bond lengths are 2.25 Å. There are a spread of Mn–H bond distances ranging from 1.96–2.06 Å. There are two inequivalent B3- sites. In the first B3- site, B3- is bonded to four H+0.76+ atoms to form BH4 tetrahedra that share an edgeedge with one MnH3N3 pentagonal pyramid. There is two shorter (1.22 Å) and two longer (1.25 Å) B–H bond length. In the second B3- site, B3- is bonded to four H+0.76+ atoms to form BH4 tetrahedra that share a cornercorner with one MnH3N3 pentagonal pyramid. There are a spread of B–H bond distances ranging from 1.21–1.26 Å. There are two inequivalent N3- sites. In the first N3- site, N3- is bonded in a distorted trigonal non-coplanar geometry to one Mn2+ and three H+0.76+ atoms. There is two shorter (1.02 Å) and one longer (1.03 Å) N–H bond length. In the second N3- site, N3- is bonded in a distorted trigonal non-coplanar geometry to one Mn2+ and three H+0.76+ atoms. All N–H bond lengths are 1.02 Å. There are eleven inequivalent H+0.76+ sites. In the first H+0.76+ site, H+0.76+ is bonded in a single-bond geometry to one N3- atom. In the second H+0.76+ site, H+0.76+ is bonded in a single-bond geometry to one B3- atom. In the third H+0.76+ site, H+0.76+ is bonded in a single-bond geometry to one N3- atom. In the fourth H+0.76+ site, H+0.76+ is bonded in a water-like geometry to one Mn2+ and one B3- atom. In the fifth H+0.76+ site, H+0.76+ is bonded in a single-bond geometry to one N3- atom. In the sixth H+0.76+ site, H+0.76+ is bonded in a single-bond geometry to one B3- atom. In the seventh H+0.76+ site, H+0.76+ is bonded in a single-bond geometry to one N3- atom. In the eighth H+0.76+ site, H+0.76+ is bonded in a distorted L-shaped geometry to one Mn2+ and one B3- atom. In the ninth H+0.76+ site, H+0.76+ is bonded in a distorted L-shaped geometry to one Mn2+ and one B3- atom. In the tenth H+0.76+ site, H+0.76+ is bonded in a single-bond geometry to one N3- atom. In the eleventh H+0.76+ site, H+0.76+ is bonded in a single-bond geometry to one B3- atom.
RuF6 is beta Np structured and crystallizes in the orthorhombic Pnma space group. The structure is zero-dimensional and consists of four 13693-08-8 molecules. Ru6+ is bonded in an octahedral geometry to six F1- atoms. All Ru–F bond lengths are 1.86 Å. There are four inequivalent F1- sites. In the first F1- site, F1- is bonded in a single-bond geometry to one Ru6+ atom. In the second F1- site, F1- is bonded in a single-bond geometry to one Ru6+ atom. In the third F1- site, F1- is bonded in a single-bond geometry to one Ru6+ atom. In the fourth F1- site, F1- is bonded in a single-bond geometry to one Ru6+ atom.
POBr3 is beta Np structured and crystallizes in the orthorhombic Pna2_1 space group. The structure is zero-dimensional and consists of four phosphorus oxybromide molecules. P5+ is bonded in a distorted tetrahedral geometry to one O2- and three Br1- atoms. The P–O bond length is 1.48 Å. There are two shorter (2.20 Å) and one longer (2.21 Å) P–Br bond lengths. O2- is bonded in a single-bond geometry to one P5+ atom. There are three inequivalent Br1- sites. In the first Br1- site, Br1- is bonded in a single-bond geometry to one P5+ atom. In the second Br1- site, Br1- is bonded in a distorted single-bond geometry to one P5+ atom. In the third Br1- site, Br1- is bonded in a single-bond geometry to one P5+ atom.
ReF6 is beta Np structured and crystallizes in the orthorhombic Pnma space group. The structure is zero-dimensional and consists of four rhenium hexafluoride molecules. Re6+ is bonded in an octahedral geometry to six F1- atoms. All Re–F bond lengths are 1.87 Å. There are four inequivalent F1- sites. In the first F1- site, F1- is bonded in a single-bond geometry to one Re6+ atom. In the second F1- site, F1- is bonded in a single-bond geometry to one Re6+ atom. In the third F1- site, F1- is bonded in a single-bond geometry to one Re6+ atom. In the fourth F1- site, F1- is bonded in a single-bond geometry to one Re6+ atom.
CrPt(NCl)5 is beta Np structured and crystallizes in the orthorhombic Pnma space group. The structure is zero-dimensional and consists of four CrPt(NCl)5 clusters. Cr6+ is bonded in a 2-coordinate geometry to four N+1.40- and one Cl1- atom. There is two shorter (1.61 Å) and two longer (2.29 Å) Cr–N bond length. The Cr–Cl bond length is 2.16 Å. Pt6+ is bonded in a rectangular see-saw-like geometry to four Cl1- atoms. There are two shorter (2.26 Å) and two longer (2.27 Å) Pt–Cl bond lengths. There are three inequivalent N+1.40- sites. In the first N+1.40- site, N+1.40- is bonded in a single-bond geometry to one Cr6+ atom. In the second N+1.40- site, N+1.40- is bonded in a 2-coordinate geometry to one Cr6+ and one N+1.40- atom. The N–N bond length is 1.30 Å. In the third N+1.40- site, N+1.40- is bonded in a 3-coordinate geometry to two equivalent N+1.40- and one Cl1- atom. The N–Cl bond length is 3.15 Å. There are four inequivalent Cl1- sites. In the first Cl1- site, Cl1- is bonded in a distorted single-bond geometry to one Pt6+ atom. In the second Cl1- site, Cl1- is bonded in a single-bond geometry to one Pt6+ atom. In the third Cl1- site, Cl1- is bonded in a single-bond geometry to one Cr6+ atom. In the fourth Cl1- site, Cl1- is bonded in a single-bond geometry to one Pt6+ and one N+1.40- atom.
BP4(SI)3 is beta Np structured and crystallizes in the orthorhombic Pnma space group. The structure is zero-dimensional and consists of four BP4(SI)3 clusters. B3- is bonded in a tetrahedral geometry to one P3+ and three I1- atoms. The B–P bond length is 1.97 Å. There are two shorter (2.23 Å) and one longer (2.25 Å) B–I bond lengths. There are two inequivalent P3+ sites. In the first P3+ site, P3+ is bonded in a tetrahedral geometry to one B3- and three S2- atoms. There are two shorter (2.09 Å) and one longer (2.10 Å) P–S bond lengths. In the second P3+ site, P3+ is bonded in a single-bond geometry to one S2- atom. The P–S bond length is 2.11 Å. There are two inequivalent S2- sites. In the first S2- site, S2- is bonded in a water-like geometry to two P3+ atoms. In the second S2- site, S2- is bonded in a water-like geometry to two P3+ atoms. The S–P bond length is 2.11 Å. There are two inequivalent I1- sites. In the first I1- site, I1- is bonded in a single-bond geometry to one B3- atom. In the second I1- site, I1- is bonded in a single-bond geometry to one B3- atom.
Insufficiencies in charging facilities limit the broad application of electric vehicles (EVs). In addition, EV can hardly represent a green option if its electricity primarily depends on fossil energy. Considering these two problems, this paper studies a battery swapping-charging system based on wind farms (hereinafter referred to as W-BSCS). In a W-BSCS, the wind farms not only supply electricity to the power grid but also cooperate with a centralized charge station (CCS), which can centrally charge EV batteries and then distribute them to multiple battery swapping stations (BSSs). The operational framework of the W-BSCS is analyzed, and some preprocessing technologies are developed to reduce complexity in modeling. Then, a joint optimal scheduling model involving a wind power generation plan, battery swapping demand, battery charging and discharging, and a vehicle routing problem (VRP) is established. Then a heuristic method based on the exhaustive search and the Genetic Algorithm is employed to solve the formulated NP-hard problem. Numerical results verify the effectiveness of the joint optimal scheduling model, and they also show that the W-BSCS has great potential to promote EVs and wind power.
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)')
The Defense Waste Processing Facility (DWPF) began processing Sludge Batch 6 (SB6), also referred to as Macrobatch 7 (MB7), in June 2010. SB6 is a blend of the heel of Tank 40 from Sludge Batch 5 (SB5), H-Canyon Np transfers and SB6 that was transferred to Tank 40 from Tank 51.1 SB6 was processed using Frit 418. Sludge is received into the DWPF Chemical Processing Cell (CPC) and is processed through the Sludge Receipt and Adjustment Tank (SRAT) and Slurry Mix Evaporator Tank (SME). The treated sludge slurry is then transferred to the Melter Feed Tank (MFT) and fed to the melter. During processing of each sludge batch, the DWPF is required to take at least one glass sample to meet the objectives of the Glass Product Control Program (GPCP) and to complete the necessary Production Records so that the final glass product may be disposed of at a Federal Repository. The DWPF requested various analyses of radioactive glass samples obtained from the melter pour stream during processing of SB6 as well as reduction/oxidation (REDOX) analysis of MFT samples to determine the impact of Argon bubbling. Sample analysis followed the Task Technical and Quality Assurance Plan (TTQAP) and an Analytical Study Plan (ASP). Four Pour Stream (PS) glass samples and two MFT slurry samples were delivered to the Savannah River National Laboratory (SRNL) from the DWPF. Table 1-1 lists the sample information for each pour stream glass sample. SB6 PS3 (S03472) was selected as the official pour stream sample for SB6 and full analysis was requested. This report details the visual observations of the as-received SB6 PS No.3 glass sample as well as results for the chemical composition, Product Consistency Test (PCT), radionuclide content, noble metals, and glass density. REDOX results will be provided for all four pour stream samples and vitrified samples of MFT-558 and MFT-568A. Where appropriate, data from other pour stream samples will be provided.
The Radiological Field Lysimeter Experiment (RadFLEx) began in 2012 and is a collaboration between the Savannah River National Laboratory (SRNL) and Clemson University. The purpose of this facility is to study the long-term (2 to 20 years) fate and transport of radionuclides in the Savannah River Site (SRS) vadose zone under natural field and meteorological conditions. The unique facility houses 48 5-L cores filled with sediments amended with various radionuclide sources. The results from these studies have been applied to the development of geochemical models used to assess the risk posed by subsurface nuclear waste disposal, environmental remediation, and waste form development. This report describes the refurbishment of the exterior of the RadFLEx facility and the synthesis and deployment of six plutonium(Pu) sources, four neptunium (Np) sources and one radium (Ra) source. It is anticipated that these newly installed lysimeter cores will be studied for up to 10 years.
A depleted uranium foil and a highly enriched uranium foil that were irradiated at the Washington State University TRIGA reactor were used to measure the relative production of 237 U and 239 U, two key isotopes of interest in nuclear forensics, as well as their decay daughters 237 Np and 239 Pu. Total fissions in the two foils were also estimated using 99 Mo measurement. A reactor model of the TRIGA was built in the SCALE neutron transport code using dimensions obtained from an existing MCNP geometry. The model was successfully used to simulate foil fissions and the reaction rate ratios in 238 U, using the laboratory-measured isotope ratios as a benchmark. The flux distribution in the foils was determined. Finally, using the continuous energy Tsunami-3d module of SCALE, a sensitivity/uncertainty analysis was performed to determine the effects on the reaction ratio caused by other reactions in the reactor and by uncertainties in the known cross-sections.
We are asked to look into the improvements for the measurement of Pu of a variety of material types (MT): MT41 (Pu-242, 20 – 60% enriched), MT42 (Pu-242 > 60% enrichment), and MT-82 (Np-237). Many of these are legacy items that were packaged years ago (Fig 1&2), and when the items are re-packaged for long-term storage, they must be re-measured using either NDA (nondestructive analysis methods or radiochemical methods. It is of interest to improve the NDA and radiochemistry to reduce the un-certainty in the measurements, as in the case of larger items (250 g to 2 kg items) the uncertainty in measurements makes it difficult to certify the mass of the material down to the 0.5 g accountability requirement per DOE orders by only doing NDA measurement. It would be of interest to look into methods to improve the detection of these materials, which would not only benefit the weapon manufacturing community, but also has the ability to significantly improve the non-proliferation community.
The LLNL Heavy-Ion Group has research interests in the features and phenomena of nuclear matter at extreme temperatures and densities. It pursues these interests through participation in two experimental collaborations (ATLAS and sPHENIX) and one joint theory/experiment topical collaboration (JETSCAPE). The group is funded through a combination of LDRD, DOE-SC NP and an award from US ATLAS Software and Computing Operations, which is ultimately funded through HEP. The group's activities in FY20 were oriented around beginning a new LDRD project and fi lling two open postdoc positions while contributing to the ATLAS jet and UPC programs as well as participating in sPHENIX. Both postdoc positions were lled, effectively doubling the side of the LLNL Heavy-Ion Group, by Qipeng Hu (starting 6/20) and Dhanush Hangal (starting 8/20).
Resolving the chemistry and physics of f-electrons is one of the grand challenges of science for energy technology. A central component of this challenge is describing covalency in actinide-ligand interactions. Crystallography, x-ray spectroscopy and computational techniques have provided reasonable, and in some cases remarkable, support for covalency in actinide-ligand interactions. Courtesy the quality of the results obtained, a significant portion of the covalency dialogue has been forwarded through solid-state and computational studies of the more stable actinides (Th, U, Pu, Np). However; recent reports have questioned whether covalency in actinide interactions increases or decreases across the series. Examination of less-stable trans-actinides (Am, Cm, Bk, Cf and Es) by the aforementioned approaches can be rapidly limited due to material availability, radiological hazards and experimental data to validate computational models. Furthermore, limited thermodynamic data exists to confirm covalency in actinide solution phase interactions that are highly relevant to the remediation and reprocessing of used nuclear fuel. The best-defined interactions of actinides with soft donors in aqueous solutions involve (poly)aminopolycarboxylate (APC) ligands. The chelate effect and binding affinity with the acetic acid APCs subgroups encourages amine interactions with the actinide metal center. In the absence of these factors, amine interactions with actinides are too weak to overcome the protective hydration shell of the dissolved ion. The ability for APCs to force actinide interactions with soft nitrogen donors (as defined by Pearson’s Hard Soft Acid Base theory) encourages the application of these ligands in a variety of processes for actinide recovery from nearly chemically identical lanthanides. The ability to functionalize the amine center of the ligand in a variety of capacities (adding additional amine groups, exchanging the conventional acetate group for an acetate group, etcetera), allows for the APC ligand to serve as a thermodynamic probe for actinide-nitrogen interactions. Perhaps the single most significant breakthrough during the previous funding cycle was the observation that covalency for the transplutonium part of the actinide series can be increasingly influenced with energy degeneracy driven covalency as the actinides become heavier. This was observed most predominantly with dipicolinic acid, but extensions of f-orbital degeneracy were found to affect aliphatic aminopolycarboxylate-actinide complexes through einsteinium.
A “screening analysis” is implemented at Pahute Mesa (PM), Nevada National Security Site (NNSS), to determine subsets of the 43 radionuclides listed in the radionuclide inventory of Finnegan et al. (2016) that are of relevance or potential relevance to the hydrologic source term (HST) for assessment of radionuclide transport in groundwater. Consideration is added to how levels of contamination are defined and whether any other radionuclides not in the inventory are relevant to the HST. A model is developed to estimate a range of possible source concentrations in groundwater that account for uncertainty in partitioning into melt glass and sorption into surrounding rock of the exchange zone for the 82 PM underground nuclear tests detonated in vertical shafts. The model also accounts for the varied hydrogeochemical settings. Transport parameters needed for the screening model calculations are developed from databases for hydrogeologic units, chemistry, mineralogy, fracture spacing, fracture aperture, fracture openness, matrix porosity, bulk density, saturation, and alteration. Of key importance is consideration of diagenetic zonation of the mineralogy related to hydrothermal alteration. The screening analysis compares model results with available groundwater radiochemistry data. Consistency between screening model results and available data helps validate the model for application to all 43 radionuclides, most of which have no measurements or only non-detect measurements of concentration in groundwater. The screening analysis determines that ten radionuclides – tritium, Sr-90, I-129, Cs-137, U-232, U-233, U-234, U-238, and the total of Pu-239 and Pu-240 are relevant to the HST. Determination of relevance is primarily based on data and/or model results indicating source concentration exceeding a maximum contaminant level (MCL) out to 100 years since the time of detonation. U-238 is relevant to assessment of contamination relative to MCLs for uranium and gross alpha particle activity. Five other radionuclides – C-14, Cl-36, Tc-99, Np-237, and Pu-238 – are determined potentially relevant to the HST based on a 0.1 MCL threshold. The screening analysis recommends additional attention to the natural daughter radionuclides of the uranium series (U-238) and thorium series (Th-232) decay chains, particularly Ra-226 and Ra-228, that were not included the inventory.
The objectives of the vadose modeling for the updated Hanford Site composite analysis (CA) are to simulate the flow and transport of water and radionuclide releases from the surface to the water table and to provide radionuclide transfer rates for the plateau to river (P2R) model, version 8.3 (CP-57037, Model Package Report: Plateau to River Groundwater Model, Version 8.3). Water additions include natural recharge and water discharged to the ground as a result of industrial processes associated with Hanford Site operations. Contaminant sources include radionuclides in water discharged to the ground during operations and radionuclides disposed “dry” in solid waste burial grounds or other means. The following 16 radionuclides were selected for this modeling effort; carbon-14 (C-14), chlorine-36 (Cl-36), tritium (H-3), iodine-129 (I-129), neptunium-237 (Np-237), rhenium-187 (Re-187), strontium-90 (Sr-90), technetium-99 (Tc-99), uranium-232 (U-232), uranium-233 (U-233), uranium-234 (U-234), uranium-235 (U-235), uranium-236 (U-236), uranium-238 (U-238), radium-226 (Ra-226), and thorium-230 (Th-230). The simulation time starts in 1943 and ends at 12070, which is 10,000 years after assumed Hanford Site closure in 2070. The parallel version of the Subsurface Transport Over Multiple Phases (STOMP1) simulator, officially named the exascale Subsurface Transport Over Multiple Phases (eSTOMP), is used to simulate flow and transport for the vadose models. The documentation for the STOMP code is comprehensive. The theoretical and numerical approaches applied in the STOMP code are documented in a published theory guide (PNNL-12030, STOMP Subsurface Transport Over Multiple Phases Version 2.0 Theory Guide). The code has undergone a rigorous verification procedure against analytical solutions, laboratory-scale experiments, and field-scale demonstrations. The application guide (PNNL-11216, STOMP Subsurface Transport Over Multiple Phases Application Guide) provides instructive examples in the application of the code to classical groundwater problems. The user’s guide (PNNL-15782, STOMP: Subsurface Transport Over Multiple Phases Version 4.0: User’s Guide) describes the general use, input file formatting, compilation, and execution of the code. The primary output of the vadose zone modeling is radionuclide transfer rates to the groundwater for input into the P2R model. The rates will be summed over the 100 by 100 m P2R grid cells that fall within the vadose zone model source domain.
The objectives of the vadose modeling for the updated Hanford Site composite analysis (CA) are to simulate the flow and transport of water and radionuclide releases from the surface to the water table and to provide radionuclide transfer rates for the plateau to river (P2R) model, version 8.3 (CP-57037, Model Package Report: Plateau to River Groundwater Model, Version 8.3). Water additions include natural recharge and water discharged to the ground as a result of industrial processes associated with Hanford Site operations. Contaminant sources include radionuclides in water discharged to the ground during operations and radionuclides disposed “dry” in solid waste burial grounds or other means. The following 16 radionuclides were selected for this modeling effort: carbon-14 (C-14), chlorine-36 (Cl-36), tritium (H-3), iodine-129 (I-129), neptunium-237 (Np-237), rhenium-187 (Re-187), strontium-90 (Sr-90), technetium-99 (Tc-99), uranium-232 (U-232), uranium-233 (U-233), uranium-234 (U-234), uranium-235 (U-235), uranium-236 (U-236), uranium-238 (U-238), radium-226 (Ra-226), and thorium-230 (Th-230). The simulation time starts in 1943 and ends at 12070, which is 10,000 years after assumed Hanford Site closure in 2070. The parallel version of the Subsurface Transport Over Multiple Phases (STOMP1) simulator, officially named the exascale Subsurface Transport Over Multiple Phases (eSTOMP), is used to simulate flow and transport for the vadose models. The documentation for the STOMP code is comprehensive. The theoretical and numerical approaches applied in the STOMP code are documented in a published theory guide (PNNL-12030, STOMP Subsurface Transport Over Multiple Phases Version 2.0 Theory Guide). The code has undergone a rigorous verification procedure against analytical solutions, laboratory-scale experiments, and field-scale demonstrations. The application guide (PNNL-11216, STOMP Subsurface Transport Over Multiple Phases Application Guide) provides instructive examples in the application of the code to classical groundwater problems. The user’s guide (PNNL-15782, STOMP: Subsurface Transport Over Multiple Phases Version 4.0: User’s Guide) describes the general use, input file formatting, compilation, and execution of the code. The primary output of the vadose zone modeling is radionuclide transfer rates to the groundwater for input into the P2R model. The rates will be summed over the 100 by 100 m P2R grid cells that fall within the vadose zone model source domain.