Experimental Design of Work Chunking for Graph Algorithms on High Bandwidth Memory Architectures.
Abstract not provided.
Slota, George↗
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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↗
Chemistry imaging and distribution analysis of rare earth elements in coal using LIBS and LA-ICP-MS instruments
Currently, demand for rare earth elements (REEs) increased significantly. Coal is actively evaluated as potential economic sources for extraction of REEs. Here, in this work, laser-induced breakdown spectroscopy (LIBS) was evaluated for rapid estimation of REEs content and their distribution in the natural coal samples. The results were compared with similar laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) measurements. Thirteen coal samples (nine standard samples and five natural samples) were used in this study. Powder samples were pressed into pellets while coal chunks were directly ablated for data recording. Pellets of the powder standard samples were used to optimize the data acquisition system and then data recorded with this optimized system was used to identify the proper data acquisition and analysis models. After establishing the proper data acquisition system and analysis model using the standard samples, natural coal samples in powder form and their chunks were utilized to record LIBS and LA-ICP-MS spectra. Multivariate calibration models were developed using four of the natural samples, which were evaluated by predicting the REE content in the fifth sample. Principal component analysis was performed on the LIBS data obtained from the natural samples and it classified all the samples with high accuracy. Two-dimensional (2D) elemental mapping on coal chunk samples was also performed using both LIBS and LA-ICP-MS to study the distribution of REEs in the samples. The resulting elemental images and their correlations can be used to infer mineral distributions.
01 COAL, LIGNITE, AND PEAT↗
A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures
A challenge of atomistic machine-learning (ML) methods is ensuring that the training data are suitable for the system being simulated, which is particularly challenging for systems with large numbers of atoms. Most atomistic ML approaches rely on the nearsightedness principle (“all chemistry is local”), using information about the position of an atom’s neighbors to predict a per-atom energy. Here, in this work, we develop a framework that exploits the nearsighted nature of ML models to systematically produce an appropriate training set for large structures. We use a per-atom uncertainty estimate to identify the most uncertain atoms and extract chunks centered around these atoms. It is crucial that these small chunks are both large enough to satisfy the ML’s nearsighted principle (that is, filling the cutoff radius) and are large enough to be converged with respect to the electronic structure calculation. We present data indicating when the electronic structure calculations are converged with respect to the structure size, which fundamentally limits the accuracy of any nearsighted ML calculator. These new atomic chunks are calculated in electronic structures, and crucially, only a single force—that of the central atom—is added to the growing training set, preventing the noisy and irrelevant information from the piece’s boundary from interfering with ML training. The resulting ML potentials are robust, despite requiring single-point calculations on only small reference structures and never seeing large training structures. We demonstrated our approach via structure optimization of a 260-atom structure and extended the approach to clusters with up to 1415 atoms.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Study on gas sensing mechanism in p-CuO/n-ZnO heterojunction sensor
Highlights: • Gas adsorption sites can be enhanced by forming ZnO:CuO heterojunction. • Ethanol can access all the surface of sensing materials due to chunk-shaped morphology. • Energy band diagram model is proposed to explain the gas sensing mechanism. - Abstract: This work describes the need of more reactive sites for gas sensing. The formation of heterojunctions may increase the number of reactive sites. With this aim, ZnO:CuO nanoparticles with heterojunction was synthesized by co-precipitation method. XRD measurements on ZnO and ZnO:CuO (90:10) nanoparticles revealed the hexagonal wurtzite structure of ZnO with the signature of CuO phases in ZnO:CuO nanoparticles. The morphological analysis by SEM and TEM showed that the nanoparticles are arranged in such a way to form distorted rod-like structures, which are stacked side by side to give a chunk-shaped morphology. High resolution TEM and Fourier transform infrared analyses revealed the presence of defects in the sample. ZnO:CuO nanoparticles showed high sensitivity toward ethanol, which is associated with the introduction of large number of reactive sites and the formation of heterojunctions with the addition of CuO. The sensing mechanism behind the sensing materials is also proposed using energy band diagrams.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
TOMOCUPY
ANL REFERENCE SF-22-102 DESCRIPTION: Tomocupy is a Python package and a command-line interface for GPU reconstruction of tomographic/laminographic data in 16-bit and 32-bit precision. It implements an efficient data processing conveyor allowing to overlap all data transfers with computations. First, independent Python threads are started for reading data chunks from the hard disk into a Python data queue and for writing reconstructed chunks from the Python queue to the hard disk. Second, CPU-GPU data transfers are overlapped with GPU computations by using CUDA streams.
NIKITIN, VIKTOR↗
An Open-source Llm Enhanced-tool Specialized In Helping Moose Related Problems And Tasks
MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.
Li, Mengnan [Idaho National Laboratory (INL), Idah↗
DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano
DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models. Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS, and supercritical). For example, an area of partial melt would likely appear as an area of low density, high conductivity, low vp, and high vp/vs. This means that these target anomalies would be given high (4 or 5) index values for the purpose of imaging the heat source. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were produced for the following datasets: - Geologic model - Alteration model - vp/vs - vp - vs - Temperature model - Seismicity (density*magnitude) - Density - Resistivity - Fault distance - Earthquake cutoff depth model
15 GEOTHERMAL ENERGY↗
Community Scale Solar on the Winnebago Reservation (Final Report)
The Winnebago Tribe of Nebraska, through their affiliated economic development entity Ho-Chunk, Inc; their affiliated non-profit Ho-Chunk Community Development Corporation; and project partner Nebraska Renewable Energy Systems conducted a community scale renewable energy project on their reservation in Nebraska. The project installed PV Solar panels on seven tribally owned commercial/retail/office facilities, on one tribally owned senior residential development, and in a ground-mount array associated with a tribal business – the first ground mount installation on our reservation. The project also installed a wind turbine at our tribal college. The project was undertaken as a part of the Tribe’s broader initiative to increase renewable energy production on the reservation, reduce the Tribe’s financial obligations for purchasing energy, and lessen our reliance upon outside sources of energy to meet our basic needs. The project enabled us to formalize and strengthen community collaborations and build our capacity to pursue additional renewable energy opportunities that will enhance our energy sovereignty.
14 SOLAR ENERGY↗
Net Metering Expansion on the Winnebago Tribe of Nebraska Reservation (Final Technical Report)
The Winnebago Tribe of Nebraska, through their affiliated economic development entity Ho-Chunk, Inc; their affiliated non-profit Ho-Chunk Community Development Corporation; and an additional project partner Nebraska Renewable Energy Systems conducted a community scale renewable energy project on their reservation in Nebraska. Over the two-year project period, we installed PV Solar systems on ten tribally owned commercial/retail/office facilities, which included both six roof mounted and four pole mount installations. The project was undertaken as a part of the Tribe’s broader initiative to increase renewable energy production on the reservation, reduce the Tribe’s financial obligations for purchasing energy, and lessen our reliance upon outside sources of energy to meet our basic needs. The project enabled us to formalize and strengthen community collaborations and build our capacity to pursue additional renewable energy opportunities that will enhance our energy sovereignty.
14 SOLAR ENERGY↗
Pythia8 Quark and Gluon Jets (float8 e4m3FN)
A float8 (e4m3FN) quantized version of the quark and gluon jet dataset originally published by Komiske, Metodiev, and Thaler (Zenodo record 3164691). Only the 20-file subset without charm and bottom quark jets is included here. All simulation parameters and jet selection criteria are identical to the original: Pythia 8.226, √s = 14 TeV Quarks from WeakBosonAndParton:qg2gmZq, gluons from WeakBosonAndParton:qqbar2gmZg with the Z decaying to neutrinos FastJet 3.3.0, anti-k_t jets with R = 0.4 p_T^jet ∈ [500, 550] GeV, |y^jet| < 1.7 There are 20 files, each in compressed NumPy format (QG_jets_fp8e4m3fn_0.npz through QG_jets_fp8e4m3fn_19.npz). Each file contains two arrays: X: (100000, M, 4) — 50k quark and 50k gluon jets, randomly sorted, padded to max multiplicity M, with particle features (pt, rapidity, azimuthal angle, pdgid) y: (100000,) — jet labels, gluon = 0, quark = 1 Since NumPy has no native fp8 dtype, X is stored as float32, but the values have been quantized through TensorFlow's float8_e4m3fn type and carry only fp8 precision. The quantization procedure is as follows: a global per-channel scale factor is computed from the absolute maximum value across all 20 chunks (with FP8_MAX = 448.0, the maximum representable value of e4m3FN). Each chunk is then scaled into the fp8 dynamic range, round-tripped through tf.experimental.float8_e4m3fn, and scaled back. This global scaling ensures a consistent quantization grid across the full dataset. The y labels are unchanged. Users should be aware that e4m3FN has limited dynamic range and precision. We recommend verifying this format is appropriate for your application; for a less aggressive reduction see the float16 and float32 versions linked below. If you use this dataset, please cite the original Zenodo record and its associated paper: Komiske, Metodiev, Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165
DiLullo, Nicholas [Brown University] (ORCID:000000↗
BSEC flux towers: CSAT3B and TRH
The data were collected as part of the BSEC project, during the period from June 2025 to May 2026. Directory "broadway" contains data collected on a multi-level flux tower (US-BWf) in the Broadway East neighborhood (1808 North Patterson Park Ave., Baltimore City, MD 21213; LAT: 39o18'40.31'' N; LONG: 76o35'12.43'' W). At each of the four measurement heights (8.5 m, 11.1 m, 13.4 m, 15.9 m), a Campbell Scientific CSAT3B sonic anemometer was operated at 50 Hz to measure virtual temperature (tc) and three velocity components (u: 270 degrees; v: 180 degrees; w: vertical), and a RM Young temperature sensor (model 41382VC) was operated at 1 Hz inside a compact aspirated radiation shield (model 43502) to measure absolute temperature (T) and relative humidity (RH). Inside directory "broadway", directory "netcdf" contains data collected each day in 5-minute chunks that have been converted to NetCDF format (before quality checking), while "4hr" contains data arranged into 4-hour chunks (also in NetCDF format) that have been through basic quality checking steps (treating data points with nonzero diagnostic codes as missing data; fixing six or fewer consecutive missing data points using linear interpolation). Users are recommended to start with data in directory "4hr", while data in directory "netcdf" can be used for reference purposes.
Baltimore↗
Method and system for preparing polycrystalline group III metal nitride
A process of preparing polycrystalline group III nitride chunks comprising the steps of (a) placing a group III metal inside a source chamber; (b) flowing a halogen-containing gas over the group III metal to form a group III metal halide; (c) contacting the group III metal halide with a nitrogen-containing gas in a deposition chamber containing a foil, the foil comprising at least one of Mo, W, Ta, Pd, Pt, Ir, or Re; (d) forming a polycrystalline group III nitride layer on the foil within the deposition chamber; (e) removing the polycrystalline group III nitride layer from the foil; and (f) comminuting the polycrystalline group III nitride layer to form the polycrystalline group III nitride chunks, wherein the removing and the comminuting are performed in any order or simultaneously.
36 MATERIALS SCIENCE↗
Method and system for preparing polycrystalline group III metal nitride
A process of preparing polycrystalline group III nitride chunks comprising the steps of (a) placing a group III metal inside a source chamber; (b) flowing a halogen-containing gas over the group III metal to form a group III metal halide; (c) contacting the group III metal halide with a nitrogen-containing gas in a deposition chamber containing a foil, the foil comprising at least one of Mo, W, Ta, Pd, Pt, Ir, or Re; (d) forming a polycrystalline group III nitride layer on the foil within the deposition chamber; (e) removing the polycrystalline group III nitride layer from the foil; and (f) comminuting the polycrystalline group III nitride layer to form the polycrystalline group III nitride chunks, wherein the removing and the comminuting are performed in any order or simultaneously.
36 MATERIALS SCIENCE↗
Scalable Data Management of the Uintah Simulation Framework for Next-Generation Engineering Problems with Radiation
The need to scale next-generation industrial engineering problems to the largest computational platforms presents unique challenges. This paper focuses on data management related problems faced by the Uintah simulation framework at a production scale of 260K processes. Uintah provides a highly scalable asynchronous many-task runtime system, which in this work is used for the modeling of a 1000 megawatt electric (MWe) ultra-supercritical (USC) coal boiler. At 260K processes, we faced both parallel I/O and visualization related challenges, e.g., the default file-per-process I/O approach of Uintah did not scale on Mira. In this paper we present a simple to implement, restructuring based parallel I/O technique. We impose a restructuring step that alters the distribution of data among processes. The goal is to distribute the dataset such that each process holds a larger chunk of data, which is then written to a file independently. This approach finds a middle ground between two of the most common parallel I/O schemes–file per process I/O and shared file I/O–in terms of both the total number of generated files, and the extent of communication involved during the data aggregation phase. To address scalability issues when visualizing the simulation data, we developed a lightweight renderer using OSPRay, which allows scientists to visualize the data interactively at high quality and make production movies. Finally, this work presents a highly efficient and scalable radiation model based on the sweeping method, which significantly outperforms previous approaches in Uintah, like discrete ordinates. The integrated approach allowed the USC boiler problem to run on 260K CPU cores on Mira.
97 MATHEMATICS AND COMPUTING↗
Preparation of MgCo 2 O 4 /graphite composites as cathode materials for magnesium-ion batteries
Magnesium-ion batteries are fabricated with MgCo 2 O 4 /graphite composites as the cathode material. MgCo 2 O 4 nanoparticles are arranged using a co-precipitation method. A three-dimensional mixing process is utilized to mechanically decorate MgCo 2 O 4 nanoparticles on graphite particles. The MgCo 2 O 4 spinel crystals of size ranging from 20 to 70 nm on micrometer-sized graphite chunks are analyzed by using X-ray diffraction and scanning electron microscopy. The electrochemical properties of the as-prepared composites are well characterized by cyclic voltammetry, charge and discharge cycling, and electrochemical impedance spectroscopy (EIS). Interestingly, the MgCo 2 O 4 /graphite composite with a relatively low proportion of MgCo 2 O 4 , compared with the other as-prepared composites, achieves the highest specific capacity of 180 mAh g –1 at a C rate of 0.05 C. EIS results suggest that the electrical conductivity of the composite material is an increasing function of the graphite proportion. The superior performance of the MgCo 2 O 4 /graphite composite could be ascribed to the decoration of nanosized MgCo 2 O 4 particles as well as to the increased conductivity provided by graphite.
25 ENERGY STORAGE↗
Instantons, colloids and convergence of the 1/N expansion for the homogeneous electron gas
We investigate non-perturbative corrections to the large N expansion of the homogeneous electron gas. These are associated with instanton solutions to the effective action of the plasmon field. We show that, although the large field behavior of that action dominates the quadratic bare Coulomb term, there are no solutions at large field, and consequently none at large density. We argue that solutions would exist at low density if the large N theory had a Wigner crystal (WC) phase. However, we argue that this is not the case. Together with the implied convergence of the large N expansion, this implies that the homogeneous electron gas with N component spins and a Coulomb interaction scaling like 1∕N can only have a WC phase below a curve in the plane of N and density, which asymptotes to zero density at infinite N. We argue that for systems with a semi-classical expansion for order parameter dynamics, and a first order quantum transition between fluid and crystal phases, there are instantons associated with the decays of meta-stable fluid and crystal phases in the appropriate regions of the phase diagram. We argue that the crystal will decay into one or more colloidal or bubble phases (Kivelson and Spivak, 2006) rather than directly into the fluid. It is possible that the bubble phases remain stable all the way to the density where the crystal solution disappears. The bubbles of fluid will expand as the density is raised and gradually convert the system into a fluid filled with chunks of crystal. The transition to a translationally invariant phase is likely to be second order. Unfortunately, the HEG does not have a crystal phase at large N, where these semi-classical ideas could be examined in detail. We suggest that the evidence for negative dielectric function at intermediate densities for N=2 is an indicator of this second order transition. While the infinite N limit does not have a negative dielectric function at any density, it is possible that the closed large N equation for the plasmon two point function, derived in Banks (2018) might capture at least the qualitative features of the second order transition.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Application of the parareal algorithm to simulations of ELMs in ITER plasma
This paper explores the application of the parareal algorithm to simulations of ELMs in ITER plasma. The primary focus of this research is identifying the parameters that lead to optimum performance. Since the plasma dynamics vary extremely fast during an ELM cycle, a straightforward application of the algorithm is not possible and a modification to the standard parareal correction is implemented. The size of the time chunks also have an impact on the performance and needs to be optimized. in conclusion, a computational gain of 7.8 is obtained with 48 processors to illustrate that the parareal algorithm can be successfully applied to ELM plasma.
97 MATHEMATICS AND COMPUTING↗