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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
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 OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
DOE OSTI2023

Record-breaking 2020 summer marine heatwaves in the western North Pacific

Record-breaking marine heatwaves (MHWs) occurred in the western North Pacific during the summer of 2020. These unprecedented MHWs were consistent with favorable large-scale conditions that are linked to an anomalous western North Pacific subtropical high (WNPSH), resulting mainly from sea surface temperature (SST) anomalies across the tropical oceans. In addition, a moderate La Niña-like pattern was also conducive to transporting warm seawater to the western North Pacific. Mixed-layer heat budgets suggest that surface heat flux contributed to the SST anomaly in the western subtropical Pacific. In contrast, oceanic heat advection dominanted in the South China Sea and the western equatorial Pacific. Numerical model experiments indicated that the tropical Indian Ocean SST anomalies were responsible for the enhanced WNPSH. The increased zonal SST gradient across the tropical Pacific also played an important role. Inter-ocean interactions can modulate climate variability through ocean-atmospheric coupling and deserve more attention when predicting MHWs within the context of global warming. Additionally, it is critical to consider MHWs as a powerful tool in detecting acute, intense thermal stress events in the coral bleaching pre-warning system.

Coral bleaching↗
DOE OSTI2021

Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk), input rasters of feature sets, and positive/negative training sites. See readme .txt files and final report for additional metadata. A submission linking the full codebase for generating machine learning output models is available under "related resources" on this page.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Role of ferroelectric polarization during growth of highly strained ferroelectric materials

In ferroelectric thin films and superlattices, the polarization is intricately linked to crystal structure. Here we show that it can also play an important role in the growth process, influencing growth rates, relaxation mechanisms, electrical properties and domain structures. This is studied by focusing on the properties of BaTiO 3 thin films grown on very thin layers of PbTiO 3 using x-ray diffraction, piezoforce microscopy, electrical characterization and rapid in-situ x-ray diffraction reciprocal space maps during the growth using synchrotron radiation. Using a simple model we show that the changes in growth are driven by the energy cost for the top material to sustain the polarization imposed upon it by the underlying layer, and these effects may be expected to occur in other multilayer systems where polarization is present during growth. This motivates the concept of polarization engineering as a complementary approach to strain engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chemical approaches to artificial photosynthesis: A molecular, dye-sensitized photoanode for O 2 production prepared by layer-by-layer self-assembly

We describe here the preparation of a family of photoanodes for water oxidation that incorporate an electron acceptor–chromophore–catalyst in single molecular assemblies on nano-indium tin oxide (nanoITO) electrodes on fluorine-doped tin oxide (FTO). The assemblies were prepared by using a layer-by-layer, Atomic Layer Deposition (ALD), self-assembly approach. In the procedure, addition of an electron acceptor viologen derivative followed by a Ru II (bpy) chromophore and a pyridyl derivative of the water oxidation catalyst [Ru(bda) (L) 2 ] (bda = 2,2'-bipyridine-6,6'-dicarboxylate) 2 , were linked by ALD by addition of the bridge precursors TiO 2 , ZrO 2 , and Al 2 O 3 as the bridging groups giving the assemblies, FTO|nanoITO|–MV 2+ –ALD MO 2 –RuP2 2+ –ALD M'O 2 –WOC. In a series of devices, the most efficient gave water oxidation with an incident photon to current efficiency of 2.2% at 440 nm. Transient nanosecond absorption measurements on the assemblies demonstrated that the slow step in the intra-assembly electron transfer is the electron transfer from the chromophore through the viologen bridge to the nanoITO electrode.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Response of Subsurface Nitrogen-Cycling Microbial Communities to Environmental Fluctuations (Final Technical Report)

Riparian floodplains are dynamic ecosystems linking terrestrial and riverine systems. These floodplains experience hydrological shifts such as changes in water table height, flooding, and drought and can be ‘hotspots’ of biogeochemical cycling due to shifting sediment moisture (and saturation) and subsurface exchanges of water, nutrients, and other compounds across different sediment layers. Subsurface microbial communities are the primary drivers of biogeochemical processes in floodplains, and thus their structure and function can directly influence both surface and groundwater quality. The microbial nitrogen (N) cycle is particularly important in floodplains as it affects nutrient availability and removal. Two functional guilds of chemoautotrophic (i.e. CO2-fixing) microorganisms are responsible for the first oxidative step of the N cycle, nitrification: ammonia-oxidizing archaea (AOA) and bacteria (AOB) catalyze the oxidation of ammonia to nitrite, while nitrite-oxidizing bacteria (NOB) oxidize nitrite to nitrate. Despite the critical role nitrification plays in N-cycling in both terrestrial and aquatic ecosystems, our understanding of the diversity, ecophysiology, and activity of nitrifying organisms in subsurface floodplain soils/sediments is extremely limited. To help address this critical knowledge gap, the overarching goal of this project was to determine how shifts in key environmental parameters and gradients impact microbial N-cycling communities/processes, with particular emphasis on nitrification, within hydrologically-variable floodplain sediments in the Wind River Basin near Riverton, Wyoming. The three specific objectives of this project were to: (1) to associate in situ environmental drivers of N cycling with distinct functional guilds; (2) determine the guild response to variation in key ecosystem drivers; and (3) develop a dynamic ecosystem model of the microbial N cycle with the Riverton subsurface using community genomic and biogeochemical data collected in the first two objectives. Over the course of this project, we employed both 16S rRNA gene amplicon sequencing and genome-resolved metagenomics to examine the phylogenetic diversity and metabolic potential of subsurface nitrifier communities within 68 samples collected across multiple sites, depths, and time points within the Riverton floodplain, allowing for both spatial and temporal investigations at different scales. This project benefitted tremendously from recent advances in high-throughput sequencing technologies coupled with dramatic improvements in the computational tools and algorithms available for analyzing such large, complex genomic datasets. By pairing these cutting-edge genomic approaches with depth-resolved sampling and detailed geochemical analyses of the Riverton floodplain, we have gained novel insights into the structure and function of subsurface nitrifier communities in relation to both hydrology and biogeochemistry. This project resulted in the most detailed and comprehensive characterization of N-cycling floodplain microbial communities to date and will hopefully inspire and pave the way for future studies using similar approaches in other floodplains. Indeed, such information is critical for understanding subsurface biogeochemical cycling and how elemental stores are altered from perturbations initiated by the water cycle within floodplains. Finally, because of the terrestrial-aquatic nature of the Riverton floodplain, results from this project are also of relevance to disciplines such as soil science, estuarine science, limnology & oceanography, biogeochemistry, geobiology, environmental engineering, as well as genomics and data science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ultrafast relaxation of lattice distortion in two-dimensional perovskites

Direct visualization of ultrafast coupling between charge carriers and lattice degrees of freedom in photoexcited semiconductors has remained a long-standing challenge and is critical for understanding the light-induced physical behaviour of materials under extreme non-equilibrium conditions. Here we obtain a direct visualization of the structural dynamics in monocrystalline 2D perovskites. We achieve this by monitoring the evolution of wavevector-resolved ultrafast electron diffraction intensity following above-bandgap high-density photoexcitation. Our analysis reveals a light-induced ultrafast reduction in antiferro-distortion resulting from a strong interaction between the electron–hole plasma and perovskite lattice, which induces an in-plane octahedra rotation towards a more symmetric phase. Correlated ultrafast spectroscopy performed at the same carrier density as ultrafast electron diffraction reveals that the creation of a dense electron–hole plasma triggers the relaxation of lattice distortion at shorter timescales by modulating the crystal cohesive energy. Then we show that the interaction between carrier gas and lattice can be altered by tailoring the rigidity of the 2D perovskite by choosing an appropriate organic spacer layer.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Sr-Nd-Pb isotope systematics of Australasian tektites: Implications for the nature and composition of target materials and possible volatile loss of Pb

The Australasian tektite (AAT) strewn field is the largest strewn field on the Earth with about ~10–30% coverage, both land and ocean, but a clearly identified source impact crater is absent despite the young age of AAT of ca. 790 ka. A genetic link between the Australasian tektites and their unequivocal parental materials is therefore largely impossible to establish. Nevertheless, the nature of the parental materials and the extent of volatilization can be constrained using the splash form tektites, carrying the chemical signatures of high-temperature processes, and the layered (so-called Muong Nong-type) tektites, which are less chemically homogenized and exceptionally abundant in the AAT field. New high-precision Sr, Nd and Pb isotopic measurements were obtained for a chemically and petrographically well-characterized suite of AAT, which included the Muong Nong-type (MN-AAT) with precisely known field locations in Laos and splash forms (SF-AAT) from different parts of the strewn field. Additionally, optically dark and light zones of the MN-AAT were also separately analyzed. Homogeneous ε Nd values from -11.8 to -11.2, combined with a narrow range of two-stage Nd model ages from 1.67 to 1.72 Ga for the entire AAT suite, point to a well-mixed source, in terms of REE, of the crustal segment from which the sedimentary material for tektites was ultimately derived. The Sr isotopic data largely overlap for SF-AAT and MN-AAT ( 87 Sr/ 86 Sr = 0.71636–0.72021) and indicate Paleozoic to Mesozoic sedimentary parentage. However, late Neogene to early Quaternary re-deposition and formation of a thick silt-sized sedimentary section with vertical stratification is required to comply with 10Be data. Lead isotope systematics documents at least three different components which can perhaps be represented by different mineral phases, such as feldspar, zircon, organic matter adsorbed on young sediments etc., sorted during fluvial transport and final deposition. In addition, the SF-AAT have systematically lower Pb contents than the MN-AAT, and generally show isotopically heavier Pb isotopic ratios. This is theoretically consistent with a preferential volatilization of lighter Pb isotopes during evaporation and considerably larger Pb loss from SF-AAT when compared to MN-AAT. Nevertheless, further experimental work would be necessary to unambiguously distinguish kinetic fractionation from source mixing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Cascading economic losses from port disruptions under capacity constrained multimodal freight networks

This study quantifies how throughput disruptions at major seaports cascade through capacity-constrained multimodal freight networks and interregional production systems. We couple an agent-based model (ABM) multimodal freight simulation that resolves rerouting, terminal queueing, and inventory drawdown under binding modal and facility capacities with a multiregional output loss input-output (MRIIM) model that propagates realized delivery shortfalls across regions and sectors. The framework is demonstrated for the Port of Los Angeles using Freight Analysis Framework flows and Bureau of Economic Analysis input-output accounts and is evaluated over a 52-week horizon under deterministic sector targeted shocks and stochastic disruption realizations with uncertain severity and duration. Results indicate nonlinear amplification: realized national losses concentrate in manufacturing and transportation/warehousing even when exogenous port shocks are dispersed, suggesting that congestion spillback and limited short-run substitution can dominate the initial shock allocation. We further evaluate a tabular reinforcement-learning (Q-learning) intervention layer that selects among a small set of implementable system level levers (truck-to-rail and truck-to-barge shift settings) without overriding shipper routing, finding that such interventions reduce total losses for moderate disruptions but yield diminishing returns once substitute modes approach capacity. By linking operational freight behavior to system wide impacts under uncertainty, the proposed ABM-MRIIM pipeline provides a reusable workflow for port disruption stress testing, identification of structurally critical sectors/corridors, and evaluation of resilience interventions under realistic capacity limits.

42 ENGINEERING↗
DOE OSTI2022

Impacts, processes and projections of the quasi-biennial oscillation

In the tropical stratosphere, deep layers of eastward and westward winds encircle the globe and descend regularly from the upper stratosphere to the tropical tropopause. With a complete cycle typically lasting almost 2.5 years, this quasi-biennial oscillation (QBO) is arguably the most predictable mode of atmospheric variability that is not linked to the changing seasons. Here, the QBO affects climate phenomena outside the tropical stratosphere, including ozone transport, the North Atlantic Oscillation and the Madden–Julian Oscillation, and its high predictability could enable better forecasts of these phenomena if models can accurately represent the coupling processes. Climate and forecasting models are increasingly able to simulate stratospheric oscillations resembling the QBO, but exhibit common systematic errors such as weak amplitude in the lowermost tropical stratosphere. Uncertainties about the waves that force the oscillation, particularly the momentum fluxes from small-scale gravity waves excited by deep convection, make its simulation challenging. Improved representation of the processes governing the QBO is expected to lead to better forecasts of the oscillation and its impacts, increased understanding of unusual events such as the two QBO disruptions observed since 2016, and more reliable future projections of QBO behaviour under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The role of non-standard translation in Candida albicans pathogenesis

ABSTRACT Candida albicans typically resides in the human gastrointestinal tract and mucosal membranes as a commensal organism. To adapt and cope with the host immune system, it has evolved a variety of mechanisms of adaptation such as stress-induced mutagenesis and epigenetic regulation. Niche-specific patterns of gene expression also allow the fungus to fine-tune its response to specific microenvironments in the host and switch from harmless commensal to invasive pathogen. Proteome plasticity produced by CUG ambiguity, on the other hand is emerging as a new layer of complexity in C. albicans adaptation, pathogenesis, and drug resistance. Such proteome plasticity is the result of a genetic code alteration where the leucine CUG codon is translated mainly as serine (97%), but maintains some level of leucine (3%) assignment. In this review, we dissect the link between C. albicans non-standard CUG translation, proteome plasticity, host adaptation and pathogenesis. We discuss published work showing how this pathogen uses the fidelity of protein synthesis to spawn novel virulence traits.

Bezerra, Ana Rita (ORCID:0000000346131711)↗
DOE OSTI2023

Quantifying Aging-Induced Irreversible Volume Change of Porous Electrodes

Automotive manufacturers are working to improve cell and pack design by increasing their performance, durability, and range. One of the critical factors to consider as the industry moves towards materials with higher energy density is the ability to consider the irreversible volume change characteristic of the accelerated SEI layer growth tied to the large volume change and particle cracking typically associated with active material strain. As the time from initial design to manufacture of electric vehicle is decreased in order to rapidly respond to consumer demands and widespread adoption of electric vehicles, the ability to link aging and volume change to end of life vehicle requirements using virtual tools is critical. In this study, apply a mechano-electrochemical model to determine the irreversible volume change at the electrode and cell level, allowing for virtual design iterations to predict the volume change at battery cell aged states.

Electrochemistry↗
DOE OSTI2024

Carbon Storage Site Mapping Inquiry Tool (MapIT)

To date, 48 projects, consisting of 139 wells, are currently under review with the Environmental Protection Agency’s (EPA) Underground Injection Control (UIC) Program for Class VI – wells used for geologic sequestration of carbon dioxide. The number of applications submitted is expected to increase in coming years with the increase of the 45Q tax credit available to projects that initiate construction prior to 2033. The amount of data collected to submit a Class VI permit is vast, and often disparate, coming from state, federal, and commercial entities, as well as field-specific data collected within an area of interest. When preparing for site selection and permitting, the initial aggregation of relevant public data can be time intensive. The Carbon Storage Site Mapping Inquiry tool (MapIT) was created to support and accelerate the discovery and accessibility of open-source data and information available across the USA. Data was aggregated and organized based on data types described within the EPA UIC Class VI permit documentation. The online tool enables users to explore hundreds of geospatial data layers and connect to additional external resources, leveraging API and REST services where possible to ensure updates to data in real time. MapIT enables users to explore state and federal data related to geologic, geophysical, structural, hydrologic, and contextual information. In addition to displaying spatial data and linking to external resources, MapIT leverages custom widgets to ensure that internal data and external data are discoverable and accessible. The widgets connect users to resources such as the USGS publications and the USGS Earthquake Catalog based on a user-defined location. This talk will describe data aggregation workflows, data types, data preparation, and tool development for MapIT. The Carbon Storage Site Mapping Inquiry Tool and underlying database are valuable, intuitive resources that empower government, academic, commercial and industry stakeholders to explore, analyze, and acquire carbon storage related data.

Morkner, Paige↗
DOE OSTI2022

Influence of carrier trapping on radiation detection properties in $\mathrm{CVD}$ grown 4H-SiC epitaxial layers with varying thickness up to 250 µm

We report low doped n-type 4H-SiC epitaxial layers of thickness 50, 150, and 250 µm grown by hot wall chemical vapor deposition were used to fabricate Ni/4H-SiC Schottky radiation detectors. The epitaxial layers were grown on the (0001) face of highly conductive bulk 4H-SiC substrates 8° offcut towards the $\langle 11\bar{2}1\rangle$ direction. The 50, 150, and 250 µm thick epilayer detectors, under optimized settings, showed energy resolutions of 2.0%, 0.78%, and 0.63%, respectively for 5.48 MeV alpha particles. Deep level transient spectroscopy studies showed that the observed variation in the detector resolution is linked to the defect parameters in the devices. Least-squares fitting of the bias dependence of the charge collection efficiency according to a drift-diffusion model, revealed minority carrier diffusion lengths of 16, 10, and 9.2 µm, respectively, implying that the detectors are not limited by minority carrier trapping. The detector performance was observed to be primarily dependent on the concentration and capture cross-sections of the lifetime killing electron traps Z 1/2 and EH 6/7 .

36 MATERIALS SCIENCE↗
DOE OSTI2021

In Operando Angle‐Resolved Photoemission Spectroscopy with Nanoscale Spatial Resolution: Spatial Mapping of the Electronic Structure of Twisted Bilayer Graphene

To pinpoint the electronic and structural mechanisms that affect intrinsic and extrinsic performance limits of 2D material devices, it is of critical importance to resolve the electronic properties on the mesoscopic length scale of such devices under operating conditions. Herein, angle‐resolved photoemission spectroscopy with nanoscale spatial resolution (nanoARPES) is used to map the quasiparticle electronic structure of a twisted bilayer graphene device. The dispersion and linewidth of the Dirac cones associated with top and bottom graphene layers are determined as a function of spatial position on the device under both static and operating conditions. The analysis reveals that microscopic rotational domains in the two graphene layers establish a range of twist angles from 9.8° to 12.7°. Application of current and electrostatic gating lead to strong electric fields with peak strengths of 0.75 V/μm at the rotational domain boundaries in the device. These proof‐of‐principle results demonstrate the potential of nanoARPES to link mesoscale structural variations with electronic states in operating device conditions and to disentangle such extrinsic factors from the intrinsic quasiparticle dispersion.

Majchrzak, Paulina↗
DOE OSTI2019

Mechanisms and Kinetics of Reduction of Solid NiO in CO/CO{sub 2} and CO/Ar Gas Mixtures

Experimental studies have been carried out on the reduction of dense nickel oxide in CO/CO{sub 2} and CO/Ar gas mixtures at temperatures between 873 K and 1273 K (600 °C and 1000 °C). Examination of the nickel product microstructures of partially reduced samples has been undertaken using high-resolution scanning electron microscopy. It has been shown that the reduction can result in the formation of a dense nickel layer at the sample surface, continuous gas pore growth in the NiO, or the discontinuous growth of the porous nickel product. The product morphologies formed, and the microstructures of the gas/metal oxide/metal interfaces, are functions of reaction temperature and gas conditions. The microstructural and kinetic evidence indicates direct links between the reduction rates, nickel product growth mechanisms, and product microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Crystal structure of encorafenib, C 22 H 27 ClFN 7 O 4 S

The crystal structure of encorafenib, C 22 H 27 ClFN 7 O 4 S, has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Encorafenib crystallizes in space group P2 1 (#4) with a = 16.17355(25), b = 9.52334(11), c = 17.12368(19) Å, β = 89.9928(22)°, V = 2637.50(4) Å 3 , and Z = 4. The crystal structure consists of alternating layers of stacked halogenated phenyl rings and the other parts of the molecules perpendicular to the a-axis. One molecule participates in two strong N–H∙∙∙N hydrogen bonds (one intra- and the other intermolecular), which are not present for the other molecule. The intermolecular hydrogen bonds link molecule 2 into a spiral chain along the b-axis. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

36 MATERIALS SCIENCE↗