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DOE OSTI2025

An Overview of the Molten Salt Thermal Properties Database–Thermophysical, Version 4.0 (MSTDB-TP V.4.0)

A central repository of thermophysical and thermochemical properties of molten salt compositions of relevance to molten salt reactors (MSRs) is vital in supporting the broad community of MSR developers, who are at various stages of developing and deploying their reactor designs. In general, these MSR designs differ significantly from developer to developer (e.g., with respect to the hardness of the neutron spectra, level of fissile loading, target multicomponent temperatures and power levels, and moderating capabilities). Therefore, the fuel and coolant salts being considered vary greatly: they may be chlorides or fluorides, they utilize different actinides at different ratios, and the cations in the melt are selected based on perceived advantages and disadvantages. Considering the general need for thermal properties, and the vastness of the array of potential candidate salt mixtures, the Molten Salt Thermal Properties Database (MSTDB) was initiated in 2018 with the goal of providing thermophysical and thermochemical characterization of key molten salt compounds and mixtures across their temperature and compositional domains. The MSTDB is thus divided into the thermophysical arm (MSTDB-TP) and the thermochemical arm (MSTDB-TC). The MSTDB is an effort funded by the Department of Energy, Office of Nuclear Energy (DOE-NE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, and the MSR Campaign. This report provides an overview of the MSTDB-TP v4.0 in terms of the data contained within, the state of the tools used to access the data, the availability of predictive models that leverage the raw data in the database, the preliminary status of developmental efforts that are currently underway, and an account of future goals for MSTDB-TP. The primary goal for the update from MSTDB-TP v.3.1 to v4.0 was the incorporation of surface tension data into the database; this property is important for thermal hydraulics modeling and species transport in other tools that have been developed under the NEAMS program. A breakdown of the surface tension data that have been added into MSTDB-TP v4.0 is provided herein, and the manner in which the quality of the data has been assessed is also documented. For MSTDB-TP v4.0, newly published thermophysical property data—primarily from collaborative experimental efforts under the MSR Campaign—have been incorporated into the database, and the resulting expansion is documented here. Because of the size to which MSTDB-TP has grown, the raw data format has now been recast into JavaScript Object Notation (JSON) format for easier connection with the MSTDB-TP application programming interface (API). Saline; the pre-existing comma-separated value (CSV) format has been deprecated but is still maintained, accessible, and up to date. As a final effort in packaging the MSTDB-TP v4.0 update, the graphical user interface (GUI) for MSTDB has been updated to allow full accessibility to the density and viscosity predictive models, which are based on Redlich-Kister expansions of MSTDB-TP raw data. Some other major aspects of this report, in terms of preliminary and future work, include: (1) documentation of the formalism and preliminary testing of a kinetic theory model that may act as a predictive model for thermal conductivity; (2) documentation of the candidate predictive models that may be considered in the future for surface tension, making use of the surface tension data now in MSTDB-TP v4.0; (3) a preliminary account of a data collection process that will enable the filling of additional gaps within MSTDB-TP, namely with data which have been collected computationally (e.g., through ab initio molecular dynamics).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

The relationship between gene traits and transcription in soil microbial communities varies by environmental stimulus

Codon and nucleotide frequencies are known to relate to the rate of gene transcription, yet how these traits shape transcriptional profiles of soil microbial communities remains unclear. Here we test the prediction that functional genes with high codon optimization and energetically lower cost nucleotides (i.e., nucleotides requiring less adenosine triphosphate (ATP) for synthesis) have higher transcriptional expression in a soil microbial community. In laboratory incubations, we subjected an agricultural soil to two separate short-term environmental changes: labile carbon (glucose) addition or a sudden 30-min increase in temperature from 20 °C to 60 °C. Using the total genomic codon frequencies to predict preferred codon usage for each taxon, we then estimated codon optimization for each transcript. On the community level, we found a higher average level of codon optimization after the addition of glucose. Synonymous nucleotide composition in the transcript pool also shifted towards energetically cheaper nucleotides, favoring uracil (U) over adenine (A) and cytosine (C) over guanine (G). Similarly, we found that encoded amino acid usage shifted towards energetically cheaper amino acids in response to labile carbon. In contrast, in communities responding to heat shock, there were no significant differences in the averaged gene traits of expressed transcripts. We used metagenome-assembled-genomes to further examine the ability of gene traits to predict transcriptional responses within and between taxa. We found that traits of individual genes could not reliably predict the level of transcription of a gene within or between taxa—highlighting the limits of this approach. However, we did find that when traits were averaged across several related genes, codon optimization was able to predict levels of transcription in metabolic pathways associated with growth and nutrient uptake in response to glucose. Similar relationships were not observed in response to heat, or for functions associated with stress—such as genes associated with sporulation or heat shock. These results demonstrate that gene traits, such as codon usage, nucleotide selection, and amino acid selection, relate to the transcriptional expression of genes in soil microbial communities and suggests that these relationships may be dependent on both gene function and the specific type of environmental stimuli.

Biological and medical sciences↗
DOE OSTI2026

Nutrient limitation shapes functional traits of mycorrhizal fungi and phosphorus-cycling bacteria across an elevation gradient

In nutrient-limited high-elevation ecosystems, plants rely on arbuscular mycorrhizal (AM) fungi to provide mineral phosphorus (P) in the form of phosphate (PO43-). AM fungi gather these nutrients from phosphorus-cycling bacteria (PCBs) that can mineralize PO43- from organic matter and solubilize mineral-bound P. How climate, soil factors, and nutrient limitation influence AM fungi and PCB assembly remains unclear. We collected soil from montane meadows across a 1,000-m elevation gradient on three replicate mountainsides and analyzed AM fungal marker genes, P-cycling genes from shotgun metagenomes, and edaphic measurements. High-elevation soils had nearly 50-fold less soil PO₄³⁻ and 60% more AM fungal hyphae than low-elevation soils. AM fungal turnover was linked to changes in pH, organic carbon, and PO₄³-. The composition of 198 P-cycling genes was influenced by the AM fungal community structure. Drivers of individual PCB functional genes, including pH and organic carbon, varied with gene phylogeny. We found a trade-off in P-cycling strategies across elevation: P-rich, low-elevation soils supported root-colonizing AM fungi and organic P-mineralizing bacteria. P-poor, high-elevation soils were dominated by stress-tolerant AM fungi and mineral P-solubilizing bacteria. Our results suggest that AM fungi and PCB community turnover across elevation are both shaped by pH, organic carbon, and P availability. With continued climate warming, the structure and function of mountaintop ecosystems might shift to resemble lower elevations, disrupting long-established and specialized microbial assemblages, with consequences for P-cycling dynamics and the total P available to plant communities.IMPORTANCEPhosphorus (P) limits plant productivity in high-elevation ecosystems, yet the microbial networks that mobilize P, including arbuscular mycorrhizal (AM) fungi and phosphorus-cycling bacteria (PCBs), remain under-characterized in these nutrient-poor soils. We show that across a 10,00-m elevation gradient, AM fungi and P-cycling gene assemblages shift predictably with pH, organic carbon, and phosphate availability. Higher elevations, with less available P, select for stress-tolerant AM fungal taxa and PCB strategies geared toward mineral solubilization, while low-elevation sites favor root colonization by AM fungi and organic P mineralization. These results suggest that nutrient limitation can constrain microbial community assembly in consistent ways across landscapes. High mountain soils are low in P and rely on a network of underground AM fungi and PCB to deliver nutrients to plants. This study shows how those underground relationships reorganize with elevation and how climate change could collapse long-standing microbial strategies by pushing high-elevation ecosystems toward lowland conditions. As soils warm and dry, the microbial scaffolding that supports alpine plant life may become increasingly unstable.

arbuscular mycorrhizal fungi↗
DOE OSTI2025

AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition

Abstract. Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.

Mahowald, Natalie M. (ORCID:000000022873997X)↗
DOE OSTI2024

Genomic insights into redox-driven microbial processes for carbon decomposition in thawing Arctic soils and permafrost

Climate change is rapidly transforming Arctic landscapes where increasing soil temperatures speed up permafrost thaw. This exposes large carbon stocks to microbial decomposition, possibly worsening climate change by releasing more greenhouse gases. Understanding how microbes break down soil carbon, especially under the anaerobic conditions of thawing permafrost, is important to determine future changes. Here, we studied the microbial community dynamics and soil carbon decomposition potential in permafrost and active layer soils under anaerobic laboratory conditions that simulated an Arctic summer thaw. The microbial and viral compositions in the samples were analyzed based on metagenomes, metagenome-assembled genomes, and metagenomic viral contigs (mVCs). Following the thawing of permafrost, there was a notable shift in microbial community structure, with fermentative Firmicutes and Bacteroidota taking over from Actinobacteria and Proteobacteria over the 60-day incubation period. The increase in iron and sulfate-reducing microbes had a significant role in limiting methane production from thawed permafrost, underscoring the competition within microbial communities. We explored the growth strategies of microbial communities and found that slow growth was the major strategy in both the active layer and permafrost. Our findings challenge the assumption that fast-growing microbes mainly respond to environmental changes like permafrost thaw. Instead, they indicate a common strategy of slow growth among microbial communities, likely due to the thermodynamic constraints of soil substrates and electron acceptors, and the need for microbes to adjust to post-thaw conditions. The mVCs harbored a wide range of auxiliary metabolic genes that may support cell protection from ice formation in virus-infected cells.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Reining in an Agentic Harness for High Energy Physics

Agentic systems now address tasks across theoretical, phenomenological, and experimental high energy physics (HEP), but their scientific capabilities remain difficult to reuse across different large language models, providers, and harnesses. We argue that stable parts of these workflows should be promoted into versioned scientific operations and exposed through common protocols. Existing general-purpose harnesses can then be specialized for HEP through task-specific sets of tools and skills, while community-maintained registries would make these capabilities discoverable and citable. We identify mismatches in conventions, assumptions, and domains of validity among independently developed operations as a potential obstacle to their composition, and discuss machine-readable scientific contracts as one possible solution. These design principles and evaluation guidelines provide a near-term path toward a portable and community-maintained agentic harness for HEP.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗
DOE OSTI2025

Durability Research Is Pivotal for Perovskite Photovoltaics

Metal halide perovskite solar cells have shown promising power conversion efficiencies, but commercialization requires that decent durability is also demonstrated. Under normal operation, solar cells are subject to a complex combination of stressors, such as visible light, ultraviolet light, heat, humidity, mechanical stress and electric potential, which complicates the understanding of failure mechanisms. Existing stress tests do not act as a time machine. In new materials systems such as perovskite photovoltaics, the tests have no known relationship to field service. In this Perspective we recommend following a durability learning cycle that interleaves photovoltaic module engineering with field testing; accelerated testing; and preconditioning and performance engineering. We advocate for field testing to demonstrate real-world performance and identify field-relevant failure modes, and urge the community to develop accelerated and qualification tests that account for device metastability, variations in material composition and different/various processing methods. In conclusion, these practices are more difficult, but more important, than the simple pursuit of higher initial efficiencies.

14 SOLAR ENERGY↗
DOE OSTI2026

Toward a Climate OSSE Framework for Satellite Mission Design

The rich history of observing system simulation experiments (OSSEs) does not yet include a well-established framework for using climate models. The need for a climate OSSE is triggered by the need to quantify the value of a particular measurement for reducing the uncertainty in climate predictions, which differ from numerical weather predictions in that they depend on future atmospheric composition rather than the current state of the weather. However, both weather and climate modeling communities share a need for motivating major observing system investments. Here, we outline a new framework for climate OSSEs that leverages the use of machine learning to calibrate climate model physics against existing satellite data. We demonstrate its application using NASA’s GISS-E3 model to objectively quantify the value of potential future improvements in spaceborne measurements of Earth’s planetary boundary layer. A mature climate OSSE framework should be able to quantitatively compare the ability of proposed observing system architectures to answer a climate-related question, thus offering added value throughout the mission design process, which is subject to increasingly rapid advances in instrument and satellite technology. Technical considerations include selection of observational benchmarks and climate projection metrics, approaches to pinpoint the sources of model physics uncertainty that dominate uncertainty in projections, and the use of instrument simulators. Community and policy-making considerations include the potential to interface with an established culture of model intercomparison projects and a growing need to economically assess the value-driven efficiency of social spending on Earth observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Integrated Metrics for County-Level Resilience Ranking Using Entropy and TOPSIS

In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.

Bhusal, Narayan [Oak Ridge National Laboratory (OR↗
DOE OSTI2025

GenomeFace v1.0

GenomeFace is meta-genome binning software. Metagenomic binning, the process of grouping DNA sequences into taxonomic units, is critical for understanding the functions, interactions, and evolutionary dynamics of microbial communities. We propose a deep learning approach to binning using two neural networks, one based on composition and another on environmental abundance, dynamically weighting the contribution of each based on characteristics of the input data. Trained on over 43,000 prokaryotic genomes, our network for composition-based binning is inspired by metric learning techniques used for facial recognition. Using a task-specific, multi-GPU accelerated algorithm to cluster the embeddings produced by our network, our binner leverages marker genes observed to be universally present in nearly all taxa to grade and select optimal clusters of sequences from a hierarchy of candidates. We evaluate our approach on four simulated datasets with known ground truth. Our linear time integration of marker genes recovers more near complete genomes than state of the art but computationally infeasible solutions using them, while being over an order of magnitude faster. Finally, we demonstrate the scalability and acuity of our approach by testing it on three of the largest metagenome assemblies ever performed. Compared to other binners, we produced 47%-183% more near complete genomes. From these datasets, we find over the genomes of over 3000 new candidate species which have never been previously cataloged, representing a potential 4% expansion of the known bacterial tree of life.

Lettich, Richard [Lawrence Berkeley National Labor↗
DOE OSTI2026

Accelerated Selectrion of Optimal Perovskite Alloys for Solar PV using a Combined Quantum and Machine Learning Hierachiral Approach

The project aims to: (i) accelerate the discovery of “Missing HP alloys” by combining quantum mechanics and artificial intelligence machine learning approaches, and (ii) analyze the stabilities of candidate alloys, including those that do not pass selection filters (and are thus expected to degrade over time) to decipher the nature of the instabilities to guide the development of durable solar cell materials. Successful candidates will be subjected to validation experiments at NREL's state-of-the-art facilities. The discoveries this effort will provide will be directly testable and implementable and will greatly impact U.S. progress in HP PV as they will provide clear direction and motivation for experimental studies including specific material synthetic targets, device optimization, and device stability protocols. A key advantage of this effort is the feedback and guidance provided by the Industry Collaborative Work Group that we established to coordinate academic and national lab research with industry needs. The proposed work will provide a basis for and direct the development of robust and reliable HP PV. It will also provide a timely, valuable and extensive roadmap to the experimental HP PV community to enable it to focus its efforts on improving and fine-tuning promising HP compositions that this effort predicts will likely be the best performers rather than wandering in the vast chemical space for decades spending enormous resources mostly evaluating unpromising candidate materials.

14 SOLAR ENERGY↗
DOE OSTI2025

Fractionation of Filamentous Algae from Mixed Biofilms

Filamentous algae, which grow in long, hair-like filaments within biofilms, play a crucial role in wastewater treatment due to their ability to produce significant biomass and their resistance to predation compared to traditional microalgal treatments. These algae can effectively uptake and utilize pollutants, particularly excessive nitrogen (ammonia, nitrate, nitrite) and phosphorus (phosphate), making filamentous algae valuable for wastewater treatment, as well as bioethanol and biodiesel production due to high lipid productions. However, each algal species possesses different capacities, necessitating a thorough genetic identification and understanding of each community. A major challenge in accurately assessing these communities is the lack of coverage in large sequencing databases which can lead to misrepresentation of the true composition and abundance of organisms and overall sequencing bias. To address this, I evaluated chemical and physical techniques for separating filamentous algae from mixed biofilms to achieve clean genetic sequencing results. I employed pH washing (0.001M HCl, 0.001M HCl, DiH2O, 0.0001M HCl, 0.001M HCl) for chemical treatment, followed by physical separation through centrifugation (5000rpm, 6500rpm) or filtration (2mm, 250um, 75um). The most successful method was deionized water washing, which yielded clear differences across stacked filters; the 2mm filtrate showed high levels of filamentous algae, with microalgae eluting in the 75um filtrate or remaining within agglutinations of algae larger filters. Base washing eluted the highest concentrations of microalgae, with larger filter sizes retaining more filamentous algae, indicating the breakdown of extracellular polymeric substances (EPS). Our downstream plans include sending the high-throughput next-generation sequencing to confirm the purity and ratios of filamentous and non-filamentous algae, as well as bacteria present, thereby validating the success of our treatments. Potential applications include creating community-based fractions for analysis, refining current sequencing data with clearer isolations, and generating designer biofilms to enhance our understanding of community interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems

ABSTRACT Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome’s functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute’s Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome’s metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Phage-based delivery of CRISPR-associated transposases for targeted bacterial editing

Phage λ, a well-characterized temperate phage, has been recently leveraged for bacterial genome editing by selectively delivering base editors into targeted bacterial species. We extend this concept by engineering phage λ to deliver CRISPR-guided transposases, accomplishing large insertions and targeted gene disruptions. To achieve this, we engineered phage λ using homologous recombination paired with Cas13a-based counterselection for precise phage modifications. Initially, we established the utility of Cas13a in phage λ by conducting minimal recoding edits, deletions, and insertions. Subsequently, we scaled up the engineering to embed the comprehensive DNA-editing CRISPR-Cas transposase (DART) system within the phage genome, creating λ-DART phages. These modified λ-DART phages were then employed to infectEscherichia coli, generating CRISPR RNA-guided transposition events in the host genome. Applying our engineered λ-DART phages to monocultures and a mixed bacterial community comprising three genera led to efficient, precise, and specific gene knockouts and insertions in the targetedE. colicells, achieving editing efficiencies surpassing 50% of the population. This research enhances phage-mediated genome editing by enabling efficient in situ gene integrations in bacteria, offering an avenue for further application in microbial community contexts. This scalable method enables flexible microbial genome editing in situ to manipulate the function and composition of diverse ecosystems.

Science & Technology - Other Topics↗
DOE OSTI2025

Metagenome-assembled genomes from topsoils along a hillslope water gradient across early snowmelt to late summer in East River, CO

Drought is changing the American Mountain West at unprecedented rates with unknown consequences to soil microbiome composition and function. As a part of LBNL Watershed Science Focus Area (SFA), we investigated shifts in microbial community and transcriptional activity on a subalpine conifer-meadow transition zone throughout the summer of 2023 as soil dried down. This work took place in Crested Butte, CO on Snodgrass mountain, using a proxy for drought conditions.Here we present metagenome assembled genomes (MAGs) for the bacterial and archaeal community at 0-10cm from three sites along a hillslope water gradient across five timepoints from early snowmelt to late summer. 42 metagenomes were sequenced at Joint Genome Institute (JGI) and can be found under the JGI GOLD (Genomes Online Database) sequencing project Gs0166660. Metagenomes were assembled through an inhouse pipeline (see methods), binned using four autobinners (concoct, maxbin2, metabat2, and vamb) and consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>70%) and contamination (<10%), and dereplicated at 95% ANI using drep. This dataset (1) a zip file of 157 MAGs (as fasta files, Gs0166660_bins_tar.gz), (2) sample metadata file with sample IGSNs (International Generic Sample Numbers) (samples.csv), (3) bounding box coordinates for the sampled locations (Gs0166660.kml), (4) metagenome assembly and coassembly metadata file listing IMG/M (Integrated Microbial Genomes/Metagenomes) metagenome accessions linking samples to metagenomes (EastRiver_Drought_ESSDive_Metadata.csv), (5) location metadata file (locations.csv), (6) file-level metadata file (flmd.csv) and (7) data dictionary (dd.csv) file.This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data and scripts associated with the manuscript "Organic Molecules are Deterministically Assembled in River Sediments"

This data package is associated with the publication "Organic Molecules are Deterministically Assembled in River Sediments" submitted to Scientific Reports (Stegen et al., 2024). The study applies community ecology methods to dissolved organic matter (DOM) chemistry from variably inundated riverbed sediments to uncover principles governing DOM composition at a reach-scale. This data package documents the workflow used to process and generate the main findings in the manuscript. The R scripts reference the raw, unprocessed Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data from another data package, available on ESS-DIVE at https://data.ess-dive.lbl.gov/view/doi:10.15485/1834208. The scripts then process the raw FTICR-MS data and generate the findings and figures presented in the associated manuscript. In brief, this study demonstrates that DOM assemblages in variably inundated sediments are primarily governed by deterministic variable selection, including sediment moisture effecting the degree of deterministic assembly. See the manuscript for more details pertaining to interpretation and implications of the findings. This data package is associated with the GitHub repository found at https://github.com/WHONDRS-Hub/ECA_2020_Sed.This data package is comprised of 6 scripts and 7 folders. The file-level metadata file (file ending in "flmd.csv") lists all files contained in this data package and descriptions for each. The data dictionary (file ending in "dd.csv) describes all tabular data columns and their respective definitions and units. The FTICR_Processing_Scripts produce the outputs found in the "Processed_Data" folder. The remaining scripts (located in the parent directory) produce the outputs found in the following four folders: (1) "MCD_Dendrograms", "MCD_Randomizations", "MCD_bNTI_Outcomes", and "OM_Null_Modeling". The fifth script additionally takes the three comma-separated values (CSV) files found in the parent directory as input ("VGC_texture.csv", "merged_weights.csv", and "ECA2_FTICR_BetaDisp.csv"). The outputs of each of the five scripts serve as the input to the following script, with the final outputs stored in the folder "OM_Null_Modeling".

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Coal-Waste-Enhanced Filaments for Additive Manufacturing of High-Temperature Plastics and Ceramic Composites

In the United States, coal waste from over a century of mining and burning coal for heat and electricity has accumulated as mountains of coal fly ash and bottom ash and acre-size ponds, coal fines and gob. These materials can be a problem for local communities and water systems. A cost-effective process to utilize high volumes of these coal wastes in a high-value product would be beneficial to those communities by reducing the amount of waste and providing jobs, manufacturing components, and materials from the waste. Many coal-to-products technologies (e.g., carbon fibers, graphene, carbon foam) rely on carefully choosing the starting material and then altering it chemically or thermally to make the products work. Due to the wide variability of composition and coal content in typical coal waste streams, many high-volume coal waste streams are likely to be unsuitable for use in those technologies. Semplastics’ technology has been shown to utilize most types of coal waste successfully without any pre-selection or pre-processing requirements other than a nominal particle-size reduction for wastes like bottom ash. This characteristic of Semplastics’ solution may enable the use of much larger volumes of a wider range of coal wastes than other coal-to-products technologies. In this project, Semplastics leveraged its unique experience with both coal waste (fly ash or coal combustion residuals), resin materials, and 3D printing to develop 3D printer filaments using common coal wastes – bituminous coal fines and fly ash – and researched the feasibility of using other forms of coal waste as fillers. Simple 3D-printed parts were successfully produced from the coal waste enhanced filaments, which were found to have improved strength and stiffness.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Application of ATONA Amplifiers to the Measurement of Uranium Isotopic Ratios by Thermal Ionization Mass Spectrometry

Uranium isotopic composition can provide valuable information about the history and provenance of a nuclear material; therefore, uranium isotopic analyses are frequently made in the nuclear forensics, safeguards, and environmental monitoring communities. These measurements have always presented challenges due to the extreme variability in the relative abundance between the major ( 235 U, 238 U) and minor ( 233 U, 234 U, 236 U) isotopes of uranium. Here, the recently developed ATONA (Atto- to Nano-Amp) amplification system paired with Faraday cup detectors has a large dynamic range and low noise floor making it ideal for measuring uranium isotopic ratios in materials of both natural and anthropogenic origin. A wide variety of certified reference materials were analyzed to investigate the utility of the ATONA amplification system for determining uranium isotopic composition in samples ranging from depleted to highly enriched. The ATONA amplifiers provide nearly an order of magnitude improvement in external reproducibility over 10 11 Ω amplifiers when measuring the minor 234 U/ 238 U ratio in isotopically natural and depleted samples and when paired with a secondary electron multiplier can measure very low relative abundance uranium isotopes (i.e., 236 U).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗