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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Hanford Waste Treatment Plant Low Activity Waste Facility Stack Effluent Monitoring - Sampling Probe Location Qualification Evaluation

The Hanford Tank Waste Treatment and Immobilization Plant low activity waste (LAW) facility stack monitor locations were qualified using scale model stacks to mitigate the risk of identifying that sampling locations do not meet the qualification criteria on the full-scale stack. As required by the American National Standards Institute/Health Physics Society (ANSI/HPS) N13.1-1999 standard, the scale model and its sampling location were geometrically similar to the actual stack and the Reynolds numbers for both the actual and model stacks were >10,000. An additional criterion is that the product of the hydraulic diameter and mean velocity (DV) of the full-scale stack must be between 1/6 DV and 6 DV of the scale model stack tests. Verification tests of the LAW stacks were performed at normal operating conditions. The minimum 1/6 DV value, along with the maximum 6 DV value from the scale model testing determines the range of conditions for which the full-scale stack may be operated and remain in compliance with the stack verification criterion. Based on these DV values, the corresponding stack flow rates for each of the LAW stacks are 815–55,758 scfm for LV-S1, 980–112,078 scfm for LV-S2, 264–22,901 scfm for LV-S3, and 981–79,832 scfm for LV-C2. The remaining criteria for the stack verification to be considered valid involve the flow angle and velocity uniformity results. First, the flow angle at the full-scale stack must be =20°. Second, the velocity uniformity at the full-scale stack must be =20% coefficient of variance (COV). Finally, the velocity uniformity results for the actual and scale model stack tests must agree within 5% COV. These criteria were met through the full-scale stack tests at the LAW facility. Flow angle results were primarily less than 10°, except for one LV-S2 Fan A result, which was 13.2°; all flow angle results were within the =20° criterion. The velocity uniformity results for each test condition ranged between 1.5 COV and 9.2% COV, which were all within the range of the target % COV values from the scale model tests. Based on these stack verification test results, the four LAW filtered exhaust stack sampling locations meet the qualification criteria provided in the ANSI/HPS N13.1-1999 standard for all fan operating configurations. This includes single-fan operating conditions for LV-S1 and LV-S2, dual-fan operations for LV-S3 at both the continuous air monitor and record sampler locations, and both the single-fan as well as the dual-fan operations for LV-C2. Further changes to the system configuration or operating conditions that are outside the bounds described in this report may require additional tests or analyses to determine compliance with the standard.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Detecting 127 Xe in an atmospheric tracer experiment

The Xcounts algorithm for calculating air concentrations of radioactive xenon isotopes (Eslinger et al., 2023) has been extended to estimate 127 Xe in addition to 131m Xe, 133m Xe, 133 Xe, and 135 Xe. The algorithm was applied to 119 samples collected with a SAUNA Q B system (Ringbom et al., 2023) during a two-month atmospheric tracer release experiment. Finally, the algorithm identified two samples with 127 Xe present from a single 1.5 h release about 3.5 km upwind of the sampler and no false detections of 127 Xe were observed in the other samples.

127Xe calibration↗
DOE OSTI2022

Hubble Asteroid Hunter

Context: The Hubble Space Telescope (HST) archives constitute a rich dataset of high-resolution images to mine for strong gravitational lenses. While many HST programmes specifically target strong lenses, they can also be present by coincidence in other HST observations. Aims: Our aim is to identify non-targeted strong gravitational lenses, without any prior selection on the lens properties, in almost two decades of images from the ESA HST archive (eHST). Methods: We used crowdsourcing on the Hubble Asteroid Hunter (HAH) citizen science project to identify strong lenses, along with asteroid trails, in publicly available large field-of-view HST images. We visually inspected 2354 objects tagged by citizen scientists as strong lenses to clean the sample and identify the genuine lenses. Results: We report the detection of 252 strong gravitational lens candidates, which were not the primary targets of the HST observations. A total of 198 of them are new, not previously reported by other studies, consisting of 45 A grades, 74 B grades and 79 C grades. The majority are galaxy-galaxy configurations. The newly detected lenses are, on average, 1.3 magnitudes fainter than previous HST searches. This sample of strong lenses with high-resolution HST imaging is ideal to follow up with spectroscopy for lens modelling and scientific analyses. Conclusions: This paper presents the unbiased search of lenses that enabled us to find a wide variety of lens configurations, including exotic lenses. We demonstrate the power of crowdsourcing in visually identifying strong lenses and the benefits of exploring large archival datasets. This study shows the potential of using crowdsourcing in combination with artificial intelligence for the detection and validation of strong lenses in future large-scale surveys such as ESA’s Euclid mission or in James Webb Space Telescope (JWST) archival images.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

X-ray absorption spectroscopy study of Mn reference compounds for Mn speciation in terrestrial surface environments

Abstract X-ray absorption spectroscopy (XAS) offers great potential to identify and quantify Mn species in surface environments by means of linear combination fit (LCF), fingerprint, and shell-fit analyses of bulk Mn XAS spectra. However, these approaches are complicated by the lack of a comprehensive and accessible spectrum library. Additionally, molecular-level information on Mn coordination in some potentially important Mn species occurring in soils and sediments is missing. Therefore, we investigated a suite of 32 natural and synthetic Mn reference compounds, including Mn oxide, oxyhydroxide, carbonate, phosphate, and silicate minerals, as well as organic and adsorbed Mn species, by Mn K-edge X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopy. The ability of XAS to infer the average oxidation state (AOS) of Mn was assessed by comparing XANES-derived AOS with the AOS obtained from redox titrations. All reference compounds were studied for their local (<5 Å) Mn coordination environment using EXAFS shell-fit analysis. Statistical analyses were employed to clarify how well and to what extent individual Mn species (groups) can be distinguished by XAS based on spectral uniqueness. Our results show that LCF analysis of normalized XANES spectra can reliably quantify the Mn AOS within ~0.1 v.u. in the range +2 to +4. These spectra are diagnostic for most Mn species investigated, but unsuitable to identify and quantify members of the manganate and Mn(III)-oxyhydroxide groups. First-derivative XANES fingerprinting allows the unique identification of pyrolusite, ramsdellite, and potentially lithiophorite within the manganate group. However, XANES spectra of individual Mn compounds can vary significantly depending on chemical composition and/or crystallinity, which limits the accuracy of XANES-based speciation analyses. In contrast, EXAFS spectra provide a much better discriminatory power to identify and quantify Mn species. Principal component and cluster analyses of k2-weighted EXAFS spectra of Mn reference compounds implied that EXAFS LCF analysis of environmental samples can identify and quantify at least the following primary Mn species groups: (1) Phyllo- and tectomanganates with large tunnel sizes (2 × 2 and larger; hollandite sensu stricto, romanèchite, todorokite); (2) tectomanganates with small tunnel sizes (2 × 2 and smaller; cryptomelane, pyrolusite, ramsdellite); (3) Mn(III)-dominated species (nesosilicates, oxyhydroxides, organic compounds, spinels); (4) Mn(II) species (carbonate, phosphate, and phyllosilicate minerals, adsorbed and organic species); and (5) manganosite. All Mn compounds, except for members of the manganate group (excluding pyrolusite) and adsorbed Mn(II) species, exhibit unique EXAFS spectra that would allow their identification and quantification in mixtures. Therefore, our results highlight the potential of Mn K-edge EXAFS spectroscopy to assess bulk Mn speciation in soils and sediments. A complete XAS-based speciation analysis of bulk Mn in environmental samples should preferably include the determination of Mn valences following the “Combo” method of Manceau et al. (2012), EXAFS LCF analyses based on principal component and target transformation results, as well as EXAFS shell-fit analyses for the validation of LCF results. For this purpose, all 32 XAS reference spectra are provided in the Online Materials1 for further use by the scientific community.

Geochemistry & Geophysics↗
DOE OSTI2026

pop-cosmos : redshifts and physical properties of KiDS-1000 galaxies

ABSTRACT Principled Bayesian inference of galaxy properties has not previously been performed for wide-area weak-lensing surveys with millions of sources. We address this gap by applying the pop-cosmos generative model to perform spectral energy distribution (SED) fitting for 4 million KiDS (Kilo-Degree Survey)-1000 galaxies. Calibrated on deep COSMOS2020 photometric data, pop-cosmos specifies a physically motivated prior over the galaxy population up to $z \simeq 6$ in stellar population synthesis (SPS) parameter space. Using the Speculator SPS emulator with GPU (graphics processing unit)-accelerated Markov Chain Monte Carlo sampling, we perform full posterior inference at 8.2 GPU seconds per galaxy, obtaining joint constraints on galaxy redshifts and physical properties. We validate photometric redshifts against $\sim \!185\,\!000$ KiDS galaxies cross-matched to Dark Energy Spectroscopic Instrument Data Release 1 spectroscopic samples, achieving low bias ($2\times 10^{-3}$), scatter ($\sigma _{\mathrm{MAD}}=0.03$), and outlier fraction (3.2 per cent) for the Bright Galaxy Survey, with comparable performance (bias $3\times 10^{-2}$, $\sigma _{\mathrm{MAD}}=0.05$, 1.0 per cent outliers) for luminous red galaxies (LRGs). Within the LRG sample, we identify massive, dusty, star-forming contaminants at $z \simeq 0.4$ satisfying standard colour selections for quenched populations. We infer trends in stellar mass, star formation, metallicity, and dust across five tomographic redshift bins consistent with established scaling relations. Using specific star formation rate constraints, we identify $\sim$7 per cent of KiDS-1000 galaxies as quenched, versus 37 per cent implied by conservative colour cuts. This enables the construction of weak-lensing samples defined by physical properties while mitigating intrinsic alignment systematics and preserving statistical power. Our analysis validates pop-cosmos out of sample, establishing it as a scalable approach for galaxy evolution and cosmological analyses with photometric surveys.

Halder, Anik [Institute of Astronomy and Kavli Ins↗
DOE OSTI2022

EE-SMOTE: An oversampling method in conjunction with information entropy for imbalanced learning

Imbalanced learning attracts great attention in various research fields. Existing literature-reported methodologies in imbalanced learning have shown drawbacks including over-generation or noisy/wrong samples generations. This paper presents EE-SMOTE, an oversampling technique based on information entropy, to support the imbalance classifications. Specifically, we propose a metric, Eigen-Entropy (EE), to identify homogenous samples from minority classes for oversampling technique, specifically, SMOTE to reach data balances for classification. Experiments on public dataset and real-world datasets demonstrate the efficacy and effectiveness of the proposed EE-SMOTE in imbalanced learning.

Huang, Jiajing↗
DOE OSTI2021

No evidence for a strong decrease of planetesimal accretion in old white dwarfs

ABSTRACT A large fraction of white dwarfs are accreting or have recently accreted rocky material from their planetary systems, thereby ‘polluting’ their atmospheres with elements heavier than helium. In recent years, the quest for mechanisms that can deliver planetesimals to the immediate vicinity of their central white dwarfs has stimulated a flurry of modelling efforts. The observed time evolution of the accretion rates of white dwarfs through their multi-Gyr lifetime is a crucial test for dynamical models of evolved planetary systems. Recent studies of cool white dwarf samples have identified a significant decrease of the mass accretion rates of cool, old white dwarfs over Gyr time-scales. Here, we revisit those results using updated white dwarf models and larger samples of old polluted H- and He-atmosphere white dwarfs. We find no compelling evidence for a strong decrease of their time-averaged mass accretion rates for cooling times between 1 and 8 Gyr. Over this period, the mass accretion rates decrease by no more than a factor of the order of 10, which is one order of magnitude smaller than the decay rate found in recent works. Our results require mechanisms that can efficiently and consistently deliver planetesimals inside the Roche radius of white dwarfs over at least 8 Gyr.

Blouin, Simon (ORCID:0000000296321436)↗
DOE OSTI2021

The Most Massive White Dwarfs in the Solar Neighborhood

We present an analysis of the most massive white dwarf candidates in the Montreal White Dwarf Database 100 pc sample. We identify 25 objects that would be more massive than 1.3M ⊙ if they had pure H atmospheres and CO cores, including two outliers with unusually high photometric mass estimates near the Chandrasekhar limit. We provide follow-up spectroscopy of these two white dwarfs and show that they are indeed significantly below this limit. We expand our model calculations for CO core white dwarfs up to M = 1.334 M ⊙ , which corresponds to the high-density limit of our equation-of-state tables, ρ = 109 g cm -3 . We find many objects close to this maximum mass of our CO core models. A significant fraction of ultramassive white dwarfs are predicted to form through binary mergers. Merger populations can reveal themselves through their kinematics, magnetism, or rapid rotation rates. We identify four outliers in transverse velocity, four likely magnetic white dwarfs (one of which is also an outlier in transverse velocity), and one with rapid rotation, indicating that at least 8 of the 25 ultramassive white dwarfs in our sample are likely merger products.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Speciation mapping of the oxidation layer on aged uranium dioxide using scanning transmission x-ray spectromicroscopy

In this study, UO 2 was aged in humid air and prepared as a thin section using a focused ion beam (FIB) instrument. The specimen was measured using synchrotron radiation spectromicroscopy techniques at the Beamline 11.0.2 STXM end station of the Advanced Light Source (ALS). Non-negative matrix factorization (NMF) methods were used to identify and map three component x-ray absorption near-edge structure (XANES) spectra in the oxygen K-edge data, revealing a surface layer of U 4 O 9 with a thickness of 206 ± 21 nm, and the bulk of the sample remaining as UO 2 . Uranium N 4,5 -edge XANES spectromicroscopy supports these results. The diffusion-controlled parabolic rate constant for UO 2 oxidation to U 4 O 9 was calculated from the observed layer thickness and compared to literature values. Complementary transmission electron microscopy (TEM) was used to image the sample and identify the phases present in various regions, confirming the STXM results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

The brown dwarf population in the star-forming region NGC 2264

ABSTRACT The brown dwarf population in the canonical star-forming region NGC 2264 is so far poorly explored. We present a deep, multiwavelength, multiepoch survey of the star-forming cluster NGC 2264, aimed to identify young brown dwarf candidates in this region. Using criteria including optical/near-infrared (IR) colours, variability, Spitzer mid-IR colour excess, extinction, and Gaia parallax and proper motion (in order of relevance), we select 902 faint red sources with indicators of youth. Within this sample, we identify 429 brown dwarf candidates based on their IR colours. The brown dwarf candidates are estimated to span a mass range from 0.01 to 0.08 M⊙. We find rotation periods for 44 sources, 15 of which are brown dwarf candidates, ranging from 3.6 h to 6.5 d. A subset of 38 brown dwarf candidates show high-level irregular variability indicative of ongoing disc accretion, similar to the behaviour of young stars.

Pearson, Samuel↗
DOE OSTI2023

Light elements Na and Al in 58 bulge spheroid stars from APOGEE

ABSTRACT We identified a sample of 58 candidate stars with metallicity [Fe/H] ≲ −0.8 that likely belong to the old bulge spheroid stellar population, and analyse their Na and Al abundances from Apache Point Observatory Galactic Evolution Experiment (APOGEE) spectra. In a previous work, we inspected APOGEE-Stellar Parameter and Chemical Abundance Pipeline abundances of C, N, O, Mg, Al, Ca, Si, and Ce in this sample. Regarding Na lines, one of them appears very strong in about 20 per cent of the sample stars, but it is not confirmed by other Na lines, and can be explained by sky lines, which affect the reduced spectra of stars in a certain radial velocity range. The Na abundances for 15 more reliable cases were taken into account. Al lines in the H band instead appear to be very reliable. Na and Al exhibit a spread in abundances, whereas no spread in N abundances is found, and we found no correlation between them, indicating that these stars could not be identified as second-generation stars that originated in globular clusters. We carry out the study of the behaviour of Na and Al in our sample of bulge stars and literature data by comparing them with chemodynamical evolution model suitable for the Galactic bulge. The Na abundances show a large spread, and the chemodynamical models follow the main data, whereas for aluminum instead, the models reproduce very satisfactorily the nearly secondary-element behaviour of aluminum in the metallicity range below [Fe/H] ≲ −1.0. For the lower-metallicity end ([Fe/H < −2.5), hypernovae are assumed to be the main contributor to yields.

Astronomy & Astrophysics↗
DOE OSTI2025

Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Impact of Acid–Base Stimulation Sequence on Mineral Stability for Tight/Impermeable Unconventional Carbonate-Rich Rocks: A Delaware Basin Case Study

We report mineral precipitation due to reactions with injected fluids during unconventional fracture stimulation is a well-recognized problem. The goal of this study is to evaluate secondary mineral precipitation and permeability attenuation under chemical injection scenarios specific to the Delaware basin. Whole cylindrical cores (2.54 cm diameter and 2.54 cm height) and ground shale (150–250 μm) from the carbonate-rich Bone Spring Formation, Delaware Basin TX (Leonardian), were reacted at 80 °C and 85 bar using a hydraulic fracturing fluid (HFF) recipe and an injection sequence typical of the Delaware Basin. The reacted shales and solutions were analyzed using a variety of laboratory- and synchrotron-based techniques to characterize both the chemical and spatial distributions of secondary mineral precipitation and identify changes in permeability and mineralogy. This carbonate-rich shale (>84% calcite) rapidly neutralized the acidic HFF. Synchrotron-based X-ray fluorescence mapping coupled with X-ray absorption spectroscopy (both bulk and micro) showed that most of the iron was in an oxidized form prior to exposure to HFF and that almost all iron(II) became fully oxidized after the reaction. Scanning electron microscopy images of the ground shale samples primarily identified iron(oxyhydr)oxide microcrystals on grain surfaces. A few small isolated iron-rich areas also contained sulfur, suggesting that some pyrite was preserved when isolated within a calcite crystal but that most was oxidized. The rapid neutralization of the acid spearhead in these carbonate-rich samples demonstrates that the acid spearhead is useful for initiating fractures in extremely calcite-rich rocks but does little to enhance rock permeability. This suggests that the impact of the acid spearhead is significantly smaller for carbonate-rich shales compared to clay-rich shales, which has broad implications for acidizing in carbonate-rich shale formations and iron transformations within these shales.

58 GEOSCIENCES↗
DOE OSTI2021

Calibration of the Hα Age–Activity Relation for M Dwarfs

In this work, we calibrate the relationship between Hα emission and M dwarf ages. We compile a sample of 892 M dwarfs with Hα equivalent width (Hα EW) measurements from the literature that are either comoving with a white dwarf of known age (21 stars) or in a known young association (871 stars). In this sample we identify 7 M dwarfs that are new candidate members of known associations. By dividing the stars into active and inactive categories according to their Hα EW and spectral type (SpT), we find that the fraction of active dwarfs decreases with increasing age, and the form of the decline depends on SpT. Using the compiled sample of age calibrators, we find that Hα EW and fractional Hα luminosity (L {sub Hα}/L {sub bol}) decrease with increasing age. Hα EW for SpT ≤ M7 decreases gradually up until ∼1 Gyr. For older ages, we found only two early M dwarfs that are both inactive and seem to continue the gradual decrease. We also found 14 mid-type M dwarfs, out of which 11 are inactive and present a significant decrease in Hα EW, suggesting that the magnetic activity decreases rapidly after ∼1 Gyr. We fit L {sub Hα}/L {sub bol} versus age with a broken power law and find an index of −0.11{sub −0.01}{sup +0.02} for ages ≲776 Myr. The index becomes much steeper at older ages, but a lack of field age-calibrators (≫1 Gyr) leaves this part of the relation far less constrained. Finally, from repeated independent measurements for the same stars, we find that 94% of them have a level of Hα EW variability ≤5 Å at young ages (<1 Gyr).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Miscentring of optical galaxy clusters based on Sunyaev–Zeldovich counterparts

ABSTRACT The ‘miscentring effect’, i.e. the offset between a galaxy cluster’s optically defined centre and the centre of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalogue from the Hyper Suprime-Cam (HSC) Survey S19A Data Release and the Sunyaev–Zeldovich cluster catalogue from Data Release 5 of the Atacama Cosmology Telescope (ACT). We obtain a sample of 186 clusters in common in the redshift range $0.1 \le z \le 1.4$ over an area of 469 deg$^2$. By modelling the distribution of centring offsets in this fiducial sample, we find a miscentred fraction (corresponding to clusters offset by more than 330 kpc) of ∼25 per cent, a value consistent with previous miscentring studies. We examine the image of each miscentred cluster in our sample and identify one of several reasons to explain the miscentring. Some clusters show significant miscentring for astrophysical reasons, i.e. ongoing cluster mergers. Others are miscentred due to non-astrophysical, systematic effects in the HSC data or the cluster-finding algorithm. After removing all clusters with clear, non-astrophysical causes of miscentring from the sample, we find a considerably smaller miscentred fraction, $\sim 10~\,\rm per\,cent$. We show that the gravitational lensing signal within 1 Mpc of miscentred clusters is considerably smaller than that of well-centred clusters, and we suggest that the ACT SZ centres are a better estimate of the true cluster potential centroid.

Ding, Jupiter (ORCID:0000000296119799)↗
DOE OSTI2024

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Population genomics of the pathogenic yeast Candida tropicalis identifies hybrid isolates in environmental samples

Candida tropicalis is a human pathogen that primarily infects the immunocompromised. Whereas the genome of one isolate, C. tropicalis MYA-3404, was originally sequenced in 2009, there have been no large-scale, multi-isolate studies of the genetic and phenotypic diversity of this species. Here, we used whole genome sequencing and phenotyping to characterize 77 isolates of C. tropicalis from clinical and environmental sources from a variety of locations. We show that most C. tropicalis isolates are diploids with approximately 2–6 heterozygous variants per kilobase. The genomes are relatively stable, with few aneuploidies. However, we identified one highly homozygous isolate and six isolates of C. tropicalis with much higher heterozygosity levels ranging from 36–49 heterozygous variants per kilobase. Our analyses show that the heterozygous isolates represent two different hybrid lineages, where the hybrids share one parent (A) with most other C. tropicalis isolates, but the second parent (B or C) differs by at least 4% at the genome level. Four of the sequenced isolates descend from an AB hybridization, and two from an AC hybridization. The hybrids are MTLa/α heterozygotes. Hybridization, or mating, between different parents is therefore common in the evolutionary history of C. tropicalis. The new hybrids were predominantly found in environmental niches, including from soil. Hybridization is therefore unlikely to be associated with virulence. In addition, we used genotype-phenotype correlation and CRISPR-Cas9 editing to identify a genome variant that results in the inability of one isolate to utilize certain branched-chain amino acids as a sole nitrogen source.

59 BASIC BIOLOGICAL SCIENCES↗