Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Synthetic acceleration”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Highly-destabilized ligand field excited states of iron carbene complexes and their relation to charge transfer state lifetimes

Lifetimes of photoexcited charge transfer (CT) states in transition metal chromophores are influenced by low-lying ligand field (LF) excited states, especially for 3d metal complexes. To manipulate interactions between LF and CT states, it is important to be able to control LF excited state energies using tunable synthetic variables. In this report, we use Fe 2p3d L 3 -edge resonant inelastic X-ray scattering (RIXS) to measure LF excited state energies of three homoleptic iron chromophores coordinated by strong-field N-heterocyclic carbenes (NHCs). We investigate the effect of oxidation state and ligand scaffold on LF energies and covalency parameters. A cyclometalated bis(NHC) ligand affords both high LF excited state energies (and thus high 10 Dq) as well as high metal–ligand covalency compared to other iron complexes with very strong-field ligands. However, for the set of complexes investigated, we do not observe meaningful correlation between the LF excited state energies and the CT excited state lifetimes. These results illustrate that targeting long-lived CT excited states necessitates control of multiple molecular excited state properties, with destabilization of the LF excited state energies proving necessary, but insufficient, to control the CT excited state lifetime in Fe carbene complexes.

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

The UV Photoinduced Ring-Closing Reaction of Cyclopentadiene Probed with Ultrafast Electron Diffraction

Conjugated cyclic organic molecules are common across many fields such as pharmaceuticals, are naturally occurring in biological systems, and are used in synthetic materials. One particular area of interest from a photochemical point of view is the formation of highly strained cyclic organics. We investigate the photoinduced reaction of cyclopentadiene, a five-membered organic ring molecule which can form strained three and four carbon rings after photoexcitation with UV light, with the gas-phase ultrafast electron diffraction instrument at the SLAC MeV-UED facility. Electron diffraction offers a direct probe sensitive to the nuclear geometry during the reaction, allowing for the determination of the distribution of products formed following photoexcitation. We observe the simultaneous formation of the highly strained ring- closed bicyclo[2.1.0]pentene and vibrationally hot cyclopentadiene within the temporal resolution of the experiment, and determine the relative yield of all reaction products. Furthermore, the experimental results are in good agreement with the predictions of trajectory simulations.

Computational Chemistry↗
DOE OSTI2025

Characterizing the hadronization of parton showers using the HOMER method

We update the HOMER method, a technique to solve a restricted version of the inverse problem of hadronization – extracting the Lund string fragmentation function f(z) from data using only observable information. Here, we demonstrate its utility by extracting f(z) from synthetic Pythia simulations using high-level observables constructed on an event-by-event basis, such as multiplicities and shape variables. Four cases of increasing complexity are considered, corresponding to e + e − collisions at a center-of-mass energy of 90 ≥ v producing either a string stretched between a $q$ and $\bar{q}$ containing no gluons; the same string containing one gluon g with fixed kinematics; the same but the gluon has varying kinematics; and the most realistic case, strings with an unrestricted number of gluons that is the end-result of a parton shower. We demonstrate the extraction of f(z) in each case, with the result of only a relatively modest degradation in performance of the HOMER method with the increased complexity of the string system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Developing nanometer scale, atomically precise metallo-catalysts with Molecular Lego

We propose to create atomically precise, highly robust, nanometer scale macromolecules that organize biomimetic metal binding groups in designed three-dimensional pockets to accelerate specific polymerization reactions. These atomically precise catalysts will create atomically precise polymers with improved mechanical and environmental properties from renewable building blocks. We seek to create and study nanometer scale catalysts that mimic metallo-enzymes in their activity and selectivity because these larger catalysts will be able to make more extensive non-covalent contacts with the transition state of the growing polymer. These catalysts will have extended lifetimes because they will better protect the metals and higher activity because additional reactive groups can be brought in close contact with the active site to enhance reactivity. They will be far more tolerant to extremes of temperature and will not denature in non-aqueous solvents because they are held together by multiple, strong, covalent bonds. Our approach to catalysts could be used to develop catalysts to replace noble metal-based catalysts when targeting other reactions, because the geometry of the active site enforced by the Molecular Lego scaffolding will access new reactivity, mimicking how nature uses earth abundant metals for much of its catalysis. We will initially target Lewis acid-based catalysts that assemble aliphatic polyesters with improved stereoselectivity, tacticity and complex alternation of monomers. Aliphatic polyesters have received growing attention as attractive, environmentally benign, and sustainable alternatives to polymers developed from petroleum feedstocks. Aliphatic polyesters are environmentally friendly because they undergo facile hydrolytic degradation to benign products, and they have excellent properties and high biocompatibility. We will assemble these catalysts using unique “Spiroligomers” (aka Molecular Lego) chemistry developed in the Schafmeister group combined with the organometallic chemistry expertise of the Dobereiner group. Polymers are an attractive synthetic target of nanoscale atomically precise catalysts because their resulting structure, stereochemistry and tacticity has profound impact on their properties (crystalline vs amorphous) and provides a readout of the catalytic mechanism. Polymer synthesis also serves as an example of “atomically precise manufacturing” where atomically precise nanoscale catalysts construct polymers with atomic precision at increasing length scales.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Pressurized in situ X-ray diffraction insights into super/subcritical carbonation reaction pathways of steelmaking slags and constituent silicate minerals

This study explores mineral carbonation of industrial stainless steelmaking slags and relevant synthetic constituent minerals via in situ pressurized X-ray diffraction, to clarify carbonation reaction pathways and efficiency for carbon storage and waste valorization. The primary mineral phases of Argon Oxygen Decarburization (AOD) and Continuous Casting (CC) slags, namely, åkermanite, bredigite, cuspidine, merwinite, and β- and γ-C 2 S, were reacted in a custom-built beryllium-capped XRD reactor filled with either water-saturated (wet) subcritical CO 2 (g), or wet supercritical CO 2(SC) , in a series of carbonation experiments. Formation of calcite and aragonite was observed for most Ca-bearing minerals, transient precipitation of metastable vaterite was observed, while carbonation of AOD and CC slags in CO 2(SC) resulted in hydrated crystalline calcium carbonates and indirect evidence of amorphous carbonate. Overall, results obtained suggest that low-pressure subcritical carbonation routes are attractive for standalone slag carbonation processes, while high-pressure supercritical carbonation routes are amiable to symbiotic integration with CO 2(SC) -generating green technologies.

42 ENGINEERING↗
DOE OSTI2023

Systems Engineering of Rhodococcus opacus to Enable Production of Drop-in Fuels from Lignocellulose

Production of drop-in fuels from lignocellulose using Rhodococcus opacus PD630 (hereafter R. opacus) is a challenging goal. During the grant period we have pushed the field forward significantly in several areas of research. Towards the end goal of accelerating the adoption of R. opacus in biofuel production, during the grant period we have expanded the phenotypic characterization of R. opacus grown in single aromatic (model lignocellulosic) compounds or their mixtures, modeling the growth conditions in lignocellulosic biomass. Harnessing the power of adaptive evolution, we produced evolved R. opacus isolates with superior lignin valorization capabilities and identified differentially expressed genes and pathways after adaptation. We used next generation multi-omic techniques such as genomic, transcriptomic, and metabolomic analyses, to identify the catabolic pathways used by R. opacus to degrade aromatic compounds and funnel these degradation products into central metabolism, as well as the aromatic transport genes required for increased tolerance and utilization. Taking this information one step further, we identified endogenous transcription factors and regulatory mechanisms important for degradation of five model aromatic compounds. To accurately estimate R. opacus growth and consumption on model lignin compounds we pioneered the use of novel extraction procedures prior to GC-MS analysis. Alongside 13 C-metabolic flux analysis, we have elucidated the metabolic routes preferred by Rhodococcus opacus during aromatic compound degradation. Finally, we used in tandem lipidomics and high-resolution mass spectrometry to identify the modulation of mycolic acids and phospholipid membrane composition modification as a strategy for aromatic tolerance in R. opacus. Being a non-model organism, R. opacus lacks the breadth of tools and technical foundation which drive biofuel research in more well-understood microbes such as Escherichia coli. To reduce this burden for use, we designed and produced new tools for genomic manipulation and engineering in R. opacus. These engineering breakthroughs support efficient genomic editing, enabling gene overexpression, repression, and genetic alteration. Using these tools, we have generated synthetically engineered strains with increased lipogenesis and growth, both positive traits required for increased lignin valorization. Optimizing engineered strains for biofuel production from lignocellulose requires extremely sophisticated synthetic rewiring of metabolism. To facilitate systems-level reorganization of metabolism in R. opacus, we created a genome-scale model that accurately predicts metabolic flux and growth rates on the aromatic compound phenol. Lignin requires extensive pre-treatment before biological degradation by R. opacus. Towards an eventual goal of degrading real-world lignin, we developed new depolymerization processes to generate lignin breakdown products (LBP). We optimized LBP storage and composition analysis techniques, enabling accurate prediction of specific LBP compound integration into cell wall components. Overall, through the work funded by this grant we generated 20 manuscripts (17 published, 3 in review/preparation), methods for increased accuracy in metabolomics of aromatic compounds, multiple genetic tools for altering the R. opacus genome, genome scale models for predicting flux through metabolic pathways, as well as multi-omic data for community use. The work funded by this grant has increased the knowledge of aromatic degradation in bacteria and advanced our efforts to optimize R. opacus for lignin valorization.

09 BIOMASS FUELS↗
DOE OSTI2021

EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on ensemble learning

Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme’s function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Titanium-, Nitrogen-Doped Carbon Flowers Catalyze Electrochemical Nitrate Reduction Reaction to Ammonia

An emerging design heuristic for electrochemical nitrate reduction (NO 3 RR) catalysts is synthesizing electron-deficient sites to facilitate binding of electron-rich NO 3 – . However, this rule has rarely been applied to metal-, nitrogen-doped carbon (MNC) catalysts. Titanium (Ti), with low electronegativity and high NO 3 RR reactivity, is a compelling MNC candidate. To date, atomically dispersed TiN x motifs have eluded synthesis due to the strong oxophilicity of Ti. Here, in this work, we leverage nitrogen-rich carbon flowers (CF) to overcome synthetic challenges and produce Ti-, N-doped carbon flower (TiCF) catalysts. Advanced materials characterization demonstrates that TiCF catalysts are a mixed phase material with 3/4 of Ti atoms in TiO 2 -like nanoparticles and 1/4 of Ti atoms in novel, atomically dispersed TiN x sites. TiCF achieves 61 ± 7% NH 3 -selectivity at −0.70 V vs RHE and 14 ± 5 mA/cm 2 to NH 3 formation (| j NH 3 |) at −0.85 V vs RHE in (0.1 M NaOH + 0.1 M NaNO 3 + 0.45 M Na 2 SO 4 ) electrolyte. Control studies show both CF morphology and Ti sites are essential for high NO 3 RR activity. Density functional theory calculations attribute the NO3RR reactivity to TiN x , which facilitates multiple bond formation with surface intermediates to promote favorable NH3 synthesis pathways. Thus, TiCF exhibits 60× higher | j NH 3 | values than bulk Ti and NH 3 yield rates (>0.06 mmol NH 3 /h/cm 2 ) that are competitive with state-of-the-art MNC catalysts (e.g., FeNC, CuNC). TiCF introduces a new class of Ti electrocatalysts, advancing the MNC design space and sustainable NH 3 production.

ammonia↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗
DOE OSTI2025

X-ray Spectroscopy Characterization of Electronic Structure and Metal–Metal Bonding in Dicobalt Complexes

Developing multimetallic complexes with tunable metal–metal interactions has long been a target of synthetic inorganic chemistry efforts due to the unique properties that such compounds can exhibit. However, understanding relationships between metal–metal bonding and chemical properties is challenging due to system-dependent factors that influence metal–metal and metal–ligand interactions, including ligand identity, coordination geometry, and metal–metal distance. In this work, we apply X-ray absorption and emission spectroscopy and quantum chemical calculations to describe electronic structure and bonding in a series of dicobalt complexes. The compounds with silane ligands and pseudo-octahedral coordination geometry exhibit Co–Co σ and multicentered bonding character, which we characterize from both the occupied and vacant perspectives via their contributions to the Co X-ray emission and absorption spectra, respectively. In contrast, the dicobalt complexes with a pseudotetrahedral coordination environment do not exhibit Co–Co bonding due to symmetry constraints on orbital overlap. We extend these insights to the theoretical evaluation of related dicobalt complexes to explain how ligand coordination and symmetry dictate the presence or absence of a Co–Co bond. In conclusion, this work highlights how fundamental insights into electronic structure and bonding through X-ray spectroscopy uncover important factors governing metal–metal interactions and guide the rational design of multimetallic complexes with tunable metal–metal bonds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Dark Energy Survey year 6 results: Magnification modeling and its impact on galaxy clustering and galaxy-galaxy lensing cosmology

Gravitational lensing magnification alters the observed spatial distribution of galaxies and must be accounted for to prevent biases in cosmological probes of the large-scale structure. We investigate its effects on the Dark Energy Survey Year 6 galaxy clustering and galaxy-galaxy lensing analyses using the fiducial lens (position tracer) sample M ag L im++. Magnification bias is parameterized by a coefficient that describes the response of the number of selected objects per unlensed area element to a change in the lensing convergence. We quantify this coefficient using the BALROG synthetic source injection catalog to account for the complexity of the selection function, and compare these results with simplified estimates. The resulting values of the magnification coefficients for each redshift bin are [3.16 ± 0.08, 2.76 ± 0.21, 4.09 ± 0.15, 4.42 ± 0.16, 4.90 ± 0.29, 4.83 ± 0.25]. Relative to Year 3, this analysis provides more precise and accurate magnification bias estimates through a larger BALROG area and reweighting to better match the data properties. Here, the cosmological results are robust when tested against various magnification parameter prior choices and also when adding cross-clustering between lens redshift bins. Neglecting magnification, however, introduces significant systematic shifts: relative to the fiducial analysis with Gaussian priors centered on the BALROG -derived estimates, we observe shifts of 1.37σ in S 8 and -0.84σ in Ω m (with cosmic shear included: -0.61σ in S 8 and -0.71σ in Ω m ), in agreement with findings from simulated data, demonstrating that magnification must be modeled to avoid biases. Freeing the magnification bias in lens bin 2 leads to unphysical negative values, further justifying its exclusion from the fiducial Year 6 analysis.

Cosmological parameters↗
DOE OSTI2025

A Fast-Pass, Desorption Electrospray Ionization Mass Spectrometry Strategy for Untargeted Metabolic Phenotyping

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) provides direct analytical readouts of small molecules that can be used to characterize the metabolic phenotypes of genetically engineered bacteria. In an effort to accelerate the time frame associated with the screening of mutant libraries, we have developed a high-throughput DESI-MSI analytical workflow implementing a single raster line-scan strategy that facilitates the collection of location-resolved molecular information from engineered strains on a subminute time scale. Evaluation of this “Fast-Pass” DESI-MSI phenotyping workflow on analytical standards demonstrated the capability of acquiring full metabolic profiling information with a throughput of ~40 s per sample. This Fast-Pass strategy was implemented in the analysis of genetically edited Escherichia coli strains that have been engineered to produce various free-fatty acids (FFAs) for applications relevant to biofuels. Due to the untargeted nature of DESI-MSI, the investigation of these strains yielded molecular information for both global metabolites and targeted detection of accumulated bioproducts, allowing simultaneous readouts of strain-specific chemical profiles and comparative measurements of FFA production levels.

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

Single-Electron Transfer Stabilizes Metastable Alane in a Bipyridine-Functionalized MOF Nanopore

Nanoconfinement of metastable hydrides such as alane (AlH 3 ) in functionalized porous frameworks represents a promising yet largely untapped strategy for high-capacity energy storage. Despite its potential, the underlying mechanisms responsible for the thermodynamic stabilization of metastable hydrides are poorly understood. Here, in this study, concepts from solution Lewis acid–base chemistry were applied to a crystalline metal–organic framework (MOF) to stabilize AlH 3 . The long-range order and synthetically versatile pore chemistry enabled us to reveal the intimate details of the hydride-host interactions. Powder X-ray diffraction (PXRD) of AlH 3 -infiltrated UiO-67bpy (Zr 6 O 4 (OH) 4 (bpydc) 6 ; bpydc 2– = 2,2′-bipyridine-5,5′-dicarboxylate) confirms that the framework maintains its crystallinity up to 250 °C and is stable under 70 MPa H 2 pressure. We find that thermodynamic stabilization arises from coupled single-electron and hydrogen-atom transfer from AlH 3 to bipyridine-containing linkers. Electron paramagnetic resonance detects a bipyridyl radical with an anisotropic g-tensor (g values of 2.00132, 2.00215, and 2.00275), consistent with Bader charge analysis predicting 0.82 e – transferred from the hydride to the MOF. These findings establish critical structure–property relationships underpinning AlH 3 stabilization and suggest general strategies for tuning the thermodynamics and kinetics of metastable materials.

Shivanna, Mohana [Sandia National Laboratories (SN↗
DOE OSTI2026

Organo-mineral interactions in active layer and permafrost soils along aging Arctic landscapes

Rising temperatures are accelerating permafrost thaw, exposing large soil organic carbon (SOC) stocks to microbial decomposition with implications for global climate. Understanding how permafrost carbon is stored and protected through associations with minerals is critical for predicting its vulnerability to decomposition upon thaw. However, how landscape age, substrate chemistry, and soil depth influence mineral associations remain relatively unexplored. We investigated organo-mineral associations in active layer and permafrost soils across a landscape age and geochemical gradient on Alaska’s North Slope, spanning three glaciated (~11,500–125,000 years) and one unglaciated site. Using selective dissolution extractions, X-ray diffraction, and Mössbauer spectroscopy, we characterized minerals and their relationship with SOC. The three recently deglaciated sites had low soil pH that decreased with age and greater abundances of pyrophosphate- and oxalate-extractable Al and Fe, whereas the oldest unglaciated site exhibited near-neutral pH, greater pyrophosphate-extractable Ca, and distinct mineralogy. Across sites, SOC was positively associated with Al and Fe mineral phases, with stronger relationships in acidic soils. Pyrophosphate-extractable Ca also showed strong relationships with SOC at the acidic sites (up to ~10x greater), suggesting that Ca-mediated protection may operate beyond traditionally recognized high-pH soils. Permafrost soils showed depth-related changes in pH, SOC, and Fe mineralogy, suggesting chemically active, heterogeneous layers may shape mineral dynamics and associated carbon. Our results highlight how landscape age, parent material, and depth create distinct geochemical environments that govern mineral-organic associations. As thaw exposes soil to new conditions, these mineral-mediated protection mechanisms may be altered, potentially affecting the permafrost carbon-climate feedback.

Synthetic Biology↗
DOE OSTI2025

DESI DR2 results. I. Baryon acoustic oscillations from the Lyman alpha forest

We present the baryon acoustic oscillation (BAO) measurements with the Lyman-𝛼 (Ly⁢𝛼) forest from the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI) survey. Our BAO measurements include both the autocorrelation of the Ly⁢𝛼 forest absorption observed in the spectra of high-redshift quasars and the cross-correlation of the absorption with the quasar positions. The total sample size is approximately a factor of 2 larger than the DR1 dataset, with forest measurements in over 820,000 quasar spectra and the positions of over 1.2 million quasars. We describe several significant improvements to our analysis in this paper, and two supporting papers describe improvements to the synthetic datasets that we use for validation and how we identify damped Ly⁢𝛼 absorbers. Our main result is that we have measured the BAO scale with a statistical precision of 1.1% along and 1.3% transverse to the line of sight, for a combined precision of 0.65% on the isotropic BAO scale at 𝑧 eff =2.33. This excellent precision, combined with recent theoretical studies of the BAO shift due to nonlinear growth, motivated us to include a systematic error term in Ly⁢𝛼 BAO analysis for the first time. We measure the ratios 𝐷 𝐻 ⁡(𝑧 eff )/𝑟 𝑑 = 8.632 ± 0.098 ± 0.026 and 𝐷 𝑀 ⁡(𝑧 eff )/𝑟 𝑑 = 38.99 ± 0.52 ± 0.12, where 𝐷 𝐻 = 𝑐/𝐻⁡(𝑧) is the Hubble distance, 𝐷 𝑀 is the transverse comoving distance, 𝑟 𝑑 is the sound horizon at the drag epoch, and we quote both the statistical and the theoretical systematic uncertainty. The companion paper presents the BAO measurements at lower redshifts from the same dataset and the cosmological interpretation.

baryon acoustic oscillations↗
DOE OSTI2021

Non-destructive quantification of anaerobic gut fungi and methanogens in co-culture reveals increased fungal growth rate and changes in metabolic flux relative to mono-culture

Abstract Background Quantification of individual species in microbial co-cultures and consortia is critical to understanding and designing communities with prescribed functions. However, it is difficult to physically separate species or measure species-specific attributes in most multi-species systems. Anaerobic gut fungi (AGF) (Neocallimastigomycetes) are native to the rumen of large herbivores, where they exist as minority members among a wealth of prokaryotes. AGF have significant biotechnological potential owing to their diverse repertoire of potent lignocellulose-degrading carbohydrate-active enzymes (CAZymes), which indirectly bolsters activity of other rumen microbes through metabolic exchange. While decades of literature suggest that polysaccharide degradation and AGF growth are accelerated in co-culture with prokaryotes, particularly methanogens, methods have not been available to measure concentrations of individual species in co-culture. New methods to disentangle the contributions of AGF and rumen prokaryotes are sorely needed to calculate AGF growth rates and metabolic fluxes to prove this hypothesis and understand its causality for predictable co-culture design. Results We present a simple, microplate-based method to measure AGF and methanogen concentrations in co-culture based on fluorescence and absorbance spectroscopies. Using samples of < 2% of the co-culture volume, we demonstrate significant increases in AGF growth rate and xylan and glucose degradation rates in co-culture with methanogens relative to mono-culture. Further, we calculate significant differences in AGF metabolic fluxes in co-culture relative to mono-culture, namely increased flux through the energy-generating hydrogenosome organelle. While calculated fluxes highlight uncertainties in AGF primary metabolism that preclude definitive explanations for this shift, our method will enable steady-state fluxomic experiments to probe AGF metabolism in greater detail. Conclusions The method we present to measure AGF and methanogen concentrations enables direct growth measurements and calculation of metabolic fluxes in co-culture. These metrics are critical to develop a quantitative understanding of interwoven rumen metabolism, as well as the impact of co-culture on polysaccharide degradation and metabolite production. The framework presented here can inspire new methods to probe systems beyond AGF and methanogens. Simple modifications to the method will likely extend its utility to co-cultures with more than two organisms or those grown on solid substrates to facilitate the design and deployment of microbial communities for bioproduction and beyond.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Dark Energy Survey Year 6 results: Redshift calibration of the MagLim++ lens sample

In this work, we derive and calibrate the redshift distribution of the MagLim++ lens galaxy sample used in the Dark Energy Survey Year 6 (DES Y6) 3 x 2pt cosmology analysis. The 3 x 2pt analysis combines galaxy clustering from the lens galaxy sample and weak gravitational lensing. The redshift distributions are inferred using the SOMPZ method - a Self-Organizing Map framework that combines deep-field multi-band photometry, wide-field data, and a synthetic source injection ( B alrog) catalog. Key improvements over the DES Year 3 (Y3) calibration include a noise-weighted SOM metric, an expanded Balrog catalogue, and an improved scheme for propagating systematic uncertainties, which allows us to generate O(10 8 ) redshift realizations that collectively span the dominant sources of uncertainty. These realizations are then combined with independent clustering-redshift measurements via importance sampling. The resulting calibration achieves typical uncertainties on the mean redshift of 1-2%, corresponding to a 20-30% average reduction relative to DES Y3. We compress the n(z) uncertainties into a small number of orthogonal modes for use in cosmological inference. Marginalizing over these modes leads to only a minor degradation in cosmological constraints. Here, this analysis establishes the MagLim++ sample as a robust lens sample for precision cosmology with DES Y6 and provides a scalable framework for future surveys.

dark energy↗