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DOE OSTI2021

Conjugation- and Aggregation-Directed Design of Covalent Organic Frameworks as White-Light-Emitting Diodes

2D covalent organic frameworks (COFs) have emerged as a promising class of organic luminescent materials due to their structural diversity, which allows the systematic tuning of organic building blocks to optimize emitting properties. However, a significant knowledge gap exists between the design strategy and the fundamental understanding of the key structural parameters that determine their photophysical properties. Here, in this work, we report two highly emissive sp 2 -C-COFs and the direct correlation of the structure (conjugation and aggregation) with their light absorption/emission, charge transfer (CT), and exciton dynamics, the key properties that determine their function as luminescent materials. We show that white light can be obtained by simply coating COFs on an LED strip or mixing the two COFs. Using the combination of time-resolved absorption and emission spectroscopy as well as computational prediction, we show that the planarity, conjugation, orientation of the dipole moment, and interlayer aggregation not only determine the light-harvesting ability of COFs but also control the exciton relaxation pathway and photoluminescent quantum yield.

14 SOLAR ENERGY↗

DOE OSTI2023

Exceptional enhancement of mechanical properties in high-entropy alloys via thermodynamically guided local chemical ordering

Understanding the local chemical ordering propensity in random solid solutions, and tailoring its strength, can guide the design and discovery of complex, paradigm-shifting multicomponent alloys. First, we present a simple thermodynamic framework, based solely on binary enthalpies of mixing, to select optimal alloying elements to control the nature and extent of chemical ordering in high-entropy alloys (HEAs). Next, we couple high-resolution electron microscopy, atom probe tomography, hybrid Monte-Carlo, special quasirandom structures, and density functional theory calculations to demonstrate how controlled additions of Al and Ti and subsequent annealing drive chemical ordering in nearly random equiatomic face-centered cubic CoFeNi solid solution. We establish that short-range ordered domains, the precursors of long-range ordered precipitates, inform mechanical properties. Specifically, a progressively increasing local order boosts the tensile yield strengths of the parent CoFeNi alloy by a factor of four while also substantially improving ductility, which breaks the so-called strength–ductility paradox. Finally, we validate the generality of our approach by predicting and demonstrating that controlled additions of Al, which has large negative enthalpies of mixing with the constituent elements of another nearly random body-centered cubic refractory NbTaTi HEA, also introduces chemical ordering and enhances mechanical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Triboplasma assisted chemical conversion in granular systems: a semi-analytic model

We present a semi-analytic model for a novel plasma-assisted chemical conversion pathway using triboplasmas generated in granular flows. Triboelectric charge relaxation is a well known phenomena where the potential generated from contact charging of particles exceeds the breakdown voltage of the background gas. In this work, we extend the triboelectric charge relaxation theory to include non equilibrium plasma energy and particle balance equations to predict the formation of dissociated and excited species that act as precursors to chemical conversion, for example in plasma-assisted ammonia synthesis. Our example case study with nitrogen background gas and teflon/aluminum tribomaterial system yielded high excited nitrogen species densities per collision that are comparable to current plasma-assisted conversion pathways. We also present a regime diagram for various gases where Paschen breakdown parameters are used to determine whether triboplasmas can be formed for a given effective work-function difference between two materials. Our sensitivity studies indicate particle velocity, particle radius, solids fraction and space charge effects play a critical role in overall plasma densities and excited species production.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Evolution of structure growth during dark energy domination: Insights from the cross-correlation of DESI galaxies with CMB lensing and galaxy magnification

We use a hybrid effective field theory (HEFT) model to constrain the evolution of low-redshift (z≲0.4) matter fluctuations by cross-correlating DESI Bright Galaxy Survey (BGS) legacy imaging with the latest cosmic microwave background (CMB) lensing maps from Planck and Atacama cosmology telescope. Our tomographic BGS analysis finds that the evolution and amplitude of matter fluctuations align with CMB-conditioned Λ cold dark matter predictions. When including DESI baryon acoustic oscillation (BAO) measurements we obtain σ8=0.876-0.067+0.051 from BGS alone. Jointly analyzing BGS and luminous red galaxy (LRG) cross-correlations with the same CMB lensing maps yields σ8=0.791±0.021. As a complementary approach we isolate the galaxy magnification signal from the cross-correlation of nonoverlapping BGS and LRG photometric redshift bins, ruling out the null-magnification hypothesis at 11σ. For the first time, we constrain quasilinear structure growth from the (finite-difference calibrated) galaxy magnification signal and find σ8=0.720±0.047(stat)±0.050(sys) assuming a linear bias model and including BAO data.

Sailer, Noah↗

DOE OSTI2025

Dark energy survey year 3 results: Cosmological constraints from cluster abundances, weak lensing, and galaxy clustering

Galaxy clusters provide a unique probe of the late-time cosmic structure and serve as a powerful independent test of the Λ⁢CDM model. This work presents the first set of cosmological constraints derived with ∼16,000 optically selected redMaPPer clusters across nearly 5000 deg 2 using DES year 3 datasets. Our analysis leverages a consistent modeling framework for galaxy cluster cosmology and DES-Y3 joint analyses of galaxy clustering and weak lensing (3 × 2⁢pt), ensuring direct comparability with the DES-Y3 3 × 2⁢pt analysis. Here, we obtain constraints of 𝑆 8 = 0.864 ± 0.035 and Ω m = $0.26⁢5^{+0.019}_{−0.031}$ from the cluster-based data vector. We find that cluster constraints and 3 × 2⁢pt constraints are consistent under the Λ⁢CDM model with a posterior predictive distribution (PPD) value of 0.53. The consistency between clusters and 3 × 2⁢pt provides a stringent test of Λ⁢CDM across different mass and spatial scales. Jointly analyzing clusters with 3 × 2⁢pt further improves cosmological constraints, yielding 𝑆 8 = $0.81⁢1^{+0.022}_{−0.020}$ and Ω m = $0.29⁢4^{+0.022}_{−0.033}$, a 24% improvement in the Ω m − 𝑆 8 figure of merit over 3 × 2⁢pt alone. Moreover, we find no significant deviation from the Planck CMB constraints with a probability to exceed (PTE) value of 0.6, significantly reducing previous 𝑆 8 tension claims. Finally, combining DES 3 × 2⁢pt, DES clusters, and Planck CMB places an upper limit on the sum of neutrino masses of ∑𝑚 𝜈 < 0.26 eV at 95% confidence under the Λ⁢CDM model. These results establish optically selected clusters as a key cosmological probe and pave the way for cluster-based analyses in upcoming stage-IV surveys such as LSST, Euclid, and Roman.

Abbott, T. M. C. [NSF’s National Optical-Infrared ↗

DOE OSTI2025

First JSNS 2 measurement of the electron neutrino flux using the 12 C⁡(𝜈 𝑒 ,𝑒 − )⁢ 12 N g.s. reaction

JSNS2 (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment searching for sterile neutrinos through the observation of ν¯μ→ν¯e appearance oscillations, using neutrinos produced by muon decay-at-rest. A key aspect of the experiment involves accurately understanding the neutrino flux and the quantities of pions and muons, which are progenitors of (anti)neutrinos, given that their production rates have yet to be measured. We present the first electron-neutrino flux measurement using C12(νe,e−)12Ng.s. reaction in JSNS2, yielding a flux of (6.7±1.6(stat)±1.7(syst))×10−9 cm−2 proton−1 at the JSNS2 detector location, located at 24 meters distance from the mercury target. This flux measurement is consistent with predictions from simulations based on hadron models.

Particle decays↗

DOE OSTI2020

Clean 2D superconductivity in a bulk van der Waals superlattice

Alternative route to a 2D superconductor Single layers of transition metal dichalcogenides exhibit exotic properties, including superconductivity. The usual route to obtaining such samples is to exfoliate a three-dimensional (3D) crystal. Devarakonda et al. instead grew a superlattice comprising alternating layers of the transition metal dichalcogenide hexagonal NbS 2 and the material Ba 3 NbS 5 (see the Perspective by Schoop). The inert Ba 3 NbS 5 layers serve to dissociate the superconducting NbS 2 layers from one another, resulting in 2D superconductivity with high carrier mobility. The combination of high mobility and reduced dimensionality may give rise to exotic quantum phases. Science , this issue p. 231 see also p. 170

Science & Technology - Other Topics↗

DOE OSTI2021

$\mathrm {K_S}^{0}$- and (anti-)$\Lambda $-hadron correlations in $pp$ collisions at ${\sqrt{s}} = 13$ TeV

Two-particle Azimuthal correlations are measured with the ALICE apparatus in pp collisions at √s = 13 TeV to explore strangeness- and multiplicity-related effects in the fragmentation of jets and the transition regime between bulk and hard production, probed with the condition that a strange meson (KS o ) or baryon (Λ) with transverse momentum p T > 3 GeV/c is produced. Azimuthal correlations between kaons or Λ hyperons with other hadrons are presented at midrapidity for a broad range of the trigger (3 < $p$$^{trigg}_{T}$ < 20 GeV/c) and associated particle p T (1 GeV/c < $p$$^{assoc}_{T}$< $p$$^{trigg}_{T}$), for minimum-bias events and as a function of the event multiplicity. The near- and away-side peak yields are compared for the case of either K S o or Λ(Λ¯) being the trigger particle with that of inclusive hadrons (a sample dominated by pions). In addition, the measurements are compared with predictions from PYTHIA 8 and EPOS LHC event generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Assessing the Impact of a Forest Canopy on Near-Surface Wind Statistics

Representing the forest canopy in atmospheric numerical models should improve simulated winds within and above the canopy up to a few hundred meters above the ground. Here, in this study, we implement a forest canopy parameterization into the Weather Research and Forecasting (WRF) Model in a large-eddy simulation (LES) mode by applying drag forces across multiple layers within the canopy height. We use unique observations from the Lidar Experiments for Assessing Flow over Forests (LEAFF) field campaign at the Wind River Experimental Forest (WREF) in the U.S. Pacific Northwest to evaluate model performance. In a 2-day case study, the canopy parameterization improved wind predictions both within and above the canopy, particularly during the daytime and at finer grid resolution. Without it, winds were frequently overpredicted above the canopy. Similarly, derived quantities such as the wind shear index also yielded estimates closer to observations with the canopy parameterization implemented. These findings suggest that representing the canopy using drag forces alone can improve simulated mean winds up to 200 m above the surface. Furthermore, second-order statistical moments of wind were more sensitive to canopy density than first-order moments, especially during the daytime. This increased sensitivity and the improved daytime performance in wind speed—evidenced by the lowest bias from observations (3% compared to 20% over diurnal cycle)—imply that winds above the canopy layer are strongly influenced by how well turbulence above the canopy is modeled. The results of this study can serve as a foundation for parameterizing forest canopy effects in coarser weather forecast models.

Energy - Wind↗

DOE OSTI2023

Water mass age structures the auxiliary metabolic gene content of free-living and particle-attached deep ocean viral communities

Viruses play important roles in the ocean’s biogeochemical cycles. Yet, deep ocean viruses are one of the most under-explored fractions of the global biosphere. Little is known about the environmental factors that control the composition and functioning of their communities or how they interact with their free-living or particle-attached microbial hosts. We analysed 58 viral communities associated with size-fractionated free-living (0.2–0.8 μm) and particle-attached (0.8–20 μm) cellular metagenomes from bathypelagic (2150–4018 m deep) microbiomes obtained during the Malaspina expedition. These metagenomes yielded 6631 viral sequences, 91% of which were novel, and 67 represented high-quality genomes. Taxonomic classification assigned 53% of the viral sequences to families of tailed viruses from the order Caudovirales. Computational host prediction associated 886 viral sequences to dominant members of the deep ocean microbiome, such as Alphaproteobacteria (284), Gammaproteobacteria (241), SAR324 (23), Marinisomatota (39), and Chloroflexota (61). Free-living and particle-attached viral communities had markedly distinct taxonomic composition, host prevalence, and auxiliary metabolic gene content, which led to the discovery of novel viral-encoded metabolic genes involved in the folate and nucleotide metabolisms. Water mass age emerged as an important factor driving viral community composition. We postulated this was due to changes in quality and concentration of dissolved organic matter acting on the host communities, leading to an increase of viral auxiliary metabolic genes associated with energy metabolism among older water masses. These results shed light on the mechanisms by which environmental gradients of deep ocean ecosystems structure the composition and functioning of free-living and particle-attached viral communities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Pipeline Structural Stability Under Nuclear Blast Pressure Loading

This report documents a set of simplified models to predict pipeline collapse under nuclear pressure loading. After a review of pipeline design literature, a set of simple expressions have been selected to represent an approximation of the threshold pressure for failure from cross- sectional yielding, cross-section buckling, and longitudinal buckling. These expressions provide a first-order approximation on load levels needed to achieve collapse. As a demonstration, the collapse pressure for a set of representative pipelines are calculated. Estimated pressure fields are also computed for a set of nuclear detonations, providing estimates of the ground range limit for pipeline collapse.

42 ENGINEERING↗

DOE OSTI2021

ABLE direct drive multi-shell NIF campaign [Slides]

The ABLE campaign is validating design and fabrication capabilities for multi-shell high-yield NIF targets. PDD double shell of outer two shells validates both hydroefficiency and shell collision efficiency for innovative NIF ignition concept. The goal is to obtain design hydro-efficiency and collision efficiency with predictive shell symmetry. The results will be used to determine if further development of this ICF concept is warranted.

42 ENGINEERING↗

DOE OSTI2026

Gearbox bearing crack growth prognostics and uncertainty quantification with physics-informed machine learning

This paper introduces the extreme theory of functional connections (X-TFC), a physics-informed machine learning algorithm, and tailors it to estimate the remaining useful life (RUL) of wind turbine gearbox bearings experiencing fatigue crack growth. Unlike purely data-driven methods, X-TFC embeds a physics model, based on Head's theory in this work, into its training objective. The core of X-TFC is a random-projection single-layer neural network trained via an extreme learning machine, which requires only limited damage progression data and solves for output weights with a least-squares optimization algorithm. A composite loss function balances the network's fit to observed degradation data against the residuals of the governing crack growth differential equation, ensuring the learned damage trajectory remains physically plausible. When applied to a vibration-based health-index (HI) dataset measured during the growth of a crack on the inner ring of a high-speed bearing in a wind turbine gearbox (Bechhoefer and Dubé, 2020), X-TFC achieves near-zero prediction bias. Even when trained on only the first 10 %–20 % of the damage progression data, with sufficient physics weighting its predictions remain monotonic and smooth, delivering high prognosability and trendability. To quantify the epistemic uncertainty, we employ a Monte Carlo ensemble of independently initialized X-TFC models trained on noise-perturbed data, which yields confidence intervals around each RUL estimate and captures both model-parameter and epistemic uncertainty. In addition to a vibration-based HI, we demonstrate that the proposed framework can be directly applied to a supervisory control and data acquisition (SCADA) data-based HI (Eftekhari Milani et al., 2026) measured during similar wind turbine gearbox bearing crack faults, preserving its accuracy and interpretability. This extension shows the versatility of our approach, which is applicable to bearings of multiple gearbox manufacturers, models, and ratings using only SCADA data. By integrating domain knowledge with machine learning, X-TFC offers a rapid, reliable tool for crack prognostics. Its adaptability to other bearing failure modes, such as pitch bearing ring cracks, positions X-TFC as a powerful enabler of data-driven, physics-informed asset management in the wind energy sector and beyond.

17 WIND ENERGY↗

DOE OSTI2025

Prediction of Redox Potentials for Different Oxidation States of U, Np, Pu, and Am in Alkaline Aqueous Solution

The redox potentials for U, Np, Pu, and Am for oxidation states +III up to +VIII in alkaline aqueous solutions were predicted using density functional theory (DFT) and small-core pseudopotentials and their basis sets, with a hybrid explicit/implicit solvent model using SHE = 4.28 V. For each oxidation state, various oxo/hydroxo complexes were evaluated resulting in a variety of one-electron redox pathways. For An(VIII/VII) couples, the predicted redox potentials for the [An(VIII)O 5 (OH)] -3 /[An(VII)O 4 (OH) 2 ] -3 , or [An(VIII)O 4 (OH) 2 ] -2 /[An(VII)O 4 (OH) 2 ] -3 couples are in good agreement with existing estimates. For An(VII/VI) redox couples, all couples, particularly [An(VII)O 4 (OH) 2 ] -3 /[An(VI)O 2 (OH) 4 ] -2 , were in agreement with experimental values for U, Np, and Pu, but the results for Am showed larger differences from the estimated potentials. The An(VI/V) couples were consistent with experiment for dioxo/tetrahydroxo couples, and the An(V/IV) couples showed acceptable agreement based on actinide-specific couples, with neutral hydroxides often favored in the +IV state. The An(IV/III) couples were consistent with literature values when modeled as soluble neutral hydroxides. The use of our approach yielded calculated redox potentials that were within ±0.2 V from experimental or estimated values consistent with our prior calculations on redox potentials of actinides from Ac to Am in acidic aqueous solutions. Finally, as a result, this supports the robustness of our DFT-based methodology for predicting actinide redox potentials, offering valuable insights for actinide chemistry in aqueous solutions.

Actinides↗

DOE OSTI2022

NIR Dataset and Models (Near-Infrared Spectroscopy Calibration Dataset for Butanediol Fermentation and Multivariate Calibration Models) [SWR-22-60]

2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be used in a variety of chemical feedstocks, liquid fuels, and biosynthetic building blocks. While 2,3-BDO can be efficiently produced by fermentation, the fermentation requires continuous monitoring and control to maximize 2,3-BDO yields and minimize inhibitory coproducts. Because of the time required for sampling and at-line measurement of fermentation samples with high pressure liquid chromatography (HPLC), the ability for operators to perform real-time modification to fermentation conditions is limited. To overcome this challenge, researchers from the National Renewable Energy Laboratory (NREL) have developed a calibration model which can predict the concentration of several analytes in real-time using near-infrared (NIR) spectra of the filtered fermentation broth. While significantly reducing the need for off-line sampling, NREL expects this technology to play a critical role in maximizing 2,3-BDO production. 2,3-butanediol (2,3-BDO) is a useful chemical platform that can be used to create a variety of products. For instance, 2,3-BDO can be (1) dehydrated and converted into methyl ethyl ketone, a liquid fuel additive or (2) deoxydehydrated into 1,3-butadiene for synthetic rubber, which can also be oligomerized in high yields to gasoline, diesel, and jet fuel. In order to maximize 2,3-BDO production, frequent measurement of fermentation samples is needed, as small changes in oxygen concentration can drive the fermentation to undesired products. For example, oxygen-deficient conditions result in glycerol production, while excess oxygen concentrations result in acetoin production. This results in the need for measuring dissolved oxygen, glucose, and xylose concentrations in order to optimize the aeration rate of the fermentation. Traditional monitoring methods occurs off-line and can take up to 30 minutes per sample. With multiple fermenters and high-pressure liquid chromatography (HPLC) injectors, resulting in the need for multiple samples, the sampling process can take hours to complete. NREL’s calibration model can predict the glucose, xylose, 2,3-BDO, acetoin, and glycerol concentrations from NIR spectra of filtered fermentation liquor samples. Using a partial least-squares (PLS) calibration model, NREL’s model can monitor the concentration of these analytes during subsequent fermentations at bench- and pilot-scale, demonstrating the utility of NIR spectroscopy combined with chemometrics for real-time, at-line monitoring of 2,3-BDO fermentations.

Wolfrum, Edward↗

DOE OSTI2023

Proxy quality control of biomass particles using thermogravimetric analysis and Gaussian process regression models

Abstract The temperature experienced by reactants during preparation in a reactor is a key component in determining the yield and homogeneity of usable chemical products such as biomass particles. Thermocouples with sensors can be used to monitor spatial temperature gradients within reactors but these sensors are often too expensive and/or invasive. The present work proposes a strategy to identify optimal machine learning models to infer the maximum effective temperature experienced by particles during oxidative biomass torrefaction using key thermochemical combustion parameters. The maximum rate of weight loss, the corresponding temperature, and fixed carbon content on a dry‐ash‐free basis are used as literature‐based predictor variables obtained from thermogravimetric analysis. The evaluation of 24 machine‐learning models using the standard tenfold cross‐validation method suggests that the exponential Gaussian process regression (GPR) model is the most effective, followed by other GPR models. These high‐performing GPR models were also utilized to predict the effective preparation temperature distribution of reactor‐produced biomass particles under eight conditions of varying residence time and air‐to‐biomass ratio. The effective preparation temperature and residence time of individual biomass particles were then encoded into the torrefaction severity factor and used to estimate the energy yield of the reactor output as a novel quality control method. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗

DOE OSTI2026

Rapid Coal-Ash Characterization using Geophysical Methods & Machine Learning

Coal combustion products (CCP) are challenging to delineate in heterogeneous field settings. Conventional methods (test pits, coring, and laboratory analyses) are labor-intensive, slow, invasive, and provide sparse spatial coverage. This study evaluates whether rapid non-invasive geophysical screening methods—induced polarization (IP), magnetic susceptibility, and nuclear magnetic resonance (NMR) —combined with surface colorimetry (RGB_24), can discriminate CCP-soil mixtures and provide reliable estimates of CCP content. Laboratory measurements were collected on five CCP-soil mixtures (series) and modeled using (i) a linear baseline, (ii) a calibrated non-linear (power-mean) model, and (iii) a machine-learning (ML) Random Forest approach, with validation via leave-one-series-out and site-specific tests. Across the five series, individual signals—particularly IP and magnetic susceptibility—were strongly predictive of ash content but were consistently outperformed by combined models. The pooled calibrated non-linear and ML models captured the observed non-linearity and achieved high accuracy and precision, improving on linear fits. Colorimetry showed the weakest direct relationship with ash content for the tested samples but improved performance when included in multi-signal models. At pre-selected 3.5% decision threshold, calibrated and ML approaches yielded near-perfect classification (Matthews correlation coefficient ˜ 1), suggesting strong practical operability for field screening. Additionally, field-analog tests highlighted the role of endmembers—accuracy declined without access to end-member measurements but was largely recovered by collecting a minimal labeled pair for local recalibration. With end members, accuracy remained high. Globally trained models performed well on three operational unknowns; however, series-specific refits provided the most accurate predictions. Overall, these results highlight the potential of combining rapid geophysics and minimal local calibration for improved coal-ash delineation.

Peshtani, Klaudio↗

DOE OSTI2025

Prediction of neutron production and energy spectrum by the inverse kinematic reaction between an incident 7 Li 3+ beam and a proton target in PHITS

A neutron source using the inverse kinematic reaction between lithium and proton, p( 7 Li, n) 7 Be, achieves forward-directed neutrons, potentially enhancing neutron yield in the forward direction. Despite the advantage, no evaluated-cross-section data for this reaction can be used in Monte Carlo simulation codes, such as PHITS. To solve this problem, this study aims to evaluate the applicability of the user-defined cross-section data, Frag data, for p ( 7 Li, n) 7 Be in PHITS. The simulations reproduced collisions between 7 Li 3+ ions and polypropylene targets. The Frag data was edited based on the JENDL-5 by utilizing the two-body collision kinematics. The neutron yield and angular distribution were investigated in the simulation. As a result, the forward neutron convergence with a reasonable neutron yield and energy spectrum was observed. The expected neutron yield in the forward 1-steradian area is 2.46 × 10 10 n/s when lithium-ion energy and current are 16.45 MeV and 0.1 mA.

43 PARTICLE ACCELERATORS↗