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DOE OSTI2025

Isotopic mass effects of tritium-fueled high-performance TFTR supershots

An increase in total stored energy correlated with the addition of tritium fuel was observed in supershots during the TFTR DT campaign. This supershot regime had strikingly high, centrally peaked ion and electron temperatures, and the largest neutron emission rates observed in TFTR. This paper presents a study of the causes of this increase in stored energy in supershots. Twenty-six supershots have been recently reanalyzed with the TRANSP plasma analysis code. Early TRANSP simulations did not accurately match the measured magnitude and time evolutions of the neutron emission rates. This mismatch is attributed to neglecting apparent increases of trace amounts of heavy impurities during neutral beam injection. The new TRANSP runs were tuned to accommodate this and match the measured global neutron emission rates. These new runs also had improved fidelity in predicting the time histories and radial dependencies of measured DT neutron emission rates. That in turn adds confidence in the simulated thermal deuterium and tritium density profiles that are needed for calculating the average hydrogenic atomic mass profiles. Six subsets of these supershots had well matched toroidal field B tor , plasma current I p , flux geometry, and total injected neutral beam power. The mix of D and T beam ions was varied for different discharges. The magnitude of the increase of the thermal ion energy W i with added tritium was relatively small, and the total W tot increase is dominated by the increase in fast beam ions with T. Analyses at times before the occurrences of deleterious MHD instabilities yielded scaling of W tot with the volume-average isotopic mass consistent with previous publications. The relative fraction of fast energy ions is expected to be small in practical tokamak reactors. Thus the increase in stored energy W tot observed in TFTR supershots does not appear likely to be significantly helpful for producing useful fusion energy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

CTA and SWGO can discover Higgsino dark matter annihilation

Thermal Higgsino dark matter (DM), with a mass near 1.1 TeV, is one of the most well-motivated and untested DM candidates. Leveraging recent hydrodynamic cosmological simulations that give DM density profiles in Milky Way analog galaxies we show that the linelike gamma-ray signal predicted from Higgsino annihilation in the Galactic Center could be detected at high significance with the upcoming Cherenkov Telescope Array (CTA) and Southern Wide-field Gamma-ray Observatory (SWGO) for all but the most pessimistic DM profiles. We perform the most sensitive search to-date for the linelike signal using 15 years of data from the Fermi Large Area Telescope, coming within an order one factor of the necessary sensitivity to detect the Higgsino for some Milky Way analog DM density profiles. We show that H.E.S.S. has subleading sensitivity relative to Fermi for the Higgsino at present. In contrast, we analyze H.E.S.S. inner Galaxy data for the thermal wino model with a mass near 2.8 TeV; we find no evidence for a DM signal and exclude the wino by over a factor of two in cross section for all DM profiles considered. In the process, we identify and attempt to correct what appears to be an inconsistency in previous H.E.S.S. inner Galaxy analyses for DM annihilation related to the analysis effective area, which may weaken the DM cross-section sensitivity claimed in those works by around an order of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Sensitivities of time-dependent temperature profile predictions for NSTX with the multi-mode model

The Multi-Mode Model (MMM) for turbulent transport was applied to a large set of well-analyzed discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate its sensitivities to a wide range of plasma conditions. MMM calculations were performed for hundreds of milliseconds in each discharge by performing time-dependent predictive simulations with the 1.5D tokamak integrated modeling code TRANSP. A closely related study (Lestz et al 2025 Plasma Phys. Control. Fusion 67 105029) concluded that MMM predicted electron and ion temperature profiles that were in reasonable agreement with NSTX observations, generally outperforming a different reduced transport model, TGLF. This finding motivates the more thorough investigation of the characteristics of the MMM predictions conducted in this work. The simulations with MMM have electron energy transport dominated by electron temperature gradient modes in the examined discharges with relatively low plasma β (ratio of kinetic plasma pressure to magnetic field pressure) and high collisionality, transitioning to a mixture of different modes for higher β and lower collisionality. The thermal ion diffusivity predicted by MMM is much smaller than the neoclassical contribution, in line with previous experimental analysis of NSTX. Nonetheless, the electron and ion temperature profiles are coupled via collisional energy exchange and thus sensitive to which transport channels are predicted. The time-dependent simulations with MMM are robust to the simulation start time, converging to remarkably similar temperature profiles later during the discharge. MMM typically overpredicts confinement relative to NSTX observations, leading to the prediction of overly steep temperature profiles. Plasmas with spatially broader temperature profiles, higher plasma β, and longer energy confinement times tend to be predicted by MMM with better agreement with the experiment. As a result, these findings provide useful context for understanding the regime-dependent tendencies of MMM in anticipation of self-consistent, time-dependent predictive simulations of NSTX-U discharges with these same modeling tools.

MMM↗

DOE OSTI2026

Metabolic flux, metabolite, and transcript analysis uncover reprogramming of metabolism toward higher seed oil

Overexpression of WRINKLED1 (WRI1), a master regulator of glycolysis and fatty acid biosynthesis, together with DIACYLGLYCEROL ACYLTRANSFERASE1 (DGAT1), which catalyzes the final step of triacylglycerol assembly, is a promising strategy for enhancing seed oil content. However, how these regulators coordinate system-wide metabolic reprogramming at the levels of gene expression, metabolite pools, and fluxes remains poorly understood. To address this, we performed 13 C-metabolic flux analysis, metabolomics, and transcriptomics on in vitro cultured pennycress (Thlaspi arvense L.) embryos overexpressing the native WRI1 and DGAT1 homologs. Here, in cultured embryos, WRI1/DGAT1 overexpression increased triacylglycerol accumulation by 28% while reducing protein content by 34%, relative to the wild type. Embryos showed ∼20-fold and 50-fold upregulation of WRI1 and DGAT1 along with induction of WRI1 target genes in glycolysis and fatty acid biosynthesis. Genes associated with photosynthesis and Calvin cycle functions were also upregulated, whereas genes encoding ribosomal proteins and seed storage proteins were strongly repressed, consistent with the observed lipid–protein tradeoff. Flux analysis revealed that enhanced triacylglycerol biosynthesis is supported by increased flux through the Rubisco shunt and cytosolic pyruvate kinase, while the oxidative pentose phosphate pathway and malic enzyme contributed little to NADPH or pyruvate supply. Metabolomic profiling revealed extensive perturbations in glycolytic intermediates, tricarboxylic acid cycle metabolites, and amino acids. In plant grown seeds, WRI1/DGAT1 lines also showed a modest but significant increase in total lipid content. Collectively, these findings reveal how WRI1 and DGAT1 reprogram central metabolism to enhance oil accumulation, with relevance to mature seeds.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Salk Institute for Biological Studies Requirements (Analysis Report)

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Between February and March 2024, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from the Salk Institute for Biological Studies (Salk) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff better to understand the needs of the researchers within the community. Material for this event included the written documentation from each of the profiled research areas, documentation about the current state of technology support, and a write-up of the discussion that took place via e-mail and video conferencing. The case studies highlighted the ongoing challenges and opportunities that Salk Institute for Biological Studies have in supporting a cross-section of established and emerging research use cases. Each case study mentioned unique challenges which were summarized into common needs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Best estimate of the planetary boundary layer height from multiple remote sensing measurements

Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions.

Zhang, Damao [Pacific Northwest National Laborator↗

DOE OSTI2025

Memoirs of Mass Accretion: Probing the Edges of Intracluster Light in Simulated Galaxy Clusters

The diffuse starlight extending throughout massive galaxy clusters, known as intracluster light (ICL), has the potential to be read as a memoir of mass accretion: informative, individual, and yet imperfect. Here, we combine dark-matter-only zoom-in simulations from the Symphony suite with the Nimbus “star-tagging” model of the stellar halo to assess how much information about the mass assembly of an individual galaxy cluster can be gleaned from idealized measurements of ICL outskirts. We show that the edges of a cluster’s stellar profile—the primary (R sp⋆,1 ) and secondary (R sp⋆,2 ) stellar “splashback” radii—are sensitive to both continuous mass accretion histories (MAHs) and discrete merger events, making them potentially powerful probes of a cluster’s past. We find that R sp⋆,1 strongly correlates with the cluster’s mass ∼1 dynamical time ago, while R sp⋆,2 traces more recent MAH to a slightly lesser degree. In combination, these features can further distinguish between clusters that have and have not undergone a major merger within the past dynamical time. We use both to predict realistic cluster MAHs with the MultiCAM framework. These outer ICL features are significantly more sensitive to mass accretion and merger histories than the stellar mass gap and halo concentration, and perform comparably to the commonly used X-ray-based tracer of relaxedness, x off . While our analysis is idealized, the relevant ICL features are potentially detectable in next-generation deep imaging of nearby clusters. This work highlights the promise of ICL measurements and lays the groundwork for more detailed forecasts of their power.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Thermo-mechanical deterioration and molecular degradation of 3D-printed methacrylate-based polymer in various chemical environments

The advancement of additive manufacturing (AM) has accelerated the development of stereolithographic (SLA) photo-curable resins, particularly methacrylate-based polymers, due to the ability to their high-resolution, robust mechanical properties, and suitability. However, their long-term performance in chemical environments remains poorly understood. This study investigates the extent and mechanisms of degradation on an SLA-printed methacrylate-based polymer subjected to various chemicals, including polar and non-polar solvents, as well as strongly acidic aqueous solutions over a 12-week accelerated aging period. A comprehensive analytical approach incorporating swelling kinetics, surface morphology, tensile and dynamic mechanical analysis (DMA), Fourier-transform infrared (FTIR) spectroscopy, and mass spectrometry (MS) was employed to characterize chemical absorption, structural integrity, and leached products. Results reveal that degradation severity is governed by both the polarity and reactivity of the chemical environment. Notably, exposure to 6 mol L -1 HNO₃ induced the most severe deterioration, with over threefold higher swelling compared to other media, and significant reductions in tensile strength, tensile modulus, and glass transition temperature (T g ). In contrast, specimens aged in non-polar solvents (xylene and dodecane) exhibited negligible chemical interaction and retained mechanical performance. FTIR and MS analyses identified acid-catalyzed hydrolysis of ester groups as prominent degradation pathways in acidic media, while diffusion-controlled plasticization prevailed in polar solvents. Furthermore, this study provides valuable insights into the chemical stability of SLA-printed polymers and develops predictive degradation profiles that are crucial for designing durable polymer systems for advanced industrial use.

36 MATERIALS SCIENCE↗

DOE OSTI2024

New York-Presbyterian and Columbia University Irving Medical Center Requirements (Analysis Report)

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Between February and June 2024, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from New York-Presbyterian (NYP), Columbia University Irving Medical Center (CUIMC), and NYSERNet for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the community. Material for this event included the written documentation from each of the profiled research areas, documentation about the current state of technology support, and a write-up of the discussion that took place via e-mail and video conferencing. The case studies highlighted the ongoing challenges and opportunities that NYP and CUIMC have in supporting a cross-section of established and emerging research use cases. Each case study mentioned unique challenges which were summarized into common needs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Experimental Analysis of Advanced Turbine for Supercritical CO2 Power Cycle

An advanced first stage high pressure turbine blade for a supercritical CO2 power cycle is tested in the Big Rig for Aerothermal Stationary Turbine Analysis (BRASTA) annular cascade at the Purdue Experimental Turbine Aerothermal Laboratory (PETAL) high pressure blow down facility alongside a baseline blade for comparison of aerodynamic performance. Both geometries are tested simultaneously using a novel off-axis rig design to allow for the blade geometries to be scaled up to achieve higher Reynolds numbers. The off-axis design necessitates the use of discrete sectors of airfoils, which are designed and additively manufactured in house using an mSLA printer. Printing the blades allows for unique routing of passage for blade surface static pressure taps that contour to the blade shape instead of requiring a straight line view from surface tap to egress, allowing all instrumentation for 15%, 50%, and 85% span to exist in the same passage. A flow conditioning gauze [1] is placed upstream of the blade passages to impart pressure, Mach, and swirl profiles to mimic the rotor relative frame inlet conditions to the blade. Downstream, a sonic valve is used to alter the backpressure to achieve different pressure ratios for different blowdown setpoints. The design of the rig, sonic wheel, and instrumentation are discussed along with the manufacturing and GD&T of the blades with respect to the build-up of this novel experimental apparatus. Initial commissioning data is obtained from inlet total pressure and temperature rakes, blade static pressure taps, and exit total pressure rakes. Oil visualization is performed using a silicone oil mixture containing titanium dioxide and pigmented for contrast of the blade suction side surface with the hub and shroud endwalls. The viscosity of the oil is tuned so that the oil does not fully thin and blow away during the time of the test, and endoscopic cameras are placed in the rig for live monitoring and recording. The preliminary oil visualization and rake profiles are presented, showcasing the operability of this additively manufactured off-axis flow path design.

Tuite, Logan [Purdue University]↗

DOE OSTI2024

Testing- and Model- Based Optimization of Coal-fired Primary Heater Design for Indirect Supercritical CO 2 Power Cycles (Final Scientific and Technical Report)

The overall objective of this project was to perform the R&D necessary to mitigate the risk associated with the design of a primary heat exchanger for a solid-fired combustion system coupled with an indirect-fired closed-loop Brayton Cycle utilizing supercritical CO 2 . The key technological hurdle was the coupling of a solid-fuel firing system with the primary heater, which poses a singular challenge, which is the management of burner performance and operational conditions in a way to manage heat exchanger tube metal temperatures and temperature ramp rates in the absence of fluid phase change on the inside of the tubes. We designed and built the first ever pseudo power system employing a simple recuperated supercritical CO 2 closed-loop Brayton Cycle coupled to a solid-fuel fired system. Advanced coupled CFD and process modeling were used to design the primary heat exchanger (PHX), which consisted of both radiative and convective sections, to limit tube metal temperatures resulting from the heat release profile of the solid fuel flame near the radiative tubes. The heat exchanger was designed to produce finished CO 2 temperatures of 600 °C a pressure of 20.7 MPa and CO 2 flow of 5.5 kg/s. The constructed PHX was capable of 1.2 MWth heat uptake. During design of the PHX, the modeling showed that most variables influencing flame shape (burner stoichiometric ratio and register velocities and swirl) were not suitable to manage heat flux to the metal surfaces. This is because they substantially increased adiabatic flame temperature through the influence of localized stoichiometric ratio. Excess air and firing rate were the two most powerful variables that could be used to control tube surface temperatures. The coupled system was operated for a total of 407 hours, with the longest continuous run of 248 hours. For 62% of the operational time, the unit was unmanned and in automatic control. The fuels used for the testing included natural gas, two Utah Bituminous coals, woody biomass, and bagasse. During the testing we were able to verify the 1.2 MWth heat uptake and we operated at a finished CO 2 temperature of 607 °C and a pressure of 20.3 MPa simultaneously. The real-time corrosion rate of the Super 304H tube CO 2 surface in the region of the radiative section of the PHX were measured, at an approximate temperature of 550 °C. The two key variables related to corrosion rate are the pressure and flow rate of the CO 2 . A technoeconomic analysis was performed at a scale of 120 MWE. The updated analysis showed that the efficiency of an sCO 2 power producing plant will be related to the pressure drop of the PHX.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

Assessment of Extinction‐, Satellite‐, and Model‐Based Vertical Cloud Condensation Nuclei (CCN) Retrieval Methods Using Airborne CCN Measurements Over the Southern Great Plains

Abstract Accurate estimates of the vertical profile of cloud condensation nuclei (CCN) concentration are crucial to better quantify aerosol‐cloud interactions. We assessed the correlation between the vertical CCN concentrations obtained from extinction‐, satellite‐, and model‐based retrieval methods and airborne CCN concentrations collected at 0.24% supersaturation within the 3, 9, 27, and 81 km regions centered over the U.S. Department of Energy's Atmospheric Radiation Measurement User Facility Southern Great Plains (SGP) site during the spring and summer of 2016. The extinction profiles at a wavelength 355 nm were provided by the ground‐based Raman lidar. Our analysis showed moderate correlation between dry‐corrected extinction and airborne CCN data. We found the retrieved number concentration of CCN (RNCCN) method showed regression best‐fit slopes close to unity and consistent prediction errors for the majority of the data. The Lenhardt et al. (2023, https://doi.org/10.5194/amt‐16‐2037‐2023 ) method showed similar conclusions but only during spring, whereas the Mamouri and Ansmann (2016, https://doi.org/10.5194/acp‐16‐5905‐2016 ) method showed poor correlation. The Shinozuka et al. (2015, https://doi.org/10.5194/acp‐15‐7585‐2015 ) satellite‐based method exhibited reasonable agreement during summer but poor correlation during periods where both high (∼1,400 #/cm 3 ) and low (∼50 #/cm 3 ) airborne CCN concentrations were observed. The Copernicus Atmosphere Monitoring Service reanalysis modeled 3‐D CCN data set showed a moderate to weak positive correlation but performed poorly at high airborne CCN concentrations. Our analysis suggests the extinction‐based RNCCN method performed better than other methods across most observation periods under the diverse meteorological conditions observed at the SGP site.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Transient Modeling and Simulation of a Generic Stable Salt Reactor

A SAM system-level model of a generic stable salt reactor has been developed to investigate thermal-hydraulic behavior and safety performance under steady and transient conditions. The model integrates information generated from a reactor physics analysis using PROTEUS and PERSENT, and a computation fluid dynamics (CFD) analysis using STAR-CCM+. A loose, iterative coupling scheme between PROTEUS and SAM is implemented to calculate the equilibrium power and temperature distributions in the steady-state critical core condition. The converged steady-state model is then used in PERSENT to calculate the four reactivity feedback temperature coefficients (Doppler, fuel density, coolant density, and core radial expansion) and kinetic parameters that are needed in SAM to model the temperature feedback effects in transient simulations. Within the fully enclosed liquid fuel pins, natural convection is the dominant heat transfer mechanism. The STAR-CCM+ model of the fuel pin considers conjugate heat transfer from the liquid fuel salt to the pin cladding and external reactor coolant. The CFD results of the axial and radial temperature profiles are used to empirically determine an effective fuel salt thermal conductivity in the SAM fuel pin model so that the temperatures predicted by the SAM model match as closely as possible the CFD results. In the central region of the fuel pin, the effective thermal conductivity is as high as similar to 60 times the physical fuel salt thermal conductivity. The whole-plant SAM model is then used to simulate an unprotected station blackout transient. The results of this simulation showed that the large negative fuel axial expansion reactivity feedback reduces fission power to similar to 2.4% nominal power. The core is cooled by natural circulation, which removes heat in the core to the emergency heat removal system, and ultimately, to the ambient. However, peak fuel salt and cladding temperatures can potentially reach as high as 1500 K, albeit briefly, if the shutdown mechanism fails to operate.

stable salt reactor; transient simulations; system↗

DOE OSTI2025

Charge Reduction and Performance Analysis of a Heat Pump Water Heater Using R290 as a Refrigerant—A Field Study

Heat pump water heaters (HPWHs) are a proven technology for water heating that has been commercialized. The adoption of HPWHs for domestic and commercial water heating is growing rapidly because of their superior performance compared with alternative water heating methods. Whereas most existing systems use R-134a as a working refrigerant, R290 has gained major attention owing to its superior thermodynamic properties. The goal of the current study is to assess the performance of residential HPWH with R290 as a direct refrigerant replacement for R134a. Two units of a 50 gal HPWH were used in this experimental study. A baseline unit contained R134a refrigerant, and a prototype unit contained R290 refrigerant. The prototype unit was developed through the modification of a commercially available HPWH unit to achieve a low charge of R290 refrigerant. Another major modification was the replacement of the baseline compressor with a compressor designed for R290. Tests were conducted in a field environment (a research and demonstration house) using programmed drawn profiles daily. The prototype that reduced the charge by 43–47% provided displayed performance comparable to the baseline unit regarding first-hour rating (FHR) and the uniform energy factor (UEF).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Integrated lipidomic and proteomic profiling reveals metabolic network disruption by SARS-CoV-2 variants

The rapid evolution of SARS-CoV-2 has produced myriad viral strains with increasing transmissibility and capacity for immune evasion. While effective vaccination campaigns have reduced the fatalities associated with SARS-CoV-2, infections continue, and a detailed understanding of how this virus manipulates host biochemical pathways remains elusive. We asked both whether the patterns of host lipid rewiring remained consistent across variants and whether the changes in the abundance of lipid classes are related to changes in the expression of the enzymes involved in their biosynthesis. We compared global nontargeted lipidomics on A549-ACE2 cells infected with the delta variant (B.1.617.2), or the omicron (B.1.1.529) variant to our previous results of global nontargeted lipidomics on A549-ACE2 cells infected with the original WA1 strain and further performed quantitative proteomics to assess changes in the host proteome. We found that metabolic rewiring, both on the lipid and the enzymatic level, is remarkably consistent across all three variants. We further mapped changes in the expression of host metabolic enzymes, linking enzyme expression to alterations in the abundance of specific lipids during infection. This analysis identified key proteins related to virus-mediated changes in lipid abundance, including fatty acid synthase (FASN), lysosomal acid lipase (LIPA), and ORMDL, a regulator of sphingolipid biosynthesis. These integrated lipidomic and proteomic experiments shed light on the importance of the complex network of host metabolism networks that support SARS-CoV-2 infection and suggest that lipid metabolism may be a promising avenue for uncovering conserved therapeutic targets.

SARS-CoV-2↗

DOE OSTI2024

Improving the modeling of near-wall interphase heat transfer in porous media models of Pebble Bed Reactors

Here, this work aims to improve capabilities for modeling localized effects in porous media models of Pebble Bed Reactors. The wall-channeling effect is the primary local phenomenon of interest in a PBR, where the presence of the reflector wall disrupts the pebble packing, causing the pebbles near the wall to pack less efficiently and creating large void regions. Accurate modeling of the near-wall region is important as it will affect core bypass flow and temperature predictions. Porous media models are commonly used for design scoping and plant-level simulations of PBRs. Although these models have some capabilities to model the near-wall region, the correlations that are available in porous media codes are often inaccurate when a multi-region model is used to discretize the near-wall region. This work employs a high-to-low analysis to study the accuracy of available interphase heat transfer closures. NekRS, a spectral element computational fluid dynamics code, is used to perform Large Eddy Simulations. These LES simulation results are compared to porous media model results from the Pronghorn porous media code. The friction term of the KTA drag closure is first improved, reducing the error in the prediction of the near-wall velocity from over 50% to less than 5%. This is combined with improvements to the form term from previous works to produce a drag closure that is capable of accurately modeling the wall-channeling effect across a variety of flow conditions. The Nusselt number predictions of several heat transfer correlations are compared to the high-fidelity results where it is found that the KTA heat transfer correlation is capable of accurately predicting the local Nusselt numbers that were determined in the high-fidelity simulation. Comparison of the radial solid temperature profiles, however, reveal discrepancies between NekRS and Pronghorn. It is discovered that the implementation of the interphase heat transfer coefficient that exists in many current porous media codes is not valid when local porosities are modeled. Instead, it is suggested that the interphase heat transfer coefficient should be dependent on the local porosity, the Nusselt number, and the local solid surface-to-volume ratio. Implementation of this change produces improvement in the agreement between the results obtained by NekRS and Pronghorn while using the KTA heat transfer correlation.

interphase heat transfer↗

DOE OSTI2025

Novel CHI3L1 ‐Associated Angiogenic Phenotypes Define Glioma Microenvironments: Insights From Multi‐Omics Integration

ABSTRACT The CHI3L1 signaling pathway significantly influences glioma angiogenesis, but its role in the tumor microenvironment (TME) remains elusive. We propose a novelCHI3L1‐associated vascular phenotype classification for glioma through integrative analyses of multiple datasets with bulk and single‐cell transcriptome, genomics, digital pathology, and clinical data. We investigated the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes through a comprehensive multi‐omics approach. We constructed the vascular‐related risk (VR) score based onCHI3L1‐associated vascular signatures (CAVS) identified by machine learning algorithms. Utilizing unsupervised consensus clustering, gliomas were stratified into three distinct vascular phenotypes: Cluster A, marked by high vascularization and stromal activation with a relatively low levels of tumor‐infiltrating lymphocytes (TILs); Cluster B, characterized by moderate vascularization and stromal activity, coupled with a high density of TILs; and Cluster C, defined by low vascularization and sparse immune cell infiltration. We observed that the CAVS effectively indicated glioma‐associated angiogenesis and immune suppression by single‐cell RNA‐seq analysis. Moreover, the high‐VR‐score group exhibited enhanced angiogenic activity, reduced immune response, resistance to immunotherapy, and poorer clinical outcomes. The VR score independently predicted glioma prognosis and, combined with a nomogram, provided a robust clinical decision‐making tool. Potential drug prediction based on transcription factors for high‐risk patients was also performed. Our study reveals thatCHI3L1‐associated vascular phenotypes shape distinct immune landscapes in gliomas, offering insights for optimizing therapeutic strategies to improve patient outcomes.

Oncology↗

DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗