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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2025

Measurement of light-by-light scattering and the Breit-Wheeler process, and search for axion-like particles in ultraperipheral PbPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

Measurements of light-by-light scattering (LbL, γγ → γγ) and the Breit-Wheeler process (BW, γγ → e + e − ) are reported in ultraperipheral PbPb collisions at a centre-of-mass energy per nucleon pair of 5.02 TeV. The data sample, corresponding to an integrated luminosity of 1.7 nb −1 , was collected by the CMS experiment at the CERN LHC in 2018. Events with an exclusively produced γγ or e + e − pair with invariant masses m γγ,ee > 5 GeV, along with other fiducial criteria, are selected. The measured BW fiducial production cross section, σ fid (γγ → e + e − ) = 263.5 ± 1.8(stat) ± 17.8(syst) μb, as well as the differential distributions for various kinematic observables, are in agreement with leading-order quantum electrodynamics predictions complemented with final-state photon radiation. The measured differential BW cross sections allow discrimination between different theoretical descriptions of the photon flux of the lead ion. In the LbL final state, 26 exclusive diphoton candidate events are observed compared with 12.0 ± 2.9 expected for the background. Combined with previous results, the observed significance of the LbL signal with respect to the background-only hypothesis is above five standard deviations. The measured fiducial LbL scattering cross section, σ fid (γγ → γγ) = 107 ± 24(stat) ± 13(syst) nb, is in agreement with next- to-leading-order predictions. Limits on the production of axion-like particles coupled to photons are set over the mass range 5–100 GeV, including the most stringent limits to date in the range of 5–10 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Full-Shape analysis of the power spectrum and bispectrum of DESI DR1 LRG and QSO samples

We present the first joint analysis of the power spectrum and bispectrum using the Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI), focusing on Luminous Red Galaxies (LRGs) and quasars (QSOs) across a redshift range of 0.4 ≤ z ≤ 2.1. By combining the two- and three-point statistics, we are able to partially break the degeneracy between the logarithmic growth rate, f(z), and the amplitude of dark matter fluctuations, σ s8 (z), which cannot be measured separately in analyses that only involve the power spectrum. In comparison with the (fiducial) Planck ΛCDM cosmology we obtain f/f fid = {0.888 -0.089 +0.186 ,0.977 -0.220 +0.182 ,1.030 -0.085 +0.368 }, σ s8 /σ fid s8 = {1.224 -0.133 +0.091 ,1.071 -0.163 +0.278 ,1.00 0 -0.223 +0.088 } respectively for the three LRG redshift bins, corresponding to a cumulative 10.1% constraint on f, and of 8.4% on σ s8 , including the systematic error budget. Additionally, we obtain constraints for the ShapeFit compressed parameters describing the isotropic scaling parameter, α iso (z), the Alcock-Paczyński parameter, α AP (z), the combined growth of structure parameter fσ s8 (z), and the combined shape parameter m(z)+n(z). Their cumulative constraints from our joint power spectrum-bispectrum analysis are respectively σ αiso = 0.9% (9% improvement with respect to our power spectrum-only analysis); σ α AP = 2.3% (no improvement with respect to power spectrum-only analysis, which is expected given that the bispectrum monopole has no significant anisotropic signal); σ fσs8 = 5.1% (9% improvement); σ m+n = 2.3% (11% improvement). These results are fully consistent with the main DESI power spectrum analysis, demonstrating the robustness of the DESI cosmological constraints, and compatible with Planck ΛCDM cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Characterization of Kariya ( Hildegardia barteri (Mast.) Kosterm) Seed Oil Fatty Acid Methyl Ester Prepared from Basic Catalytic Transesterification

The rising global energy demand, alongside concerns regarding environmental deterioration due to the use of fossil fuels, has spurred extensive investigation into renewable energy alternatives. Biomass-derived biodiesel, especially from lesser-known oil sources, emerges as a promising option. This research focuses on analyzing the fatty acid methyl esters (FAMEs) derived from Kariya (Hildegardia barteri (Mast.) Kosterm) seed oil through basic catalytic transesterification using gas chromatography–flame ionization detector (GC–FID) analysis, assessing its potential as a biodiesel feedstock. Oil extraction from Kariya seeds was carried out using three solvents (n-hexane, ethanol, and a 1:1 blend of hexane and ethanol), followed by transesterification with methanol. Gas chromatography–mass spectrometry (GC–MS) and GC–FID analyses were utilized to identify and quantify FAMEs in the resulting biodiesel. The results revealed various FAMEs, including methyl myristate, methyl palmitate, methyl stearate, methyl oleate, methyl linoleate, and methyl linolenate. Significant differences in FAME composition were observed among the samples, with hexane–ethanol Kariya oil biodiesel (HE-KOB) showing the highest FAME content (76.1%). This combination of solvents exhibited synergistic effects on the composition of HE-KOB, suggesting potential optimization strategies for biodiesel production. Fourier transform infrared spectroscopy (FTIR) provided additional insights into the molecular composition of the biodiesel samples, confirming their biodiesel nature through the identified functional groups such as methyl, methylene, hydrocarbon, ester, aldehyde, and alkene. Thermogravimetric analysis (TGA) for thermal decomposition also gave an insight into FAME composition and its contribution to the degree of conversion of biodiesel to energy. These findings highlight the feasibility of utilizing Kariya seed oil as a biodiesel feedstock, emphasizing the importance of solvent selection and transesterification conditions in optimizing FAME yield and composition. This research contributes to the exploration of underutilized oil sources for sustainable biodiesel production, aligning with the global shift towards cleaner and renewable energy sources.

09 BIOMASS FUELS↗

DOE OSTI2026

How Inverter-Based Resources (IBRs) Affect Protection Relay Elements

This presentation starts with the details of IEEE 2800-2022 requirements of IBR for balanced and unbalanced faults. Then, it shows how to design a droop based GFM control to be IEEE 2800-2022 compliant. An performance evaluation is carried out to see if three OEM GFM inverters 2800-2022 compliant or not. And finally we show how IEEE 2800-2022 complaint and non-compliant GFM inverter will impact the transmission line protection elements, such as directional element and FID. Finally, recommendations are made if the GFM is not IEEE 2800-2022 compliant to disable the protection elements that rely on reliable and regulated negative sequence current.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Measurements of inclusive and differential Higgs boson production cross sections at $\sqrt{\text{s}} = 13.6$ TeV in the H → γγ decay channel

Inclusive and differential cross sections for Higgs boson production in proton-proton collisions at a centre-of-mass energy of 13.6 TeV are measured using data collected with the CMS detector at the LHC in 2022, corresponding to an integrated luminosity of 34.7 fb −1 . Events with the diphoton final state are selected, and the measured inclusive fiducial cross section is ${\sigma }_{\text{fid}}={74}\pm {11}{\left({\text{stat}}\right)}_{-4}^{+5}\left({\text{syst}}\right)$ fb, in agreement with the standard model prediction of 67.8 ± 3.8 fb. Differential cross sections are measured as functions of several observables: the Higgs boson transverse momentum and rapidity, the number of associated jets, and the transverse momentum of the leading jet in the event. Within the uncertainties, the differential cross sections agree with the standard model predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Microstructure prediction for Ti-22Al-25Nb in laser powder bed fusion

This work presents a physics-informed framework for predicting solidification morphology and defect susceptibility in additively manufactured Ti–22Al–25Nb across a broad processing space. The framework integrates solidification microstructure selection (SMS) analysis with a single-track defect-based printability map to establish a unified methodology linking processing parameters to both interfacial morphology and manufacturability. Thermal gradients G and solidification rates R are first computed using the Thermo-Calc Additive Manufacturing (TC-AM) module, a finite-interface-dissipation (FID) phase-field (PF) model coupled with CALPHAD method is then employed to systematically distinguish planar and dendritic regimes as functions of $G$ and $R$. By superimposing the printability map onto the morphology projections, a comprehensive process–structure framework is obtained. Across most processing conditions, the predicted microstructure is predominantly dendritic, while planar growth emerges only under selected laser power $P$ and scan speed $v$ combinations. In addition to morphology classification, the framework quantifies the dendritic area fraction and introduces a width-based morphology descriptor to characterize the spatial extent of planar/dendritic regions within the melt pool. It provides mechanistic insight into the interplay between solidification physics and defect formation, offering practical guidance for parameter selection and microstructural control in Ti–22Al–25Nb additive manufacturing (AM).

36 MATERIALS SCIENCE↗

DOE OSTI2025

Bioconversion of self-neutralized chemically depolymerized lignin streams into polyhydroxyalkanoates

Lignin is a chemically complex, diverse, and abundant plant polymer mainly composed of aromatic monomers. These aromatic monomers make lignin a potential source of aromatics and a viable substitute for petrochemically-derived aromatics. However, the structural recalcitrance of lignin requires harsh reagents from the chemical process and expensive catalysts for effective depolymerization. This chemical process often results in poor yields of chemical intermediate mixtures of varying bioavailability and/or toxicity. Furthermore, the cost of additional reagents required to separate or detoxify these intermediates makes processing lignin impractical. We report progress towards the use of such chemically depolymerized lignin streams by employing bacterial strains to produce polyhydroxyalkanoates (PHA). PHAs are a group of biodegradable microbial polyesters that have potential as a replacement for petroleum-based plastics. In this study we utilized two distinct lignin streams obtained after chemical depolymerization of lignin under alkaline and acidic pH in the presence of catalysts. We mixed the alkali-treated depolymerized stream with the acid-treated depolymerized stream to create a solution of neutral-pH chemically depolymerized lignin (CDL). We found moderate to substantial growth of both native and non-native PHA producers on the mixture of CDL as well as its aliphatic and aromatic components. PHA was detected by Sudan Black B staining in C. necator H16, P. putida KT2440, and E. coli LSBJ STQKAB grown on mixed CDL as the sole carbon source. For C. necator H16 and E. coli LSBJ STQKAB we found that PHA content was greater when grown on mixed CDL when compared to their preferred carbon source by GC-FID quantification. Our study provided progress towards a cost-competitive, sustainable, and industrially relevant use for lignin.

Winkler, Gordon L. W. [Univ. of Wisconsin, Madison↗

DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗