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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Symbol alphabets in QCD and flag cluster algebras

The full 245-letter symbol alphabet for all planar massless two-loop six-point Feynman integrals was recently determined in arXiv:2412.19884 and arXiv:2501.01847. In a parallel mathematical development, it was shown in arXiv:2408.14956 that there is an embedding of the cluster algebra associated to the partial flag variety $\mathcal{Fl}$ $2,n-2;n$ , which describes the kinematics of n massless particles, into that of the Grassmannian Gr(n–2, 2n–4). In this paper we connect these developments by showing that most of the rational symbol letters can be expressed in terms of flag cluster variables, and that all of the algebraic symbol letters arise from infinite mutation sequences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bacteria-mediated dsRNA delivery for mosquito-borne virus control

Mosquito-borne viruses represent an increasing global public health threat, exacerbated by urbanisation and climate change, thus making effective mosquito control essential. RNA interference (RNAi), a sequence-specific gene regulation mechanism, can be a flexible vector control tool. RNAi effectors, such as double-stranded RNA (dsRNA), can target mosquito genes or the viruses they carry, disrupting development or suppressing infection. However, current RNAi delivery methods are ineffective. Engineered bacterial symbionts offer a promising alternative for delivery, as they can produce dsRNA directly within mosquitoes. However, bacterial RNAi delivery in mosquitoes remains underexplored. We review emerging genetic tools, insights from RNAi and bacteria–mosquito interactions to outline priorities for realising bacterial RNAi as an efficient and sustainable vector control strategy.

Biological and medical sciences↗
DOE OSTI2025

Calibration of RAFM Micromechanical Model for Creep Using Bayesian Optimization for Functional Output

A Bayesian optimization procedure is presented for calibrating a multimechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8–9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600°C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework (Huang et al., 2021, “Bayesian optimization of functional output in inverse problems,” Optim. Eng., 22, pp. 2553–2574). Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. In conclusion, validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel.

42 ENGINEERING↗
DOE OSTI2024

Unveiling the microbial realm with VEBA 2.0: a modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic and viral multi-omics from either short- or long-read sequencing

Abstract The microbiome is a complex community of microorganisms, encompassing prokaryotic (bacterial and archaeal), eukaryotic, and viral entities. This microbial ensemble plays a pivotal role in influencing the health and productivity of diverse ecosystems while shaping the web of life. However, many software suites developed to study microbiomes analyze only the prokaryotic community and provide limited to no support for viruses and microeukaryotes. Previously, we introduced the Viral Eukaryotic Bacterial Archaeal (VEBA) open-source software suite to address this critical gap in microbiome research by extending genome-resolved analysis beyond prokaryotes to encompass the understudied realms of eukaryotes and viruses. Here we present VEBA 2.0 with key updates including a comprehensive clustered microeukaryotic protein database, rapid genome/protein-level clustering, bioprospecting, non-coding/organelle gene modeling, genome-resolved taxonomic/pathway profiling, long-read support, and containerization. We demonstrate VEBA’s versatile application through the analysis of diverse case studies including marine water, Siberian permafrost, and white-tailed deer lung tissues with the latter showcasing how to identify integrated viruses. VEBA represents a crucial advancement in microbiome research, offering a powerful and accessible software suite that bridges the gap between genomics and biotechnological solutions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing

Metal halide perovskite (MHP) solar cells exhibit a metastable response to bias governed by coupled ionic–electronic processes, complicating the conventional reciprocity relation between luminescence intensity and device open-circuit voltage (V oc ). This limits the use of luminescence as a diagnostic for device screening or accelerated stress testing, motivating new approaches that can interpret photoluminescence (PL) signals under nonequilibrium conditions. From the artificial intelligence perspective, we develop an explainable deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) layers, and an attention mechanism to learn spatiotemporal features from operando photoluminescence PL image sequences. The model achieves a mean absolute error of ±0.027 V in predicting open-circuit voltage transients and reduces extreme-tail errors by up to 78% compared to physics-based reciprocity calculations. Gradient-weighted Class Activation Mapping (Grad-CAM) provides interpretability by highlighting physically meaningful regions such as electrode edges and emergent defect features. From the engineering application perspective, this framework enables accurate, contactless prediction of device V oc and identification of degradation-relevant features during accelerated aging of perovskite solar cells. This approach demonstrates how explainable AI can enhance operando diagnostics and reliability analysis in photovoltaic devices under nonequilibrium conditions.

14 SOLAR ENERGY↗
DOE OSTI2024

High phenotypic and genotypic plasticity among strains of the mushroom-forming fungus Schizophyllum commune

Schizophyllum commune is a mushroom-forming fungus notable for its distinctive fruiting bodies with split gills. It is used as a model organism to study mushroom development, lignocellulose degradation and mating type loci. It is a hypervariable species with considerable genetic and phenotypic diversity between the strains. In this study, we systematically phenotyped 16 dikaryotic strains for aspects of mushroom development and 18 monokaryotic strains for lignocellulose degradation. There was considerable heterogeneity among the strains regarding these phenotypes. The majority of the strains developed mushrooms with varying morphologies, although some strains only grew vegetatively under the tested conditions. Growth on various carbon sources showed strain-specific profiles. The genomes of seven monokaryotic strains were sequenced and analyzed together with six previously published genome sequences. Moreover, the related species Schizophyllum fasciatum was sequenced. Although there was considerable genetic variation between the genome assemblies, the genes related to mushroom formation and lignocellulose degradation were well conserved. These sequenced genomes, in combination with the high phenotypic diversity, will provide a solid basis for functional genomics analyses of the strains of S. commune.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Flow matching meets biology and life science: a survey

Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological research and discovery, enabling breakthroughs in molecule design, protein generation, catalysis discovery, drug discovery, and beyond. At the same time, biological applications have served as valuable testbeds for evaluating the capabilities of generative models. Recently, flow matching has emerged as a powerful and efficient alternative to diffusion-based generative modeling, with growing interest in its application to problems in biology and life sciences. This paper presents the first comprehensive survey of recent developments in flow matching and its applications in biological domains. We begin by systematically reviewing the foundations and variants of flow matching, and then categorize its applications into three major areas: biological sequence modeling, molecule generation and design, and peptide and protein generation. For each, we provide an in-depth review of recent progress. We also summarize commonly used datasets and software tools, and conclude with a discussion of potential future directions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Spatiotemporal forecasting of the edge localized modes in tokamak plasmas using neural networks

Artificial intelligence techniques have been increasingly adopted by the plasma and fusion science to address problems like plasma reconstruction, surrogate modeling, and tokamak/stellarator optimization. A key focus in sustained fusion research is the prediction and mitigation of edge-localized-modes (ELMs), instabilities that occur in short, periodic bursts and can cause erosion to the tokamak vessel wall. Recent research has demonstrated the power of neural networks in approximating continuous functions. In this work, we build spatiotemporal forecasting models that can predict the onset of ELMs and their evolution at early stages. We leverage recent advances in generative modeling, sequence-to-sequence modeling, and Fourier neural operators to propose architectures and training strategies that can learn to forecast short to long term dynamics of the noisy signals due to ELMs. We benchmark the developed model against a state-of-the-art foundation model using the beam emission spectroscopy (BES) data that captures the plasma fluctuations due to ELMs over a 8 x 8 spatial grid. Our models demonstrate high accuracy, outperforming the baselines, in predicting the evolution of BES signals during ELM events. Furthermore, the developed models exhibit high accuracy in predicting the rapid rise and relaxation of the signals due to ELMs within 30–80 µs.

edge localized modes↗
DOE OSTI2024

The extent of multiallelic, co‐editing of LIGULELESS1 in highly polyploid sugarcane tunes leaf inclination angle and enables selection of the ideotype for biomass yield

Summary Sugarcane ( Saccharum spp. hybrid) is a prime feedstock for commercial production of biofuel and table sugar. Optimizing canopy architecture for improved light capture has great potential for elevating biomass yield. LIGULELESS1 ( LG1 ) is involved in leaf ligule and auricle development in grasses. Here, we report CRISPR/Cas9‐mediated co‐mutagenesis of up to 40 copies/alleles of the putative LG1 in highly polyploid sugarcane (2 n = 100–120, x = 10–12). Next generation sequencing revealed co‐editing frequencies of 7.4%–100% of the LG1 reads in 16 of the 78 transgenic lines. LG1 mutations resulted in a tuneable leaf angle phenotype that became more upright as co‐editing frequency increased. Three lines with loss of function frequencies of ~12%, ~53% and ~95% of lg1 were selected following a randomized greenhouse trial and grown in replicated, multi‐row field plots. The co‐edited LG1 mutations were stably maintained in vegetative progenies and the extent of co‐editing remained constant in field tested lines L26 and L35. Next generation sequencing confirmed the absence of potential off targets. The leaf inclination angle corresponded to light transmission into the canopy and tiller number. Line L35 displaying loss of function in ~12% of the lg1 NGS reads exhibited an 18% increase in dry biomass yield supported by a 56% decrease in leaf inclination angle, a 31% increase in tiller number, and a 25% increase in internode number. The scalable co‐editing of LG1 in highly polyploid sugarcane allows fine‐tuning of leaf inclination angle, enabling the selection of the ideotype for biomass yield.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Expanding the genetic toolkit: adenine and cytosine base editors for gene disruption in Aspergillus niger

Despite revolutionizing fungal genetic engineering, conventional CRISPR/Cas9-mediated knockouts rely on DNA double-strand breaks (DSBs), which can cause unwanted insertions and deletions, chromosomal abnormalities, and cytotoxicity. Base editors such as adenine base editors (ABEs), which convert A‧T to G‧C, and cytosine base editors (CBEs), which convert C‧G to T‧A, offer a safer alternative by enabling predictable, target-specific single-nucleotide changes without introducing DSBs. To overcome the limitations of traditional genome editing in filamentous fungi, we developed efficient base-editing systems in Aspergillus niger . For the first time, we constructed an ABE in A. niger , achieving up to 80% editing efficiency and inducing predictable A-to-G mutations at the intended intron sites, disrupting gene function through mRNA mis-splicing. We also developed a highly efficient CBE system, capable of introducing premature stop codons with 50–100% efficiency. To broaden the editing scope, we implemented a Cas9-NG variant recognizing a relaxed PAM sequence requiring only a single guanine (G), enabling editing at start codons and splice sites. Leveraging this expanded scope, we established gene disruption approaches by targeting start codons via ABE-mediated A-to-G conversions (ATG-to-GTG and ATG-to-ACG) and CBE-mediated C-to-T conversion (ATG-to-ATA). Additionally, our base-editing systems enable multiplex gRNA delivery and marker-free editing of multiple genes. Collectively, the scope-expanding strategies increase the number of genes targetable for disruption by base-editing in A. niger by 26.3% and enable near-complete coverage of 96% of the coding genes. Overall, this work demonstrates the potential of ABE and CBE systems as versatile, efficient, and safer alternatives to DSBs-based gene disruption in filamentous fungi.

Aspergillus↗
DOE OSTI2026

Near-Real-Time Material Tracking: Combining Vis–NIR Spectroscopy with Flow Sensing for Accurate Nd(III) Quantification

A fiber-optic visible–near-infrared (vis–NIR) absorption spectroscopy and flow sensor system has been developed for near-real-time tracking of Nd mass in the effluent stream from a column in a fume hood. The approach leverages two unique data streams and a partial least-squares regression (PLSR) model trained on vis–NIR absorption spectra of Nd(III) (0–1.5 M) in 1 M HNO 3 . In-line volumetric flow rate and vis–NIR spectra are measured in sequence after a chromatography column. The time stamps from each data stream are then synchronized, which allows integrated volumes to be combined with Nd(III) molarities predicted by a PLSR model to accurately calculate the Nd mass flowing through the column. This integrated measurement provides instantaneous mass flow and accumulates these data over time to obtain the total mass processed. The methodology developed in this study contributes critical technical infrastructure to improve monitoring capabilities to support chemical separations and the production of strategic materials and isotopes.

Irvine, Sawyer B. [Oak Ridge National Laboratory (↗
DOE OSTI2024

Additive manufacturing of sandwich panels with continuous fiber reinforced high modulus composite facings

Abstract An improved approach consisting of a combination of fiber placement and fused filament fabrication is introduced for the additive manufacture (AM) of structural grade sandwich beams. Here, sandwich beams are additively manufactured using in‐situ deposition and consolidation of continuous fiber unidirectional facings made from a commingled yarn system of e‐glass fiber (~50% vol.) and amorphous PET, and a hexagonal honeycomb core structure made from PETG. Both facings and the sandwich core are manufactured on a single machine, in one sequence (skin‐core‐skin), employing the benefit of matrix compatibility to create autohesion at the interfaces. Flexural and transverse shear rigidity are determined experimentally and compared with analytical predictions and show correlation to within 3%. Flexural strength and core shear strength are also measured. Post‐mortem examinations show that core fracture and core facing debond were the dominant failure mode in flexure. Single cantilever beam tests were performed to evaluate core facing debond toughness. Subsequently, surface preheat using infrared heaters was utilized to increase autohesion between core and facing. The results show debond toughness was increased 4 times using infrared heating. This research effort presents a manufacturing approach that has the potential for the AM of stiff, well bonded, structural grade sandwich beams, in an integrated sequence, employing in‐situ consolidation to the facings, without the need for the use of intermediate adhesives for skin‐to‐core bonding. Highlights An improved additive manufacturing technique for making sandwich panels is developed. Sandwich panel facings have fiber volume fractions of approximately 50%. Surface preheat improves core‐to‐facing debond toughness by a factor of 4. Top and bottom facings are consolidated during manufacture leading to better properties. Experimental results are compared to analytical predictions and show good correlation.

17 WIND ENERGY↗
DOE OSTI2024

Self-Assembling and Pore-Forming Peptoids as Antimicrobial Biomaterials

Bacterial infections have been a serious threat to mankind throughout history. Natural antimicrobial peptides (AMPs) and their membrane-disruption mechanism have generated an immense interest in the design and development of synthetic mimetics that could overcome the intrinsic drawbacks of AMPs, such as their susceptibility to proteolytic degradation. Herein, by exploiting the self-assembly and pore-forming capabilities of sequence-defined peptoids, we discovered a new family of low molecular weight peptoid antibiotics that exhibit excellent broad-spectrum activity and high selectivity toward a panel of clinically significant Gram-positive and Gram-negative bacterial strains, including vancomycin-resistant E. faecalis (VREF), methicillin-resistant S. aureus (MRSA), methicillin-resistant S. epidermidis (MRSE), E. coli, P. aeruginosa, and K. pneumoniae. Tuning peptoid sidechain chemistry and structure enabled us to tune the efficacy of antimicrobial activity. Mechanistic studies using Transmission Electron Microscopy (TEM), bacterial membrane depolarization and lysis, and time-kill kinetics assays along with molecular dynamics simulations reveal that these peptoids kill both Gram-positive and Gram-negative bacteria through a membrane-disruption mechanism. In conclusion, these robust and biocompatible peptoid-based antibiotics can provide a valuable tool for combating the emerging drug resistance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Genome resources for three modern cotton lines guide future breeding efforts

Cotton ( Gossypium hirsutum L.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars (‘UGA230’, ‘UA48’ and ‘CSX8308’) and updating the ‘TM-1’ cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from ‘Pima’ cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Strangers in a foreign land: ‘Yeastizing’ plant enzymes

Abstract Expressing plant metabolic pathways in microbial platforms is an efficient, cost‐effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour‐intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost‐effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data‐driven machine learning framework designed to extract ‘yeastizing’ rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design‐build‐test cycle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Development of a pattern recognition algorithm for reconstructing multi-particle events in the Mu2e detector

Mu2e is an upcoming experiment at Fermilab and its main goal is to search for the Charged Lepton Flavor Violation (CLFV) in the coherent transition of a muon into an electron on an Al target. In Mu2e, multi-particle events can occur simultaneously within the same time region and it is crucial to accurately identify each particle track, including signals, to improve the robustness of track finding methods and enhance reconstruction efficiency. ¯p annihilation is one of the background events and produces multiple particles that can mimic signal events. Additionally, photons from radiative pion captures can produce a γ → $e+e−$ pair, which can be used to calibrate the Mu2e momentum scale and the resolution. The Mu2e track reconstruction sequence begins by grouping hits produced in the tracker based on time and z coordinate information, called TimeCluster, and selected hits are processed to reconstruct helices and determine their momentum. The current pattern recognition algorithms identify a single helix per TimeCluster for single track events. A new pattern recognition algorithm is being developed to reconstruct multi-particle events and its features for finding multiple tracks and the current evaluation results are reported.

Kitagawa, H. [Pisa U.]↗
DOE OSTI2024

Exon disruptive variants in Populus trichocarpa associated with wood properties exhibit distinct gene expression patterns

Abstract Forest trees may harbor naturally occurring exon disruptive variants (DVs) in their gene sequences, which potentially impact important ecological and economic phenotypic traits. However, the abundance and molecular regulation of these variants remain largely unexplored. Here, 24,420 DVs were identified by screening 1014Populus trichocarpafull genomes. The identified DVs were predominantly heterozygous with allelic frequencies below 5% (only 26% of DVs had frequencies greater than 5%). Using common garden‐grown trees, DVs were assessed for gene expression variation in the developing xylem, revealing that their gene expression can be significantly altered, particularly for homozygous DVs (in the range of 27%–38% of cases depending on the studied common garden). DVs were further investigated for their correlations with 13 wood quality traits, revealing that, among the 148 discovered DV associations, 15 correlated with more than one wood property and six genes had more than one DV in their coding sequences associated with wood traits. Approximately one‐third of DVs correlated with wood property variation also showed significant gene expression variation, confirming their non‐spurious impact. These findings offer potential avenues for targeted introduction of homozygous mutations using tree biotechnology, and while the exact mechanisms by which DVs may directly influence wood formation remain to be unraveled, this study lays the groundwork for further investigation.

Genetics & Heredity↗
DOE OSTI2024

The 1.3 Å resolution structure of the truncated group Ia type IV pilin from Pseudomonas aeruginosa strain P1

The type IV pilus is a diverse molecular machine capable of conferring a variety of functions and is produced by a wide range of bacterial species. The ability of the pilus to perform host-cell adherence makes it a viable target for the development of vaccines against infection by human pathogens such as Pseudomonas aeruginosa . Here, the 1.3 Å resolution crystal structure of the N-terminally truncated type IV pilin from P. aeruginosa strain P1 (ΔP1) is reported, the first structure of its phylogenetically linked group (group I) to be discussed in the literature. The structure was solved from X-ray diffraction data that were collected 20 years ago with a molecular-replacement search model generated using AlphaFold ; the effectiveness of other search models was analyzed. Examination of the high-resolution ΔP1 structure revealed a solvent network that aids in maintaining the fold of the protein. On comparing the sequence and structure of P1 with a variety of type IV pilins, it was observed that there are cases of higher structural similarities between the phylogenetic groups of P. aeruginosa than there are between the same phylogenetic group, indicating that a structural grouping of pilins may be necessary in developing antivirulence drugs and vaccines. These analyses also identified the α–β loop as the most structurally diverse domain of the pilins, which could allow it to serve a role in pilus recognition. Studies of ΔP1 in vitro polymerization demonstrate that the optimal hydrophobic catalyst for the oligomerization of the pilus from strain K122 is not conducive for pilus formation of ΔP1; a model of a three-start helical assembly using the ΔP1 structure indicates that the α–β loop and the D-loop prevent in vitro polymerization.

Bragagnolo, Nicholas↗