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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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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Rearrangement collision theory of phonon-driven exciton dissociation

Understanding the processes governing the dissociation of excitons to free charge carriers in semiconductors and insulators is of central importance for photovoltaic applications. Dyson's S-matrix formalism provides a framework for computing scattering rates between quasiparticle states derived from the same underlying Hamiltonian, often reducing to familiar Fermi's “golden rule” like expressions at first order. By presenting a rigorous formalism for multichannel scattering, we extend this approach to describe scattering between composite quasiparticles and, in particular, the process of exciton dissociation mediated by the electron-phonon interaction. Subsequently, we derive rigorous expressions for the exciton dissociation rate, a key quantity of interest in optoelectronic materials, which enforce correct energy conservation and may be readily used in ab initio calculations. We apply our formalism to a three-dimensional model system to compare temperature-dependent exciton rates obtained for different scattering channels.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Metabolic Redox Coupling Controls Methane Production in Permafrost‐Affected Peatlands Through Organic Matter Quality‐Dependent Energy Allocation

ABSTRACT Permafrost thaw represents one of Earth's largest climate feedback risks, potentially releasing vast carbon (C) stores as greenhouse gases (GHG). However, our ability to predict emissions remains limited by poor understanding of how changing organic matter (OM) composition affects microbial carbon processing. We test a metabolism‐centered redox framework, which views microbial processes as coupled oxidative‐reductive reactions, to mechanistically explain how organic matter metabolite quality controls greenhouse gas production in permafrost‐affected peatland ecosystems. Rather than relying solely on geochemical redox measurements, our approach examines how microbes balance electron flow through metabolic pathways. Using active layer peat (9–19 cm) from contrasting environments (bog and fen), we employed multi‐omics approaches, including metabolomics, metagenomics, and metatranscriptomics, to link OM chemistry to microbial function. Our results reveal distinct dissolved organic matter metabolite composition, with fen systems enriched in compounds with higher substrate quality (low molecular weight (MW) sugars with high H:C ratios and low aromaticity) and bog systems dominated by compounds with lower substrate quality (high MW phenols with lower H:C ratios and higher aromaticity). In fen samples, these sugar‐like compounds correlated with higher oxidative metabolism and methanogenesis, supported by increased glycolysis gene expression. Initially, electrons from increased oxidative metabolism were balanced through nitrate and sulfate reduction, but as these electron acceptors were depleted, methanogenesis increased to maintain redox balance. Fen samples showed rapid degradation of both high‐ and low‐substrate‐quality compounds, suggesting sufficient energy for efficient C cycling. Conversely, bog samples exhibited more polyphenolic compounds, lower glycolysis activity, and higher stress‐related gene expression, suggesting energy was diverted towards cell maintenance under acidic conditions rather than C processing. This approach suggests that predicting greenhouse gas emissions requires an understanding of how organic matter quality shapes microbial energy allocation strategies, providing a mechanistic framework for improving emission predictions from permafrost‐affected peatlands and similar ecosystems.

Biodiversity & Conservation↗
DOE OSTI2024

Sentinel

Network intrusion detection systems (NIDS) are commonplace in network security but they frequently employ algorithms that are computational demanding requiring hardware and software with significant power requirements. Two examples of such resource-intensive algorithms used for network security are regular expression matching and broader signature pattern matching which are commonly used in deep packet inspection (DPI). Network security algorithms that have large power requirements may be a challenge for low-power internet-of-things (IoT) environments, which generally lack the power resources to implement complex security measures like computationally expensive DPI at the edge. Furthermore, IoT environments incorporating 5G standalone networks have network latency constraints beyond just power that make DPI at the edge even more difficult. Programmable logic is ideally suited for machine learning inference for DPI because of its deep instruction level parallelism and single-cycle memory access. Machine learning approaches for DPI have been explored before using the programmable logic of field programmable gate arrays (FPGA) as a potential solution for NIDS approaches that would be power-suitable for IoT. However, those previous programmable logic NIDS approaches utilize either a supervised or unsupervised learning model. Sentinel utilizes the ensemble of these two machine learning approaches known as a semi-supervised approach which has shown promise in NIDS implementations. Sentinel provides a programmable logic implementation of a semi-supervised approach for DPI which operates at much lower power and latency than a GPU implementation with negligible loss of accuracy due to quantization through a logistic regressor.

Anderson, MatthewW [Idaho National Laboratory (INL↗
DOE OSTI2026

Rational engineering to enhance C8-fatty acid biosynthesis in Picosynechococcus sp. PCC 7002

Medium-chain fatty acids (e.g., C8-C14) are important biofuel precursors that can be produced from CO2 by cyanobacteria and other photoautotrophs. However, cyanobacteria naturally direct a relatively small fraction of fixed carbon to lipid synthesis, primarily producing long-chain membrane-bound fatty acids. We investigated whether mitigating kinetic bottlenecks within the fatty acid biosynthesis (FAB) pathway could enhance flux to free fatty acid (FFA) production in Picosynechococcus sp. PCC 7002. Previous in vitro studies proposed that the FAB initiating enzyme FabH is the primary rate-controlling enzyme in PCC 7002. We hypothesized that enhancing fatty acid initiation could increase in vivo FFA production while shifting the kinetic bottleneck further downstream in the pathway. We enabled C8-FFA accumulation by knocking out the native acyl-acyl carrier protein synthetase gene (aas) and expressing the highly active Cuphea palustris-derived mutant thioesterase CpFatB1.2-M4-287 (CupTE), which selectively catalyzes C8 chain termination. We then expressed the diatom-derived Chaetoceros sp. GSL56 FabH ortholog (chKASIII), to enhance initiation, and a medium-chain selective E. coli ketosynthase ecFabF[I108F] (ecFabF∗), to enhance elongation. When expressed individually or in combination with ecFabF∗, chKASIII slowed growth and decreased net carbon fixation rates relative to the parental Δaas-CupTE strain. However, co-expression of chKASIII, ecFabF∗, and CupTE redirected a larger fraction of fixed carbon toward FFA production, increasing relative carbon flux to C8-FFA and decreasing the projected minimum selling price by four-fold. This work demonstrates how systems metabolic engineering can be applied to enhance C8-FFA production in cyanobacteria while highlighting the unpredictable physiological consequences of host metabolic burden.

09 BIOMASS FUELS↗
DOE OSTI2024

The Essence of Cryptol: A Denotational Cryptol Interpreter in Coq for Foundational Assurances for Quantum Resistant Cryptosystems

Systems of the utmost consequence need a means to establish authenticity of software and data. Cryptosystems implement authentication, but can be vulnerable to cryptographic and implementation attacks. With the threat of quantum cryptographic attacks, “post-quantum” cryptosystems (PQCs) must be henceforth used in these systems. However, the new cryptography needs new ways to, rigorously and machine-checkably, prove systems free of vulnerabilities. We propose a retargetable capability to rapidly instantiate proven correct postquantum cryptosystems through novel proof-carrying synthesis and proof-automation technique, extending those proven successful on existing systems. This capability is crucial to meeting the cryptographic requirements for future high-consequence systems. Since specifications for high consequence cryptography are presently captured in a domain specific language known as Cryptol. While this can enable convenient fully automated reasoning about Cryptol specificaitons and implementations via the Software Analysis Workbench (SAW), Cryptol has expressivity gaps, so that cryptosystems with probabilistic programming features like Falcon cannot be fully expressed in the language. Moreover, SAW’s automation fails for programs and specificaitons with inductive and recursive structure, as in the Sphincs+ PQC. Finally, Cryptol and SAW together represent some 200,000 lines of unverified Haskell, so that the any guarantees about high consequence cryptography are presently contingent on a large, unverified, yet trusted computing base. The first step of the larger project of agile, assured crpytography is therefore to provide a formal, mechanized semantics for Cryptol, so that the specifications expressed by cryptographers in Cryptol can be reasoned about and compiled into performant implementations with a foundational, machine checkable certificate of correctness. This report describes our work on this first step, culminating in the design of a certified denotational interpreter, in Coq, for core Cryptol.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Complex transcriptional regulations of a hyperparasitic quadripartite system in giant viruses infecting protists

Abstract Hyperparasitism is a common pattern in nature that is not limited to cellular organisms. Giant viruses infecting protists can be hyperparasitized by smaller ones named virophages. In addition, both may carry episomal DNA molecules known as transpovirons in their particles. They all share transcriptional regulatory elements that dictate the expression of their genes within viral factories built by giant viruses in the host cytoplasm. This suggests the existence of interactions between their respective transcriptional networks. Here we investigatedAcanthamoeba castellaniicells infected by a giant virus (megavirus chilensis), and coinfected with a virophage (zamilon vitis) and/or a transpoviron (megavirus vitis transpoviron). Infectious cycles were monitored through time-course RNA sequencing to decipher the transcriptional program of each partner and its impact on the gene expression of the others. We found highly diverse transcriptional responses. While the giant virus drastically reshaped the host cell transcriptome, the transpoviron had no effect on the gene expression of any of the players. In contrast, the virophage strongly modified the giant virus gene expression, albeit transiently, without altering the protein composition of mature viral particles. The virophage also induced the overexpression of transpoviron genes, likely through the indirect upregulation of giant virus-encoded transcription factors. Together, these analyses document the intricated transcriptionally regulated networks taking place in the infected cell.

Science & Technology - Other Topics↗
DOE OSTI2025

The architecture of resilience: a genome assembly of Myrothamnus flabellifolia sheds light on desiccation tolerance and sex determination

Myrothamnus flabellifolia is a dioecious resurrection plant endemic to southern Africa that has become an important model for understanding desiccation tolerance. Despite its ecological and medicinal significance, genomic and transcriptomic resources for the species are limited. We generated a chromosome-level, haplotype-resolved reference genome assembly and annotation for M. flabellifolia and conducted transcriptomic profiling across a natural dehydration–rehydration time course in the field. Genome architecture and sex determination were characterized, and co-expression network and cis-regulatory element (CRE) enrichment analyses were used to investigate dynamic responses to desiccation. The 1.28-Gb genome exhibits unusually consistent chromatin architecture with unique chromosome organization across highly divergent haplotypes. We identified an XY sexual system with a small sex-determining region on Chromosome 8. Transcriptomic responses varied with dehydration severity, pointing to early suppression of growth, progressive activation of protective mechanisms, and subsequent return to homeostasis upon rehydration. Late embryogenesis abundant and early light-induced protein transcripts were dynamically regulated and showed enrichment of abscisic acid and stress-responsive CREs pointing toward conserved responses. Together, this study provides foundational resources for understanding the genomic architecture and reproductive biology of M. flabellifolia and offers new insights into the mechanisms of desiccation tolerance.

chromosome structure↗
DOE OSTI2025

A split luciferase system for studying coronavirus Mpro dimerization in vitro and in living cells

The main protease enzyme (Mpro) of coronaviruses cleaves the viral polyprotein into functional units essential for virus replication. Prior work has demonstrated that Mpro functions as a homodimer. However, studies on the mechanism of dimerization have been challenging because the purified protease is mostly dimeric, dimerization-defective mutants lack proteolytic activity, and robust cell-based assays have yet to be reported. To enable work on Mpro dimerization, we have developed a quantitative luciferase-based SARS-CoV-2 (SARS2) Mpro biosensor that accurately reports protein dimerization in living cells and, upon purification, also in vitro. Co-transfection of cells with a construct expressing Mpro fused to the 18 kDa LargeBiT of luciferase (LgBiT) and a second construct with Mpro fused to the 1 kDa SmallBiT of luciferase (SmBiT) results in a reconstitution of luciferase activity in a dose-dependent manner that requires conserved residues within the dimerization interface. Proteolytic activity is dispensable for dimerization and, uniquely, a C145A catalytically inactive mutant exhibits enhanced dimerization signal likely due to lower cytotoxicity. Mpro enzymes from multiple different coronaviruses also dimerize in this system, indicating mechanistic conservation. Interestingly, this dimerization biosensor also provides a quantitative read-out of inhibitor-facilitated dimerization. Covalent SARS2 Mpro inhibitors such as nirmatrelvir cause a 3- to 5-fold increase in luciferase activity. Together with corroborating structural, biophysical, and molecular dynamics experiments, our studies support a model in which covalent Mpro inhibitors such as nirmatrelvir simultaneously block catalytic activity and induce allosteric stabilization of the dimeric complex.

SARS-CoV-2 main protease (Mpro/3CLpro)↗
DOE OSTI2025

Integrating an Industrial Source and Commercial Algae Farm with Innovative CO 2 Transfer Membrane and Improved Strain Technologies

This report describes the overall findings of the research project “Integrating an Industrial Source and Commercial Algae Farm with Innovative CO 2 Transfer Membrane and Improved Strain Technologies”. The motivation and goal for this project were to increase the carbon utilization efficiency (CUE) and areal productivity for algal cultivations, thereby reducing CO 2 costs to cultivation operations and improving economics. This was achieved through a combination of enhanced delivery of inorganic carbon and improved strains of Nannochloropsis oceanica capable of higher rates of bicarbonate uptake and metabolism. The project succeeded in these goals. First, a bubble-free, membrane-based technology was developed for delivering CO 2 to cultivations, which increased the CUE from the 15-20% that is standard in the industry to more than 65%. In addition, N. oceanica was modified to express a bicarbonate transporter protein, BicA, which enabled the cells to grow more quickly. In addition, advances were made in the protein engineering of carbonic anhydrase (CA) for enhanced stability and catalytic performance, the computational fluid dynamics modeling of algal cultivation systems, and in the life-cycle assessment and technoeconomic analysis of algal production. Together, these outcomes contribute to advancing algal cultivation as an economically viable platform for production of fuels, materials, and other chemical products. The project results aid in addressing the dual challenges of reducing atmospheric CO 2 levels and achieving green energy solutions.

09 BIOMASS FUELS↗
DOE OSTI2025

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗
DOE OSTI2024

Central transcriptional regulator controls photosynthetic growth and carbon storage in response to high light

Carbon capture and biochemical storage are some of the primary drivers of photosynthetic yield and productivity. To elucidate the mechanisms governing carbon allocation, we designed a photosynthetic light response test system for genetic and metabolic carbon assimilation tracking, using microalgae as simplified plant models. The systems biology mapping of high light-responsive photophysiology and carbon utilization dynamics between two variants of the same Picochlorum celeri species, TG1 and TG2 elucidated metabolic bottlenecks and transport rates of intermediates using instationary 13 C-fluxomics. Simultaneous global gene expression dynamics showed 73% of the annotated genes responding within one hour, elucidating a singular, diel-responsive transcription factor, closely related to the CCA1/LHY clock genes in plants, with significantly altered expression in TG2. Transgenic P. celeri TG1 cells expressing the TG2 CCA1/LHY gene, showed 15% increase in growth rates and 25% increase in storage carbohydrate content, supporting a coordinating regulatory function for a single transcription factor.

09 BIOMASS FUELS↗
DOE OSTI2026

Fidelity Comparison of Time-Bin and Fock State Encoding in Hybrid Quantum Systems Under Channel and Transduction Effects

Future quantum networks are expected to integrate a heterogeneous combination of quantum systems, owing to the diverse advantages offered by different physical platforms in terms of scalability, coherence times, and interfacing capabilities. Within the context of this emerging quantum communication paradigm, this paper presents an analytical comparison of two photonic encoding schemes—time-bin and Fock state (single rail)—in hybrid quantum systems where flying qubits are entangled with stationary matter qubits. We evaluate their resilience against fiber channel and quantum transduction effects by calculating the fidelity of the final states relative to their ideal forms. Employing the characteristic function approach, we derive analytical fidelity expressions and investigate their dependence on parameters such as transmissivity, noise levels, fiber length, and source generation success probability. Additionally, we simulate the scenario with a dedicated QuTiP software implementation to verify the validity of the theoretical models. Our findings reveal that due to its inherent single-mode structure, the Fock state encoding consistently outperforms time-bin encoding in fidelity, as this structure significantly minimizes susceptibility to losses compared to the two-mode nature of the time-bin scheme. This analysis offers valuable insights for future hybrid quantum communication and information processing applications.

Fiorini, Francesco [Pisa U.]↗
DOE OSTI2024

Principles of two-dimensional terahertz spectroscopy of collective excitations: The case of Josephson plasmons in layered superconductors

Two-dimensional terahertz spectroscopy (2DTS), a terahertz analog of nuclear magnetic resonance, is a new technique poised to address many open questions in complex condensed matter systems. The conventional theoretical framework used ubiquitously for interpreting multidimensional spectra of discrete quantum level systems is, however, insufficient for the continua of collective excitations in strongly correlated materials. Here, in this work, we develop a theory for 2DTS of a model collective excitation, the Josephson plasma resonance in layered superconductors. Starting from a mean-field approach at temperatures well below the superconducting phase transition, we obtain expressions for the multidimensional nonlinear responses that are amenable to intuition derived from the conventional single-mode scenario. We then consider temperatures near the superconducting critical temperature T c , where dynamics beyond mean-field become important and conventional intuition fails. As fluctuations proliferate near T c , the dominant contribution to nonlinear response comes from an optical parametric drive of counterpropagating Josephson plasmons, which gives rise to 2D spectra that are qualitatively different from the mean-field predictions. As such, and in contrast to one-dimensional spectroscopy techniques, such as third harmonic generation, 2DTS can be used to directly probe thermally excited finite-momentum plasmons and their interactions. Our theory can readily be tested in cuprates, and we discuss implications beyond the present context of Josephson plasmons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

A Review and Revalidation of Equivalency Factors in H-Canyon

In recent years, an effort has begun to validate and reaffirm long-existing limits and equivalency factors in use at the H-Canyon (HCA) facility located at the Savanah River Site. Equivalency factors (EF) are coefficients used to express the reactivity of a quantity of a given isotope in terms of an equivalent amount of another isotope’s reactivity, commonly termed Fissile Gram Equivalent (FGE). For criticality safety purposes, EFs are often used to conservatively determine if a given mixed-isotope system is within previously established limits. These EFs are calculated by taking ratios of established subcritical limits for a given system or process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

SCF Framework, HF Stability, and RPA Correlation for Jordan–Wigner-Transformed Spin Hamiltonians on Arbitrary Coupling Topologies

Mapping spins to fermions via the Jordan–Wigner (JW) transformation can render mean-field (Hartree–Fock, HF) descriptions effective for strongly correlated spin systems. As established in recent work, the application of such approaches is not limited by the nonlocal structure of JW strings or by site ordering because string operators can be absorbed into Thouless rotations of a Slater determinant, and the variational optimization of a unitary Lie-algebraic similarity transformation removes any ordering dependence. Leveraging these ideas, we develop a self-consistent field (SCF) scheme that expresses the mean-field energy as a functional of the single-particle density matrix, providing an alternative to gradient-based optimization of Thouless parameters. We derive the analytical orbital Hessian to diagnose HF stability and compute the ground-state correlation energy through the random-phase approximation (RPA). Benchmark results for the XXZ and J 1 –J 2 model on one- and two-dimensional lattices demonstrate that RPA significantly improves mean-field accuracy.

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

Improving the Transformation Efficiency of Synechococcus sp. PCC 7002 via Methylome-Guided Premethylation of DNA

Cyanobacteria are promising microbial platforms for a diverse set of biotechnology applications, from living materials to photosynthetic chemical production, but are less well characterized than commonly engineered microbes such as Escherichia coli. This study facilitates genetic engineering in Synechococcus sp. PCC 7002, a fast-growing, halotolerant, and naturally competent strain, by identifying ten native methylation motifs and designing shuttle strains that mimic the native methylation state by expressing a subset of heterologous methyltransferases. DNA methylation in E. coli with as few as two active methyltransferases increased transformation efficiency up to 30-fold across four distinct integration sites in PCC 7002. This work provides an experimental framework to bypass native restriction-modification systems for efficient genome editing and metabolic engineering in nonmodel bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Non-conformity Scores for High-Quality Uncertainty Quantification from Conformal Prediction

High-quality uncertainty quantification (UQ) is a critical component of enabling trust in deep learning (DL) models and is especially important if DL models are to be deployed in high-consequence applications. Conformal prediction (CP) methods represent an emerging nonparametric approach for producing UQ that is easily interpretable and, under weak assumptions, provides a guarantee regarding UQ quality. This report describes the research outputs of an Exploratory Express Laboratory Directed Research and Development (LDRD) project at Sandia National Laboratories. This project focused on how best to implement CP methods for DL models. This report introduces new methodology for obtaining high-quality UQ from DL models using CP methods, describes a novel system of assessing UQ quality, and provides experimental results that demonstrate the quality of the new methodology and utility of the UQ quality assessment system. Avenues for future research and discussion of potential impacts at Sandia and in the wider research community are also given.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A core of cell wall proteins functions in wall integrity responses in Arabidopsis thaliana

Abstract Cell walls surround all plant cells, and their composition and structure are tightly regulated to maintain cellular and organismal homeostasis. In response to wall damage, the cell wall integrity (CWI) system is engaged to ameliorate effects on plant growth. Despite the central role CWI plays in plant development, our current understanding of how this system functions at the molecular level is limited. Here, we investigated the transcriptomes of etiolated seedlings of mutants of Arabidopsis thaliana with defects in three major wall polysaccharides, pectin ( quasimodo2 ), cellulose ( cellulose synthase3 je5 ), and xyloglucan ( xyloglucan xylosyltransferase1 and 2 ), to probe whether changes in the expression of cell wall‐related genes occur and are similar or different when specific wall components are reduced or missing. Many changes occurred in the transcriptomes of pectin‐ and cellulose‐deficient plants, but fewer changes occurred in the transcriptomes of xyloglucan‐deficient plants. We hypothesize that this might be because pectins interact with other wall components and/or integrity sensors, whereas cellulose forms a major load‐bearing component of the wall; defects in either appear to trigger the expression of structural proteins to maintain wall cohesion in the absence of a major polysaccharide. This core set of genes functioning in CWI in plants represents an attractive target for future genetic engineering of robust and resilient cell walls.

59 BASIC BIOLOGICAL SCIENCES↗