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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Systematic identification of transcriptional activation domains from non-transcription factor proteins in plants and yeast

Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Effect of dilute Rh on oxygen dissociation, spillover, and the oxidation of Cu across many orders of magnitude pressure

Knowledge of how trace amounts of more reactive metals influence the oxidation rate and mechanism of Cu surfaces is essential for developing strategies to optimize the performance of Cu-based catalysts. We find that the addition of 1% Rh to Cu(111) increases the initial O 2 dissociation rate by approximately 9-fold. CO poisoning experiments reveal that single Rh atoms activate O 2 and facilitate the spillover of atomic oxygen to Cu sites. Scanning tunneling microscopy (STM) and in situ X-ray photoelectron spectroscopy (XPS) support this mechanism, showing enhanced surface oxygen near Rh atoms. Here, a density functional theory (DFT)-based model demonstrates that Rh binds the O 2 precursor 0.15 eV more strongly than Cu(111) and lowers the O 2 dissociation barrier by 0.02 eV. Both single-crystal and nanoparticle experiments show that at low oxygen pressures, Rh enhances Cu oxidation, whereas at higher pressures, it inhibits deeper oxidation, as evidenced by in situ ultraviolet-visible (UV-vis) spectra.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Understanding, inhibiting, and engineering membrane transporters with high-throughput mutational screens

Promiscuous membrane transporters play vital roles across domains of life, mediating the uptake and efflux of structurally and chemically diverse substrates. Although many transporter structures have been solved, the fundamental rules of polyspecific transport remain inscrutable. In recent years, high-throughput genetic screens have solidified as powerful tools for comprehensive, unbiased measurements of variant function and hypothesis generation, but have had infrequent application and limited impact in the transporter field. In this primer, we describe the principles of high-throughput screening methods available for studying polyspecific transporters and comment on the necessity and potential of high-throughput methods for deciphering these transporters in particular. We present several screening approaches which could provide a fundamental understanding of the molecular basis of function and promiscuity in transporters. Here, we further posit how this knowledge can be leveraged to design inhibitors that combat multidrug resistance and engineer transporters as needed tools for synthetic biology and biotechnology applications.

EPIs↗
DOE OSTI2024

Validating the rhenium proxy for rock organic carbon oxidation using weathering profiles

Chemical weathering over geological timescales acts as a source or sink of atmospheric carbon dioxide (CO 2 ), while influencing long-term redox cycling and atmospheric oxygen (O 2 ) at Earth's surface. There is a growing recognition that the oxidative weathering of rock organic carbon (OC petro ) can release more CO 2 than is locally drawn down by silicate weathering, and may vary due to changes in erosion and climate. The element rhenium (Re) has emerged as a proxy to track the oxidative weathering of OC petro , yet uncertainties in its application remain namely that we lack a systematic assessment of the comparative mobility of Re and OC petro during sedimentary rock weathering. Here we measure Re and OC petro loss across gradients in rock weathering at 9 global sites, spanning a range of initial OC petro values from ~0.2 % to >10 %. We use titanium to account for volume changes during weathering and assess Re and OC petro loss alongside major elements that reflect silicate (Na, Mg), carbonate (Ca, Mg) and sulfide (S) weathering. Across the dataset, Re loss is correlated with OC petro loss but not with loss of any other major element. Further, across the weathering profiles, the average molar ratio of OC petro to Re loss was 0.84 ± 0.15, with 8 out of 9 sites having a ratio >0.74. At one site (Marcellus Shale), the average ratio was lower at 0.58 ± 0.11. The excess loss of Re matches expectations that, typically, between ~0 and 20 % of the Re liberated by sedimentary rock weathering derives from silicate or sulfide phases, while some OC petro may be physically or chemically protected from weathering. Overall, our measurements provide validation for the Re proxy of OC petro oxidation and allow future work to further improve our knowledge of regional and global-scale rates of this important source of CO 2 in the geochemical carbon cycle.

58 GEOSCIENCES↗
DOE OSTI2025

Quantification of the REE 3+ aqua ions and chloride species in aqueous fluids by in situ Raman spectroscopy using perturbations of the water band

Acidic NaCl-rich aqueous fluids play a crucial role in forming hydrothermal rare earth elements (REE) mineral deposits. Aqueous REE mobility is mostly controlled by the stabilities of REE 3+ and REE chloride species. Our current knowledge of REE speciation is based on solubility data, thermodynamic models and in situ spectroscopic measurements, sometimes coupled with molecular simulations. Here, in this study, we investigate Nd and Yb speciation in pH2 Cl-bearing solutions at 25 °C and 0.1 MPa with variable Cl/REE ratios using Raman Spectroscopy in solutions with 0.1 to 0.6 mol/kg NdCl 3 or YbCl 3 and 0.2 to 3.2 mol/kg NaCl. Due to the challenges in resolving the REE-Cl band, we developed a new method using the water vibrational mode and multivariate curve resolution (MCR) analysis. The Raman spectra for the vibrational band of water (2700 to 3900 cm –1 ) were collected at 25 °C and fitted by three Gaussian sub-peaks, then quantified using MCR analysis to de-convolute the water band into bulk H 2 O and the perturbations caused by of Cl – , REE 3+ , and REE chloride species. REE speciation based on the perturbations of the water band indicates that REE 3+ aqua ions dominate acidic solutions at 25 °C, but up to ~20 mol% YbCl 2+ forms at high YbCl 3 concentrations. The new method is promising for quantifying in situ speciation of the REE 3+ aqua ions and REE chloride species in aqueous fluids while providing information on the hydration of ions. This method improves our molecular level understanding of REE aqueous species stability and their role in REE mobilization during fluid-rock interaction.

58 GEOSCIENCES↗
DOE OSTI2026

Understanding and mitigating degradation in amine-based sorbents for CO 2 direct air capture

The success of direct air capture (DAC) of CO 2 depends on sorbents that combine high capacity, low energy requirements, and long-term durability. Amine-based sorbents-including solid-supported aminopolymers, grafted amines, and amine-functionalized resins-remain the leading candidates, but their limited lifetimes drive up costs and constrain deployment. In this review, we outline the current understanding of amine-based sorbent degradation with an emphasis on clearly identifying what is known about structure-property-performance relationships, as well as important knowledge gaps. More specifically, we discuss how polymer chemistry, sorbent design variables, and environmental and process conditions contribute to performance loss. In parallel, we outline how advances in spectroscopy, modeling, and accelerated testing are beginning to illuminate chemical and physical degradation mechanisms. Looking forward, we identify future research directions that will be critical for gaining a deeper understanding of degradation, as well as opportunities for developing innovative mitigation strategies for improving the lifetime of amine-based sorbents.

organic↗
DOE OSTI2025

Data-driven reduced-order models for port-Hamiltonian systems with operator inference

Hamiltonian operator inference has been developed in Sharma et al. (2022) to learn structure-preserving reduced-order models (ROMs) for Hamiltonian systems. The method constructs a low-dimensional model using only data and knowledge of the functional form of the Hamiltonian. The resulting ROMs preserve the intrinsic structure of the system, ensuring that the mechanical and physical properties of the system are maintained. In this work, we extend this approach to port-Hamiltonian systems, which generalize Hamiltonian systems by including energy dissipation, external input, and output. Based on snapshots of the system’s state and output, together with the information about the functional form of the Hamiltonian, reduced operators are inferred through optimization and are then used to construct data-driven ROMs. To further alleviate the complexity of evaluating nonlinear terms in the ROMs, a hyper-reduction method via discrete empirical interpolation is applied. Accordingly, we derive error estimates for the ROM approximations of the state and output. Lastly, we demonstrate the structure preservation, as well as the accuracy of the proposed port-Hamiltonian operator inference framework, through numerical experiments on a linear mass–spring-damper problem and a nonlinear Toda lattice problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Infrared nanoimaging and nanospectroscopy of electrochemical energy storage materials and interfaces

Electrochemical interfaces are central to the function and performance of energy storage devices. Thus, the development of new methods to characterize these interfaces, in conjunction with electrochemical performance, is essential for bridging the existing knowledge gaps and accelerating the development of energy storage technologies. Of particular need is the ability to characterize surfaces or interfaces in a non-destructive way with adequate resolution to discern individual structural and chemical building blocks. To this end, sub-diffraction-limit low-energy infrared optical probes that exploit near-field interactions within atomic force microscopy platforms, such as pseudoheterodyne nanoimaging, photothermal nanoimaging and nanospectroscopy, and nanoscale Fourier transform infrared spectroscopy, are all powerful emerging techniques. These are capable of non-destructive surface probing and imaging at nanometer resolution. This review outlines recent efforts to characterize ex situ, in situ, and operando electrode materials and electrochemical interfaces in rechargeable batteries with these advanced infrared near-field probes.

25 ENERGY STORAGE↗
DOE OSTI2026

Fabrication, oxidation, and combustion of nanoscale magnesium diboride and tetraboride

The difficult ignition and low combustion efficiency of boron particles decrease the performance of boron-loaded, fuel-rich propellants for solid fuel ramjets and ducted rockets. One approach to solving this problem involves the use of magnesium diboride (MgB 2 ), which ignites easier than boron. Magnesium tetraboride (MgB 4 ) offers greater energy density owing to its higher boron content. However, the effect of B/Mg ratio on the ignition and combustion is unknown. Additionally, while nanoscale MgB₂ particles and quasi-2D structures are promising energetic additives, the oxidation and combustion properties of nanoscale MgB₄ have not been explored. To address these knowledge gaps, the present work included synthesis and high-energy ball milling of MgB 2 and MgB 4 powders, thermogravimetric analysis (TGA) of their oxidation, and combustion experiments with thin layers of the obtained powders. Comparison of two synthesis routes (a solid-state reaction in a tube furnace and combustion synthesis) has shown that the former is the superior method for producing magnesium borides. TGA has revealed that oxidation of both MgB 2 and MgB 4 results in a high conversion into the oxides (88–91 %), far exceeding the low conversion of boron (62.5 %). MgB 4 begins to oxidize rapidly at a much lower temperature (∼900 °C) than MgB 2 (∼1200 °C). The burning rates of milled MgB 2 and MgB 4 are about eight and five times, respectively, faster than that of submicron boron. Magnesium borides exhibit a stable, sustained boron flame, needed for high combustion efficiency, whereas physical Mg/B mixtures undergo Mg-driven "flash" combustion.

Boron↗
DOE OSTI2026

Time-dependent-bases with local CUR decomposition method for accelerating turbulent combustion simulations

Here, this study presents a novel reduced-order modeling framework, Time-Dependent Bases with Local CUR decomposition (TDB-L-CUR), designed to efficiently and accurately approximate the species transport equations in reacting flow simulations. The method extends the existing TDB-CUR approach for chemically reacting flows (Jung et al. Comput. Methods Appl. Mech. Engrg. 437 (2025) 117758), which leverages matrix decomposition techniques to form a global-in-space, time-dependent low-dimensional manifold. While TDB-CUR performs well in homogeneous systems, it may be less well-suited to spatially heterogeneous systems such as turbulent flames, where higher-rank approximations are typically required. The proposed TDB-L-CUR framework introduces two methodological extensions to the baseline approach. First, it applies unsupervised clustering to partition the physical domain into distinct regions, enabling spatially localized manifold construction, thereby reducing the rank required for the reduced-order representation. Second, it incorporates a computational singular perturbation (CSP)-based scheme for identifying and penalizing fast species, allowing for spatio-temporally adaptive mitigation of chemical stiffness. The proposed framework is validated on a hierarchy of test cases, including a one-dimensional premixed flame, a two-dimensional nonpremixed ignition case with vortex interaction, and a three-dimensional turbulent premixed flame. TDB-L-CUR significantly improves accuracy over TDB-CUR while further reducing computational cost. The fully on-the-fly formulation of TDB-L-CUR (i.e., requiring no offline training or prior knowledge) makes it a robust and scalable tool for reduced-order modeling of reactive flows.

Local manifold↗
DOE OSTI2024

Cluster expansion by transfer learning for phase stability predictions

Recent progress towards universal machine-learned interatomic potentials holds considerable promise for materials discovery. Yet the accuracy of these potentials for predicting phase stability may still be limited. In contrast, cluster expansions provide accurate phase stability predictions but are computationally demanding to parameterize from first principles, especially for structures of low dimension or with a large number of components, such as interfaces or multimetal catalysts. We overcome this trade-off via transfer learning. Using Bayesian inference, we incorporate prior statistical knowledge from machine-learned and physics-based potentials, enabling us to sample the most informative configurations and to efficiently fit first-principles cluster expansions. Furthermore, this algorithm is tested on Pt:Ni, showing robust convergence of the mixing energies as a function of sample size with reduced statistical fluctuations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bayesian prior construction for uncertainty quantification in first-principles statistical mechanics

First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into downstream calculations, in addition to any uncertainty inherent to DFT calculations. Bayesian frameworks address this by quantifying uncertainty and incorporating expert knowledge through priors. However, constructing effective priors remains challenging. This work introduces and describes practical strategies for building Bayesian cluster expansions, focusing on basis truncation, hyperparameter selection, and ground state replication. We analyze multiple basis truncation schemes, compare cross-validation to the evidence-approximation for hyperparameter optimization, and provide methods to find and enforce ground-state-preserving models through priors. Additionally, we compare the uncertainties between different approximations to DFT (LDA, PBE, SCAN) against the uncertainty introduced with the use of cluster expansion surrogate models. These approaches are demonstrated on the BCC Li x Mg 1-x and Li x Al 1-x alloys, which are both of interest for solid-state Li batteries. Our results provide guidelines for constructing and utilizing Bayesian cluster expansions, thereby improving the transparency of materials modeling. Furthermore, the approaches and insights developed in this work can be transferred to a wide range of cluster expansion surrogate models, including the atomic cluster expansion and related machine-learned interatomic potential architectures.

Alloy theory↗
DOE OSTI2025

Computational and experimental analysis of structural and Thermophysical properties of LiX-KX (X = chloride, iodide, or bromide) molten salts

Molten salts containing lithium (Li) and potassium (K) halides, including chlorides (Cl), bromides (Br), and iodides (I), are pivotal in advanced technological applications including nuclear reactor technologies, thermal batteries, and industrial pyrochemical processes. Despite their significance, the availability of the temperature-dependent structural and thermophysical properties of these molten salts remain scarce. Here, this study addresses this knowledge gap by predicting and measuring the key properties such as coordination number, radial distribution function, density, heat capacity, and volumetric thermal expansion of molten LiI-KI, LiBr-KBr and LiCl-KCl eutectic mixtures. Utilizing ab initio molecular dynamics (AIMD) simulations, we predict these properties of LiX-KX (X=Br,I, Cl) across various temperatures, with chlorides as a benchmark. Experimental measurements using Archimedes methods and Differential Scanning Calorimetry validate the densities and heat capacities of these molten salts. Our comprehensive analysis of the structural and thermophysical properties provides critical insights into the behavior of these molten salts at various temperatures, enhancing the understanding necessary for their application in advanced technologies.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Simulators for quantum network modeling: A comprehensive review

Quantum network research is exploring new networking protocols, physics-based hardware and novel experiments to demonstrate how quantum distribution will work over large distances. Current work explores much of these concepts in simulations, that are developed to understand how quantum networking will be set up and researchers can experiment virtually. Exposing flaws in network designs, like unsustainable topologies, or develop protocols that efficiently utilize network resources, simulators can also help assess whether workloads are balanced across virtual machines in the network. However, much of these simulation models come without reliable verification methods, for testing performance in real deployments. Here, in this paper, we present a review of, to the best of our knowledge, currently used toolkits for modeling quantum networks. With these toolkits and standardized validation techniques, we can lay down the foundations for more accurate and reliable quantum network simulators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A grid-scale study of demand bidding by large industrial users

A demand bidding mechanism for engaging large industrial electricity users in the operation of the power grid is presented. Demand bidding is formulated as an optimization problem based on a modified version of the alternating current optimal power flow problem, and can be interpreted as a tâtonnement process between the grid operator and electricity users. Here, the work provides the first – to the authors’ knowledge – grid-scale case study of demand bidding, using a synthetic grid structure in the footprint of the grid of Texas. Results reveal that the demand bidding lowers overall power generation costs, but economic benefits plateau as the number of participants increases. Transmission line and transformer capacity constraints become the limiting factors, revealing that expanding and fortifying the transmission infrastructure is key to expanding demand-side participation. Demand bidding does not substantially alter the optimal operation of existing bidding entities when the number of bidders increases, thereby supporting existing bidders to stay in the system and encouraging new ones to join.

Chlor-alkali plant↗
DOE OSTI2025

Damage progression and failure of SiC/SiC composite tubes under hard-contact radial expansion

The response of silicon carbide (SiC) fiber-reinforced SiC matrix (SiC/SiC) composite cladding to mechanical interaction with fissile fuel is a knowledge gap that must be overcome to design and assess SiC-based cladding systems for advanced nuclear applications. This study developed the relevant mechanical testing capability and identified the failure behavior and the critical microstructural features and processing defects. Sections of SiC composite tube were subjected to a modified expansion-due-to-compression (EDC) test in an X-ray computed tomography microscope: a polyurethane plug pressed surrogate Al 2 O 3 into the inner walls of the SiC/SiC composite tubes to achieve hard contact. A pure EDC test with just a polyurethane plug was also performed as a reference. Through the use of displacement fields, digital volume correlation revealed inhomogeneous deformation fields in the tubes, even for pure EDC, which was related to the inherent defects in the structure. Deep learning–aided segmentation and systematic data analysis revealed that the presence of inhomogeneous deformation applied by the hard contact was exaggerated by the presence of inner surface imperfections left behind from the matrix densification process. In conclusion, the findings provide insights into the applications, highlighting the necessity for improvements in inner surface roughness and the incorporation of localized contacts in pellet–cladding mechanical interaction computational models.

Composites↗
DOE OSTI2025

Upstream considerations for gas fermentation processes

Gas fermentation enables the production of fuels, chemicals, and foods from gaseous carbon sources and could serve as a technology for valorizing carbon that may otherwise be emitted to the atmosphere. In this review, we focus on upstream feedstock considerations: the supply of carbon and the supply of electrical power. Electrical power serves a dual role, providing both process energy and biochemical redox potential (via hydrogen or reduced intermediates). We define gas fermentation as bioprocesses involving gaseous feedstocks metabolized by microbes, distinct from microbial electrosynthesis. Trends in CO 2 point sources and low-carbon electricity systems are analyzed, highlighting opportunities and challenges for future deployment. This review synthesizes current knowledge and identifies key R&D priorities for process integration at industrial scale.

09 BIOMASS FUELS↗
DOE OSTI2024

Ensemble Monte Carlo calculations with five novel moves

We introduce five novel types of Monte Carlo (MC) moves that brings the number of moves of ensemble MC calculations from three to eight. So far such calculations have relied on affine invariant stretch moves that were originally introduced by Christen (2007), walk moves by Goodman and Weare (2010) and quadratic moves by Militzer (2023). Ensemble MC methods have been very popular because they harness information about the fitness landscape from a population of walkers rather than relying on expert knowledge. Here we modified the affine method and employed a simplex of points to set the stretch direction. We adopt the simplex concept to quadratic moves. We also generalize quadratic moves to arbitrary order. Finally, we introduce directed moves that employ the values of the probability density while all other types of moves rely solely on the location of the walkers. We apply all algorithms to the Rosenbrock density in 2 and 20 dimensions and to the ring potential in 12 and 24 dimensions. We evaluate their efficiency by comparing error bars, autocorrelation time, travel time, and the level of cohesion that measures whether any walkers were left behind. Our code is open source.

97 MATHEMATICS AND COMPUTING↗