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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A universal and constant rate of gene content change traces pangenome flux to LUCA

Abstract Prokaryotic genomes constantly undergo gene flux via lateral gene transfer, generating a pangenome structure consisting of a conserved core genome surrounded by a more variable accessory genome shell. Over time, flux generates change in genome content. Here, we measure and compare the rate of genome flux for 5655 prokaryotic genomes as a function of amino acid sequence divergence in 36 universally distributed proteins of the informational core (IC). We find a clock of gene content change. The long-term average rate of gene content flux is remarkably constant across all higher prokaryotic taxa sampled, whereby the size of the accessory genome—the proportion of the genome harboring gene content difference for genome pairs—varies across taxa. The proportion of species-level accessory genes per genome, varies from 0% (Chlamydia) to 30%–33% (Alphaproteobacteria, Gammaproteobacteria, and Clostridia). A clock-like rate of gene content change across all prokaryotic taxa sampled suggest that pangenome structure is a general feature of prokaryotic genomes and that it has been in existence since the divergence of bacteria and archaea.

Microbiology↗
DOE OSTI2022

Finite Element Modeling of Electrochemical Polishing of Niobium in Hydrofluoric-Sulfuric Acid Electrolyte

Niobium (Nb) used in superconducting radio-frequency cavities requires smooth surface to achieve optimal performance. In this work, a finite element model that coupled electrochemistry, heat transfer, and fluid dynamics was developed to investigate the electrochemical polishing mechanisms of Nb, using experimentally measured polarization results of coupon samples as validations. The current and potential distribution, oxide growth kinetics of Nb in a complex cavity geometry was investigated as a function of temperature and coolant flow. A low temperature coolant with intermediate flow rate was found to reduce surface current and ensure oxide uniformity. These results could shed light on the design of future particle accelerators.

SRF accelerator cavity↗
DOE OSTI2021

A Lattice Litany for Transition Metal Oxides

In this tribute to K Alex Müeller, I describe how his early insights have influenced future decades of research on perovskite ferroelectrics and more broadly transition metal oxides (TMOs) and related quantum materials. I use his influence on my own research journey to discuss impacts in three areas: structural phase transitions, precursor structure, and quantum paraelectricity. I emphasize materials functionality in ground, metastable, and excited states arising from competitions among lattice, charge, and spin degrees of freedom, which results in highly tunable landscapes and complex networks of multiscale configurations controlling macroscopic functions. I discuss competitions between short- and long-range forces as particularly important in TMOs (and related materials classes) because of their localized and directional metal orbitals and the polarizable oxygen ions. I emphasize crucial consequences of elasticity and metal–oxygen charge transfer

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Development of a calculation framework coupling Monte Carlo neutronics, subchannel thermal-hydraulics and 1.5D fuel thermo-mechanics

A coupled calculation system consisting of the Monte Carlo neutronic solver SERPENT and the fuel behaviour solver TRANSURANUS was extended with the sub-channel thermal-hydraulic solver SUBCHANFLOW to form a coupled calculation framework for multi-physics reactor analyses. The coupling between the 3 solvers was accomplished via a driver program written in Perl that runs the solvers, processes and transfers the output data between the solvers and monitors the convergence. While some features of the coupling are still under development, a steady-state coupled calculation resulting in convergence was performed for a single hexagonal fuel assembly to demonstrate that the base functionalities work as expected. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

6Li as a three-body system in the (p, 3He) reaction at astrophysical energies

Several astrophysical processes are governed by the occurrence of nuclear reactions involving light nuclei at energies below the Coulomb barrier. Among other effects, their understanding is challenged by the appearance of clustered structures in the ground-state configuration of some of these nuclei, which may have a significant impact on the reaction cross section. In this contribution, we focus on the Li 6 (p, He 3 ) He 4 reaction, to probe the role of clustered configurations of Li 6 . In particular, we consider a three-body ab-initio calculation, based on the hyperspherical harmonics (HH) method, of the Li 6 wave function (WF), together with a more phenomenological three-body model. We observe that the HH WF entails a degree of clustering much larger than obtained from the phenomenological WFs. However, the corresponding reaction cross section, evaluated as a direct two-nucleon transfer in distorted-wave Born approximation, still follows the scaling with the clustering strength already pointed out in a previous work [1] and exhibits an energy trend very similar to that obtained with realistic phenomenological WFs. This opens up interesting perspectives towards constraining

Perrotta, S.↗
DOE OSTI2022

Theoretical Modeling of Electrochemical Proton-Coupled Electron Transfer

Proton-coupled electron transfer (PCET) plays an essential role in a wide range of electrocatalytic processes. Here, a vast array of theoretical and computational methods have been developed to study electrochemical PCET. These methods can be used to calculate redox potentials and pKa values for molecular electrocatalysts, proton-coupled redox potentials and bond dissociation free energies for PCET at metal and semiconductor interfaces, and reorganization energies associated with electrochemical PCET. Periodic density functional theory can also be used to compute PCET activation energies and perform molecular dynamics simulations of electrochemical interfaces. Various approaches for maintaining a constant electrode potential in electronic structure calculations and modeling complex interactions in the electric double layer (EDL) have been developed. Theoretical formulations for both homogeneous and heterogeneous electrochemical PCET spanning the adiabatic, nonadiabatic, and solvent-controlled regimes have been developed and provide analytical expressions for the rate constants and current densities as functions of applied potential. The quantum mechanical treatment of the proton and inclusion of excited vibronic states have been shown to be critical for describing experimental data, such as Tafel slopes and potential dependent kinetic isotope effects. The calculated rate constants can be used as input to microkinetic models and voltammogram simulations to elucidate complex electrocatalytic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Tracking of Marangoni driven motion during laser powder bed fusion

Laser Powder Bed Fusion (LPBF) can be used to create Functionally Graded Materials (FGMs) by selectively distributing property augmenting particles throughout the build matrix. With this method parts can be created with nonhomogeneous properties tuned to spatially varied, pre-determined design constraints. To unlock this capability, the fluid motion that occurs in the molten liquid phase must be thoroughly understood. More importantly, the actual tracking of motion during the LPBF process is needed. Further, a numerical model of heat transfer and fluid flow in LPBF process was created. Using results from the model the motion of particles starting at many different locations was tracked. The average change in position for these particles based on starting location was calculated. The effects of non-dimensional parameters on melt pool size were examined. These results were validated experimentally and compared to what is available in the literature.

42 ENGINEERING↗
DOE OSTI2020

Proton-coupled electron transfer across benzimidazole bridges in bioinspired proton wires

Designing molecular platforms for controlling proton and electron movement in artificial photosynthetic systems is crucial to efficient catalysis and solar energy conversion. The transfer of both protons and electrons during a reaction is known as proton-coupled electron transfer (PCET) and is used by nature in myriad ways to provide low overpotential pathways for redox reactions and redox leveling, as well as to generate bioenergetic proton currents. Herein, we describe theoretical and electrochemical studies of a series of bioinspired benzimidazole-phenol (BIP) derivatives and a series of dibenzimidazole-phenol (BI 2 P) analogs with each series bearing the same set of terminal proton-accepting (TPA) groups. The set of TPAs spans more than 6 pK a units. These compounds have been designed to explore the role of the bridging benzimidazole(s) in a one-electron oxidation process coupled to intramolecular proton translocation across either two (the BIP series) or three (the BI 2 P series) acid/base sites. These molecular constructs feature an electrochemically active phenol connected to the TPA group through a benzimidazole-based bridge, which together with the phenol and TPA group form a covalent framework supporting a Grotthuss-type hydrogen-bonded network. Infrared spectroelectrochemistry demonstrates that upon oxidation of the phenol, protons translocate across this well-defined hydrogen-bonded network to a TPA group. The experimental data show the benzimidazole bridges are non-innocent participants in the PCET process in that the addition of each benzimidazole unit lowers the redox potential of the phenoxyl radical/phenol couple by 60 mV, regardless of the nature of the TPA group. Using a series of hypothetical thermodynamic steps, density functional theory calculations correctly predicted the dependence of the redox potential of the phenoxyl radical/phenol couple on the nature of the final protonated species and provided insight into the thermodynamic role of dibenzimidazole units in the PCET process. This information is crucial for developing molecular “dry proton wires” with these moieties, which can transfer protons via a Grotthuss-type mechanism over long distances without the intervention of water molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Mechanistic Investigation of Dimethylmercury Formation Mediated by a Sulfide Mineral Surface

Mercury (Hg) pollution is a global environmental problem, but many key transformations among 29 various Hg species are not well understood. Although anaerobic microorganisms are the primary 30 producers of monomethylmercury (MMeHg), the abiotic formation of dimethylmercury (DMeHg) 31 from MMeHg have been suggested to account for a large portion of DMeHg in the ocean. It has 32 been shown that abiotic formation of DMeHg from MMeHg can be facilitated by reduced sulfur 33 groups on mineral surfaces. However, a detailed mechanistic understanding of this reaction is 34 lacking. Here we perform density functional theory (DFT) calculations to explore mechanisms of 35 DMeHg formation on the 110 surface of a CdS(s) (hawleyite) nanoparticle. We show that 36 coordination of MMeHg substituents to adjacent reduced sulfur groups protruding from the surface 37 facilitate DMeHg formation and that the reaction proceeds through direct transmethylation from 38 one MMeHg substituent to another. Solvation effects were found to play an important role in the 39 surface reconstruction of the nanoparticle and in decreasing the energetic barrier for DMeHg 40 formation. We also investigate DMeHg formation from MMeHg mediated by organic dithiols and 41 compare the results to those mediated by the mineral surface. In contrast to the surface reaction, 42 the calculations suggest that dithiol-mediated formation of DMeHg instead proceeds through a 43 mechanism in which Hg–Hg interactions activate a methyl group from one MMeHg substituent 44 for intramolecular transfer to the adjacent MMeHg substituent. These findings refine previously 45 proposed mechanisms of abiotic DMeHg formation and fill gaps in our understanding of global 46 Hg cycling in the environment.

Lian, Peng↗
DOE OSTI2025

Bottom-Up Simulation, Reconstruction, and Quantification of Macromolecule Sequences from Experimental Polymerizations

Motivated by the canonical sequence–structure–function paradigm, tools to characterize chemical patterning in natural biomacromolecules, from proteins to nucleic acids, have grown exponentially in recent years. However, analogous strategies for synthetic macromolecules remain in nascent stages, complicated by sequence polydispersity and analytical limitations. To address this, we have developed a comprehensive and open-source Python package, PRISM (polymer rate insights and sequence modeling), an end-to-end workflow that provides a path from experimental kinetics measurements to quantitative and qualitative metrics for describing chemical patterning in stochastic polymers. First, a numerical integration strategy was constructed to simulate and fit experimental data from reversible addition–fragmentation chain transfer (RAFT) polymerization kinetics, enabling the facile estimation of relevant reactivity ratios. These ratios were then used in a mechanism-specific stochastic kinetic simulation strategy to simulate sequence ensembles corresponding to model systems spanning experimental copolymers, classes of statistical polymers (e.g., alternating, block, and gradient), and multiblock copolymers. Lastly, inspired by sequence homology metrics from bioinformatics, we introduce visualization strategies and quantitative metrics to facilitate comparisons of different sequence ensembles. As the sequence–structure–function paradigm becomes increasingly central in de novo design of synthetic macromolecules, this toolkit provides a first step toward accurate and representative sequence description and featurization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Colloidal CdSe nanocrystals are inherently defective

Abstract Colloidal CdSe nanocrystals (NCs) have shown promise in applications ranging from LED displays to medical imaging. Their unique photophysics depend sensitively on the presence or absence of surface defects. Using simulations, we show that CdSe NCs are inherently defective; even for stoichiometric NCs with perfect ligand passivation and no vacancies or defects, we still observe that the low energy spectrum is dominated by dark, surface-associated excitations, which are more numerous in larger NCs. Surface structure analysis shows that the majority of these states involve holes that are localized on two-coordinate Se atoms. As chalcogenide atoms are not passivated by any Lewis base ligand, varying the ligand should not dramatically change the number of dark states, which we confirm by simulating three passivation schemes. Our results have significant implications for understanding CdSe NC photophysics, and suggest that photochemistry and short-range photoinduced charge transfer should be much more facile than previously anticipated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Programmable Gene Knockdown in Diverse Bacteria Using Mobile‐CRISPRi

Abstract Facile bacterial genome sequencing has unlocked a veritable treasure trove of novel genes awaiting functional exploration. To make the most of this opportunity requires powerful genetic tools that can target all genes in diverse bacteria. CRISPR interference (CRISPRi) is a programmable gene‐knockdown tool that uses an RNA‐protein complex comprised of a single guide RNA (sgRNA) and a catalytically inactive Cas9 nuclease (dCas9) to sterically block transcription of target genes. We previously developed a suite of modular CRISPRi systems that transfer by conjugation and integrate into the genomes of diverse bacteria, which we call Mobile‐CRISPRi. Here, we provide detailed protocols for the modification and transfer of Mobile‐CRISPRi vectors for the purpose of knocking down target genes in bacteria of interest. We further discuss strategies for optimizing Mobile‐CRISPRi knockdown, transfer, and integration. We cover the following basic protocols: sgRNA design, cloning new sgRNA spacers into Mobile‐CRISPRi vectors, Tn 7 transfer of Mobile‐CRISPRi to Gram‐negative bacteria, and ICE Bs1 transfer of Mobile‐CRISPRi to Bacillales. © 2020 The Authors. Basic Protocol 1 : sgRNA design Basic Protocol 2 : Cloning of new sgRNA spacers into Mobile‐CRISPRi vectors Basic Protocol 3 : Tn 7 transfer of Mobile‐CRISPRi to Gram‐negative bacteria Basic Protocol 4 : ICE Bs1 transfer of Mobile‐CRISPRi to Bacillales Support Protocol 1 : Quantification of CRISPRi repression using fluorescent reporters Support Protocol 2 : Testing for gene essentiality using CRISPRi spot assays on plates Support Protocol 3 : Transformation of E. coli by electroporation Support Protocol 4 : Transformation of CaCl 2 ‐competent E. coli

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data‐Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM

Abstract Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large‐amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN‐based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics‐based gravity wave (GW) parameterizations as a test case. WACCM has complex, state‐of‐the‐art parameterizations for orography‐, convection‐, and front‐driven GWs. Convection‐ and orography‐driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto‐encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO 2 ). However, their performance is significantly improved by applying transfer learning, for example, re‐training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data‐driven parameterizations for various processes, including (but not limited to) GWs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine Learning-Assisted Recovery of Delicate Kinetic Information from Transient Reactor Experiments

Identifying active sites and their roles in chemical reaction steps remains a vital challenge in heterogeneous catalysis. Transient experiments offer a unique way to probe active sites and distinguish subtle kinetic features. Although physics-based analysis methods may be well-developed, they can be highly susceptible to experimental noise, and smoothing methods may erase or even distort important features; a smooth curve is not always the best curve. We demonstrate a new workflow for the direct interpretation of intrinsic kinetic information from exit flux curves measured in transient reactor experiments. This workflow contains three artificial neural networks (ANNs), including a noise reducer, a concentration predictor, and a rate predictor to analyze experimental data, followed by the virtual TAP (VTAP) physics-based reactor model and density functional theory (DFT) calculations of adsorption energies on specific sites. We use this workflow to analyze the data from experiments titrating Pt/Al 2 O 3 and Pt/SiO 2 catalysts with carbon monoxide (CO) in the temporal analysis of products (TAP) reactor. Our workflow separates the time-evolving chemical reaction and mass transfer information contained in the TAP pulse response. The existence of strong- and weak-binding sites on the Pt/Al 2 O 3 catalyst is observed in the catalyst titration experiment in the transient reactor. The structures of the strong- and weak-binding sites are then identified by using DFT calculations. We find that the Pt/SiO 2 catalyst has only strong-binding sites, which aligns with the inactive support effect of SiO 2 . We demonstrate how machine learning methods provide unique insights with high-resolution data analysis that cannot be achieved by using state-of-the-art physics-based methods.

Adsorption↗
DOE OSTI2025

Identification of key steps in the evolution of anaerobic methanotrophy in Candidatus Methanovorans (ANME-3) archaea

Despite their large environmental impact and multiple independent emergences, the processes leading to the evolution of anaerobic methanotrophic archaea (ANME) remain unclear. This work uses comparative metagenomics of a recently evolved but understudied ANME group, “Candidatus Methanovorans” (ANME-3), to identify evolutionary processes and innovations at work in ANME, which may be obscured in earlier evolved lineages. We identified horizontal transfer of hdrA homologs and convergent evolution in carbon and energy metabolic genes as potential early steps in Methanovorans evolution. We also identified the erosion of genes required for methylotrophic methanogenesis along with horizontal acquisition of multiheme cytochromes and other loci uniquely associated with ANME. The assembly and comparative analysis of multiple Methanovorans genomes offers important functional context for understanding the niche-defining metabolic differences between methane-oxidizing ANME and their methanogen relatives. Furthermore, this work illustrates the multiple evolutionary modes at play in the transition to a globally important metabolic niche.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Structure and Potential–Dependent Selectivity in Redox–Metallopolymers: Electrochemically Mediated Multicomponent Metal Separations

Electro-responsive functional materials can play a critical role in selective metal recovery and recycling due to the need for molecular differentiation between transition metals in complex mixtures. Redox-active metallopolymers are a promising platform for electrochemical separations, offering versatile structural tuning and fast electron transfer. First, through a judicious selection of polymer structure between a main-chain metallopolymer (polyferrocenylsilane) and a pendant-group metallopolymer (polyvinylferrocene), charge-transfer interactions and binding strength toward competing metal ions are tuned, which as a result, dictate selectivity. For example, almost an order of magnitude increase in separation factor between chromate and meta-vanadate can be achieved, depending on polymer structure. Second, these metallopolymer electrodes exhibit potential-dependent selectivity that can even flip ion preference, based solely on electrical means—indicating a control parameter that is orthogonal to structural modifications. Finally, this work presents a framework for evaluating electrochemical separations in multicomponent ion mixtures and elucidates the underlying charge-transfer mechanisms resulting in molecular selectivity through a combination of spectroscopy and electronic structure calculations. Furthermore, the findings demonstrate the applicability of redox-metallopolymers in tailored electrochemical separations for environmental remediation, value-added metal recovery, waste recycling, and even mining processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Rational Design of Weakly‐Solvating Molecules for Salt‐In‐Pre‐Ionic‐Liquid Electrolytes for Li Metal Batteries

Lithium metal batteries (LMBs) promise step‐changes in energy densities but suffer from poor cycle life due to unstable electrolyte‐lithium interfaces. Conventional carbonate electrolytes exhibit excessive lithium‐ion solvation and low oxidative stability, leading to rapid capacity loss. Herein, we report a rationally designed weakly‐solvating cyclic sulfonamide, 1‐trifluoromethanesulfonyl)amide pyrrolidine (TFMSPyr), which integrates an electron‐withdrawing trifluoromethanesulfonyl functional group at pyrrolidinic‐N. TFMSPyr acts as a pre‐ionic‐liquid solvent that forms intrinsically localized, anion‐dominated solvation, coupling molecular architecture, solvation topology, and transport dynamics. As a result, LiFSI based salt‐in‐pre‐ionic‐liquid (SIPIL) electrolytes exhibit high lithium‐ion transference number, oxidative stability > 5 V versus Li/Li + and anion‐derived solid electrolyte interphases (SEI). Li||Cu cells with SIPIL deliver a first cycle Coulombic efficiency (CE) of ≈ 99% with average CE of 99.2% for 100 cycles, and lithium half‐cells with lithium iron phosphate (LFP) cathode exhibit 82% capacity retention after 400 cycles with CE of 99.98%. In anode‐free full cells, 95% of initial capacity is retained after 63 cycles with an average CE of 99.5%. These results demonstrate that molecular engineering of solvents offers a powerful pathway to stabilize lithium metal interfaces and enable practical Anodeless LMBs.

25 ENERGY STORAGE↗
DOE OSTI2023

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes

Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. Here, we leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. J. Chem. Phys. 159, 024120 (2023)] to train three machine learning (ML) models for transition temperature (T 1/2 ) prediction using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set RF and recursive feature addition RF models perform best, achieving moderate correlation to experimental T 1/2 values. We then compare ML T 1/2 predictions to those from three previously identified best-performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict T 1/2 more accurately than the best-performing DFAs. In addition, we study ML model predictions for a set of 18 SCO complexes for which only estimated T 1/2 values are available. Upon excluding outliers from this set, the RF-RFA RF model shows a strong correlation to estimated T 1/2 values with a Pearson’s r of 0.82. In contrast, DFA-predicted T 1/2 values have large errors and show no correlation to estimated T 1/2 values over the same set of complexes. Overall, our study demonstrates slightly superior performance of ML models in comparison with some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗