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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Carbene Functionalization of Monolayer Tungsten Disulfide for Enhanced Quantum Emission

Semiconducting two-dimensional (2D) transition metal dichalcogenides (TMDs) are promising materials for an array of applications, ranging from conventional field-effect transistors, photodetectors, and light-emitting diodes to their more recent use in quantum photonic technologies. Chemical functionalization of 2D TMDs with organic ligands and adlayers provides an additional means for customizing their electronic and optical properties. While many pathways have been reported for the chemical functionalization of 2D TMDs, their frequent reliance on solution-based methods results in limited control over adlayer thickness and coverage, thus hindering utility in high-performance applications. Here, in this study, we describe the vapor-phase functionalization of a 2D TMD with carbene ligands, specifically tungsten disulfide (WS 2 ) with N-heterocyclic carbenes (NHCs), resulting in molecularly smooth, thin, and uniform adlayers. Reacting NHCs with monolayer WS 2 reduces the broad photoluminescence background observed at cryogenic temperatures by 58%, which facilitates the detection of single-photon emitters from strained monolayer WS 2 , as indicated by second order correlation values ( g (2) ) as low as 0.17 ± 0.07. Chemical characterization coupled with density functional theory calculations suggests that the NHC adlayer has a dual defect-passivation and doping effect on monolayer WS 2 that results in enhanced single-photon emission. Overall, this study establishes vapor-phase carbene functionalization as a homogeneous surface modification scheme for tailoring the quantum emission properties of semiconducting 2D TMDs.

N-heterocyclic carbenes↗
DOE OSTI2024

LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning

Sequence-function data provides valuable information about the protein functional landscape but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barcoding strategy with nanopore sequencing to rapidly generate sequence-function data for entire protein-coding genes. LevSeq integrates into existing protein engineering workflows and comes with open-source software for data analysis and visualization. The pipeline facilitates data-driven protein engineering by consolidating sequence-function data to inform directed evolution and provide the requisite data for machine learning-guided protein engineering (MLPE). LevSeq enables quality control of mutagenesis libraries prior to screening, which reduces time and resource costs. Simulation studies demonstrate LevSeq’s ability to accurately detect variants under various experimental conditions. Lastly, we show LevSeq’s utility in engineering protoglobins for new-to-nature chemistry. Widespread adoption of LevSeq and sharing of the data will enhance our understanding of protein sequence-function landscapes and empower data-driven directed evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Electron capture of superheavy nuclei with realistic lepton wave functions

The superheavy nuclei push the periodic table of the elements and the chart of the nuclides to their limits, providing a unique laboratory for studies of the electron-nucleus interactions. The most important weak decay mode in known superheavy nuclei is electron capture (EC). In the standard calculations of EC, the lepton wave functions are usually considered in the lowest-order approximation. In this work, we investigate the sensitivity of EC rates on the choice of the electron wave functions by (i) assuming the single-particle approximation for the electron wave functions, and (ii) carrying out Dirac-Hartree-Fock (DHF) calculations. The nuclear response is generated based on the state-of-the-art quasiparticle random phase approximation employing relativistic nuclear energy density functional theory. Here, we show that using the improved lepton wave functions reduces the EC rates up to 40% in the superheavy nucleus oganesson (𝑍=118). Interestingly, because of screening effects, the difference between the EC rates obtained with the DHF and single-particle calculations is fairly small.

Atomic orbital↗
DOE OSTI2025

Further steps toward the next generation of covariant energy density functionals

The present study aims at further development of covariant energy density functionals (CEDFs) towards more accurate description of binding energies across the nuclear chart. Infinite basis corrections to binding energies in the fermionic and bosonic sectors of the covariant density functional theory are taken into account in the fitting protocol within the covariant density functional theory. In addition, total electron binding energies are used in the conversion of atomic binding energies into nuclear ones. Their dependence on neutron excess is investigated across the nuclear chart within the atomic approach. Furthermore, these factors were disregarded in the previous generation of covariant energy density functionals, but their omission leads to substantial global calculation errors for physical quantities of interest. For example, these errors for binding energies are of the order of 0.8 MeV or higher for the three major classes of covariant energy density functionals.

Binding energy & masses↗
DOE OSTI2024

Entropy-Infused Deep Learning Loss Function for Capturing Extreme Values in Wind Power Forecasting

Extreme scenarios in wind power generation occur with higher frequency and larger magnitude in the recent years due to the ever-increasing extreme meteorological factors. Accurate forecasting of the occurrence of extreme values in wind power generation is of great concern to ensure reliable power system operation. Recently, deep learning models have surged in popularity for wind power forecasting, with the mean squared error (MSE) loss function being commonly used. However, the MSE loss function, being sensitive to extreme values, disproportionately penalizes larger errors, cannot adequately capture the extreme values present in wind energy data, and novel loss functions have seldom been tailored for wind power forecasting. To this end, in this paper, we introduce a novel loss function specifically crafted to capture extreme values in wind power forecasting. The experimental results with four fundamental deep learning methods on open source wind power dataset validate that the new loss function is efficient and superior in all cases compared to MSE in capturing extreme values while maintaining forecasting performance.

17 WIND ENERGY↗
DOE OSTI2024

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems

ABSTRACT Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome’s functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute’s Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome’s metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1967

Theoretical studies on the relationship between the thermionic work functions of refractory-metal intermetallic compounds and their electronic and crystal structures Final summary report

The Group VIA-Re binary systems are characterized by the presence of several intermediate phases. The u phase is present in all of the systems, but the a-Mn phases occur in only the Mo-Re and W-Re systems. The occurrence of these intermediate phases appears to be governed largely by electronic factors and is predictable on the basis of electron/atom ratio. The thermionic work functions of the intermediate phase materials were experimentally determined. function and the screening of the outermost electrons in the Group VIA metals was found. A correlation between the work As might be expected for purely metallic substances, the work functions were relatively high, in the range 4. 5 to 5. 0 eV, and in each case were bracketed by the work functions of the pure constituents. A discontinuous change in the average work function with composition is proposed, based on a thermodynamical treatment of the electron vapor.

REFRACTORY METAL↗
NASA NTRS2017

Curved Displacement Transfer Functions for Geometric Nonlinear Large Deformation Structure Shape Predictions

For shape predictions of structures under large geometrically nonlinear deformations, Curved Displacement Transfer Functions were formulated based on a curved displacement, traced by a material point from the undeformed position to deformed position. The embedded beam (depth-wise cross section of a structure along a surface strain-sensing line) was discretized into multiple small domains, with domain junctures matching the strain-sensing stations. Thus, the surface strain distribution could be described with a piecewise linear or a piecewise nonlinear function. The discretization approach enabled piecewise integrations of the embedded-beam curvature equations to yield the Curved Displacement Transfer Functions, expressed in terms of embedded beam geometrical parameters and surface strains. By entering the surface strain data into the Displacement Transfer Functions, deflections along each embedded beam can be calculated at multiple points for mapping the overall structural deformed shapes. Finite-element linear and nonlinear analyses of a tapered cantilever tubular beam were performed to generate linear and nonlinear surface strains and the associated deflections to be used for validation. The shape prediction accuracies were then determined by comparing the theoretical deflections with the finiteelement- generated deflections. The results show that the newly developed Curved Displacement Transfer Functions are very accurate for shape predictions of structures under large geometrically nonlinear deformations.

displacement theory↗
NASA NTRSDate not supplied

On The Incorporation of Both Function-Driven Requirements and Topology Optimization in The Development of Lunar Launch & Landing Pads

In the design of additively manufactured structures, there can be a constant battle between focusing on function-driven design and topology optimization in an attempt to improve a products ability to meet all requirements. Function-driven optimization is defined as the process of identifying the best design parameters that satisfy project functionality requirements. Function-driven optimization is typically implemented by a designer(s) who, instead of computational limitations, has human bias for the input or inputs determined. On the other hand, topology optimization is defined as a mathematical method that optimizes material layout within a given design space, for a given set of loads, boundary conditions, and constraints with the goal of maximizing the performance of the system. In topology optimization, the work is defined by the limited mathematical inputs and computationally available goals/restraints. If not evaluated simultaneously, these two approaches can result in fundamentally different design concepts meant to address the same requirements. NASA’s Artemis program includes the development of a base camp near the lunar South Pole. After site selection is made, one of the first infrastructure elements needed will be a launch and landing pad (LLP). The construction of such a pad will prevent damage to nearby structures from plume-surface interaction (PSI) while also providing a stable platform for the lander. These benefits reduce the required distance between the established landing zones and other settlement infrastructure. In this paper, the authors evaluate the requirements associated with lunar LLP development, application of both functional and topological optimization techniques, methodologies to combine the two techniques, and potential impacts on final design of such a pad.

In-situ↗
NASA NTRS2023

A Theoretical and Computational Revisit of Conversions Between Whitham’s F-Function and Equivalent Area

This paper proves mathematically that the integral transforms between Whitham’s F-function and equivalent area are the inverse transforms of each other if and only if the slope of the equivalent area at the origin is zero. This mathematical fact contradicts the accepted unconditional inverse relation between Whitham’s F-function and equivalent area in the sonic boom research literature. Piecewise linear approximations of an F-function and of the second derivative of an equivalent area are used to derive numerical formulas for conversions between Whitham’s F-function and equivalent area. Numerical results are included to show convergence of the numerical conversions as the maximum length of the segments for piecewise linear approximations goes to zero. These numerical conversions are approximately the inverse transforms of each other when the second derivative of an equivalent area is continuous and the slope of the equivalent area at the origin is zero.

Whitham's F-function↗
DOE OSTI2024

Plant-specific microbial diversity facilitates functional redundancy at the soil-root interface

Abstract Aims Plant-specific microbial diversity reflecting host-microbe coevolution was frequently shown at the structural level but less on the functional scale. We studied the microbiome of three compartments at the soil root interface (root endosphere, rhizosphere, bulk soil) of medicinal plants cultivated under organic management in Egypt. The study aimed to examine the impact of the rhizosphere on microbial community composition and diversity in desert agricultural soil, as well as to identify specific functions associated with the rhizosphere. Methods The microbiome community structure, diversity, and microbial functioning were evaluated through the utilization of 16S rRNA gene amplicon and shotgun metagenome sequencing. Results We found the typical rhizosphere effect and plant-species-specific enrichment of bacterial diversity. The annual plants Calendula officinalis and Matricaria chamomilla ( Asteraceae ) were more similar than the perennial Solanum distichum ( Solanaceae ). Altogether, plant species explained 50.5% of the variation in bacterial community structures in the rhizosphere. Our results indicate a stronger effect of the plant species in terms of modulating bacterial community structures in the rhizosphere than in root endosphere samples. The plant-driven rhizosphere effect could be linked to redundant plant beneficial functions in the microbiome, while enrichment of specific genes related to amino acid ion transport and metabolism, carbohydrate transport and metabolism, defense mechanisms, and secondary metabolites biosynthesis were more specific. Conclusions The study explores the microbiome continuum at the soil-root interface of medicinal plant species, revealing significant bacterial community structure shifts and plant specificity. The study provides insights into the essential microbiome components contributing to rhizosphere functionality.

Wicaksono, Wisnu Adi (ORCID:0000000215561981)↗
DOE OSTI2025

Automated workflow for non-empirical Wannier-localized optimal tuning of range-separated hybrid functionals

Here, we introduce an automated workflow for generating non-empirical Wannier-localized optimally-tuned screened range-separated hybrid (WOT-SRSH) functionals. WOT-SRSH functionals have been shown to yield highly accurate fundamental band gaps, band structures, and optical spectra for bulk and 2D semiconductors and insulators. Our workflow automatically and efficiently determines the WOT-SRSH functional parameters for a given crystal structure and composition, approximately enforcing the correct screened long-range Coulomb interaction and an ionization potential ansatz. In contrast to previous manual tuning approaches, our tuning procedure relies on a new search algorithm that only requires a few hybrid functional calculations with minimal user input. We demonstrate our workflow on 23 previously studied semiconductors and insulators, reporting the same high level of accuracy. By automating the tuning process and improving its computational efficiency, the approach outlined here enables applications of the WOT-SRSH functional to compute spectroscopic and optoelectronic properties for a wide range of materials.

Gant, Stephen E. [University of California, Berkel↗
DOE OSTI2024

Functional diversification within the heme-binding split-barrel family

Due to neofunctionalization, a single fold can be identified in multiple proteins that have distinct molecular functions. Depending on the time that has passed since gene duplication and the number of mutations, the sequence similarity between functionally divergent proteins can be relatively high, eroding the value of sequence similarity as the sole tool for accurately annotating the function of uncharacterized homologs. Here, we combine bioinformatic approaches with targeted experimentation to reveal a large multifunctional family of putative enzymatic and nonenzymatic proteins involved in heme metabolism. This family (homolog of HugZ (HOZ)) is embedded in the “FMN-binding split barrel” superfamily and contains separate groups of proteins from prokaryotes, plants, and algae, which bind heme and either catalyze its degradation or function as nonenzymatic heme sensors. In prokaryotes these proteins are often involved in iron assimilation, whereas several plant and algal homologs are predicted to degrade heme in the plastid or regulate heme biosynthesis. In the plant Arabidopsis thaliana, which contains two HOZ subfamilies that can degrade heme in vitro (HOZ1 and HOZ2), disruption of AtHOZ1 (AT3G03890) or AtHOZ2A (AT1G51560) causes developmental delays, pointing to important biological roles in the plastid. In the tree Populus trichocarpa, a recent duplication event of a HOZ1 ancestor has resulted in localization of a paralog to the cytosol. Structural characterization of this cytosolic paralog and comparison to published homologous structures suggests conservation of heme-binding sites. This study unifies our understanding of the sequence-structure-function relationships within this multilineage family of heme-binding proteins and presents new molecular players in plant and bacterial heme metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Revisiting Artifacts of Kohn–Sham Density Functionals for Biosimulation

We revisit the problem of unphysical charge density delocalization/fractionalization induced by the self-interaction error of common approximate Kohn–Sham (KS) density functional theory functionals on simulation of small to medium-sized proteins in a vacuum. Aside from producing unphysical electron densities and total energies, the vanishing of the HOMO–LUMO gap associated with the unphysical charge delocalization leads to an unphysical low-energy spectrum and catastrophic failure of most popular solvers for the KS self-consistent field (SCF) problem. We apply a robust quasi-Newton SCF solver to obtain solutions for some of these difficult cases. The anatomy of the charge delocalization is revealed by the natural deformation orbitals obtained from the density matrix difference between the Hartree–Fock and KS solutions; the charge delocalization not only can occur between charged fragments (such as in zwitterionic polypeptides) but also involves neutral fragments. The vanishing-gap phenomenon and troublesome SCF convergence are both attributed to the unphysical KS Fock operator eigenspectra of molecular fragments (e.g., amino acids or their side chains). Analysis of amino acid pairs suggests that the unphysical charge delocalization can be partially ameliorated by the use of some range-separated hybrid functionals but not by semilocal or standard hybrid functionals. Last, we demonstrate that solutions without the unphysical charge delocalization can be located even for semilocal KS functionals highly prone to such defects, but such solutions have non-Aufbau character and are unstable with respect to mixing of the non-overlapping “frontier” orbitals. Caution should be exercised when unexpectedly small (or vanishing) HOMO–LUMO gaps and atypical SCF convergence patterns (e.g., oscillatory) are observed in KS DFT simulations in any context (bio or otherwise).

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

Oligoether-Functionalized PEDOT: Combining an EDOT Backbone with Polar Side Chains for Solution Processability and High Electrical Conductivity

Conjugated polymers functionalized with oligoether (OE)-based side chains are a key class of materials for various organic electronic applications, including transparent electrodes, thermoelectrics, electrochromic displays, and electrochemical transistors. Herein, we report a highly soluble OE-functionalized poly(3,4-ethylenedioxythiophene) (PEDOT) homopolymer, prepared by direct (hetero)arylation polymerization, which allows solution processing to yield films with comparable redox properties to oxidatively polymerized PEDOT. This polymer, PEDOT(OE3), when optimally oxidatively doped, reaches among the highest electrical conductivities of any OE-functionalized polymer and is comparable to more synthetically complex OE-functionalized polymers. X-ray scattering and spectroscopy were utilized to rationalize the transport properties resulting from varying the doping level. Comparison of PEDOT(OE3) to a series of OE-functionalized dioxythiophenes with varying amounts of 3,4-ethylenedioxythiophene units allows for a deeper understanding of structure–property relationships through charge-transport models. Ultimately, PEDOT(OE3) is shown to be a material with exceptional charge-transport properties and is promising for various applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A global soil plasmidome resource unveils functional and ecological roles of plasmids in soil microbiomes

Plasmids play significant roles in microbial adaptation to ecosystems, yet their dynamics remain poorly understood due to identification challenges. We present the Global Soil Plasmidome Resource (GSPR), a comprehensive dataset of 98,728 plasmid sequences amassed from 6860 terrestrial microbial communities and isolates. We explore this resource through various computational approaches, including phylogenetic diversity analysis, host prediction, and extensive functional annotation, to understand the contribution of plasmids to the genetic and functional diversity in soil, correlating these findings with sample type, as well as the soil habitat they were retrieved from. Our analysis reveals insights into plasmid-encoded functions such as effector modules, quorum sensing, and stress resistance, which may contribute to their persistence and microbial adaptation in soil. Furthermore, CRISPR analysis suggests a prevalent role of these elements related to intra-plasmid competition. By contrasting plasmids from cultivated and uncultivated organisms, we identify important functions that expand existing knowledge of plasmid roles in these habitats. This study represents a notable step forward in elucidating plasmid diversity and function within soil microbiomes and establishes a foundational framework for exploring their roles in natural environments.

Fiamenghi, Mateus B↗
DOE OSTI2025

CRISPRi-ART enables functional genomics of diverse bacteriophages using RNA-binding dCas13d

Bacteriophages constitute one of the largest reservoirs of genes of unknown function in the biosphere. Even in well-characterized phages, the functions of most genes remain unknown. Experimental approaches to study phage gene fitness and function at genome scale are lacking, partly because phages subvert many modern functional genomics tools. Here we leverage RNA-targeting dCas13d to selectively interfere with protein translation and to measure phage gene fitness at a transcriptome-wide scale. We find CRISPR Interference through Antisense RNA-Targeting (CRISPRi-ART) to be effective across phage phylogeny, from model ssRNA, ssDNA and dsDNA phages to nucleus-forming jumbo phages. Using CRISPRi-ART, we determine a conserved role of diverse rII homologues in subverting phage Lambda RexAB-mediated immunity to superinfection and identify genes critical for phage fitness. CRISPRi-ART establishes a broad-spectrum phage functional genomics platform, revealing more than 90 previously unknown genes important for phage fitness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A functional microbiome catalogue crowdsourced from North American rivers

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires knowledge of the spatial drivers of river microbiomes. However, understanding of the core microbial processes governing river biogeochemistry is hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we used a community science effort to accelerate the sampling, sequencing and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). GROWdb profiles the identity, distribution, function and expression of microbial genomes across river surface waters covering 90% of United States watersheds. Specifically, GROWdb encompasses microbial lineages from 27 phyla, including novel members from 10 families and 128 genera, and defines the core river microbiome at the genome level. GROWdb analyses coupled to extensive geospatial information reveals local and regional drivers of microbial community structuring, while also presenting foundational hypotheses about ecosystem function. Building on the previously conceived River Continuum Concept, we layer on microbial functional trait expression, which suggests that the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures, so that it can be widely accessed across disciplines for watershed predictive modelling and microbiome-based management practices.

59 BASIC BIOLOGICAL SCIENCES↗