Search NASA⌕ Search

SEARCH · Search NASA

Results for “functioning”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Dataset for manuscript "Rotational Memory Function of SPC/E water"

Memory effect are essential for dynamics of condensed materials and are responsible for non-exponential relaxation of correlation functions of dynamic variables through the memory function entering the memory equation. Memory functions of dipole rotations for polar liquids have never been calculated. We present here calculations of memory functions and single-dipole rotations and of the overall system dipole moment for SPC/E water measured by dielectric spectroscopy. The memory functions for single-particle and collective dynamics turn out to be nearly identical. This result validates theories of dielectric spectroscopy in terms of single-particle time correlation function and the connection between the collective and single-particle relaxation times in terms of the Kirkwood factor. The dataset includes single particle and system dipole moments, including their time-dependence.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Developing a media formulation to sustain ex vivo chloroplast function

Chloroplasts are critical organelles in plants and algae responsible for accumulating biomass through photosynthetic carbon fixation and cellular maintenance through metabolism in the cell. Chloroplasts are increasingly appreciated for their role in biomanufacturing, as they can produce many useful molecules, and a deeper understanding of chloroplast regulation and function would provide more insight for the biotechnological applications of these organelles. However, traditional genetic approaches to manipulate chloroplasts are slow, and generation of transgenic organisms to study their function can take weeks to months, significantly delaying the pace of research. To develop chloroplasts themselves as a quicker and more defined platform, we isolated chloroplasts from the green algae, Chlamydomonas reinhardtii, and examined their photosynthetic function after extraction. Combined with a metabolic modeling approach using flux-balance analysis, we identified key metabolic reactions essential to chloroplast function and leveraged this information into reagents that can be used in a “chloroplast media” capable of maintaining chloroplast photosynthetic function over time ex vivo compared to buffer alone. We envision this could serve as a model platform to enable more rapid design-build-test-learn cycles to study and improve chloroplast function in combination with genetic modifications and potentially as a starting point for the bottom-up design of a synthetic organelle-containing cell.

Chlamydomonas reinhardtii↗
DOE OSTI2025

SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions

Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses learnable activation functions on the edges of the computational graph followed by summation on nodes. The learnable edge activation functions in the original implementation are basis spline functions (B-Spline). Here, we present a model in which learnable grids of B-Spline activation functions are replaced by grids of re-weighted sine functions (SineKAN). We evaluate numerical performance of our model on a benchmark vision task. We show that our model can perform better than or comparable to B-Spline KAN models and an alternative KAN implementation based on periodic cosine and sine functions representing a Fourier Series. Further, we show that SineKAN has numerical accuracy that could scale comparably to dense neural networks (DNNs). Compared to the two baseline KAN models, SineKAN achieves a substantial speed increase at all hidden layer sizes, batch sizes, and depths. Current advantage of DNNs due to hardware and software optimizations are discussed along with theoretical scaling. Additionally, properties of SineKAN compared to other KAN implementations and current limitations are also discussed.

Reinhardt, Eric↗
DOE OSTI2026

Citric Acid Functionalized Natural Fibers to Enhance Thermal Stability and Moisture Resistance in Polylactic Acid Composites

Cellulosic fibers can impart many unique benefits into composite applications, such as reduced weight or structural reinforcement; however, these materials also increase hygroscopicity and decrease thermal stability, restricting broader applications. The present work adapted an experimental process for functionalizing the cellulose surface using citric acid (CA) for three fibers: a 100% cellulose bleached soft Kraft pulp (e.g., creafill) and two natural fibers with similar composition but different fiber morphology, flax fiber and banana fiber. The process uses CA with a sodium hypophosphite (SHP) catalyst to chemically functionalize fiber surfaces, and the reaction mechanism was investigated through Fourier Transform Infrared Spectroscopy (FTIR), which suggested a grafting mechanism rather than a surface-based crosslinking between neighboring sites. Functionalized fibers were compounded into polylactic acid (PLA) at 20 wt.% to better understand how this functionalization might impact critical performance properties like thermal stability, crystallization, thermal mechanical properties, and water uptake of these composites. The study demonstrated varying levels of efficacy for the functionalization of cellulosic fibers with CA/SHP and the fiber with the most open microstructure, e.g., banana fiber, exhibited the largest change in its properties with a 38% reduction in water uptake compared to untreated banana fiber composites. Parallel evaluation of the functionalization process for different fibers demonstrates the importance of fiber morphology on surface modification and can enable their use in composites by demonstrating the efficacy of this potentially low-cost, low-toxicity method for reducing hygroscopicity and improving thermal stability.

citric acid↗
DOE OSTI2024

Measuring the Conditional Luminosity and Stellar Mass Functions of Galaxies by Combining the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys Data Release 9, Survey Validation 3, and Year 1 Data

In this investigation, we leverage the combination of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 9, Survey Validation 3, and Year 1 data sets to estimate the conditional luminosity functions and conditional stellar mass functions (CLFs and CSMFs) of galaxies across various halo mass bins and redshift ranges. To support our analysis, we utilize a realistic DESI mock galaxy redshift survey (MGRS) generated from a high-resolution Jiutian simulation. An extended halo-based group finder is applied to both MGRS catalogs and DESI observation. By comparing the r- and z-band luminosity functions (LFs) and stellar mass functions (SMFs) derived using both photometric and spectroscopic data, we quantified the impact of photometric redshift (photo-z) errors on the galaxy LFs and SMFs, especially in the low-redshift bin at the low-luminosity/mass end. By conducting prior evaluations of the group finder using MGRS, we successfully obtain a set of CLF and CSMF measurements from observational data. We find that at low redshift, the faint-end slopes of CLFs and CSMFs below ~10 9 h –2 L ⊙ (or h –2 M ⊙ ) evince a compelling concordance with the subhalo mass functions. After correcting the cosmic variance effect of our local Universe following Chen et al., the faint-end slopes of the LFs/SMFs turn out to also be in good agreement with the slope of the halo mass function.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

CAZyme domain architectures suggest fine-scale functional differentiation among anaerobic fungi and bacteria during lignocellulose conversion to volatile fatty acids

Anaerobic fermentation with microbial communities (microbiomes) is an emerging platform for conversion of lignocellulosic biomass to biofuels and bioproducts. The process relies on diverse anaerobic microbes that interact to deconstruct and convert lignocellulosic biomass into a range of products, such as volatile fatty acids (VFAs), which can be achieved by arresting methanogenesis during fermentation. However, defining the distinct functional roles played by various fungi and bacteria during anaerobic biodegradation remains poorly understood. Here, we performed parallel enrichment experiments from cow faeces, goat faeces, and anaerobic digester sludge, selecting for fungal or bacterial dominated communities that convert sorghum biomass into VFAs. Subsequently we reconstructed metabolic networks across these enrichments based on recovered bacterial metagenome-assembled genomes (MAGs) and fungal isolate genomes and profiled their metabolic activity using metatranscriptomics to identify potential functional niches. Our findings implicate diverse bacteria affiliated with the Bacteroidales and Lachnospiraceae in the direct conversion of lignocellulosic biomass to propionate and butyrate, respectively, whereas Neocallimastix-dominated fungal enrichments converted lignocellulose to lactate, acetate and formate. Analysis of carbohydrate-active enzymes (CAZymes) revealed fine-scale differences between microbes that expressed unique multi-functional enzymes linking two or more CAZymes together with distinct carbohydrate binding motifs, implicating lignocellulose structure as a key driver of selection and niche differentiation. Most of these multi-functional enzymes localized complementary degradation functions together, likely conferring synergistic degradation effects within and between microbiome members. We anticipate that these findings will help inform efforts to develop synthetic microbiomes with tailored functionality for low-cost conversion of lignocellulosic biomass to fuels and bio-based chemicals.

Lawson, Christopher E [University of Toronto;]↗
DOE OSTI2026

Learning the generating functional for variance reduction in lattice QCD

The generating functional in quantum field theory provides the natural framework for constructing correlation functions as derivatives with respect to source operators. We present a methodology that leverages machine-learned normalizing flows to reduce the variance of arbitrary $N$-point correlation functions of bosonic operators in lattice gauge field theory calculations by encoding a representation of the generating functional. We show that it is possible to systematically approach noiseless estimators of correlation functions in this framework. We demonstrate this methodology with applications to calculations of glueball correlation functions and Wilson loops in Quantum Chromodynamics and Yang-Mills theory. The results show up to three orders of magnitude variance reduction.

Abbott, Ryan [Columbia U.] (ORCID:0000000258778005↗
DOE OSTI2026

DNA Crystals as a Template for Patterned Functional Materials

DNA nanotechnology offers a wide toolkit of molecular functionalities and scales, including intricate motifs less than 10 nm and periodic structures exceeding 100 µm. At larger scales, however, there are often significant tradeoffs for DNA structures, namely stability and mechanical strength. This work describes the design, synthesis, and characterization of a functionalized DNA crystal. Using a ligated DNA crystal grants significant freedom for various functional materials to be applied, in this case, semiconducting cadmium sulfide and palladium metal. Properties investigated in this study include stability, mechanical strength, and optoelectronic properties such as photoluminescence (PL) and electric conductivity. Significant changes are observed based on the functional material applied to DNA crystals. The Young's modulus of the crystal varies by about five orders of magnitude when functionalized with palladium. PL and semiconductive behaviors were observed when cadmium sulfide was attached. These crystals represent an expansion of the capabilities of DNA structures at these length scales, and additionally a platform for future studies exchanging the materials or altering the ligation scheme.

CdS↗
DOE OSTI2025

The developments in modifying functionality of lignin and its application in biocomposites

With the advancement of sustainable material innovations, renewable natural biopolymers are gradually replacing traditional metal and petroleum-based synthetic materials due to their environmental friendliness, biodegradability, and economic advantages. Lignin, the second most abundant natural aromatic polymer in the plant kingdom, has emerged as a key candidate raw material for the development of green polymer systems because of its unique phenylpropane unit network structure, high carbon content, and rich functional group characteristics. However, challenges such as the inherent structural complexity, chemical inertness, and uneven molecular weight distribution of lignin limit its direct application. By employing modification strategies such as chemical functionalization and physical regulation, researchers can precisely control its reactivity, thermal stability, and interfacial compatibility, enabling the preparation of high-performance lignin-based functional composites. Here, this paper systematically reviews the principles and methodological advancements in lignin's multi-dimensional modification technology. It analyzes the mechanisms by which various chemical and physical modification techniques enhance the mechanical properties, functional responsiveness, and environmental adaptability of materials, and discusses innovative applications in fields such as intelligent packaging, biomedical materials, and energy storage devices. Furthermore, this review addresses the key challenges encountered in the high-value transformation of lignin, with the aim of offering a theoretical framework and technical pathway for the transformative development of lignin from agricultural and forestry by-products to functional material platforms.

Functional composites↗
DOE OSTI2024

Monte Carlo Explicitly Correlated Second-Order Many-Body Green’s Function Calculations of Semiconductor Band Gaps

A systematically converging series of ab initio, post-density-functional, size-consistent, electron-correlated approximations is desired for predictive computing of felectronic band structures of insulating, semiconducting, and metallic solids. A series that meets all of these desiderata (except the applicability to metals) is ab initio many-body Green's function theory based on Gaussian-type-orbital (GTO) basis sets. Here, its leading-order approximation, the second-order Green's function (GF2) method in the diagonal and frequency-independent approximations with the aug-cc-pVDZ basis set, is applied to the fundamental band gaps of three semiconductors (diamond, silicon, and silicon carbide in the zincblende structure) using cluster models. Corrections are made to the basis-set-incompleteness errors by the explicit-correlation (F12) ansatz (GF2-F12) for the valence band edges. The crystals are modeled as surface-passivated clusters of increasing sizes, whose wave functions are expanded by up to 2709 GTO basis functions. Immense computational costs of these calculations are overcome by the highly scalable stochastic algorithm of the Monte Carlo GF2-F12 method, whose operation cost per state increases only as a cubic power of system size, which has a tiny memory footprint and easily achieves near-perfect parallel efficiency on thousands of CPUs or on hundreds of GPUs. The correlated, F12-corrected highest-occupied and lowest-unoccupied molecular-orbital energy (HOMO-LUMO) gap is 5.78 ± 0.07 eV for C 87 H 76 as compared with the experimental value of the fundamental (indirect) band gap of bulk diamond at 5.48 eV. The correlated, F12-corrected HOMO-LUMO gaps for Si 75 H 76 and Si 32 C 43 H 76 are 2.56 ± 0.15 eV and 3.50 ± 0.12 eV, respectively, which are expected to decrease further with increasing cluster sizes. As a result, the experimental fundamental (indirect) band gaps of bulk silicon and silicon carbide are 1.17 eV and 2.42 eV, respectively.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
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

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↗
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↗