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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Metagenome-Guided Proteomic Quantification of Reductive Dehalogenases in the Dehalococcoides mccartyi -Containing Consortium SDC-9

At groundwater sites contaminated with chlorinated ethenes, fermentable substrates are often added to promote reductive dehalogenation by indigenous or augmented microorganisms. Contemporary bioremediation performance monitoring relies on nucleic acid biomarkers of key organohalide-respiring bacteria, such as Dehalococcoides mccartyi (Dhc). In this work, metagenome sequencing of the commercial, Dhc-containing consortium, SDC-9, identified 12 reductive dehalogenase (RDase) genes, including pceA (two copies), vcrA, and tceA, and allowed for specific detection and quantification of RDase peptides using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Shotgun (i.e., untargeted) proteomics applied to the SDC-9 consortium grown with tetrachloroethene (PCE) and lactate identified 143 RDase peptides, and 36 distinct peptides that covered greater than 99% of the protein-coding sequences of the PceA, TceA, and VcrA RDases. Quantification of RDase peptides using multiple reaction monitoring (MRM) assays with 13 C-/ 15 N-labeled peptides determined 1.8 × 10 3 TceA and 1.2 × 10 2 VcrA RDase molecules per Dhc cell. Ultimately, the MRM mass spectrometry approach allowed for sensitive detection and accurate quantification of relevant Dhc RDases and has potential utility in bioremediation monitoring regimes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data

Expression-based omics technologies (e.g. proteomics, metabolomics, transcriptomics, etc.) increasingly rely on supervised and unsupervised machine learning (ML) models to find key biomolecules distinguishing conditions, identify natural groupings in biological data, or generate predictions for outcomes of interest. Fitting ML models to omics data presents several challenges, including handling missing data, selecting a normalization method, choosing a valid model, and optimizing hyperparameters, all requiring statistical programming skills to address these challenges. Thus, the open-source web application SLOPE was designed to lower the barrier to ML modeling for omics data. SLOPE supports the fitting of 15 ML models (10 supervised and 5 unsupervised) tailored to omics datasets, such as proteomics, metabolomics, lipidomics, and transcriptomics. SLOPE offers several omics-specific features, including methods for handling missingness (imputation, conversion, removal), normalization tests, ranking of models based on the structure of a user’s data and user input, and optimal hyperparameter selections using cross-validation splits. By streamlining ML workflows for omics analysis, SLOPE address critical gaps in existing online web tools, facilitating a broader adoption of these models for omics research. Here, SLOPE is applied to data from a lignin exposure study to highlight the workflow for fitting both supervised and unsupervised models to data.

lipidomics↗
DOE OSTI2022

Atomic Edge-Guided Polyethylene Crystallization on Monolayer Two-Dimensional Materials

In this report we combine an advanced synthesis of two-dimensional (2D) materials (MoSe 2 ) having well-defined atomic edge configurations with ab initio and atomistic molecular dynamics (MD) simulations to study how atomic edges interact with polyethylene (HDPE) chains in a dilute solution assembly process. Our results reveal that Mo-terminated zigzag (Mo-ZZ) edges act as preferred nucleation sites and strongly interact with HDPE chains. The HDPE chains align in parallel with the Mo-ZZ edges and form arrays of lamellae that are perpendicular to the edges. Interestingly, atomic edge configurations are observed to dramatically change such interactions. The crystallization discrepancy at different edges was demonstrated on the same piece of MoSe 2 with different types of edges. The ab initio and MD simulations between n-alkane (n = 5 and 25), a segment of HDPE, and MoSe 2 suggest that the atomic structures of MoSe 2 can affect their interactions with n-alkane chains. Following the Mo-ZZ edge preferred nucleation principle, controlled long-range alignment of HDPE lamellae can be realized by creating multilayer MoSe 2 with parallel atomic steps. This research opens a pathway toward an atomic level understanding of polymer–2D nanomaterial interactions. It also bridges the gap between atomic-level and long-range mesoscopic structures and introduces a novel strategy for long-range structural control.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Theoretical Calculation Guided Design of Single-Atom Catalysts toward Fast Kinetic and Long-Life Li–S Batteries

Lithium–sulfur (Li–S) batteries are enticing next-generation energy storage technologies due to their high theoretical energy density, environmental friendliness, and low cost. Yet, low conductivity of sulfur species, dissolution of polysulfides, poor conversion from sulfur reduction, and lithium sulfide (Li 2 S) oxidation reactions during discharge–charge processes hinder their practical applications. Herein, under the guidance of density functional theory calculations, we have successfully synthesized large-scale single atom vanadium catalysts seeded on graphene to achieve high sulfur content (80 wt % sulfur), fast kinetic (a capacity of 645 mAh g –1 at 3 C rate), and long-life Li–S batteries. Both forward (sulfur reduction) and reverse reactions (Li 2 S oxidation) are significantly improved by the single atom catalysts. This finding is confirmed by experimental results and consistent with theoretical calculations. The ability of single metal atoms to effectively trap the dissolved lithium polysulfides (LiPSs) and catalytically convert the LiPSs/Li 2 S during cycling significantly improved sulfur utilization, rate capability, and cycling life. Our work demonstrates an efficient design pathway for single atom catalysts and offers solutions for the development of high energy/power density Li–S batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Electrolyte-Guided Design of Electroreductive CO Coupling on Copper Surfaces

Engineering the interfacial distribution of electrolytic ions can aid in modulating the electrocatalyst performance and efficiency. Using a hybrid quantum-classical modeling approach, we describe how predictive tuning of the solution microenvironment on copper can enhance the efficiency of CO 2 reduction (CO 2 R) to C 2 products. We elucidate how competing electrolyte constituents in mixed electrolyte solutions stimulate restructuring of the electrochemical double layer (EDL) and stabilize the OCCO* dimer (* denotes surface adsorbed), with predictions validated in flow reactors using copper gas diffusion electrodes (Cu-GDEs). Furthermore, our findings highlight how molecular-scale electrolyte engineering with informed models of the EDL can be leveraged to tailor CO 2 R activity and selectivity toward C 2 products.

36 MATERIALS SCIENCE↗
DOE OSTI2025

System and Machine Learning-Guided Materials Design for High-Pressure Hydrogen Compression

Cost-effective and reliable hydrogen compression remains a challenging barrier in the widespread adoption of hydrogen as an energy carrier. The prevailing technology of mechanical compression suffers from several drawbacks, some of which can be addressed by nonmechanical compression strategies (e.g., electrochemical or metal hydride-based thermal compression). Thermally driven metal hydride compression strategies typically rely on multistage metal hydride-based compressors; however, discovering or optimizing low-stability metal hydrides that can pressurize hydrogen upward of 1000 bar is difficult, both with respect to computational predictions and experimental validation. Here, in this study, we (1) demonstrate that simple machine learning-derived design rules can inform the rational design of alloying strategies yielding low-stability hydrides, (2) validate their experimental pressure–composition–temperature (PCT) isotherms up to 875 bar, and (3) utilize a dynamic system-level model of a metal hydride compressor design to evaluate their performance under realistic operating conditions. Importantly, this analysis yields predicted operational efficiencies of both 2-stage (90–875 bar) and 3-stage (20–875 bar) metal hydride compressors to enable further evaluation of this technology and its techno-economic outlook.

alloy optimization↗
DOE OSTI2023

Active Learning Guided Computational Discovery of Plant-Based Redoxmers for Organic Nonaqueous Redox Flow Batteries

Organic nonaqueous redox flow batteries (ONRFBs) are promising energy storage devices due to their scalability and reliance on sourceable materials. However, finding suitable redox-active organic molecules (redoxmers) for these batteries remains a challenge. Using plant-based compounds as precursors for these redoxmers can decrease their costs and environmental toxicity. In this computational study, flavonoid molecules have been examined as potential redoxmers for ONRFBs. Flavone and isoflavone derivatives were selected as catholyte (positive charge carrier) and anolyte (negative charge carrier) molecules, respectively. To drive their redox potentials to the opposite extremes, in silico derivatization was performed using a novel algorithm to generate a library of > 40000 candidate molecules that penalizes overly complex structures. A multiobjective Bayesian optimization based active learning algorithm was then used to identify best redoxmer candidates in these search spaces. Furthermore, our study provides methodologies for molecular design and optimization of natural scaffolds and highlights the need of incorporating expert chemistry awareness of the natural products and the basic rules of synthetic chemistry in machine learning.

25 ENERGY STORAGE↗
DOE OSTI2025

Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity

The enzymatic depolymerization of poly(ethylene terephthalate) (PET) is emerging as a leading chemical recycling technology for waste polyester. As part of this endeavor, new candidate enzymes identified from natural diversity can serve as useful starting points for enzyme evolution and engineering. In this study, we improved upon HMM searches by applying an iterative machine learning strategy to identify 400 putative PET-degrading enzymes (PET hydrolases) from naturally occurring homologs. Using high-throughput (HTP) experimental techniques, we successfully expressed and purified >200 enzyme candidates and assayed them for PET hydrolysis activity as a function of pH, temperature, and substrate crystallinity. From this library, we discovered 91 previously unknown PET hydrolases, 35 of which retain activity at pH 4.5 on crystalline material, which are conditions relevant to developing more efficient commercial processes. Notably, four enzymes showed equal to or higher activity than LCC-ICCG, a benchmark PET hydrolase, at this challenging condition in our screening assay, and 11 of which have pH optima <7. Using these data, we identified regions of PETases statistically correlated to activity at lower pH. We additionally investigated the effect of condition-specific activity data on trained machine learning predictors and found a precision (putative hit rate) improvement of up to 30% compared to a Hidden Markov Model alone. Our findings show that by pointing enzyme discovery toward conditions of interest with multiple rounds of experimental and machine learning, we can discover large sets of active enzymes and explore factors associated with activity at those conditions.

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

Structure-Guided Engineering of a Scoulerine 9-O-Methyltransferase Enables the Biosynthesis of Tetrahydropalmatrubine and Tetrahydropalmatine in Yeast

Benzylisoquinoline alkaloids (BIAs) are an important class of plant natural products with diverse pharmacological properties. Microbial platforms can produce BIAs through heterologous biosynthesis more efficiently than native plant hosts and allow for the generation of currently inaccessible BIA biosynthetic intermediates and unnatural derivatives. However, much remains unknown regarding the structures, substrate scopes, and mechanisms of many of the enzymes involved in BIA biosynthesis, which hampers efforts toward engineering these enzymes to produce alternative products or act in non-native biosynthetic contexts. In this paper, we present multiple crystal structures of two scoulerine 9- O -methyltransferase (S9OMT) variants from Thalictrum flavum which catalyze an essential step in the biosynthesis of berberine. The crystal structures revealed the structural basis for substrate and cofactor recognition by Tf S9OMT and provide further insight into the structure and function of S9OMTs. A structural comparison of the Tf S9OMT and T. flavum norcoclaurine 6OMT ( Tf 6OMT) crystal structures identified important residues for enzyme regiospecificity, which were confirmed via mutagenesis and in vitro assays. Several Tf S9OMT mutants with expanded substrate scopes toward various 1-benzylisoquinoline substrates were generated. The rationally engineered Tf S9OMT mutants with altered regiospecificity were tested in a yeast-based scoulerine production platform and enabled de novo production of tetrahydropalmatrubine and tetrahydropalmatine.

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

A Chemist’s Guide to Solid-State Qubits: Diamond Color Centers as Embedded Vacancy-Centered Molecules

Diamond point defects or color centers are at the crux of solid-state quantum technology applications, from computing to sensing. Modeling molecular qubits has advanced by leaps and bounds mainly due to the advanced quantum mechanical (QM) tools in the arsenal of quantum chemists for modeling (isolated) molecular systems, which should be easily translatable for analogous systems in the solid state. Here, we simulate a series of diamond defects: a carbon vacancy (V C ) and second row substitutions that are adjacent to a V C or D C V C , with D = B, N, and O, to evaluate their magnetic and optical properties. We applied molecular orbital principles, an embedded cluster method, and a multiconfigurational QM theory to computationally characterize the above-mentioned defects for a multitude of spin, charge, and excited states. Our computational approach delivered high-quality QM insights that compare well with experiments. Furthermore, with this framework, we confirm the spin-triplets B C V C – and O C V C 0 to be analogous to N C V C – , with calculated bright transitions that respectively correspond to UV (3.80 eV) and blue (2.65 eV) light absorption and emission lifetimes (τ) of 2.3 and 5.6 ns, which are higher in energy and more short-lived (and thus brighter) than that for N C V C – : green (2.26 eV), τ = 9.5 ns.

Martirez, John Mark P. [Princeton Plasma Physics L↗