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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Progressive evolutions of non-fused ring electron acceptors toward efficient organic solar cells with improved photocurrent and reduced energy loss

Resolving competitive counterbalance between high external quantum efficiency (EQE) response and low energy loss is critical for achieving efficient organic solar cells (OSCs), including those based on low-cost non-fused-ring electron acceptor (NFREA). In this work, we performed a comparable study by utilizing four NFREAs with progressive evolutions in molecular structures, energetic levels, spectral coverages, crystallinity, etc, to pair with polymer donor J52. By regulating the terminal and side chains of NFREAs, the enhanced intramolecular charge transfer (ICT) effect allows O-PC-EH possessing the broader spectral coverage for higher photocurrent, the reduced energetic offset enables O-PC-EH-based device owning higher luminescence property for lower energy loss, the strengthened crystallinity endows O-PC-EH-based blend better charge transport property for less charge recombination. With those comprehensive managements from molecule to blend properties, the J52:O-PC-EH-based OSCs achieved the best efficiency of 14.4% (certified: 14.1%) with benefits in all the three device parameters. Here, this work reveals the importance of fine-tuning molecular structures of NFREAs according to paired polymer donor for maximizing the device performances.

14 SOLAR ENERGY↗
DOE OSTI2020

Spatially Offset Femtosecond Stimulated Raman Spectroscopy: Observing Exciton Transport through a Vibrational Lens

To design better molecular electronic devices, we need a strong understanding of how charges or excitons propagate, as many efficiency losses arise during transport. Exciton transport has been difficult to study because excitons tend to be short-lived, have short diffusion lengths, and can easily recombine. Here, we debut spatially offset femtosecond stimulated Raman spectroscopy (SO-FSRS), a three-pulse ultrafast microscopy technique. By offsetting the photoexcitation beam, we can monitor Raman spectral changes as a function of both time and position. We used SO-FSRS on 6,13-bis(triisopropylsilylethynyl) pentacene, a well-studied organic semiconductor used in photovoltaics and field-effect transistors. Furthermore, we demonstrated that the fast exciton and free charge carrier transport axes are identical and observed that exciton transport is less anisotropic by a factor of ~3. SO-FSRS is the first technique that directly tracks molecular structural evolution during exciton transport, which can provide roadmaps for tailor-making molecules for specific electronic devices.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Nanobubble Formation and Coverage during High Current Density Alkaline Water Electrolysis

Gas bubbles are a necessary byproduct of water electrolysis whereby hydrogen and oxygen are produced from water. These attached gases reduce the electrode’s active area, which necessitates a deep understanding of the bubble life cycle starting from nanobubbles. Synchronized with the electrochemistry, the time evolution of the surface nanobubble size and coverage is resolved using grazing incidence small-angle X-ray scattering (GISAXS) and correlated with optical microscopy and theoretical calculations to show that a significant portion of the surface is covered in nanobubbles after larger micron-sized bubbles are observed. Further, these nanobubbles increase in number and decrease in size, toward 2 nm diameter, with the charge passed. The trend in size and number is consistent with an increase in supersaturation, which reduces the nascent bubble size. Altogether, this study suggests a significant portion of the surface contains nanobubbles and that strategies to reduce the dissolved hydrogen would be effective at reducing the nanobubble surface coverage.

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

Unraveling Triplet Formation Mechanisms in Acenothiophene Chromophores

The evolution of molecular platforms for singlet fission (SF) chromophores has fueled the quest for new compounds capable of generating triplets quantitatively at fast time scales. As the exploration of molecular motifs for SF has diversified, a key challenge has emerged in identifying when the criteria for SF have been satisfied. Here, we show how covalently bound molecular dimers uniquely provide a set of characteristic optical markers that can be used to distinguish triplet pair formation from processes that generate an individual triplet. These markers are contained within (i) triplet charge-transfer excited state absorption features, (ii) kinetic signatures of triplet-triplet annihilation processes, and (iii) the modulation of triplet formation rates using bridging moieties between chromophores. Our assignments are verified by time-resolved electron paramagnetic resonance (EPR) measurements, which directly identify triplet pairs by their electron spin and polarization patterns. Here we apply these diagnostic criteria to dimers of acenothiophene derivatives in solution that were recently reported to undergo efficient intermolecular SF in condensed media. While the electronic structure of these heteroatom-containing chromophores can be broadly tuned, the effect of their enhanced spin-orbit coupling and low-energy nonbonding orbitals on their SF dynamics has not been fully determined. We find that SF is fast and efficient in tetracenothiophene but that anthradithiophene exhibits fast intersystem crossing due to modifications of the singlet and triplet excited state energies upon functionalization of the heterocycle. We conclude that it is not sufficient to assign SF based on comparisons of the triplet formation kinetics between monomer and multichromophore systems.

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

Common origin of sterol biosynthesis points to a feeding strategy shift in Neoproterozoic animals

Steranes preserved in sedimentary rocks serve as molecular fossils, which are thought to record the expansion of eukaryote life through the Neoproterozoic Era (~1000-541 Ma). Scientists hypothesize that ancient C 27 steranes originated from cholesterol, the major sterol produced by living red algae and animals. Similarly, C 28 and C 29 steranes are thought to be derived from the sterols of prehistoric fungi, green algae, and other microbial eukaryotes. However, recent work on annelid worms–an advanced group of eumetazoan animals–shows that they are also capable of producing C 28 and C 29 sterols. In this paper, we explore the evolutionary history of the 24-C sterol methyltransferase (smt) gene in animals, which is required to make C 28+ sterols. We find evidence that the smt gene was vertically inherited through animals, suggesting early eumetazoans were capable of C 28+ sterol synthesis. Our molecular clock of the animal smt gene demonstrates that its diversification coincides with the rise of C 28 and C 29 steranes in the Neoproterozoic. This study supports the hypothesis that early eumetazoans were capable of making C 28+ sterols and that many animal lineages independently abandoned its biosynthesis around the end-Neoproterozoic, coinciding with the rise of abundant eukaryotic prey.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Ultrahigh-pressure crystallographic passage towards metallic hydrogen

The structural evolution of molecular hydrogen H 2 under multi-megabar compression and its relation to atomic metallic hydrogen is a key unsolved problem in condensed-matter physics. Although dozens of crystal structures have been proposed by theory, only one, the simple hexagonal-close-packed (hcp) structure of only spherical disordered H 2 , has been previously confirmed in experiments. Through advancing nano-focused synchrotron X-ray probes, here we report the observation of the transition from hcp H 2 to a post-hcp structure with a six-fold larger supercell at pressures above 212 GPa, indicating the change of spherical H 2 to various ordered configurations. Theoretical calculations based on our XRD results found a time-averaged structure model in the space group $P\bar{6}2c$ with alternating layers of spherically disordered H 2 and new graphene-like layers consisting of H 2 trimers (H 6 ) formed by the association of three H 2 molecules. Here, this supercell has not been reported by any previous theoretical study for the post-hcp phase, but is close to a number of theoretical models with mixed-layer structures. The evidence of a structural transition beyond hcp establishes the trend of H 2 molecular association towards polymerization at extreme pressures, giving clues about the nature of the molecular-to-atomic transition of metallic hydrogen. Considering the spectroscopic behaviours that show strong vibrational and bending peaks of H 2 up to 400 GPa, it would be prudent to speculate the continuation of hydrogen molecular polymerization up to its metallization.

Ji, Cheng [Center for High Pressure Science and Te↗
DOE OSTI2020

Anoxic photogeochemical oxidation of manganese carbonate yields manganese oxide

The oxidation states of manganese minerals in the geological record have been interpreted as proxies for the evolution of molecular oxygen in the Archean eon. In this paper we report that an Archean manganese mineral, rhodochrosite (MnCO 3 ), can be photochemically oxidized by light under anoxic, abiotic conditions. Rhodochrosite has a calculated bandgap of about 5.4 eV, corresponding to light energy centering around 230 nm. Light at that wavelength would have been present on Earth’s surface in the Archean, prior to the formation of stratospheric ozone. We show experimentally that the photooxidation of rhodochrosite in suspension with light centered at 230 nm produced H 2 gas and manganite (γ-MnOOH) with an apparent quantum yield of 1.37 × 10 –3 moles hydrogen per moles incident photons. Our results suggest that manganese oxides could have formed abiotically on the surface in shallow waters and on continents during the Archean eon in the absence of molecular oxygen.

58 GEOSCIENCES↗
DOE OSTI2024

Evolution and persistence of SiO emission in nanosecond laser ablation plumes

Analyzing laser-produced plasmas in a controlled oxygen-containing environment provides insight into the formation and evolution of molecular species through gas-phase oxidation. This study explores the role of ambient pressure and oxygen availability in forming SiO molecular species in laser ablation plumes. The self-emission emanating during the reactive ablation of Si targets was characterized by optical emission spectroscopy and optical time-of-flight techniques. Our results showed that the SiO species formation was greatly influenced by both the ambient pressure and oxygen availability. The intensity and the persistence of SiO emission bands are lower at higher oxygen concentrations, indicating they are depopulated by the formation of more complex silicon oxides. The oxygen partial pressure effects on plume chemistry showed that SiO formation is favored even with a minimal oxygen concentration in the environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Genomic factors shaping codon usage across the Saccharomycotina subphylum

Codon usage bias, or the unequal use of synonymous codons, is observed across genes, genomes, and between species. It has been implicated in many cellular functions, such as translation dynamics and transcript stability, but can also be shaped by neutral forces. We characterized codon usage across 1,154 strains from 1,051 species from the fungal subphylum Saccharomycotina to gain insight into the biases, molecular mechanisms, evolution, and genomic features contributing to codon usage patterns. We found a general preference for A/T-ending codons and correlations between codon usage bias, GC content, and tRNA-ome size. Codon usage bias is distinct between the 12 orders to such a degree that yeasts can be classified with an accuracy >90% using a machine learning algorithm. We also characterized the degree to which codon usage bias is impacted by translational selection. We found it was influenced by a combination of features, including the number of coding sequences, BUSCO count, and genome length. Our analysis also revealed an extreme bias in codon usage in the Saccharomycodales associated with a lack of predicted arginine tRNAs that decode CGN codons, leaving only the AGN codons to encode arginine. Analysis of Saccharomycodales gene expression, tRNA sequences, and codon evolution suggests that avoidance of the CGN codons is associated with a decline in arginine tRNA function. Consistent with previous findings, codon usage bias within the Saccharomycotina is shaped by genomic features and GC bias. However, we find cases of extreme codon usage preference and avoidance along yeast lineages, suggesting additional forces may be shaping the evolution of specific codons.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models

Abstract Background A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. Results Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp 614 Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro 323 Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. Conclusions These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Advancing Multiscale Simulation of Plasma-Surface Interfaces

We report the development of an atomistic-informed, surface-state-dependent predictive model for particle exchange in a carbon-tungsten plasma-surface interface. The predictive model uses machine learning (ML) techniques to learn the energy and angular distributions for particle exchange and rate functions for surface state evolution from molecular dynamics simulations of cumulative bombardment of tungsten by energetic carbon ions. Each predictive component is sensitive to the energy and trajectory of incident plasma species and the surface state. The surface state is represented by a set of surface state descriptors, which were derived from the atomistic surface state for each independent carbon bombardment event. These descriptors are representative of the composition and degree of amorphization of the outermost angstrom of surface material and were chosen to optimize predictive performance for particle exchange at the interface. The distributions for particle exchange (reflection/sputtering) are demonstrated to vary with each surface state descriptor, motivating the development of surface-state-dependent particle exchange models for plasma simulations. The performance of various ML methods was compared, including polynomial quantile regression, artificial neural networks, k-nearest neighbors, and random forest algorithms, with polynomial regression performing the best for interpolation and extrapolation of learned relationships. In addition to the particle exchange model, a neutral network was developed and used to identify data sufficiency throughout surface descriptor space, which will enable real-time feedback during future data production to ensure data is produced where it is most needed, and we provide commentary on improvements to the data production workflow for future endeavors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Analytical and Computational Advances, Opportunities, and Challenges in Marine Organic Biogeochemistry in an Era of “Omics”

Advances in sampling tools, analytical methods, and data handling capabilities have been fundamental to the growth of marine organic biogeochemistry over the past four decades. There has always been a strong feedback between analytical advances and scientific advances. However, whereas advances in analytical technology were often the driving force that made possible progress in elucidating the sources and fate of organic matter in the ocean in the first decades of marine organic biogeochemistry, today process-based scientific questions should drive analytical developments. Several paradigm shifts and challenges for the future are related to the intersection between analytical progress and scientific evolution. Untargeted “molecular headhunting” for its own sake is now being subsumed into process-driven targeted investigations that ask new questions and thus require new analytical capabilities. However, there are still major gaps in characterizing the chemical composition and biochemical behavior of macromolecules, as well as in generating reference standards for relevant types of organic matter. Field-based measurements are now routinely complemented by controlled laboratory experiments and in situ rate measurements of key biogeochemical processes. And finally, the multidisciplinary investigations that are becoming more common generate large and diverse datasets, requiring innovative computational tools to integrate often disparate data sets, including better global coverage and mapping. Here, we compile examples of developments in analytical methods that have enabled transformative scientific advances since 2004, and we project some challenges and opportunities in the near future. We believe that addressing these challenges and capitalizing on these opportunities will ensure continued progress in understanding the cycling of organic carbon in the ocean.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Updated HIV-1 Consensus Sequences Change but Stay Within Similar Distance From Worldwide Samples

HIV consensus sequences are used in various bioinformatic, evolutionary, and vaccine related research. Since the previous HIV-1 subtype and CRF consensus sequences were constructed in 2002, the number of publicly available HIV-1 sequences have grown exponentially, especially from non-EU and US countries. Here, we reconstruct 90 new HIV-1 subtype and CRF consensus sequences from 3,470 high-quality, representative, full genome sequences in the LANL HIV database. While subtypes and CRFs are unevenly spread across the world, in total 89 countries were represented. For consensus sequences that were based on at least 20 genomes, we found that on average 2.3% (range 0.8–10%) of the consensus genome site states changed from 2002 to 2021, of which about half were nucleotide state differences and the rest insertions and deletions. Interestingly, the 2021 consensus sequences were shorter than in 2002, and compared to 4,674 HIV-1 worldwide genome sequences, the 2021 consensuses were somewhat closer to the worldwide genome sequences, i.e., showing on average fewer nucleotide state differences. Some subtypes/CRFs have had limited geographical spread, and thus sampling of subtypes/CRFs is uneven, at least in part, due to the epidemiological dynamics. Thus, taken as a whole, the 2021 consensus sequences likely are good representations of the typical subtype/CRF genome nucleotide states. The new consensus sequences are available at the LANL HIV database.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

YeastWGD2025

Supplementary data for Discovery of additional ancient genome duplications in yeasts wgd_syn / - directory containing wgd syn output for all contiguous genomes [dataset] Tree - phylogeny [dataset]Duplications - duplication table from OrthoFinder output KOannotations - KEGG annotations used for enrichment analysis IPRannotations - InterPro annotations used for enrichment analysis DipodascalesOrthogroups - formatted orthogroup assignments for Dipodascales genes.fa and .gff3 files for each new genome assembly are also provided, those these are not required to replicate the analysis

Genomics↗
DOE OSTI2021

Redshift evolution of the H2/H i mass ratio in galaxies

ABSTRACT In this paper, we present an attempt to estimate the redshift evolution of the molecular to neutral gas mass ratio within galaxies (at fixed stellar mass). For a sample of five nearby grand design spirals located on the main-sequence (MS) of star-forming galaxies, we exploit maps at 500 pc resolution of stellar mass and star formation rate (M⋆ and SFR). For the same cells, we also have estimates of the neutral (MH i) and molecular ($M_{\rm H_2}$) gas masses. To compute the redshift evolution, we exploit two relations: (i) one between the molecular-to-neutral mass ratio and the total gas mass (Mgas), whose scatter shows a strong dependence with the distance from the spatially resolved MS, and (ii) the one between $\log (M_{\rm {H_2}}/M_{\star })$ and log (MH i/M⋆). For both methods, we and that $M_{\rm H_2}$/MH i within the optical radius slightly decreases with redshift, contrary to common expectations of galaxies becoming progressively more dominated by molecular hydrogen at high redshifts. We discuss possible implications of this trend on our understanding of the internal working of high-redshift galaxies.

Morselli, Laura (ORCID:0000000307532571)↗