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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Digital holography for real-time non-invasive monitoring of larval fish at power plant intakes

Effective evaluation of technological and operational approaches to reduce entrainment of marine organisms at cooling water intake structures (CWIS) requires accurate organism-sensing systems. Current detection methods lead to large temporal data gaps, require tedious manual analysis, and are fatal to organisms. In this report we describe integrating deep learning with a non-lethal, non-intrusive imaging method—digital holography—to rapidly detect fish larvae. Laboratory experiments demonstrated that the instrument could successfully image fish larvae at flow rates exceeding ranges seen in CWIS. Holograms of two fish larvae species, in the presence of bubbles and detritus, were collected to build a large database for training a lightweight convolutional neural network. The model achieves 97% extraction accuracy in quantifying larvae, and distinguishing them from other particles, including detritus and bubbles, when applied to a dataset of manually classified images, exceeding previous metrics for non-lethal, accurate, and real-time detection. These results demonstrate the potential of in situ holographic imaging for monitoring endangered larval fish species at power plant intake structures, and for high-fidelity, real-time applications in monitoring aquatic ichthyoplankton.

endangered species↗
DOE OSTI2025

Optical Absorption Spectroscopy Probes Water Wire and Its Ordering in a Hydrogen-Bond Network

Water wires, quasi-one-dimensional chains composed of hydrogen-bonded (H-bonded) water molecules, play a fundamental role in numerous chemical, physical, and physiological processes. Yet direct experimental detection of water wires has been elusive so far. Based on advanced many-body theory that includes electron-hole interactions, we report that optical absorption spectroscopy can serve as a sensitive probe of water wires and their ordering. In both liquid and solid water, the main peak of the spectrum is discovered to be a charge-transfer exciton. In water, the charge-transfer exciton is strongly coupled to the H-bonding environment where the exciton is excited between H-bonded water molecules with a large spectral intensity. In regular ice, the spectral weight of the charge-transfer exciton is enhanced by a collective excitation occurring on proton-ordered water wires, whose spectral intensity scales with the ordering length of water wire. The spectral intensity and excitonic interaction strength reaches its maximum in ice XI, where the long-range ordering length yields the most pronounced spectral signal. Our findings suggest that water wires, which widely exist in important physiological and biological systems and other phases of ice, can be directly probed by this approach. Published by the American Physical Society 2025

Tang, Fujie (ORCID:0000000197615359)↗
DOE OSTI2022

A Brief Overview of Reactive Transport Codes Used in CO 2 Applications

Reactive transport modelling consists of computational and numerical models that describe the coupled physical, chemical, mechanical, and biological processes interacting with each other over a broad range of spatial and temporal scales. This report reviews some of the reactive transport codes available in the literature for carbon dioxide (CO 2 ) applications and can assist the scientific community with the applicability of each code to solve a specific problem. Approaches implemented in these codes can be categorized in three groups: (i) continuum-scale, (ii) pore-scale, and (iii) hybrid-scale approaches. Theoretical foundations, numerical implementations, and application examples using the models are described. In the continuum approach, flow and transport are formulated in terms of a representative control volume of the medium and coupled with geochemical reactions. The medium is characterized by bulk parameters such as porosity, permeability, or reactive surface area. In the pore-scale approach, each point of space in the pore network is occupied by either a fluid or solid phase. The pore-scale approach requires an exact knowledge of the spatial and temporal phase distribution. In the hybrid-scale approach, media are described by multiple characteristic length-scales, with some regions using a pore-scale approach while others used a continuum approach.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Genetic architecture of leaf morphological and physiological traits in a Populus deltoides ‘Danhong’ × P. simonii ‘Tongliao1’ pedigree revealed by quantitative trait locus analysis

Understanding the genetic architecture of leaf morphological and physiological traits will help plant breeders develop high biomass poplar genotypes. Quantitative trait locus (QTL) studies combining next-generation sequencing techniques can advance our understanding of the genetic basis of complex traits. In this study, we measured 13 leaf morphological and physiological traits and identified quantitative trait loci (QTLs) in a Populus deltoides ‘Danhong’ × P. simonii ‘Tongliao1’ F1 population (500 progenies) using a high-density genetic map constructed by whole genome re-sequencing. This linkage map consisted of 5796 single nucleotide polymorphism (SNP) markers assigned to 19 linkage groups (LGs), spanning 2683.80 centimorgans (cM) of genetic length, with an average marker density of 0.46 cM. We identified 109 QTLs on 18 LGs for leaf morphological traits and 55 QTLs on 14 LGs for leaf physiological traits. One-hundred eight putative candidate genes were identified within the candidate genomic region. Co-expression network and gene ontology enrichment analyses suggested that these candidate genes were involved in the photosynthetic process. The differential expression patterns of the CYCLIN (Potri.015G112200) and RED CHLOROPHYLL REDUCTASE (Potri.007G043600) genes between two parents indicated their potential roles in leaf morphological and physiological traits. These findings decipher the genetic architecture of leaf morphological and physiological traits in the P. deltoides ‘Danhong’ × P. simonii ‘Tongliao1’ pedigree and provide candidate genes for future poplar genetic improvement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Path sampling of recurrent neural networks by incorporating known physics

Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledge or intuition about the system. While the recurrent nature of these networks allows them to model arbitrarily long memories in the time series used in training, it makes it harder to impose prior knowledge or intuition through generic constraints. In this work, we present a path sampling approach based on principle of Maximum Caliber that allows us to include generic thermodynamic or kinetic constraints into recurrent neural networks. We show the method here for a widely used type of recurrent neural network known as long short-term memory network in the context of supplementing time series collected from different application domains. These include classical Molecular Dynamics of a protein and Monte Carlo simulations of an open quantum system continuously losing photons to the environment and displaying Rabi oscillations. Our method can be easily generalized to other generative artificial intelligence models and to generic time series in different areas of physical and social sciences, where one wishes to supplement limited data with intuition or theory based corrections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Network-medicine framework for studying disease trajectories in U.S. veterans

A better understanding of the sequential and temporal aspects in which diseases occur in patient’s lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

WORKSHOP ON URBAN SCALE PROCESSES AND THEIR REPRESENTATION IN HIGH SPATIAL RESOLUTION EARTH SYSTEM MODELS

It is essential that numerical models used to study physics, chemistry, and biology affecting the Earth system at regional and global scales represent the effects of urban areas on climate and the effects of changing the climate on urban areas. At the same time, it is essential to develop state of the art, simple and accurate urban models to better understand the relevant processes and also toaddress issues related to urban security against the spectra of chemical, biological and radiological (CBR) hazards. Towards bringing these communities together, a 2.5-day international workshop was held at Argonne National Laboratory (ANL) in the Chicago area on May 22-24, 2019. This workshop brought together national and international experts to develop a roadmap for a better understanding of the issues associated with urban areas and at enhancing the capabilities of regional and global Earth System models (ESMs) in representing the atmospheric dynamics and chemistry, unique aspects of the biosphere and land use, and human dimensions of the urban environment. This workshop was especially important to those developing very high-resolution versions of regional and global models. The workshop also discussed existing datasets, including in situ and satellite observations, and the emerging smart city sensing technologies and their possible use in model development (e.g., the NSF-funded Array of Things network currently operating in Chicago and expanding to other cities).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Labile assembly of a tardigrade protein induces biostasis

Abstract Tardigrades are microscopic animals that survive desiccation by inducing biostasis. To survive drying tardigrades rely on intrinsically disordered CAHS proteins, which also function to prevent perturbations induced by drying in vitro and in heterologous systems. CAHS proteins have been shown to form gels both in vitro and in vivo, which has been speculated to be linked to their protective capacity. However, the sequence features and mechanisms underlying gel formation and the necessity of gelation for protection have not been demonstrated. Here we report a mechanism of fibrillization and gelation for CAHS D similar to that of intermediate filament assembly. We show that in vitro, gelation restricts molecular motion, immobilizing and protecting labile material from the harmful effects of drying. In vivo, we observe that CAHS D forms fibrillar networks during osmotic stress. Fibrillar networking of CAHS D improves survival of osmotically shocked cells. We observe two emergent properties associated with fibrillization; (i) prevention of cell volume change and (ii) reduction of metabolic activity during osmotic shock. We find that there is no significant correlation between maintenance of cell volume and survival, while there is a significant correlation between reduced metabolism and survival. Importantly, CAHS D's fibrillar network formation is reversible and metabolic rates return to control levels after CAHS fibers are resolved. This work provides insights into how tardigrades induce reversible biostasis through the self‐assembly of labile CAHS gels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Challenges in spatial metabolomics and proteomics for functional tissue unit and single-cell resolution

While transcriptomics is the most broadly applied technology for global spatial and single cell measurements in healthy and diseased tissues. Transcripts are often used as a proxy for protein and even metabolite measurements, but it has become commonly accepted that extrapolating this kind of information is a poor proxy and not a substitute for direct measurement. Within the last decade advanced developments of mass spectrometry-based assays have made these direct measurements not only possible, but routine. Where mass spectrometry has become an enabling technology, and various methods can now detect hundreds of metabolites and thousands of proteins from samples. Not only can this be performed within bulk measurements, but much effort has been directed into translating these measurements to single cells and tissues at cellular resolution. The information obtained from mass spectrometry is now able to trace metabolic events and decipher feedback loops across anatomical regions, connecting genetic and metabolic networks that define phenotypes. Herein, we will broadly overview developments in the field over the past decade, leading into several case studies which highlight the direct measurement of metabolites, proteins, and proteoforms from thinly sliced tissues. Much of this work is feasible due to multidisciplinary team science, and we offer brief perspective on paths forward and the challenges that persist with adoption and application of spatial omics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Nutrient Exposure Alters Microbial Composition, Structure, and Mercury Methylating Activity in Periphyton in a Contaminated Watershed

The conversion of mercury (Hg) to monomethylmercury (MMHg) is a critical area of concern in global Hg cycling. Periphyton biofilms may harbor significant amounts of MMHg but little is known about the Hg-methylating potential of the periphyton microbiome. Therefore, we used high-throughput amplicon sequencing of the 16S rRNA gene, ITS2 region, and Hg methylation gene pair (hgcAB) to characterize the archaea/bacteria, fungi, and Hg-methylating microorganisms in periphyton communities grown in a contaminated watershed in East Tennes(see United States). Furthermore, we examined how nutrient amendments (nitrate and/or phosphate) altered periphyton community structure and function. We found that bacterial/archaeal richness in experimental conditions decreased in summer and increased in autumn relative to control treatments, while fungal diversity generally increased in summer and decreased in autumn relative to control treatments. Interestingly, the Hg-methylating communities were dominated by Proteobacteria followed by Candidatus Atribacteria across both seasons. Surprisingly, Hg methylation potential correlated with numerous bacterial families that do not contain hgcAB, suggesting that the overall microbiome structure of periphyton communities influences rates of Hg transformation within these microbial mats. To further explore these complex community interactions, we performed a microbial network analysis and found that the nitrate-amended treatment resulted in the highest number of hub taxa that also corresponded with enhanced Hg methylation potential. This work provides insight into community interactions within the periphyton microbiome that may contribute to Hg cycling and will inform future research that will focus on establishing mixed microbial consortia to uncover mechanisms driving shifts in Hg cycling within periphyton habitats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal

In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Single-cell proteomics of Arabidopsis leaf mesophyll reveals dynamic protein responses to water-deficit stress

Background The application of single-cell omics tools to biological systems can provide unique insights into diverse cellular populations and their heterogeneous responses to internal and external perturbations. Thus far, most single-cell studies in plant systems have been limited to RNA-sequencing approaches, which only provide indirect readouts of cellular functions. Results Here, we present a single-cell proteomics workflow for plant cells that integrates tape-sandwich protoplasting, piezoelectric cell sorting, nanoPOTS sample preparation, and ion mobility-based MS data acquisition method for label-free single-cell proteomics analysis of Arabidopsis leaf mesophyll cells. From a single leaf protoplast, over 3,000 proteins were quantified with high precision. The workflow is demonstrated to identify stress associated changes in protein abundance by analyzing 117 protoplasts from well-watered and water-deficit stressed plants. Additionally, we describe a new approach for constructing covarying protein networks at the single-cell level and demonstrate how single-cell protein covariation analysis can reveal previously unrecognized protein functions while also capturing stress-induced changes in protein–protein dynamics. Conclusions The label-free scProteomic approach presented here represents a significant advance through the demonstration of a facile protoplast isolation method combined with deep and precise proteomic coverage of Arabidopsis leaf mesophyll cell types. We believe this study will serve as an informative reference to future plant scProteomic investigations.

Arabidopsis↗
DOE OSTI2020

Artificial Iron Proteins: Modeling the Active Sites in Non-Heme Dioxygenases

An important class of non-heme dioxygenases contains a conserved Fe binding site that consists of a 2-His-1-carboxylate facial triad. Results from structural biology show that, in the resting state, these proteins are six-coordinate with aqua ligands occupying the remaining three coordination sites. Here, we have utilized biotin–streptavidin (Sav) technology to design new artificial Fe proteins (ArMs) that have many of the same structural features found within active sites of these non-heme dioxygenases. An Sav variant was isolated that contains the S 112 E mutation, which installed a carboxylate side chain in the appropriate position to bind to a synthetic Fe II complex confined within Sav. Structural studies using X-ray diffraction (XRD) methods revealed a facial triad binding site that is composed of two N donors from the biotinylated ligand and the monodentate coordination of the carboxylate from S 112 E. Two aqua ligands complete the primary coordination sphere of the Fe II center with both involved in hydrogen bond networks within Sav. The corresponding Fe III protein was also prepared and structurally characterized to show a six-coordinate complex with two exogenous acetato ligands. The Fe III protein was further shown to bind an exogenous azido ligand through replacement of one acetato ligand. Spectroscopic studies of the ArMs in solution support the results found by XRD.

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

Structural asymmetry governs the assembly and GTPase activity of McrBC restriction complexes

Abstract McrBC complexes are motor-driven nucleases functioning in bacterial self-defense by cleaving foreign DNA. The GTP-specific AAA + protein McrB powers translocation along DNA and its hydrolysis activity is stimulated by its partner nuclease McrC. Here, we report cryo-EM structures of Thermococcus gammatolerans McrB and McrBC, and E. coli McrBC. The McrB hexamers, containing the necessary catalytic machinery for basal GTP hydrolysis, are intrinsically asymmetric. This asymmetry directs McrC binding so that it engages a single active site, where it then uses an arginine/lysine-mediated hydrogen-bonding network to reposition the asparagine in the McrB signature motif for optimal catalytic function. While the two McrBC complexes use different DNA-binding domains, these contribute to the same general GTP-recognition mechanism employed by all G proteins. Asymmetry also induces distinct inter-subunit interactions around the ring, suggesting a coordinated and directional GTP-hydrolysis cycle. Our data provide insights into the conserved molecular mechanisms governing McrB family AAA + motors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome

We present Microbe-seq, a high-throughput single-microbe method that yields strain-resolved genomes from complex microbial communities. We encapsulate individual microbes into droplets with microfluidics and liberate their DNA, which we amplify, tag with droplet-specific barcodes, and sequence. We use Microbe-seq to explore the human gut microbiome; we collect stool samples from a single individual, sequence over 20,000 microbes, and reconstruct nearly-complete genomes of almost 100 bacterial species, including several with multiple subspecies strains. We use these genomes to probe genomic signatures of microbial interactions: we reconstruct the horizontal gene transfer (HGT) network within the individual and observe far greater exchange within the same bacterial phylum than between different phyla. We probe bacteria-virus interactions; unexpectedly, we identify a significant in vivo association between crAssphage, an abundant bacteriophage, and a single strain of Bacteroides vulgatus. Microbe-seq contributes high-throughput culture-free capabilities to investigate genomic blueprints of complex microbial communities with single-microbe resolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Post survey report for AOP Assignable Asset collection of Crested Butte, CO

This report contains details of the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) assignable asset (AA) flight over Crested Butte, Colorado (CO). The report is being prepared for Lawrence Berkeley National Lab (LBNL), the primary clients for the flights who contracted the NEON AOP for flights during June 2018. The report includes a summary of the acquired flight lines over the planned area, results of calibration surveys, and quality assurance (QA) results of the acquired data. Detailed reports on the acquired data and processing results were also delivered with the data. LBNL contracted the AA NEON AOP flights to observe watersheds of interest at Crested Butte with remotely sensed data including high resolution LiDAR, imaging spectroscopy, and high-resolution camera imagery. The remote sensing imagery acquired by the AOP intendeds to support LBNL on an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in the area surrounding the Rocky Mountain Biological Laboratory (RMBL), one of the most prominent ecological study sites in the United States.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
DOE OSTI2020

Bidirectional titration of yeast gene expression using a pooled CRISPR guide RNA approach

Most classic genetic approaches utilize binary modifications that preclude the identification of key knockdowns for essential genes or other targets that only require moderate modulation. As a complementary approach to these classic genetic methods, we describe a plasmid-based library methodology that affords bidirectional, graded modulation of gene expression enabled by tiling the promoter regions of all 969 genes that comprise the ito977 model of Saccharomyces cerevisiae’s metabolic network. When coupled with a CRISPR-dCas9–based modulation and next-generation sequencing, this method affords a library-based, bidirection titration of gene expression across all major metabolic genes. We utilized this approach in two case studies: growth enrichment on alternative sugars, glycerol and galactose, and chemical overproduction of betaxanthins, leading to the identification of unique gene targets. In particular, we identify essential genes and other targets that were missed by classic genetic approaches.

59 BASIC BIOLOGICAL SCIENCES↗