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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Genome-resolved metagenomics of eukaryotic populations during early colonization of premature infants and in hospital rooms

Background: Fungal infections are a significant cause of mortality and morbidity in hospitalized preterm infants, yet little is known about eukaryotic colonization of infants and of the neonatal intensive care unit as a possible source of colonizing strains. This is partly because microbiome studies often utilize bacterial 16S rRNA marker gene sequencing, a technique that is blind to eukaryotic organisms. Knowledge gaps exist regarding the phylogeny and microdiversity of eukaryotes that colonize hospitalized infants, as well as potential reservoirs of eukaryotes in the hospital room built environment. Results: Genome-resolved analysis of 1174 time-series fecal metagenomes from 161 premature infants revealed fungal colonization of 10 infants. Relative abundance levels reached as high as 97% and were significantly higher in the first weeks of life (p = 0.004). When fungal colonization occurred, multiple species were present more often than expected by random chance (p = 0.008). Twenty-four metagenomic samples were analyzed from hospital rooms of six different infants. Compared to floor and surface samples, hospital sinks hosted diverse and highly variable communities containing genomically novel species, including from Diptera (fly) and Rhabditida (worm) for which genomes were assembled. With the exception of Diptera and two other organisms, zygosity of the newly assembled diploid eukaryote genomes was low. Interestingly, Malassezia and Candida species were present in both room and infant gut samples. Conclusions: Increased levels of fungal co-colonization may reflect synergistic interactions or differences in infant susceptibility to fungal colonization. Discovery of eukaryotic organisms that have not been sequenced previously highlights the benefit of genome-resolved analyses, and low zygosity of assembled genomes could reflect inbreeding or strong selection imposed by room conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A Quantitative Study of Deep Learning Training on Heterogeneous Supercomputers

Deep learning (DL) has become a key technique for solving complex problems in scientific research and discovery. DL training for science is substantially challenging because it has to deal with massive quantities of multi-dimensional data. High- performance computing (HPC) supercomputers are increasingly being employed for meeting the exponentially growing demand for DL. Multiple GPUs and high-speed interconnect network are needed for supporting DL on HPC systems. However, the excessive use of GPUs without considering effective benefits leads to inefficient resource utilization of these expensive setups. In this paper, we conduct a quantitative analysis to gauge the efficacy of DL workloads on the latest HPC system and identify viability of next-generation DL-optimized heterogeneous supercomputers for enabling researchers to develop more efficient resource management and distributed DL middleware. We evaluate well- known DL models with large-scale datasets using the popular TensorFlow framework, and provide a thorough evaluation in- cluding scalability, accuracy, variability, storage resource, GPU- GPU/GPU-CPU data transfer, and GPU utilization. Our analysis reveals that the latest heterogeneous supercomputing cluster shows varying performance trend as compared to the existing literature for single- and multi-node training. To the best of our knowledge, this is the first work to conduct such a quantitative and comprehensive study of DL training on a supercomputing system with multiple GPUs.

Han, Jingoo↗
DOE OSTI2016

Conservation of the C-type lectin fold for accommodating massive sequence variation in archaeal diversity-generating retroelements

Diversity-generating retroelements (DGRs) provide organisms with a unique means for adaptation to a dynamic environment through massive protein sequence variation. The potential scope of this variation exceeds that of the vertebrate adaptive immune system. DGRs were known to exist only in viruses and bacteria until their recent discovery in archaea belonging to the ‘microbial dark matter’, specifically in organisms closely related to Nanoarchaeota. However, Nanoarchaeota DGR variable proteins were unassignable to known protein folds and apparently unrelated to characterized DGR variable proteins. To address the issue of how Nanoarchaeota DGR variable proteins accommodate massive sequence variation, we determined the 2.52 Å resolution limit crystal structure of one such protein, AvpA, which revealed a C-type lectin (CLec)-fold that organizes a putative ligand-binding site that is capable of accommodating 1013 sequences. This fold is surprisingly reminiscent of the CLec-folds of viral and bacterial DGR variable protein, but differs sufficiently to define a new CLec-fold subclass, which is consistent with early divergence between bacterial and archaeal DGRs. The structure also enabled identification of a group of AvpA-like proteins in multiple putative DGRs from uncultivated archaea. These variable proteins may aid Nanoarchaeota and these uncultivated archaea in symbiotic relationships. Our results have uncovered the widespread conservation of the CLec-fold in viruses, bacteria, and archaea for accommodating massive sequence variation. In addition, to our knowledge, this is the first report of an archaeal CLec-fold protein.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deep-branching acetogens in serpentinized subsurface fluids of Oman

Little is known of acetogens in contemporary serpentinizing systems, despite widely supported theories that serpentinite-hosted environments supported the first life on Earth via acetogenesis. To address this knowledge gap, genome-resolved metagenomics was applied to subsurface fracture water communities from an area of active serpentinization in the Samail Ophiolite, Sultanate of Oman. Two deeply branching putative bacterial acetogen types were identified in the communities belonging to the Acetothermia (hereafter, types I and II) that exhibited distinct distributions among waters with lower and higher water–rock reaction (i.e., serpentinization influence), respectively. Metabolic reconstructions revealed contrasting core metabolic pathways of type I and II Acetothermia, including in acetogenic pathway components (e.g., bacterial- vs. archaeal-like carbon monoxide dehydrogenases [CODH], respectively), hydrogen use to drive acetogenesis, and chemiosmotic potential generation via respiratory (type I) or canonical acetogen ferredoxin-based complexes (type II). Notably, type II Acetothermia metabolic pathways allow for use of serpentinization-derived substrates and implicate them as key primary producers in contemporary hyperalkaline serpentinite environments. Phylogenomic analyses indicate that 1) archaeal-like CODH of the type II genomes and those of other serpentinite-associated Bacteria derive from a deeply rooted horizontal transfer or origin among archaeal methanogens and 2) Acetothermia are among the earliest evolving bacterial lineages. The discovery of dominant and early-branching acetogens in subsurface waters of the largest near-surface serpentinite formation provides insight into the physiological traits that likely facilitated rock-supported life to flourish on a primitive Earth and possibly on other rocky planets undergoing serpentinization.

Science & Technology - Other Topics↗
NASA NTRS1986

FASAC Technical Assessment Report: Soviet Space Science Research

This report is the work of a panel of eight US scientists who surveyed and assessed Soviet research in the spare sciences. All of the panelists were very familiar with Soviet research through their knowledge of the published scientific literature and personal contacts with Soviet and other foreign colleagues. In addition, all of the panelists reviewed considerable additional open literature--scientific, and popular, including news releases. The specific disciplines of Soviet space science research examined in detail for the report were: solar-terrestrial research, lunar and planetary research, space astronomy and astrophysics, and, life sciences. The Soviet Union has in the past carried out an ambitious program in lunar exploration and, more recently, in studies of the inner planets, Mars and especially Venus. The Soviets have provided scientific data about the latter planet which has been crucial for studies of the planet's evolution. Future programs envision an encounter with Halley's Comet, in March 1986, and missions to Mars and asteroids. The Soviet programs in the life sciences and solar-terrestrial research have been long-lasting and systematically pursued. Much of the ground-based and space-based research in these two disciplines appears to be motivated by the requirement to establish long-term human habitation in near-Earth space. The Soviet contributions to new discoveries and understanding in observational space astronomy and astrophysics have been few. This is in significant contrast to the very excellent theoretical work contributed by Soviet scientists in this discipline.

Lanzerotti, L. J.↗
DOE OSTI2024

DOE BSSD Performance Management Metrics Report Q1

Microbes play key roles in our biosphere, from driving global nutrient cycling to impacting plant, animal and human health and disease. Complex data from microbial genomes, proteins, and metabolites provide a window into these tiny engines that drive life on our planet. Yet these data are dispersed among researchers’ laboratories and various repositories, making it difficult to access. This calls for new ways of managing data, improving data interoperability, advancing community standards, and creating an infrastructure where data are shared efficiently. We have built the National Microbiome Data Collaborative (NMDC) to advance how scientists create, use, and reuse data to redefine the way we understand and harness the power of microbes. The vision of the National Microbiome Data Collaborative (NMDC) is to drive a microbiome data sharing network connecting data, people, and ideas to advance microbiome innovation and discovery. The NMDC was launched in 2019 and brought together DOE National Laboratories to collaborate across resources, capabilities, and expertise. The NMDC team was strategically assembled to include software developers, microbial researchers, metadata experts, and multi-omics specialists. The diversity of the NMDC team reflects the inherently interdisciplinary nature of microbiome science, and we leverage the strengths of the DOE National Laboratory system. Towards BER’s goal of advancing an iterative systems biology approach to the understanding of microbial genomes, the NMDC serves as a foundation for infrastructure, data standards, and community building. Together with the flagship DOE User Facilities, the Joint Genome Institute (JGI) and the Environmental Molecular Sciences Laboratory (EMSL), we are developing core capabilities in metadata standards for environmental descriptors and sample handling and processing; standardized bioinformatic workflows; an interface for data search and access; and robust community engagement activities. The NMDC production platform supports long-term data infrastructure and community building for BER’s bioenergy and environmental research goals. Our approach leverages lessons learned and an ambitious framework for collaborative, interdisciplinary data infrastructure to support microbiome research. The NMDC supports data, information, and knowledge access through three defined software tools – the Submission Portal, NMDC EDGE, and the Data Portal – driven by community needs. Herein, we describe the value proposition for the microbiome research community, our overarching strategy, and challenges and opportunities for developing the NMDC as both an infrastructure and community engagement program.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Surface basicity controls C–C coupling rates during carbon dioxide-assisted methane coupling over bifunctional Ca/ZnO catalysts

Carbon dioxide-assisted coupling of methane offers an approach to chemically upgrade two greenhouse gases and components of natural gas to produce ethylene and syngas. Prior research on this reaction has concentrated efforts on catalyst discovery, which has indicated that composites comprised of both reducible and basic oxides are especially promising. There is a need for detailed characterization of these bifunctional oxide systems to provide a more fundamental understanding of the active sites and their roles in the reaction. We studied the dependence of physical and electronic properties of Ca-modified ZnO materials on Ca content via X-ray photoelectron and absorption spectroscopies, electron microscopy, and infrared spectroscopic temperature-programmed desorption (IR-TPD). It was found that introduction of only 0.6 mol% Ca onto a ZnO surface is necessary to induce significant improvement in the catalytic production of C 2 species: C 2 selectivity increases from 5% on un-modified ZnO to 58%, at similar conversions. Evidence presented shows that this selectivity increase results from the formation of an interface between the basic CaO and reducible ZnO phases. Furthermore, the basicity of these interface sites correlates directly with catalytic activity over a wide composition range, and this relationship indicates that moderate CO 2 adsorption strength is optimal for CH 4 coupling. These results demonstrate, for the first time to our knowledge, a volcano-type relationship between CO 2 -assisted CH 4 coupling activity and catalyst surface basicity, which can inform further catalyst development.

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

Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future

Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Evaluation of a scientific data search infrastructure

The ability to search over large scientific datasets has become crucial to next-generation scientific discoveries as data generated from scientific facilities grow dramatically. In previous work, we developed and deployed ScienceSearch, a search infrastructure for scientific data which uses machine learning to automate metadata creation. Our current deployment is deployed atop a container based platform at a HPC center. In this article, we present an evaluation and discuss our experiences with the ScienceSearch infrastructure. Specifically, we present a performance evaluation of ScienceSearch's infrastructure focusing on scalability trends. The obtained results show that ScienceSearch is able to serve up to 130 queries/min with latency under 3 s. We discuss our infrastructure setup and evaluation results to provide our experiences and a perspective on opportunities and challenges of our search infrastructure.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

The SMART Theory and Modeling Team: An Integrated Element of Mission Development and Science Analysis

When targeting physical understanding of space plasmas, our focus is gradually shifting away from discovery-type investigations to missions and studies that address our basic understanding of processes we know to be important. For these studies, theory and models provide physical predictions that need to be verified or falsified by empirical evidence. Within this paradigm, a tight integration between theory, modeling, and space flight mission design and execution is essential. NASA's Magnetospheric MultiScale (MMS) mission is a pathfinder in this new era of space research. The prime objective of MMS is to understand magnetic reconnection, arguably the most fundamental of plasma processes. In particular, MMS targets the microphysical processes, which permit magnetic reconnection to operate in the collisionless plasmas that permeate space and astrophysical systems. More specifically, MMS will provide closure to such elemental questions as how particles become demagnetized in the reconnection diffusion region, which effects determine the reconnection rate, and how reconnection is coupled to environmental conditions such as magnetic shear angles. Solutions to these problems have remained elusive in past and present spacecraft missions primarily due to instrumental limitations - yet they are fundamental to the large-scale dynamics of collisionless plasmas. Owing to the lack of measurements, most of our present knowledge of these processes is based on results from modern theory and modeling studies of the reconnection process. Proper design and execution of a mission targeting magnetic reconnection should include this knowledge and have to ensure that all relevant scales and effects can be resolved by mission measurements. The SMART mission has responded to this need through a tight integration between instrument and theory and modeling teams. Input from theory and modeling is fed into all aspects of science mission design, and theory and modeling activities are tailored to SMART needs during mission development and science analysis. In this presentation, we will present an overview of SMART theory and modeling team activities. In particular, we will provide examples of science objectives derived from state-of-the art models, and of recent research results that continue to be utilized in SMART mission development.

Hesse, Michael↗
DOE OSTI2024

Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics

Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulating phenomenological constitutive laws that can accurately capture the observed material response. However, even though neural network-based constitutive laws have been shown to generalize proficiently, the generated representations are not easily interpretable due to their high number of trainable parameters. Sparse regression approaches exist that allow for obtaining interpretable expressions, but the user is tasked with creating a library of model forms which by construction limits their expressiveness to the functional forms provided in the libraries. Here, in this work, we propose to train regularized physics-augmented neural network-based constitutive models utilizing a smoothed version of $L^0$-regularization. This aims to maintain the trustworthiness inherited by the physical constraints, but also enables interpretability which has not been possible thus far on any type of machine learning-based constitutive model where model forms were not assumed a priori but were actually discovered. During the training process, the network simultaneously fits the training data and penalizes the number of active parameters, while also ensuring constitutive constraints such as thermodynamic consistency. We show that the method can reliably obtain interpretable and trustworthy constitutive models for compressible and incompressible hyperelasticity, yield functions, and hardening models for elastoplasticity, using synthetic and experimental data. This work aims to set a new paradigm for interpretable machine learning models in the broad area of solid mechanics where low and limited data is available along with prior knowledge of physical constraints that the learned maps need to obey. This paradigm can potentially be extended to a broader spectrum of scientific exploration.

Data-driven constitutive models↗
DOE OSTI2021

A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

The global nature of the ITER project along with its projected ~ petabyte per day data generation presents a unique challenge, but also an opportunity for the fusion community to rethink, optimize, and enhance our scientific discovery process. Recognizing this, collaborative research with computational scientists was undertaken over the past several years to create a framework for large-scale data movement across wide-area networks (WANs), to enable global near-real time analysis of fusion data. This would broaden the available computational resources for analysis/simulation, and increase the number of researchers actively participating in experiments. An official demonstration of this framework for fast, large data transfer and real-time analysis was carried out between the KSTAR tokamak in Daejeon, Korea and PPPL in Princeton, USA. Streaming large data transfer, with near real-time movie creation and analysis of the KSTAR Electron Cyclotron Emission imaging (ECEI) data, was performed using the I/O framework ADIOS, and comparisons made at PPPL with simulation results from the XGC1 code. These demonstrations were made possible utilizing an optimized network configuration at PPPL, which achieved over 8.8 Gbps (88% utilization) in throughput tests from NFRI to PPPL. This demonstration showed the feasibility for large-scale data analysis of KSTAR data, and provides a nascent framework to enable use of globally distributed computational and personnel resources in pursuit of scientific knowledge from the ITER experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Omics-Based Comparison of Fungal Virulence Genes, Biosynthetic Gene Clusters, and Small Molecules in Penicillium expansum and Penicillium chrysogenum

Penicillium expansum is a ubiquitous pathogenic fungus that causes blue mold decay of apple fruit postharvest, and another member of the genus, Penicillium chrysogenum, is a well-studied saprophyte valued for antibiotic and small molecule production. While these two fungi have been investigated individually, a recent discovery revealed that P. chrysogenum can block P. expansum-mediated decay of apple fruit. To shed light on this observation, we conducted a comparative genomic, transcriptomic, and metabolomic study of two P. chrysogenum (404 and 413) and two P. expansum (Pe21 and R19) isolates. Global transcriptional and metabolomic outputs were disparate between the species, nearly identical for P. chrysogenum isolates, and different between P. expansum isolates. Further, the two P. chrysogenum genomes revealed secondary metabolite gene clusters that varied widely from P. expansum. This included the absence of an intact patulin gene cluster in P. chrysogenum, which corroborates the metabolomic data regarding its inability to produce patulin. Additionally, a core subset of P. expansum virulence gene homologues were identified in P. chrysogenum and were similarly transcriptionally regulated in vitro. Molecules with varying biological activities, and phytohormone-like compounds were detected for the first time in P. expansum while antibiotics like penicillin G and other biologically active molecules were discovered in P. chrysogenum culture supernatants. Our findings provide a solid omics-based foundation of small molecule production in these two fungal species with implications in postharvest context and expand the current knowledge of the Penicillium-derived chemical repertoire for broader fundamental and practical applications.

Bartholomew, Holly P. (ORCID:0000000292726399)↗
NASA NTRS1993

(abstract) The Impactor Flux in the Pluto-Charon System

The impactor flux on Pluto and Charon consists of long-period comets from the Oort cloud passing through the region, and intermediate-period comets evolving inward to (or outward from) short-period orbits. The source of the intermediate-period comets is likely an extended disk of remnant planetesimals beyond the orbit of Neptune, commonly referred to as the Kuiper belt, though the Oort cloud may be an additional source of some short-period comets. The Pluto-Charon system actually circulates within the Kuiper belt, and with an aphelion of 49.3 AU, moves through a region where remnant planetesimal orbits would likely be stable over the age of the solar system. The recent discovery of two objects at Kuiper belt distances, 1992 QB 1 and 1993 FW, and the fact that the orbit determined for 1992 QB 1 is likely of low eccentricity and low inclination, have provided strong observational evidence for the Kuiper belt. Weisman et al (1989) showed that, for the expected number of objects in the Kuiper belt necessary to provide the short-period comet flux, cometary cratering on Pluto and Charon is dominated by Kuiper belt comets. We will provide updated estimates of the numbers, based on an improved understanding of Oort cloud and Kuiper belt dynamics since 1989, and on improved estimates for the radii and masses for Pluto and Charon. Because Pluto and Charon actually penetrate the stable region of the Kuiper belt beyond 40 AU, expected cratering rates for the pair are substantially higher than for Triton, which is located at the inner edge of the Kuiper belt. However, the Pluto-Charon system is tidally evolved and this may have led to resurfacing events at various times in its history. Thus, detailed predictions of the integrated crater density or comparisons with Triton are not very meaningful without a knowledge of that history. Differences in the suspected compositions of Pluto and Charon may also lead to different surface rheologies and thus, different retention times for cratered surfaces.

Pluto↗
DOE OSTI2022

Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats

Abstract Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Functional Domain Walls as Active Elements for Energy Technology

In the past five years in the duration of this project (July 2011-July 2016), we have made a wide range of achievements in both basic research and energy applications along the direction planned in the original proposal. These achievements were reflected by 13 articles published in peer-reviewed journals including Nature Communications, Nano Letters, etc., and one currently in revision at Science. These papers have been accumulatively cited for more than 660 times as of October 2016, according to Web of Science statistics. Specifically, we have made impactful discoveries in the following fields. Basic Research. We have investigated in depth the materials physics of the representative quantum material, VO 2 , on which most of our project is anchored. We have discovered that independent diffusion of heat and charge in the absence of quasiparticles in metallic VO 2 leads to an anomalously low electronic thermal conductivity, dramatically violating the Wiedemann-Franz law, which is a robust law governing behavior of normal conductors stating that free electrons transport heat proportionally to the charge they transport. In addition, we have discovered a peculiar thermal rectification effect based on its phase transition, as well as a gating response of the phase transition. In parallel to the work on VO 2 , we have also made breakthroughs in investigation of transition metal dichalcogenides (TMDs): we have experimentally demonstrate a strong anisotropy in in-plane thermal conductivity of black phosphorous, discovered a new, unusual member of the TMDs family, ReS 2 , where the bulk behaves as monolayers due to electronic and vibrational decoupling, unusual interaction between physi-sorbed molecules and 2D semiconductors, and thermally driven crossover from indirect toward direct bandgap in some 2D TMDs. Applications. Based on the understanding and knowledge gained from the basic investigation, we have developed novel tools and devices for energy applications. These include a nanowire based microthermometer for quantitative evaluation of electron beam heating in electron microscopy, giant-amplitude, high-work density microactuators and torsional micromuscles, as well as nanoscale thermometers and powermeters, all based on the VO 2 phase transition.

2D materials↗
DOE OSTI2025

Tunable Few-Layer van der Waals Crystals and Heterostructures as Emerging Energy and Quantum Materials (Final Technical Report)

2D and layered (van der Waals) semiconductors offer extraordinary opportunities for manipulating optically excited charge carriers, many-body excitations, and non-charge based quantum numbers. To date, research has focused on a limited group of materials, mostly transition metal dichalcogenides in the monolayer limit. Other van der Waals semiconductors, and especially few-layer to multilayer crystals and their heterostructures, carry large potential for the discovery of phenomena of interest for future energy and information technologies. But they remain largely unexplored, often due to a lack of access to high-quality materials and approaches for measuring their properties at the relevant scales. The goal of this project was to develop an EPSCoR-State/National Laboratory Partnership that addresses the challenges of preparing high-quality van der Waals semiconductors and of probing their structure, composition, and especially their optoelectronic and photonic properties, near the atomic scale using electron microscopy techniques. A central component of the project was the development of advanced methods for electron microscopy and electron-excited spectroscopy, taking advantage of unique samples as well as leading capabilities and expertise at the partner institutions. Efforts to advance leading-edge techniques was supported by ancillary developments, such as precision sample preparation for electron microscopy/spectroscopy, coordinated chemical imaging, and analytical electron microscopy. Experiments in materials synthesis and technique development were closely linked to theory and computation. The results obtained under this project yielded multifaceted benefits to the involved partners and their institutions, DOE-BES, the wider scientific community, and society at large, particularly in the State of Nebraska through dividends from knowledge and human capital generated under the project.

36 MATERIALS SCIENCE↗