Showing papers by "Oak Ridge National Laboratory published in 2020"
University of Exeter1, École Normale Supérieure2, Norwich Research Park3, University of Groningen4, Wageningen University and Research Centre5, Ludwig Maximilian University of Munich6, Max Planck Society7, Commonwealth Scientific and Industrial Research Organisation8, Université Paris-Saclay9, Stanford University10, National Oceanic and Atmospheric Administration11, National Institute for Space Research12, Bermuda Institute of Ocean Sciences13, University of Southampton14, PSL Research University15, National Institute for Environmental Studies16, Japan Agency for Marine-Earth Science and Technology17, University of Maryland, College Park18, University of Leeds19, International Institute of Minnesota20, Flanders Marine Institute21, ETH Zurich22, University of East Anglia23, German Aerospace Center24, Woods Hole Research Center25, University of Illinois at Urbana–Champaign26, University of Toulouse27, Japan Meteorological Agency28, Plymouth Marine Laboratory29, University of Paris30, Hobart Corporation31, Oeschger Centre for Climate Change Research32, Tsinghua University33, National Center for Atmospheric Research34, Appalachian State University35, University of Colorado Boulder36, University of Washington37, Atlantic Oceanographic and Meteorological Laboratory38, Princeton University39, Met Office40, Leibniz Institute of Marine Sciences41, Auburn University42, University of Tasmania43, VU University Amsterdam44, Oak Ridge National Laboratory45, Sun Yat-sen University46, Nanjing University47
TL;DR: In this paper, the authors describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
Abstract: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ± 0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budget imbalance BIM of −0.1 GtC yr−1 indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in EFOS was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was 5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in EFOS relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2019; Le Quere et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).
1,764 citations
TL;DR: The extent of the trait data compiled in TRY is evaluated and emerging patterns of data coverage and representativeness are analyzed to conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements.
Abstract: Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
882 citations
TL;DR: An overview of the field of Variational Quantum Algorithms is presented and strategies to overcome their challenges as well as the exciting prospects for using them as a means to obtain quantum advantage are discussed.
Abstract: Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will likely not be available in the near future. Current quantum devices have serious constraints, including limited numbers of qubits and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which use a classical optimizer to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essentially all applications that researchers have envisioned for quantum computers, and they appear to the best hope for obtaining quantum advantage. Nevertheless, challenges remain including the trainability, accuracy, and efficiency of VQAs. Here we overview the field of VQAs, discuss strategies to overcome their challenges, and highlight the exciting prospects for using them to obtain quantum advantage.
842 citations
TL;DR: In this article, the authors provide a detailed review of the deformation mechanisms of HEAs with the complex concentrated alloys (CCAs) with the FCC and BCC structures, highlighting both successes and limitations.
769 citations
Australian National University1, Argonne National Laboratory2, Scripps Health3, Iowa State University4, Western New England University5, Michigan State University6, National Institute of Advanced Industrial Science and Technology7, Microsoft8, University of Colorado Denver9, Oak Ridge National Laboratory10, Pacific Northwest National Laboratory11, University of Nebraska–Lincoln12, Nanjing University13, Sandia National Laboratories14, Moscow State University15, Kyocera16, Cray17, Purdue University18, Old Dominion University19, University of Rochester20
TL;DR: A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented, which include fragmentation methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory.
Abstract: A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods such as the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features of LibCChem, as it is the associated problem of power consumption as the power of computers increases dramatically. The process by which a complex program suite such as GAMESS is maintained and developed is considered. Future developments are briefly summarized.
575 citations
University of Bremen1, Duke University2, Max Planck Society3, University of Bristol4, University of Costa Rica5, Western Washington University6, University of Minnesota7, University of Bonn8, Karlsruhe Institute of Technology9, Free University of Berlin10, Oak Ridge National Laboratory11, Ulsan National Institute of Science and Technology12, University of Regensburg13, University of Warwick14, Los Alamos National Laboratory15, Claude Bernard University Lyon 116, University of Newcastle17, Dassault Systèmes18, Facultad de Ciencias Exactas y Naturales19, Argonne National Laboratory20, University of Luxembourg21
TL;DR: An overview of the recently developed capabilities of the DFTB+ code is given, demonstrating with a few use case examples, and the strengths and weaknesses of the various features are discussed, to discuss on-going developments and possible future perspectives.
Abstract: DFTB+ is a versatile community developed open source software package offering fast and efficient methods for carrying out atomistic quantum mechanical simulations. By implementing various methods approximating density functional theory (DFT), such as the density functional based tight binding (DFTB) and the extended tight binding method, it enables simulations of large systems and long timescales with reasonable accuracy while being considerably faster for typical simulations than the respective ab initio methods. Based on the DFTB framework, it additionally offers approximated versions of various DFT extensions including hybrid functionals, time dependent formalism for treating excited systems, electron transport using non-equilibrium Green’s functions, and many more. DFTB+ can be used as a user-friendly standalone application in addition to being embedded into other software packages as a library or acting as a calculation-server accessed by socket communication. We give an overview of the recently developed capabilities of the DFTB+ code, demonstrating with a few use case examples, discuss the strengths and weaknesses of the various features, and also discuss on-going developments and possible future perspectives.
491 citations
Pacific Northwest National Laboratory1, United States Geological Survey2, Smithsonian Tropical Research Institute3, Smithsonian Conservation Biology Institute4, University of Minnesota5, Joint Global Change Research Institute6, University of Maryland, College Park7, Duke University8, Boston University9, Swiss Federal Institute for Forest, Snow and Landscape Research10, University of California, Berkeley11, Stanford University12, University of Florida13, Lawrence Berkeley National Laboratory14, Northern Arizona University15, Goddard Space Flight Center16, University of Birmingham17, University of Natural Resources and Life Sciences, Vienna18, Technische Universität München19, University of Wisconsin-Madison20, Columbia University21, Oak Ridge National Laboratory22, Los Alamos National Laboratory23
TL;DR: The authors show that shifts in forest dynamics are already occurring, and the emerging pattern is that global forests are tending toward younger stands with faster turnover as old-growth forest with stable dynamics are dwindling.
Abstract: Forest dynamics arise from the interplay of environmental drivers and disturbances with the demographic processes of recruitment, growth, and mortality, subsequently driving biomass and species composition. However, forest disturbances and subsequent recovery are shifting with global changes in climate and land use, altering these dynamics. Changes in environmental drivers, land use, and disturbance regimes are forcing forests toward younger, shorter stands. Rising carbon dioxide, acclimation, adaptation, and migration can influence these impacts. Recent developments in Earth system models support increasingly realistic simulations of vegetation dynamics. In parallel, emerging remote sensing datasets promise qualitatively new and more abundant data on the underlying processes and consequences for vegetation structure. When combined, these advances hold promise for improving the scientific understanding of changes in vegetation demographics and disturbances.
476 citations
TL;DR: In this paper, the authors trained two auto-regressive language models (Transformer-XL and XLNet) on 80 billion amino acids from 200 million protein sequences (UniRef100) and one auto-encoder model on 393 billion amino acid from 2.1 billion protein sequences taken from the Big Fat Database (BFD).
Abstract: Motivation Natural Language Processing (NLP) continues improving substantially through auto-regressive (AR) and auto-encoding (AE) Language Models (LMs). These LMs require expensive computing resources for self-supervised or un-supervised learning from huge unlabelled text corpora. The information learned is transferred through so-called embeddings to downstream prediction tasks. Computational biology and bioinformatics provide vast gold-mines of structured and sequentially ordered text data leading to extraordinarily successful protein sequence LMs that promise new frontiers for generative and predictive tasks at low inference cost. As recent NLP advances link corpus size to model size and accuracy, we addressed two questions: (1) To which extent can High-Performance Computing (HPC) up-scale protein LMs to larger databases and larger models? (2) To which extent can LMs extract features from single proteins to get closer to the performance of methods using evolutionary information? Methodology Here, we trained two auto-regressive language models (Transformer-XL and XLNet) and two auto-encoder models (BERT and Albert) on 80 billion amino acids from 200 million protein sequences (UniRef100) and one language model (Transformer-XL) on 393 billion amino acids from 2.1 billion protein sequences taken from the Big Fat Database (BFD), today’s largest set of protein sequences (corresponding to 22- and 112-times, respectively of the entire English Wikipedia). The LMs were trained on the Summit supercomputer, using 936 nodes with 6 GPUs each (in total 5616 GPUs) and one TPU Pod, using V3-512 cores. Results We validated the feasibility of training big LMs on proteins and the advantage of up-scaling LMs to larger models supported by more data. The latter was assessed by predicting secondary structure in three- and eight-states (Q3=75- 83, Q8=63-72), localization for 10 cellular compartments (Q10=74) and whether a Preprint. Under review. protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabelled data (only protein sequences) captured important biophysical properties of the protein alphabet, namely the amino acids, and their well orchestrated interplay in governing the shape of proteins. In the analogy of NLP, this implied having learned some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC slightly reduced the gap between models trained on evolutionary information and LMs. Additionally, our results highlighted the importance of bi-directionality when processing proteins as the uni-directional TransformerXL was outperformed by its bi-directional counterparts.
409 citations
TL;DR: It is shown that seasonally variable regimes become more variable, and the combined influence of seasonality and magnitude of climate variables will affect future water availability.
Abstract: Both seasonal and annual mean precipitation and evaporation influence patterns of water availability impacting society and ecosystems Existing global climate studies rarely consider such patterns from non-parametric statistical standpoint Here, we employ a non-parametric analysis framework to analyze seasonal hydroclimatic regimes by classifying global land regions into nine regimes using late 20th century precipitation means and seasonality These regimes are used to assess implications for water availability due to concomitant changes in mean and seasonal precipitation and evaporation changes using CMIP5 model future climate projections Out of 9 regimes, 4 show increased precipitation variation, while 5 show decreased evaporation variation coupled with increasing mean precipitation and evaporation Increases in projected seasonal precipitation variation in already highly variable precipitation regimes gives rise to a pattern of "seasonally variable regimes becoming more variable" Regimes with low seasonality in precipitation, instead, experience increased wet season precipitation
400 citations
TL;DR: An in-depth understanding of lignin properties and their influences on biomass conversion can provide clues to improve biomass utilization and significantly increase the economic viability of biorefinery.
357 citations
Pacific Northwest National Laboratory1, Lawrence Berkeley National Laboratory2, National Center for Computational Sciences3, Brookhaven National Laboratory4, Argonne National Laboratory5, Intel6, University of Texas at Arlington7, State University of New York System8, Pennsylvania State University9, Oak Ridge National Laboratory10, Washington University in St. Louis11, Wellesley College12, Maria Curie-Skłodowska University13, Iowa State University14, Academy of Sciences of the Czech Republic15, University of Tennessee at Martin16, Université libre de Bruxelles17, Facebook18, Russian Academy of Sciences19, University of Minnesota20, University of Washington21, United States Naval Research Laboratory22, Georgia Institute of Technology23, University of St Andrews24, Universidad Autónoma Metropolitana25, University of California, San Diego26, Saarland University27, Sandia National Laboratories28, University of Illinois at Urbana–Champaign29, University of Iceland30, Australian National University31, Florida Institute of Technology32, University of Science and Technology of China33, Oswaldo Cruz Foundation34, Cardiff University35, Louisiana State University36, Chinese Academy of Sciences37, National Autonomous University of Mexico38, University of Florida39, Los Alamos National Laboratory40, University of Oviedo41, Prince of Songkla University42, Ames Laboratory43, University of Utah44, Northwestern University45, Universal Display Corporation46, Federal University of Pernambuco47, CD-adapco48, Cray49, Massachusetts Institute of Technology50, Nvidia51, University of Tennessee52, Shandong Normal University53, University of Cambridge54, Advanced Micro Devices55, Technische Universität München56, Stanford University57, Wuhan University of Technology58, Stony Brook University59
TL;DR: The NWChem computational chemistry suite is reviewed, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.
Abstract: Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.
Pacific Northwest National Laboratory1, Washington University in St. Louis2, University of Pittsburgh3, State University of New York System4, Oak Ridge National Laboratory5, Argonne National Laboratory6, Los Alamos National Laboratory7, Northern Illinois University8, Environmental Molecular Sciences Laboratory9
TL;DR: In this paper, an atomically dispersed Co and N co-doped carbon (CoN4C12) catalyst with porphyrin-like sites is reported with an improved activity and durability in PEM fuel cell conditions.
Abstract: The development of catalysts free of platinum-group metals and with both a high activity and durability for the oxygen reduction reaction in proton exchange membrane fuel cells is a grand challenge. Here we report an atomically dispersed Co and N co-doped carbon (Co–N–C) catalyst with a high catalytic oxygen reduction reaction activity comparable to that of a similarly synthesized Fe–N–C catalyst but with a four-time enhanced durability. The Co–N–C catalyst achieved a current density of 0.022 A cm−2 at 0.9 ViR-free (internal resistance-compensated voltage) and peak power density of 0.64 W cm−2 in 1.0 bar H2/O2 fuel cells, higher than that of non-iron platinum-group-metal-free catalysts reported in the literature. Importantly, we identified two main degradation mechanisms for metal (M)–N–C catalysts: catalyst oxidation by radicals and active-site demetallation. The enhanced durability of Co–N–C relative to Fe–N–C is attributed to the lower activity of Co ions for Fenton reactions that produce radicals from the main oxygen reduction reaction by-product, H2O2, and the significantly enhanced resistance to demetallation of Co–N–C. Platinum-group-metal-free, non-iron catalysts are highly desirable for the oxygen reduction reaction at proton exchange membrane (PEM) fuel cell cathodes, as they avoid the detrimental Fenton reactions. Now, a cobalt and nitrogen co-doped carbon catalyst with atomically dispersed porphyrin-like CoN4C12 sites is reported with an improved activity and durability in PEM fuel cell conditions.
TL;DR: The ability of single metal atoms to effectively trap the dissolved lithium polysulfides (LiPSs) and catalytically convert the LiPSs/Li2S during cycling, significantly improved sulfur utilization, rate capability and cycling life.
Abstract: Lithium–sulfur (Li–S) batteries are promising next-generation energy storage technologies due to their high theoretical energy density, environmental friendliness, and low cost. However, low conduc...
University of Maryland, College Park1, University of New Hampshire2, Potsdam Institute for Climate Impact Research3, Joint Global Change Research Institute4, Netherlands Environmental Assessment Agency5, Durham University6, National Institute for Environmental Studies7, International Institute for Applied Systems Analysis8, University of Bern9, Max Planck Society10, University of Hong Kong11, National Center for Atmospheric Research12, University of Copenhagen13, Ludwig Maximilian University of Munich14, Goddard Space Flight Center15, Princeton University16, Oak Ridge National Laboratory17, Food and Agriculture Organization18, University of Maryland Center for Environmental Science19
TL;DR: The Land-Use Harmonization 2 (LUH2) project is presented, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs to enable new and improved estimates of the combined effects of landUse on the global carbon–climate system.
Abstract: . Human land-use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth surface, with consequences for climate and other ecosystem services. In the future, land-use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community is developing the next generation of advanced Earth System Models (ESMs) to estimate the combined effects of human activities (e.g. land use and fossil fuel emissions) on the carbon-climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, are required as input for these models. Here we present results from the Land-use Harmonization 2 (LUH2) project, with the goal to smoothly connect updated historical reconstructions of land-use with new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land-use patterns, underlying land-use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds off a similar effort from CMIP5, and is now provided at higher resolution (0.25 × 0.25 degree), over a longer time domain (850–2100, with extensions to 2300), with more detail (including multiple crop and pasture types and associated management practices), using more input datasets (including Landsat remote sensing data), updated algorithms (wood harvest and shifting cultivation), and is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5, and are designed to enable new and improved estimates of the combined effects of land-use on the global carbon-climate system.
TL;DR: The room temperature X-ray structure of unliganded SARS-CoV-2 3CL Mpro is reported, revealing the ligand-free structure of the active site and the conformation of the catalytic site cavity at near-physiological temperature, which suggests that the room temperature structure may provide more relevant information at physiological temperatures for aiding in molecular docking studies.
Abstract: The COVID-19 disease caused by the SARS-CoV-2 coronavirus has become a pandemic health crisis. An attractive target for antiviral inhibitors is the main protease 3CL Mpro due to its essential role in processing the polyproteins translated from viral RNA. Here we report the room temperature X-ray structure of unliganded SARS-CoV-2 3CL Mpro, revealing the ligand-free structure of the active site and the conformation of the catalytic site cavity at near-physiological temperature. Comparison with previously reported low-temperature ligand-free and inhibitor-bound structures suggest that the room temperature structure may provide more relevant information at physiological temperatures for aiding in molecular docking studies.
TL;DR: The conceptual underpinnings of the raft hypothesis are reviewed and critically discuss the supporting and contradicting evidence in cells, focusing on why controversies about the composition, properties, and even the very existence of lipid rafts remain unresolved.
TL;DR: A new analysis on gene expression data from cells in bronchoalveolar lavage fluid from COVID-19 patients that were used to sequence the virus identifies a critical imbalance in RAS represented by decreased expression of ACE in combination with increases in ACE2, renin, angiotensin, key RAS receptors, kinogen and many kallikrein enzymes that activate it, and both bradykinin receptors.
Abstract: Neither the disease mechanism nor treatments for COVID-19 are currently known. Here, we present a novel molecular mechanism for COVID-19 that provides therapeutic intervention points that can be addressed with existing FDA-approved pharmaceuticals. The entry point for the virus is ACE2, which is a component of the counteracting hypotensive axis of RAS. Bradykinin is a potent part of the vasopressor system that induces hypotension and vasodilation and is degraded by ACE and enhanced by the angiotensin1-9 produced by ACE2. Here, we perform a new analysis on gene expression data from cells in bronchoalveolar lavage fluid (BALF) from COVID-19 patients that were used to sequence the virus. Comparison with BALF from controls identifies a critical imbalance in RAS represented by decreased expression of ACE in combination with increases in ACE2, renin, angiotensin, key RAS receptors, kinogen and many kallikrein enzymes that activate it, and both bradykinin receptors. This very atypical pattern of the RAS is predicted to elevate bradykinin levels in multiple tissues and systems that will likely cause increases in vascular dilation, vascular permeability and hypotension. These bradykinin-driven outcomes explain many of the symptoms being observed in COVID-19.
TL;DR: In this article, an electrocatalyst with confined reaction volume by coating Cu catalysts with nitrogen-doped carbon layers was developed, which achieved an ethanol Faradaic efficiency of (52 ± 1)% and an ethanol cathodic energy efficiency of 31%.
Abstract: The carbon dioxide electroreduction reaction (CO2RR) provides ways to produce ethanol but its Faradaic efficiency could be further improved, especially in CO2RR studies reported at a total current density exceeding 10 mA cm−2. Here we report a class of catalysts that achieve an ethanol Faradaic efficiency of (52 ± 1)% and an ethanol cathodic energy efficiency of 31%. We exploit the fact that suppression of the deoxygenation of the intermediate HOCCH* to ethylene promotes ethanol production, and hence that confinement using capping layers having strong electron-donating ability on active catalysts promotes C–C coupling and increases the reaction energy of HOCCH* deoxygenation. Thus, we have developed an electrocatalyst with confined reaction volume by coating Cu catalysts with nitrogen-doped carbon. Spectroscopy suggests that the strong electron-donating ability and confinement of the nitrogen-doped carbon layers leads to the observed pronounced selectivity towards ethanol. The electroreduction of CO2 to ethanol could enable the clean production of fuels using renewable power. This study shows how confinement effects from nitrogen-doped carbon layers on copper catalysts enable selective ethanol production from CO2 with a Faradaic efficiency of up to 52%.
Free University of Berlin1, Leipzig University2, University of Wyoming3, Wageningen University and Research Centre4, University of Göttingen5, Florida International University6, Martin Luther University of Halle-Wittenberg7, University of Montpellier8, University of Toulouse9, Oak Ridge National Laboratory10, Max Planck Society11, Morton Arboretum12, University of Manchester13
TL;DR: A holistic view of the belowground economy is taken and it is shown that root-mycorrhizal collaboration can short circuit a one-dimensional economic spectrum, providing an entire space of economic possibilities.
Abstract: Plant economics run on carbon and nutrients instead of money. Leaf strategies aboveground span an economic spectrum from "live fast and die young" to "slow and steady," but the economy defined by root strategies belowground remains unclear. Here, we take a holistic view of the belowground economy and show that root-mycorrhizal collaboration can short circuit a one-dimensional economic spectrum, providing an entire space of economic possibilities. Root trait data from 1810 species across the globe confirm a classical fast-slow "conservation" gradient but show that most variation is explained by an orthogonal "collaboration" gradient, ranging from "do-it-yourself" resource uptake to "outsourcing" of resource uptake to mycorrhizal fungi. This broadened "root economics space" provides a solid foundation for predictive understanding of belowground responses to changing environmental conditions.
TL;DR: In this article, the authors highlight recent progress and challenges related to the integration of lithium metal anodes in solid-state batteries, and highlight the challenges of integrating anodes with solid electrolytes.
Abstract: In this Perspective, we highlight recent progress and challenges related to the integration of lithium metal anodes in solid-state batteries. While prior reports have suggested that solid electroly...
TL;DR: It is reported that disordered rock salt can be used as a fast-charging anode that can reversibly cycle two lithium ions at an average voltage of about 0.6 volts versus a Li/Li+ reference electrode, alleviating a major safety concern (short-circuiting related to Li dendrite growth).
Abstract: Rechargeable lithium-ion batteries with high energy density that can be safely charged and discharged at high rates are desirable for electrified transportation and other applications1-3. However, the sub-optimal intercalation potentials of current anodes result in a trade-off between energy density, power and safety. Here we report that disordered rock salt4,5 Li3+xV2O5 can be used as a fast-charging anode that can reversibly cycle two lithium ions at an average voltage of about 0.6 volts versus a Li/Li+ reference electrode. The increased potential compared to graphite6,7 reduces the likelihood of lithium metal plating if proper charging controls are used, alleviating a major safety concern (short-circuiting related to Li dendrite growth). In addition, a lithium-ion battery with a disordered rock salt Li3V2O5 anode yields a cell voltage much higher than does a battery using a commercial fast-charging lithium titanate anode or other intercalation anode candidates (Li3VO4 and LiV0.5Ti0.5S2)8,9. Further, disordered rock salt Li3V2O5 can perform over 1,000 charge-discharge cycles with negligible capacity decay and exhibits exceptional rate capability, delivering over 40 per cent of its capacity in 20 seconds. We attribute the low voltage and high rate capability of disordered rock salt Li3V2O5 to a redistributive lithium intercalation mechanism with low energy barriers revealed via ab initio calculations. This low-potential, high-rate intercalation reaction can be used to identify other metal oxide anodes for fast-charging, long-life lithium-ion batteries.
TL;DR: Flat optics for direct image differentiation is demonstrated, allowing us to significantly shrink the required optical system size, significantly reducing the size and complexity of conventional optical systems.
Abstract: Image processing has become a critical technology in a variety of science and engineering disciplines. Although most image processing is performed digitally, optical analog processing has the advantages of being low-power and high-speed, but it requires a large volume. Here, we demonstrate flat optics for direct image differentiation, allowing us to significantly shrink the required optical system size. We first demonstrate how the differentiator can be combined with traditional imaging systems such as a commercial optical microscope and camera sensor for edge detection with a numerical aperture up to 0.32. We next demonstrate how the entire processing system can be realized as a monolithic compound flat optic by integrating the differentiator with a metalens. The compound nanophotonic system manifests the advantage of thin form factor as well as the ability to implement complex transfer functions, and could open new opportunities in applications such as biological imaging and computer vision. Vertical integration of a metalens to realize compound nanophotonic systems for optical analog image processing is realized, significantly reducing the size and complexity of conventional optical systems.
TL;DR: In this paper, the authors reviewed the recent advancements of plant-based reinforced composites, focusing on strategies and breakthroughs in enhancing the NFRCs' performance, including fiber modification, fiber hybridization, lignocellulosic fillers incorporation, conventional processing techniques, additive manufacturing (3D printing), and new fiber source exploration.
Abstract: Demands for reducing energy consumption and environmental impacts are the major driving factors for the development of natural fiber–reinforced composites (NFRCs) in many sectors. Compared with synthesized fiber, natural fiber provides several advantages in terms of biodegradability, light weight, low price, life-cycle superiority, and satisfactory mechanical properties. However, the inherent features of plant-based natural fibers have presented challenges to the development and application of NFRCs, such as variable fiber quality, limited mechanical properties, water absorption, low thermal stability, incompatibility with hydrophobic matrices, and propensity to agglomeration. Substantial research has recently been conducted to address these challenges for improved performance of NFRCs and their applications. This article reviews the recent advancements of plant-based NFRCs, focusing on strategies and breakthroughs in enhancing the NFRCs’ performance, including fiber modification, fiber hybridization, lignocellulosic fillers incorporation, conventional processing techniques, additive manufacturing (3D printing), and new fiber source exploration. The sustainability of plant-based NFRCs using life-cycle assessment and the burgeoning applications of NFRCs with emphasis on the automotive industry are also discussed.
Max Planck Society1, Woods Hole Research Center2, University of Valencia3, Karlsruhe Institute of Technology4, Wageningen University and Research Centre5, University of Jena6, Universidade Nova de Lisboa7, Université Paris-Saclay8, University of Augsburg9, Commonwealth Scientific and Industrial Research Organisation10, Chiba University11, University of Illinois at Urbana–Champaign12, Nanjing Normal University13, National Center for Atmospheric Research14, University of Exeter15, Tuscia University16, Central Maine Community College17, Ludwig Maximilian University of Munich18, Oak Ridge National Laboratory19
TL;DR: In this paper, the authors provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods and sets of predictor variables were employed.
Abstract: . FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the
atmosphere. Since eddy covariance flux towers have a relatively small
footprint and are distributed unevenly across the world, upscaling the
observations is necessary to obtain global-scale estimates of
biosphere–atmosphere exchange. Based on cross-consistency checks with
atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global
vegetation models (DGVMs), here we provide a systematic assessment of the
latest upscaling efforts for gross primary production (GPP) and net
ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine
learning methods, forcing data sets and sets of predictor variables were
employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM
ensembles ( R2>0.94 at 1 ∘ spatial resolution)
while the majority of DGVMs show, for 70 % of the land surface, values
outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from
FLUXCOM members vary within 106 and 130 PgC yr −1 with the largest
uncertainty in the tropics. Seasonal variations in independent SIF estimates
agree better with FLUXCOM GPP (mean global pixel-wise R2∼0.75 ) than with GPP from DGVMs (mean global pixel-wise
R2∼0.6 ). Seasonal variations in FLUXCOM NEE show good
consistency with atmospheric inversion-based net land carbon fluxes,
particularly for temperate and boreal regions ( R2>0.92 ).
Interannual variability of global NEE in FLUXCOM is underestimated compared
to inversions and DGVMs. The FLUXCOM version which also uses meteorological
inputs shows a strong co-variation in interannual patterns with inversions
( R2=0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger
uptake than inversion and DGVM-based estimates, particularly in the tropics
with discrepancies of up to several hundred grammes of carbon per square metre per year. These
discrepancies can only partly be reconciled by carbon loss pathways that are
implicit in inversions but not captured by the flux tower measurements such
as carbon emissions from fires and water bodies. We hypothesize that a
combination of systematic biases in the underlying eddy covariance data, in
particular in tall tropical forests, and a lack of site history effects on
NEE in FLUXCOM are likely responsible for the too strong tropical carbon
sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for
CO2 fertilization effects, carbon flux trends are not realistic.
Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP
as well as seasonal NEE variations provide useful constraints of global
carbon cycling, while interannual variability patterns from FLUXCOM are
valuable but require cautious interpretation. Exploring the diversity of
Earth observation data and of machine learning concepts along with improved
quality and quantity of flux tower measurements will facilitate further
improvements of the FLUXCOM approach overall.
TL;DR: In this article, the effects of carbon fiber reinforcement on the structure and mechanical properties of 3D printed parts are investigated within the body of literature, and current and potential applications of additively manufactured carbon fiber composites in the context of desktop 3D printing and big area additive manufacturing are discussed.
Abstract: While polymer additive manufacturing (AM) has advanced significantly over the past few decades, the limitations in material properties, speed of manufacture, and part size have relegated this technology to the space of rapid prototyping rather than the legitimate manufacture of end-use parts. Carbon fiber offers a low density, a low coefficient of thermal expansion, and high thermal conductivity and is an ideal material for bringing polymer-based AM from the realm of form and fit to that of form, fit, and function. Use of carbon fiber in AM can improve material properties, reduce the time required to manufacture functional parts compared with traditional subtractive technologies, and reduce warping, thereby enabling a larger possible build envelope. Therefore, the addition of carbon fiber to various AM technologies is of increasing interest in academic and industrial communities. This paper examines the work performed in this fast-growing area to date. Specifically, the effects of fiber reinforcement on the structure and mechanical properties of 3D printed parts are investigated within the body of literature. Upper bounds for tensile properties of carbon fiber composites are theoretically evaluated and compared with experimentally measured values. Moreover, current and potential applications of additively manufactured carbon fiber composites in the context of desktop 3D printing and big area AM are discussed. Recent innovations and industry breakthroughs in this field are also examined. This review is intended to organize and synthesize the present body of work surrounding AM of carbon fiber reinforced plastics, identify the most promising technologies, and prescribe viable research and development paths forward to advance AM from the application space of rapid prototyping to that of functional, load-bearing, end-use parts.
TL;DR: This Review provides an overview of the situations where MOFs undergo destruction due to external stimuli and offers guidelines to avoid unwanted degradation happened to the framework and highlights cases that utilize MOF instability to fabricate varying materials.
Abstract: Metal-organic frameworks (MOFs), constructed from organic linkers and inorganic building blocks, are well-known for their high crystallinity, high surface areas, and high component tunability. The stability of MOFs is a key prerequisite for their potential practical applications in areas including storage, separation, catalysis, and biomedicine since it is essential to guarantee the framework integrity during utilization. However, MOFs are prone to destruction under external stimuli, considerably hampering their commercialization. In this Review, we provide an overview of the situations where MOFs undergo destruction due to external stimuli such as chemical, thermal, photolytic, radiolytic, electronic, and mechanical factors and offer guidelines to avoid unwanted degradation happened to the framework. Furthermore, we discuss possible destruction mechanisms and their varying derived products. In particular, we highlight cases that utilize MOF instability to fabricate varying materials including hierarchically porous MOFs, monolayer MOF nanosheets, amorphous MOF liquids and glasses, polymers, metal nanoparticles, metal carbide nanoparticles, and carbon materials. Finally, we provide a perspective on the utilization of MOF destruction to develop advanced materials with a superior hierarchy for various applications.
TL;DR: The superior proton-exchange membrane fuel cell performance of SnNC cathode catalysts under realistic, hydrogen–air fuel cell conditions, particularly after NH3 activation treatment, makes them a promising alternative to today’s state-of-the-art Fe-based catalysts.
Abstract: This contribution reports the discovery and analysis of a p-block Sn-based catalyst for the electroreduction of molecular oxygen in acidic conditions at fuel cell cathodes; the catalyst is free of platinum-group metals and contains single-metal-atom actives sites coordinated by nitrogen. The prepared SnNC catalysts meet and exceed state-of-the-art FeNC catalysts in terms of intrinsic catalytic turn-over frequency and hydrogen–air fuel cell power density. The SnNC-NH3 catalysts displayed a 40–50% higher current density than FeNC-NH3 at cell voltages below 0.7 V. Additional benefits include a highly favourable selectivity for the four-electron reduction pathway and a Fenton-inactive character of Sn. A range of analytical techniques combined with density functional theory calculations indicate that stannic Sn(iv)Nx single-metal sites with moderate oxygen chemisorption properties and low pyridinic N coordination numbers act as catalytically active moieties. The superior proton-exchange membrane fuel cell performance of SnNC cathode catalysts under realistic, hydrogen–air fuel cell conditions, particularly after NH3 activation treatment, makes them a promising alternative to today’s state-of-the-art Fe-based catalysts. For oxygen reduction and hydrogen oxidation reactions, proton-exchange membrane fuel cells typically rely on precious-metal-based catalysts. A p-block single-metal-site tin/nitrogen-doped carbon is shown to exhibit promising electrocatalytic and fuel cell performance.
TL;DR: The low saturation field and the superlattice nature of MnBi4Te7 make it an ideal system to investigate rich emergent phenomena and an intrinsic natural heterostructural Z2 antiferromagnetic topological insulator with low out-of-plane saturation fields.
Abstract: Magnetic topological insulators (TI) provide an important material platform to explore quantum phenomena such as quantized anomalous Hall effect and Majorana modes, etc. Their successful material realization is thus essential for our fundamental understanding and potential technical revolutions. By realizing a bulk van der Waals material MnBi4Te7 with alternating septuple [MnBi2Te4] and quintuple [Bi2Te3] layers, we show that it is ferromagnetic in plane but antiferromagnetic along the c axis with an out-of-plane saturation field of ~0.22 T at 2 K. Our angle-resolved photoemission spectroscopy measurements and first-principles calculations further demonstrate that MnBi4Te7 is a Z2 antiferromagnetic TI with two types of surface states associated with the [MnBi2Te4] or [Bi2Te3] termination, respectively. Additionally, its superlattice nature may make various heterostructures of [MnBi2Te4] and [Bi2Te3] layers possible by exfoliation. Therefore, the low saturation field and the superlattice nature of MnBi4Te7 make it an ideal system to investigate rich emergent phenomena.
TL;DR: The combination of the intrinsic activity and stability of single Co sites, along with unique catalyst architecture, provide new insight into designing efficient PGM-free electrodes with improved performance and durability.
Abstract: Increasing catalytic activity and durability of atomically dispersed metal-nitrogen-carbon (M-N-C) catalysts for the oxygen reduction reaction (ORR) cathode in proton-exchange-membrane fuel cells remains a grand challenge. Here, a high-power and durable Co-N-C nanofiber catalyst synthesized through electrospinning cobalt-doped zeolitic imidazolate frameworks into selected polyacrylonitrile and poly(vinylpyrrolidone) polymers is reported. The distinct porous fibrous morphology and hierarchical structures play a vital role in boosting electrode performance by exposing more accessible active sites, providing facile electron conductivity, and facilitating the mass transport of reactant. The enhanced intrinsic activity is attributed to the extra graphitic N dopants surrounding the CoN4 moieties. The highly graphitized carbon matrix in the catalyst is beneficial for enhancing the carbon corrosion resistance, thereby promoting catalyst stability. The unique nanoscale X-ray computed tomography verifies the well-distributed ionomer coverage throughout the fibrous carbon network in the catalyst. The membrane electrode assembly achieves a power density of 0.40 W cm-2 in a practical H2 /air cell (1.0 bar) and demonstrates significantly enhanced durability under accelerated stability tests. The combination of the intrinsic activity and stability of single Co sites, along with unique catalyst architecture, provide new insight into designing efficient PGM-free electrodes with improved performance and durability.
TL;DR: This Review summarizes recent progress in exploring the intrinsic magnetism of atomically thin van der Waals materials, manipulation of their magnetism by tuning the interlayer coupling, and device structures for spin- and valleytronic applications.
Abstract: Ultrathin van der Waals materials and their heterostructures offer a simple, yet powerful platform for discovering emergent phenomena and implementing device structures in the two-dimensional limit. The past few years has pushed this frontier to include magnetism. These advances have brought forth a new assortment of layered materials that intrinsically possess a wide variety of magnetic properties and are instrumental in integrating exchange and spin–orbit interactions into van der Waals heterostructures. This Review Article summarizes recent progress in exploring the intrinsic magnetism of atomically thin van der Waals materials, manipulation of their magnetism by tuning the interlayer coupling, and device structures for spin- and valleytronic applications. This Review summarizes recent progress in exploring the intrinsic magnetism of atomically thin van der Waals materials, manipulation of their magnetism by tuning the interlayer coupling, and device structures for spin- and valleytronic applications.