Showing papers by "Oak Ridge National Laboratory published in 2021"

Variational Quantum Algorithms

Abstract: Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers, owing to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will probably 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 parameterized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essentially all applications that researchers have envisaged for quantum computers, and they appear to be 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. The advent of commercial quantum devices has ushered in the era of near-term quantum computing. Variational quantum algorithms are promising candidates to make use of these devices for achieving a practical quantum advantage over classical computers.

Electrochemical ammonia synthesis via nitrate reduction on Fe single atom catalyst.

Abstract: Electrochemically converting nitrate, a widespread water pollutant, back to valuable ammonia is a green and delocalized route for ammonia synthesis, and can be an appealing and supplementary alternative to the Haber-Bosch process. However, as there are other nitrate reduction pathways present, selectively guiding the reaction pathway towards ammonia is currently challenged by the lack of efficient catalysts. Here we report a selective and active nitrate reduction to ammonia on Fe single atom catalyst, with a maximal ammonia Faradaic efficiency of ~ 75% and a yield rate of up to ~ 20,000 μg h−1 mgcat.−1 (0.46 mmol h−1 cm−2). Our Fe single atom catalyst can effectively prevent the N-N coupling step required for N2 due to the lack of neighboring metal sites, promoting ammonia product selectivity. Density functional theory calculations reveal the reaction mechanisms and the potential limiting steps for nitrate reduction on atomically dispersed Fe sites. Developing green and delocalized routes for ammonia synthesis is highly important but still very challenging. Here the authors report an efficient ammonia synthesis process via nitrate reduction to ammonia on Fe single atom catalyst.

Guidelines for performing lignin-first biorefining

Abstract: The valorisation of the plant biopolymer lignin is now recognised as essential to enabling the economic viability of the lignocellulosic biorefining industry. In this context, the “lignin-first” biorefining approach, in which lignin valorisation is considered in the design phase, has demonstrated the fullest utilisation of lignocellulose. We define lignin-first methods as active stabilisation approaches that solubilise lignin from native lignocellulosic biomass while avoiding condensation reactions that lead to more recalcitrant lignin polymers. This active stabilisation can be accomplished by solvolysis and catalytic conversion of reactive intermediates to stable products or by protection-group chemistry of lignin oligomers or reactive monomers. Across the growing body of literature in this field, there are disparate approaches to report and analyse the results from lignin-first approaches, thus making quantitative comparisons between studies challenging. To that end, we present herein a set of guidelines for analysing critical data from lignin-first approaches, including feedstock analysis and process parameters, with the ambition of uniting the lignin-first research community around a common set of reportable metrics. These guidelines comprise standards and best practices or minimum requirements for feedstock analysis, stressing reporting of the fractionation efficiency, product yields, solvent mass balances, catalyst efficiency, and the requirements for additional reagents such as reducing, oxidising, or capping agents. Our goal is to establish best practices for the research community at large primarily to enable direct comparisons between studies from different laboratories. The use of these guidelines will be helpful for the newcomers to this field and pivotal for further progress in this exciting research area.

New roads and challenges for fuel cells in heavy-duty transportation

Abstract: The recent release of hydrogen economy roadmaps for several major countries emphasizes the need for accelerated worldwide investment in research and development activities for hydrogen production, storage, infrastructure and utilization in transportation, industry and the electrical grid. Due to the high gravimetric energy density of hydrogen, the focus of technologies that utilize this fuel has recently shifted from light-duty automotive to heavy-duty vehicle applications. Decades of development of cost-effective and durable polymer electrolyte membrane fuel cells must now be leveraged to meet the increased efficiency and durability requirements of the heavy-duty vehicle market. This Review summarizes the latest market outlooks and targets for truck, bus, locomotive and marine applications. Required changes to the fuel-cell system and operating conditions for meeting Class 8 long-haul truck targets are presented. The necessary improvements in fuel-cell materials and integration are also discussed against the benchmark of current passenger fuel-cell electric vehicles. Fuel cells are increasingly being considered for powertrains of heavy-duty transportation. Cullen et al. survey the technical challenges of fuel cells at both the system and materials level for transportation application and outline the roadmap for future development.

Chemical vapour deposition of Fe-N-C oxygen reduction catalysts with full utilization of dense Fe-N4 sites.

Abstract: Replacing scarce and expensive platinum (Pt) with metal–nitrogen–carbon (M–N–C) catalysts for the oxygen reduction reaction in proton exchange membrane fuel cells has largely been impeded by the low oxygen reduction reaction activity of M–N–C due to low active site density and site utilization. Herein, we overcome these limits by implementing chemical vapour deposition to synthesize Fe–N–C by flowing iron chloride vapour over a Zn–N–C substrate at 750 °C, leading to high-temperature trans-metalation of Zn–N4 sites into Fe–N4 sites. Characterization by multiple techniques shows that all Fe–N4 sites formed via this approach are gas-phase and electrochemically accessible. As a result, the Fe–N–C catalyst has an active site density of 1.92 × 1020 sites per gram with 100% site utilization. This catalyst delivers an unprecedented oxygen reduction reaction activity of 33 mA cm−2 at 0.90 V (iR-corrected; i, current; R, resistance) in a H2–O2 proton exchange membrane fuel cell at 1.0 bar and 80 °C. Replacing platinum with metal–nitrogen–carbon catalysts for the oxygen reduction reaction in proton exchange membrane fuel cells has been impeded by low activity. These limitations have now been overcome by the trans-metalation of Zn–N4 sites into Fe–N4 sites.

ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing

Abstract: Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models taken from NLP. These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The LMs were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw protein LM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks. The first was a per-residue prediction of protein secondary structure (3-state accuracy Q3=81%-87%); the second were per-protein predictions of protein sub-cellular localization (ten-state accuracy: Q10=81%) and membrane vs. water soluble (2-state accuracy Q2=91%). For the per-residue predictions the transfer of the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without using evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that protein LMs learned some of the grammar of the language of life. To facilitate future work, we released our models at https://github.com/agemagician/ProtTrans.

General synthesis of single-atom catalysts with high metal loading using graphene quantum dots.

Abstract: Transition-metal single-atom catalysts present extraordinary activity per metal atomic site, but suffer from low metal-atom densities (typically less than 5 wt% or 1 at.%), which limits their overall catalytic performance. Here we report a general method for the synthesis of single-atom catalysts with high transition-metal-atom loadings of up to 40 wt% or 3.8 at.%, representing several-fold improvements compared to benchmarks in the literature. Graphene quantum dots, later interweaved into a carbon matrix, were used as a support, providing numerous anchoring sites and thus facilitating the generation of high densities of transition-metal atoms with sufficient spacing between the metal atoms to avoid aggregation. A significant increase in activity in electrochemical CO2 reduction (used as a representative reaction) was demonstrated on a Ni single-atom catalyst with increased Ni loading. Transition-metal single-atom catalysts display excellent activity per metal atom site, but suffer from low metal atom densities (typically less than 5 wt% or 1 at.%), which limits their overall catalytic performance. Now, the use of a graphene-quantum-dot primary support, later interweaved into a carbon matrix, has enabled the synthesis of single-atom catalysts with high transition-metal atom loadings of up to 40 wt% or 3.84 at.%.

Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2

Abstract: Atmospheric carbon dioxide concentration ([CO2 ]) is increasing, which increases leaf-scale photosynthesis and intrinsic water-use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO2 ] increase and thus climate change. However, ecosystem CO2 responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO2 ]-driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO2 ] (iCO2 ) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre-industrial times. Established theory, supported by experiments, indicates that iCO2 is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO2 responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO2 responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO2 , albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.

Gradient-cell–structured high-entropy alloy with exceptional strength and ductility

Abstract: Most multicomponent high-entropy alloys (HEAs) lose ductility with increasing strength, similar to conventional materials. We controllably introduced novel gradient nano-scaled dislocation-cell str...

Root traits as drivers of plant and ecosystem functioning: current understanding, pitfalls and future research needs

Abstract: The effects of plants on the biosphere, atmosphere and geosphere are key determinants of terrestrial ecosystem functioning. However, despite substantial progress made regarding plant belowground components, we are still only beginning to explore the complex relationships between root traits and functions. Drawing on the literature in plant physiology, ecophysiology, ecology, agronomy and soil science, we reviewed 24 aspects of plant and ecosystem functioning and their relationships with a number of root system traits, including aspects of architecture, physiology, morphology, anatomy, chemistry, biomechanics and biotic interactions. Based on this assessment, we critically evaluated the current strengths and gaps in our knowledge, and identify future research challenges in the field of root ecology. Most importantly, we found that belowground traits with the broadest importance in plant and ecosystem functioning are not those most commonly measured. Also, the estimation of trait relative importance for functioning requires us to consider a more comprehensive range of functionally relevant traits from a diverse range of species, across environments and over time series. We also advocate that establishing causal hierarchical links among root traits will provide a hypothesis-based framework to identify the most parsimonious sets of traits with the strongest links on functions, and to link genotypes to plant and ecosystem functioning.

A trade-off between plant and soil carbon storage under elevated CO2.

Abstract: Terrestrial ecosystems remove about 30 per cent of the carbon dioxide (CO2) emitted by human activities each year1, yet the persistence of this carbon sink depends partly on how plant biomass and soil organic carbon (SOC) stocks respond to future increases in atmospheric CO2 (refs. 2,3). Although plant biomass often increases in elevated CO2 (eCO2) experiments4–6, SOC has been observed to increase, remain unchanged or even decline7. The mechanisms that drive this variation across experiments remain poorly understood, creating uncertainty in climate projections8,9. Here we synthesized data from 108 eCO2 experiments and found that the effect of eCO2 on SOC stocks is best explained by a negative relationship with plant biomass: when plant biomass is strongly stimulated by eCO2, SOC storage declines; conversely, when biomass is weakly stimulated, SOC storage increases. This trade-off appears to be related to plant nutrient acquisition, in which plants increase their biomass by mining the soil for nutrients, which decreases SOC storage. We found that, overall, SOC stocks increase with eCO2 in grasslands (8 ± 2 per cent) but not in forests (0 ± 2 per cent), even though plant biomass in grasslands increase less (9 ± 3 per cent) than in forests (23 ± 2 per cent). Ecosystem models do not reproduce this trade-off, which implies that projections of SOC may need to be revised. A synthesis of elevated carbon dioxide experiments reveals that when plant biomass is strongly stimulated by elevated carbon dioxide levels, soil carbon storage declines, and where biomass is weakly stimulated, soil carbon accumulates.

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Abstract: . The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the primary future climate projections within the Coupled Model Intercomparison Project Phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century encompassing the Tier 1 experiments (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by 1.15 °C) reached at the upper end of the 5–95 % envelope of the highest scenario, SSP5-8.5. This is due to both the wider range of radiative forcing that the new scenarios cover and to higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensembles' spread, according to a set of initial condition ensemble simulations available under SSP3-7.0. The same experiments suggest a tendency for internal variability to decrease along the course of the century, a new result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP, but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades in mid-century, as represented in SSP5-3.4OS, does not affect the end outcome in terms of temperature and precipitation changes by 2100, which return to the same level as those reached by the gradually increasing SSP4-3.4. Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level show all scenarios reaching 1.5 °C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering 20–28 years from present. 2 °C of warming is reached as early as the late '30s by the ensemble mean under SSP5-8.5, but as late as the late '50s under SSP1-2.6. The highest warming level considered, 5 °C, is reached only by the ensemble mean under SSP5-8.5, and not until the mid-90s.

High-entropy materials for catalysis: A new frontier

Abstract: Entropy plays a pivotal role in catalysis, and extensive research efforts have been directed to understanding the enthalpy-entropy relationship that defines the reaction pathways of molecular species. On the other side, surface of the catalysts, entropic effects have been rarely investigated because of the difficulty in deciphering the increased complexities in multicomponent systems. Recent advances in high-entropy materials (HEMs) have triggered broad interests in exploring entropy-stabilized systems for catalysis, where the enhanced configurational entropy affords a virtually unlimited scope for tailoring the structures and properties of HEMs. In this review, we summarize recent progress in the discovery and design of HEMs for catalysis. The correlation between compositional and structural engineering and optimization of the catalytic behaviors is highlighted for high-entropy alloys, oxides, and beyond. Tuning composition and configuration of HEMs introduces untapped opportunities for accessing better catalysts and resolving issues that are considered challenging in conventional, simple systems.

Challenges for and Pathways toward Li-Metal-Based All-Solid-State Batteries

Abstract: Author(s): Albertus, P; Anandan, V; Ban, C; Balsara, N; Belharouak, I; Buettner-Garrett, J; Chen, Z; Daniel, C; Doeff, M; Dudney, NJ; Dunn, B; Harris, SJ; Herle, S; Herbert, E; Kalnaus, S; Libera, JA; Lu, D; Martin, S; McCloskey, BD; McDowell, MT; Meng, YS; Nanda, J; Sakamoto, J; Self, EC; Tepavcevic, S; Wachsman, E; Wang, C; Westover, AS; Xiao, J; Yersak, T

From Materials to Cell: State-of-the-Art and Prospective Technologies for Lithium-Ion Battery Electrode Processing.

Abstract: Electrode processing plays an important role in advancing lithium-ion battery technologies and has a significant impact on cell energy density, manufacturing cost, and throughput. Compared to the extensive research on materials development, however, there has been much less effort in this area. In this Review, we outline each step in the electrode processing of lithium-ion batteries from materials to cell assembly, summarize the recent progress in individual steps, deconvolute the interplays between those steps, discuss the underlying constraints, and share some prospective technologies. This Review aims to provide an overview of the whole process in lithium-ion battery fabrication from powder to cell formation and bridge the gap between academic development and industrial manufacturing.

Constraints on Dark Matter Properties from Observations of Milky Way Satellite Galaxies

Abstract: We perform a comprehensive study of Milky Way (MW) satellite galaxies to constrain the fundamental properties of dark matter (DM). This analysis fully incorporates inhomogeneities in the spatial distribution and detectability of MW satellites and marginalizes over uncertainties in the mapping between galaxies and DM halos, the properties of the MW system, and the disruption of subhalos by the MW disk. Our results are consistent with the cold, collisionless DM paradigm and yield the strongest cosmological constraints to date on particle models of warm, interacting, and fuzzy dark matter. At 95% confidence, we report limits on (i) the mass of thermal relic warm DM, mWDM>6.5 keV (free-streaming length, λfs≲10h-1 kpc), (ii) the velocity-independent DM-proton scattering cross section, σ0 2.9×10-21 eV (de Broglie wavelength, λdB≲0.5 kpc). These constraints are complementary to other observational and laboratory constraints on DM properties.

Electric Drive Technology Trends, Challenges, and Opportunities for Future Electric Vehicles

Abstract: The transition to electric road transport technologies requires electric traction drive systems to offer improved performances and capabilities, such as fuel efficiency (in terms of MPGe, i.e., miles per gallon of gasoline-equivalent), extended range, and fast-charging options. The enhanced electrification and transformed mobility are translating to a demand for higher power and more efficient electric traction drive systems that lead to better fuel economy for a given battery charge. To accelerate the mass-market adoption of electrified transportation, the U.S. Department of Energy (DOE), in collaboration with the automotive industry, has announced the technical targets for light-duty electric vehicles (EVs) for 2025. This article discusses the electric drive technology trends for passenger electric and hybrid EVs with commercially available solutions in terms of materials, electric machine and inverter designs, maximum speed, component cooling, power density, and performance. The emerging materials and technologies for power electronics and electric motors are presented, identifying the challenges and opportunities for even more aggressive designs to meet the need for next-generation EVs. Some innovative drive and motor designs with the potential to meet the DOE 2025 targets are also discussed.

ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning

Abstract: Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models taken from NLP. These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The LMs were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw protein LM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks. The first was a per-residue prediction of protein secondary structure (3-state accuracy Q3=81%-87%); the second were per-protein predictions of protein sub-cellular localization (ten-state accuracy: Q10=81%) and membrane vs. water-soluble (2-state accuracy Q2=91%). For the per-residue predictions the transfer of the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without using evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that protein LMs learned some of the grammar of the language of life. To facilitate future work, we released our models at https://github.com/agemagician/ProtTrans.

Correlated insulating states at fractional fillings of the WS2/WSe2 moiré lattice

Abstract: The strong electron interactions in the minibands formed in moire superlattices of van der Waals materials, such as twisted graphene and transition metal dichalcogenides, make such systems a fascinating platform with which to study strongly correlated states1–19. In most systems, the correlated states appear when the moire lattice is filled by an integer number of electrons per moire unit cell. Recently, correlated states at fractional fillings of 1/3 and 2/3 holes per moire unit cell have been reported in the WS2/WSe2 hetero-bilayer, hinting at the long-range nature of the electron interaction16. Here we observe a series of correlated insulating states at fractional fillings of the moire minibands on both electron- and hole-doped sides in angle-aligned WS2/WSe2 hetero-bilayers, with certain states persisting at temperatures up to 120 K. Simulations reveal that these insulating states correspond to ordering of electrons in the moire lattice with a periodicity much larger than the moire unit cell, indicating a surprisingly strong and long-range interaction beyond the nearest neighbours. Twisted bilayers of WS2 and WSe2 have correlated states that correspond to real-space ordering of the electrons on a length scale much longer than the moire pattern.

A starting guide to root ecology: strengthening ecological concepts and standardising root classification, sampling, processing and trait measurements

Abstract: In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting-edge, meaningful and integrated knowledge. Consideration of the below-ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below-ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below-ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine-root vs coarse-root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I-VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers' views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning.

Early initiation of prophylactic anticoagulation for prevention of coronavirus disease 2019 mortality in patients admitted to hospital in the United States: cohort study.

Abstract: OBJECTIVE: To evaluate whether early initiation of prophylactic anticoagulation compared with no anticoagulation was associated with decreased risk of death among patients admitted to hospital with coronavirus disease 2019 (covid-19) in the United States DESIGN: Observational cohort study SETTING: Nationwide cohort of patients receiving care in the Department of Veterans Affairs, a large integrated national healthcare system PARTICIPANTS: All 4297 patients admitted to hospital from 1 March to 31 July 2020 with laboratory confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and without a history of anticoagulation MAIN OUTCOME MEASURES: The main outcome was 30 day mortality Secondary outcomes were inpatient mortality, initiating therapeutic anticoagulation (a proxy for clinical deterioration, including thromboembolic events), and bleeding that required transfusion RESULTS: Of 4297 patients admitted to hospital with covid-19, 3627 (844%) received prophylactic anticoagulation within 24 hours of admission More than 99% (n=3600) of treated patients received subcutaneous heparin or enoxaparin 622 deaths occurred within 30 days of hospital admission, 513 among those who received prophylactic anticoagulation Most deaths (510/622, 82%) occurred during hospital stay Using inverse probability of treatment weighted analyses, the cumulative incidence of mortality at 30 days was 143% (95% confidence interval 131% to 155%) among those who received prophylactic anticoagulation and 187% (151% to 229%) among those who did not Compared with patients who did not receive prophylactic anticoagulation, those who did had a 27% decreased risk for 30 day mortality (hazard ratio 073, 95% confidence interval 066 to 081) Similar associations were found for inpatient mortality and initiation of therapeutic anticoagulation Receipt of prophylactic anticoagulation was not associated with increased risk of bleeding that required transfusion (hazard ratio 087, 071 to 105) Quantitative bias analysis showed that results were robust to unmeasured confounding (e-value lower 95% confidence interval 177 for 30 day mortality) Results persisted in several sensitivity analyses CONCLUSIONS: Early initiation of prophylactic anticoagulation compared with no anticoagulation among patients admitted to hospital with covid-19 was associated with a decreased risk of 30 day mortality and no increased risk of serious bleeding events These findings provide strong real world evidence to support guidelines recommending the use of prophylactic anticoagulation as initial treatment for patients with covid-19 on hospital admission

Local electronic structure variation resulting in Li 'filament' formation within solid electrolytes.

Abstract: Solid electrolytes hold great promise for enabling the use of Li metal anodes. The main problem is that during cycling, Li can infiltrate along grain boundaries and cause short circuits, resulting in potentially catastrophic battery failure. At present, this phenomenon is not well understood. Here, through electron microscopy measurements on a representative system, Li7La3Zr2O12, we discover that Li infiltration in solid oxide electrolytes is strongly associated with local electronic band structure. About half of the Li7La3Zr2O12 grain boundaries were found to have a reduced bandgap, around 1–3 eV, making them potential channels for leakage current. Instead of combining with electrons at the cathode, Li+ ions are hence prematurely reduced by electrons at grain boundaries, forming local Li filaments. The eventual interconnection of these filaments results in a short circuit. Our discovery reveals that the grain-boundary electronic conductivity must be a primary concern for optimization in future solid-state battery design. Solid electrolytes are promising for enabling the use of Li metal anodes but Li infiltration along grain boundaries can lead to battery failure. Li infiltration in a model solid oxide electrolyte is now found to be strongly associated with local electronic band structure.

Towards data-driven next-generation transmission electron microscopy.

Abstract: Electron microscopy touches on nearly every aspect of modern life, underpinning materials development for quantum computing, energy and medicine. We discuss the open, highly integrated and data-driven microscopy architecture needed to realize transformative discoveries in the coming decade.

Interfacial Chemistry Enables Stable Cycling of All-Solid-State Li Metal Batteries at High Current Densities.

Abstract: The application of flexible, robust, and low-cost solid polymer electrolytes in next-generation all-solid-state lithium metal batteries has been hindered by the low room-temperature ionic conductivity of these electrolytes and the small critical current density of the batteries. Both issues stem from the low mobility of Li+ ions in the polymer and the fast lithium dendrite growth at the Li metal/electrolyte interface. Herein, Mg(ClO4)2 is demonstrated to be an effective additive in the poly(ethylene oxide) (PEO)-based composite electrolyte to regulate Li+ ion transport and manipulate the Li metal/electrolyte interfacial performance. By combining experimental and computational studies, we show that Mg2+ ions are immobile in a PEO host due to coordination with ether oxygen and anions of lithium salts, which enhances the mobility of Li+ ions; more importantly, an in-situ formed Li+-conducting Li2MgCl4/LiF interfacial layer homogenizes the Li+ flux during plating and increases the critical current density up to a record 2 mA cm-2. Each of these factors contributes to the assembly of competitive all-solid-state Li/Li, LiFePO4/Li, and LiNi0.8Mn0.1Co0.1O2/Li cells, demonstrating the importance of surface chemistry and interfacial engineering in the design of all-solid-state Li metal batteries for high-current-density applications.

Measurement of the anomalous precession frequency of the muon in the Fermilab Muon g-2 experiment

Abstract: The Muon g-2 Experiment at Fermi National Accelerator Laboratory (FNAL) has measured the muon anomalous precession frequency $\omega_a$ to an uncertainty of 434 parts per billion (ppb), statistical, and 56 ppb, systematic, with data collected in four storage ring configurations during its first physics run in 2018. When combined with a precision measurement of the magnetic field of the experiment's muon storage ring, the precession frequency measurement determines a muon magnetic anomaly of $a_{\mu}({\rm FNAL}) = 116\,592\,040(54) \times 10^{-11}$ (0.46 ppm). This article describes the multiple techniques employed in the reconstruction, analysis and fitting of the data to measure the precession frequency. It also presents the averaging of the results from the eleven separate determinations of \omega_a, and the systematic uncertainties on the result.