Showing papers by "Purdue University published in 2021"

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Deep learning for the design of photonic structures

Abstract: Innovative approaches and tools play an important role in shaping design, characterization and optimization for the field of photonics. As a subset of machine learning that learns multilevel abstraction of data using hierarchically structured layers, deep learning offers an efficient means to design photonic structures, spawning data-driven approaches complementary to conventional physics- and rule-based methods. Here, we review recent progress in deep-learning-based photonic design by providing the historical background, algorithm fundamentals and key applications, with the emphasis on various model architectures for specific photonic tasks. We also comment on the challenges and perspectives of this emerging research direction. The application of deep learning to the design of photonic structures and devices is reviewed, including algorithm fundamentals.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Abstract: U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package

Abstract: This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.

A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing

Abstract: The Normalized Difference Vegetation Index (NDVI), one of the earliest remote sensing analytical products used to simplify the complexities of multi-spectral imagery, is now the most popular index used for vegetation assessment. This popularity and widespread use relate to how an NDVI can be calculated with any multispectral sensor with a visible and a near-IR band. Increasingly low costs and weights of multispectral sensors mean they can be mounted on satellite, aerial, and increasingly—Unmanned Aerial Systems (UAS). While studies have found that the NDVI is effective for expressing vegetation status and quantified vegetation attributes, its widespread use and popularity, especially in UAS applications, carry inherent risks of misuse with end users who received little to no remote sensing education. This article summarizes the progress of NDVI acquisition, highlights the areas of NDVI application, and addresses the critical problems and considerations in using NDVI. Detailed discussion mainly covers three aspects: atmospheric effect, saturation phenomenon, and sensor factors. The use of NDVI can be highly effective as long as its limitations and capabilities are understood. This consideration is particularly important to the UAS user community.

Engineering single-atomic ruthenium catalytic sites on defective nickel-iron layered double hydroxide for overall water splitting.

Abstract: Rational design of single atom catalyst is critical for efficient sustainable energy conversion. However, the atomic-level control of active sites is essential for electrocatalytic materials in alkaline electrolyte. Moreover, well-defined surface structures lead to in-depth understanding of catalytic mechanisms. Herein, we report a single-atomic-site ruthenium stabilized on defective nickel-iron layered double hydroxide nanosheets (Ru1/D-NiFe LDH). Under precise regulation of local coordination environments of catalytically active sites and the existence of the defects, Ru1/D-NiFe LDH delivers an ultralow overpotential of 18 mV at 10 mA cm−2 for hydrogen evolution reaction, surpassing the commercial Pt/C catalyst. Density functional theory calculations reveal that Ru1/D-NiFe LDH optimizes the adsorption energies of intermediates for hydrogen evolution reaction and promotes the O–O coupling at a Ru–O active site for oxygen evolution reaction. The Ru1/D-NiFe LDH as an ideal model reveals superior water splitting performance with potential for the development of promising water-alkali electrocatalysts. Rational design of single atom catalyst is critical for efficient sustainable energy conversion. Single-atomic-site ruthenium stabilized on defective nickel-iron layered double hydroxide nanosheets achieve superior HER and OER performance in alkaline media.

Pandemics: Implications for research and practice in industrial and organizational psychology

Abstract: Pandemics have historically shaped the world of work in various ways. With COVID-19 presenting as a global pandemic, there is much speculation about the implications of this crisis for the future of work and for people working in organizations. In this article, we discuss 10 of the most relevant research and practice topics in the field of industrial and organizational psychology that will likely be strongly influenced by COVID-19. For each of these topics, the pandemic crisis is creating new work-related challenges, but it is also presenting various opportunities. The topics discussed herein include occupational health and safety, work–family issues, telecommuting, virtual teamwork, job insecurity, precarious work, leadership, human resources policy, the aging workforce, and careers. This article sets the stage for further discussion of various ways in which I-O psychology research and practice can address the issues that COVID-19 creates for work and organizational processes that are affecting workers now and will shape the future of work and organizations in both the short and long term. This article concludes by inviting I-O psychology researchers and practitioners to address the challenges and opportunities of COVID-19 head-on by proactively adapting the work that we do in support of workers, organizations, and society as a whole.

THE CONCISE GUIDE TO PHARMACOLOGY 2021/22: Enzymes.

Abstract: The Concise Guide to PHARMACOLOGY 2021/22 is the fifth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of nearly 1900 human drug targets with an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (www.guidetopharmacology.org), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes over 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point-in-time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/bph.15542. Enzymes are one of the six major pharmacological targets into which the Guide is divided, with the others being: G protein-coupled receptors, ion channels, nuclear hormone receptors, catalytic receptors and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid-2021, and supersedes data presented in the 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate.

Prevalence of Clinical and Subclinical Myocarditis in Competitive Athletes With Recent SARS-CoV-2 Infection: Results From the Big Ten COVID-19 Cardiac Registry.

Abstract: Importance Myocarditis is a leading cause of sudden death in competitive athletes Myocardial inflammation is known to occur with SARS-CoV-2 Different screening approaches for detection of myocarditis have been reported The Big Ten Conference requires comprehensive cardiac testing including cardiac magnetic resonance (CMR) imaging for all athletes with COVID-19, allowing comparison of screening approaches Objective To determine the prevalence of myocarditis in athletes with COVID-19 and compare screening strategies for safe return to play Design, Setting, and Participants Big Ten COVID-19 Cardiac Registry principal investigators were surveyed for aggregate observational data from March 1, 2020, through December 15, 2020, on athletes with COVID-19 For athletes with myocarditis, presence of cardiac symptoms and details of cardiac testing were recorded Myocarditis was categorized as clinical or subclinical based on the presence of cardiac symptoms and CMR findings Subclinical myocarditis classified as probable or possible myocarditis based on other testing abnormalities Myocarditis prevalence across universities was determined The utility of different screening strategies was evaluated Exposures SARS-CoV-2 by polymerase chain reaction testing Main Outcome and Measure Myocarditis via cardiovascular diagnostic testing Results Representing 13 universities, cardiovascular testing was performed in 1597 athletes (964 men [604%]) Thirty-seven (including 27 men) were diagnosed with COVID-19 myocarditis (overall 23%; range per program, 0%-76%); 9 had clinical myocarditis and 28 had subclinical myocarditis If cardiac testing was based on cardiac symptoms alone, only 5 athletes would have been detected (detected prevalence, 031%) Cardiac magnetic resonance imaging for all athletes yielded a 74-fold increase in detection of myocarditis (clinical and subclinical) Follow-up CMR imaging performed in 27 (730%) demonstrated resolution of T2 elevation in all (100%) and late gadolinium enhancement in 11 (407%) Conclusions and Relevance In this cohort study of 1597 US competitive athletes with CMR screening after COVID-19 infection, 37 athletes (23%) were diagnosed with clinical and subclinical myocarditis Variability was observed in prevalence across universities, and testing protocols were closely tied to the detection of myocarditis Variable ascertainment and unknown implications of CMR findings underscore the need for standardized timing and interpretation of cardiac testing These unique CMR imaging data provide a more complete understanding of the prevalence of clinical and subclinical myocarditis in college athletes after COVID-19 infection The role of CMR in routine screening for athletes safe return to play should be explored further

X-ray computed tomography

Abstract: X-ray computed tomography (CT) can reveal the internal details of objects in three dimensions non-destructively. In this Primer, we outline the basic principles of CT and describe the ways in which a CT scan can be acquired using X-ray tubes and synchrotron sources, including the different possible contrast modes that can be exploited. We explain the process of computationally reconstructing three-dimensional (3D) images from 2D radiographs and how to segment the 3D images for subsequent visualization and quantification. Whereas CT is widely used in medical and heavy industrial contexts at relatively low resolutions, here we focus on the application of higher resolution X-ray CT across science and engineering. We consider the application of X-ray CT to study subjects across the materials, metrology and manufacturing, engineering, food, biological, geological and palaeontological sciences. We examine how CT can be used to follow the structural evolution of materials in three dimensions in real time or in a time-lapse manner, for example to follow materials manufacturing or the in-service behaviour and degradation of manufactured components. Finally, we consider the potential for radiation damage and common sources of imaging artefacts, discuss reproducibility issues and consider future advances and opportunities. This Primer on X-ray computed tomography explores the different experimental configurations for three-dimensional data acquisition as well as the fundamentals of three-dimensional data reconstruction, segmentation and analysis with examples across the physical and life sciences.

Neurotoxic reactive astrocytes induce cell death via saturated lipids

Abstract: Astrocytes regulate the response of the central nervous system to disease and injury and have been hypothesized to actively kill neurons in neurodegenerative disease1–6. Here we report an approach to isolate one component of the long-sought astrocyte-derived toxic factor5,6. Notably, instead of a protein, saturated lipids contained in APOE and APOJ lipoparticles mediate astrocyte-induced toxicity. Eliminating the formation of long-chain saturated lipids by astrocyte-specific knockout of the saturated lipid synthesis enzyme ELOVL1 mitigates astrocyte-mediated toxicity in vitro as well as in a model of acute axonal injury in vivo. These results suggest a mechanism by which astrocytes kill cells in the central nervous system. Astrocytes can respond to diseases and injuries of the central nervous system by driving the death of neurons and mature oligodendrocytes through the delivery of long-chain saturated fatty acids contained in lipoparticles.

Development of Quantum Interconnects (QuICs) for Next-Generation Information Technologies

Abstract: Just as classical information technology rests on a foundation built of interconnected information-processing systems, quantum information technology (QIT) must do the same. A critical component of such systems is the interconnect, a device or process that allows transfer of information between disparate physical media, for example, semiconductor electronics, individual atoms, light pulses in optical fiber, or microwave fields. While interconnects have been well engineered for decades in the realm of classical information technology, quantum interconnects (QuICs) present special challenges, as they must allow the transfer of fragile quantum states between different physical parts or degrees of freedom of the system. The diversity of QIT platforms (superconducting, atomic, solid-state color center, optical, etc.) that will form a quantum internet poses additional challenges. As quantum systems scale to larger size, the quantum interconnect bottleneck is imminent, and is emerging as a grand challenge for QIT. For these reasons, it is the position of the community represented by participants of the NSF workshop on Quantum Interconnects that accelerating QuIC research is crucial for sustained development of a national quantum science and technology program. Given the diversity of QIT platforms, materials used, applications, and infrastructure required, a convergent research program including partnership between academia, industry and national laboratories is required.

Transistors based on two-dimensional materials for future integrated circuits

Abstract: Field-effect transistors based on two-dimensional (2D) materials have the potential to be used in very large-scale integration (VLSI) technology, but whether they can be used at the front end of line or at the back end of line through monolithic or heterogeneous integration remains to be determined. To achieve this, multiple challenges must be overcome, including reducing the contact resistance, developing stable and controllable doping schemes, advancing mobility engineering and improving high-κ dielectric integration. The large-area growth of uniform 2D layers is also required to ensure low defect density, low device-to-device variation and clean interfaces. Here we review the development of 2D field-effect transistors for use in future VLSI technologies. We consider the key performance indicators for aggressively scaled 2D transistors and discuss how these should be extracted and reported. We also highlight potential applications of 2D transistors in conventional micro/nanoelectronics, neuromorphic computing, advanced sensing, data storage and future interconnect technologies. This Review examines the development of field-effect transistors based on two-dimensional materials and considers the challenges that need to be addressed for the devices to be incorporated into very large-scale integration (VLSI) technology.

Association of Intensive Care Unit Patient Load and Demand With Mortality Rates in US Department of Veterans Affairs Hospitals During the COVID-19 Pandemic.

Abstract: Importance Although strain on hospital capacity has been associated with increased mortality in nonpandemic settings, studies are needed to examine the association between coronavirus disease 2019 (COVID-19) critical care capacity and mortality. Objective To examine whether COVID-19 mortality was associated with COVID-19 intensive care unit (ICU) strain. Design, Setting, and Participants This cohort study was conducted among veterans with COVID-19, as confirmed by polymerase chain reaction or antigen testing in the laboratory from March through August 2020, cared for at any Department of Veterans Affairs (VA) hospital with 10 or more patients with COVID-19 in the ICU. The follow-up period was through November 2020. Data were analyzed from March to November 2020. Exposures Receiving treatment for COVID-19 in the ICU during a period of increased COVID-19 ICU load, with load defined as mean number of patients with COVID-19 in the ICU during the patient’s hospital stay divided by the number of ICU beds at that facility, or increased COVID-19 ICU demand, with demand defined as mean number of patients with COVID-19 in the ICU during the patient’s stay divided by the maximum number of patients with COVID-19 in the ICU. Main Outcomes and Measures All-cause mortality was recorded through 30 days after discharge from the hospital. Results Among 8516 patients with COVID-19 admitted to 88 VA hospitals, 8014 (94.1%) were men and mean (SD) age was 67.9 (14.2) years. Mortality varied over time, with 218 of 954 patients (22.9%) dying in March, 399 of 1594 patients (25.0%) dying in April, 143 of 920 patients (15.5%) dying in May, 179 of 1314 patients (13.6%) dying in June, 297 of 2373 patients (12.5%) dying in July, and 174 of 1361 (12.8%) patients dying in August (P Conclusions and Relevance This cohort study found that although facilities augmented ICU capacity during the pandemic, strains on critical care capacity were associated with increased COVID-19 ICU mortality. Tracking COVID-19 ICU demand may be useful to hospital administrators and health officials as they coordinate COVID-19 admissions across hospitals to optimize outcomes for patients with this illness.

Multiscale modeling meets machine learning: What can we learn?

Abstract: Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

The Miocene: The Future of the Past

Abstract: The Miocene epoch (23.03–5.33 Ma) was a time interval of global warmth, relative to today. Continental configurations and mountain topography transitioned towards modern conditions, and many flora and fauna evolved into the same taxa that exist today. Miocene climate was dynamic: long periods of early and late glaciation bracketed a ∼2 Myr greenhouse interval – the Miocene Climatic Optimum (MCO). Floras, faunas, ice sheets, precipitation, pCO2, and ocean and atmospheric circulation mostly (but not ubiquitously) covaried with these large changes in climate. With higher temperatures and moderately higher pCO2 (∼400–600 ppm), the MCO has been suggested as a particularly appropriate analogue for future climate scenarios, and for assessing the predictive accuracy of numerical climate models – the same models that are used to simulate future climate. Yet, Miocene conditions have proved difficult to reconcile with models. This implies either missing positive feedbacks in the models, a lack of knowledge of past climate forcings, or the need for re‐interpretation of proxies, which might mitigate the model‐data discrepancy. Our understanding of Miocene climatic, biogeochemical, and oceanic changes on broad spatial and temporal scales is still developing. New records documenting the physical, chemical, and biotic aspects of the Earth system are emerging, and together provide a more comprehensive understanding of this important time interval. Here we review the state‐of‐the‐art in Miocene climate, ocean circulation, biogeochemical cycling, ice sheet dynamics, and biotic adaptation research as inferred through proxy observations and modelling studies.

Food consumption behavior during the COVID-19 pandemic.

Abstract: We conducted an online consumer survey in May 2020 in two major metropolitan areas in the United States to investigate food shopping behaviors and consumption during the pandemic lockdown caused by COVID-19. The results of this study parallel many of the headlines in the popular press at the time. We found that about three-quarters of respondents were simply buying the food they could get due to out of stock situations and about half the participants bought more food than usual. As a result of foodservice closures, consumers indicated purchasing more groceries than normal. Consumers attempted to avoid shopping in stores, relying heavily on grocery delivery and pick-up services during the beginning of the pandemic when no clear rules were in place. Results show a 255% increase in the number of households that use grocery pickup as a shopping method and a 158% increase in households that utilize grocery delivery services. The spike in pickup and delivery program participation can be explained by consumers fearing COVID-19 and feeling unsafe. Food consumption patterns for major food groups seemed to stay the same for the majority of participants, but a large share indicated that they had been snacking more since the beginning of the pandemic which was offset by a sharp decline in fast food consumption.

Quantum Simulators: Architectures and Opportunities

Abstract: Quantum simulators are a promising technology on the spectrum of quantum devices from specialized quantum experiments to universal quantum computers. These quantum devices utilize entanglement and many-particle behaviors to explore and solve hard scientific, engineering, and computational problems. Rapid development over the last two decades has produced more than 300 quantum simulators in operation worldwide using a wide variety of experimental platforms. Recent advances in several physical architectures promise a golden age of quantum simulators ranging from highly optimized special purpose simulators to flexible programmable devices. These developments have enabled a convergence of ideas drawn from fundamental physics, computer science, and device engineering. They have strong potential to address problems of societal importance, ranging from understanding vital chemical processes, to enabling the design of new materials with enhanced performance, to solving complex computational problems. It is the position of the community, as represented by participants of the NSF workshop on "Programmable Quantum Simulators," that investment in a national quantum simulator program is a high priority in order to accelerate the progress in this field and to result in the first practical applications of quantum machines. Such a program should address two areas of emphasis: (1) support for creating quantum simulator prototypes usable by the broader scientific community, complementary to the present universal quantum computer effort in industry; and (2) support for fundamental research carried out by a blend of multi-investigator, multi-disciplinary collaborations with resources for quantum simulator software, hardware, and education.

Ultrawhite BaSO4 Paints and Films for Remarkable Daytime Subambient Radiative Cooling.

Abstract: Radiative cooling is a passive cooling technology that offers great promises to reduce space cooling cost, combat the urban island effect, and alleviate the global warming. To achieve passive daytime radiative cooling, current state-of-the-art solutions often utilize complicated multilayer structures or a reflective metal layer, limiting their applications in many fields. Attempts have been made to achieve passive daytime radiative cooling with single-layer paints, but they often require a thick coating or show partial daytime cooling. In this work, we experimentally demonstrate remarkable full-daytime subambient cooling performance with both BaSO4 nanoparticle films and BaSO4 nanocomposite paints. BaSO4 has a high electron band gap for low solar absorptance and phonon resonance at 9 μm for high sky window emissivity. With an appropriate particle size and a broad particle size distribution, the BaSO4 nanoparticle film reaches an ultrahigh solar reflectance of 97.6% and a high sky window emissivity of 0.96. During field tests, the BaSO4 film stays more than 4.5 °C below ambient temperature or achieves an average cooling power of 117 W/m2. The BaSO4-acrylic paint is developed with a 60% volume concentration to enhance the reliability in outdoor applications, achieving a solar reflectance of 98.1% and a sky window emissivity of 0.95. Field tests indicate similar cooling performance to the BaSO4 films. Overall, our BaSO4-acrylic paint shows a standard figure of merit of 0.77, which is among the highest of radiative cooling solutions while providing great reliability, convenient paint form, ease of use, and compatibility with the commercial paint fabrication process.

Roadmap of Spin–Orbit Torques

Abstract: Spin–orbit torque (SOT) is an emerging technology that enables the efficient manipulation of spintronic devices. The initial processes of interest in SOTs involved electric fields, spin–orbit coupling, conduction electron spins, and magnetization. More recently, interest has grown to include a variety of other processes that include phonons, magnons, or heat. Over the past decade, many materials have been explored to achieve a larger SOT efficiency. Recently, holistic design to maximize the performance of SOT devices has extended material research from a nonmagnetic layer to a magnetic layer. The rapid development of SOT has spurred a variety of SOT-based applications. In this article, we first review the theories of SOTs by introducing the various mechanisms thought to generate or control SOTs, such as the spin Hall effect, the Rashba-Edelstein effect, the orbital Hall effect, thermal gradients, magnons, and strain effects. Then, we discuss the materials that enable these effects, including metals, metallic alloys, topological insulators, 2-D materials, and complex oxides. We also discuss the important roles in SOT devices of different types of magnetic layers, such as magnetic insulators, antiferromagnets, and ferrimagnets. Afterward, we discuss device applications utilizing SOTs. We discuss and compare three- and two-terminal SOT-magnetoresistive random access memories (MRAMs); we mention various schemes to eliminate the need for an external field. We provide technological application considerations for SOT-MRAM and give perspectives on SOT-based neuromorphic devices and circuits. In addition to SOT-MRAM, we present SOT-based spintronic terahertz generators, nano-oscillators, and domain-wall and skyrmion racetrack memories. This article aims to achieve a comprehensive review of SOT theory, materials, and applications, guiding future SOT development in both the academic and industrial sectors.

Free-standing ultrathin lithium metal-graphene oxide host foils with controllable thickness for lithium batteries

Abstract: Thin (≤20 μm) and free-standing Li metal foils would enable precise prelithiation of anode materials and high-energy-density Li batteries. Existing Li metal foils are too thick (typically 50 to 750 μm) or too mechanically fragile for these applications. Here, we developed a facile and scalable process for the synthesis of an ultrathin (0.5 to 20 μm), free-standing and mechanically robust Li metal foil within a graphene oxide host. In addition to low areal capacities of ~0.1 to 3.7 mAh cm−2, this Li foil also has a much-improved mechanical strength over conventional pure Li metal foil. Our Li foil can improve the initial Coulombic efficiency of graphite (93%) and silicon (79.4%) anodes to around 100% without generating excessive Li residue, and increases the capacity of Li-ion full cells by 8%. The cycle life of Li metal full cells is prolonged by nine times using this thin Li composite anode. Thin Li foils are desirable for high-energy Li battery applications. Here, Cui and team devise a fabrication route for ultrathin (less than 20 μm) Li foils that show promise for improving existing anodes including silicon, graphite and metallic Li.

Linking void and interphase evolution to electrochemistry in solid-state batteries using operando X-ray tomography.

Abstract: Despite progress in solid-state battery engineering, our understanding of the chemo-mechanical phenomena that govern electrochemical behaviour and stability at solid–solid interfaces remains limited compared to at solid–liquid interfaces. Here, we use operando synchrotron X-ray computed microtomography to investigate the evolution of lithium/solid-state electrolyte interfaces during battery cycling, revealing how the complex interplay among void formation, interphase growth and volumetric changes determines cell behaviour. Void formation during lithium stripping is directly visualized in symmetric cells, and the loss of contact that drives current constriction at the interface between lithium and the solid-state electrolyte (Li10SnP2S12) is quantified and found to be the primary cause of cell failure. The interphase is found to be redox-active upon charge, and global volume changes occur owing to partial molar volume mismatches at either electrode. These results provide insight into how chemo-mechanical phenomena can affect cell performance, thus facilitating the development of solid-state batteries. Understanding electrochemical behaviour and stability at solid–solid interfaces remains challenging. Operando synchrotron X-ray computed microtomography loss reveals that reconfiguration of interfacial contact is critical to explain cell failure during solid-state battery cycling.

Preclinical characterization of an intravenous coronavirus 3CL protease inhibitor for the potential treatment of COVID19.

Abstract: COVID-19 caused by the SARS-CoV-2 virus has become a global pandemic. 3CL protease is a virally encoded protein that is essential across a broad spectrum of coronaviruses with no close human analogs. PF-00835231, a 3CL protease inhibitor, has exhibited potent in vitro antiviral activity against SARS-CoV-2 as a single agent. Here we report, the design and characterization of a phosphate prodrug PF-07304814 to enable the delivery and projected sustained systemic exposure in human of PF-00835231 to inhibit coronavirus family 3CL protease activity with selectivity over human host protease targets. Furthermore, we show that PF-00835231 has additive/synergistic activity in combination with remdesivir. We present the ADME, safety, in vitro, and in vivo antiviral activity data that supports the clinical evaluation of PF-07304814 as a potential COVID-19 treatment.

Highly stretchable multilayer electronic circuits using biphasic gallium-indium.

Abstract: Stretchable electronic circuits are critical for soft robots, wearable technologies and biomedical applications. Development of sophisticated stretchable circuits requires new materials with stable conductivity over large strains, and low-resistance interfaces between soft and conventional (rigid) electronic components. To address this need, we introduce biphasic Ga–In, a printable conductor with high conductivity (2.06 × 106 S m−1), extreme stretchability (>1,000%), negligible resistance change when strained, cyclic stability (consistent performance over 1,500 cycles) and a reliable interface with rigid electronics. We employ a scalable transfer-printing process to create various stretchable circuit board assemblies that maintain their performance when stretched, including a multilayer light-emitting diode display, an amplifier circuit and a signal conditioning board for wearable sensing applications. The compatibility of biphasic Ga–In with scalable manufacturing methods, robust interfaces with off-the-shelf electronic components and electrical/mechanical cyclic stability enable direct conversion of established circuit board assemblies to soft and stretchable forms. Conductors made of a mixture of liquid and solid domains of Ga–In alloy can be stretched over 1,000%, keeping almost constant conductivity, and used to connect commercial electronic components and realize stretchable multilayer printed circuit boards.

Blockchain Governance—A New Way of Organizing Collaborations?

Abstract: The recent emergence of blockchains may be considered a critical turning point in organizing collaborations. We outline the historical background and the fundamental features of blockchains and pre...