Room‐temperature phosphorescence (RTP) polymers, whose emission can persist for a long period after photoexcitation, are of great importance for practical applications. Herein, dynamic covalent ...boronic ester linkages with internal B−N coordination are incorporated into a commercial epoxy matrix. The reversible dissociation of B−N bonds upon loading provides an efficient energy dissipation pathway for the epoxy network, while the rigid epoxy matrix can inhibit the quenching of triplet excitons in boronic esters. The obtained polymers exhibit enhanced mechanical toughness (12.26 MJ m−3), ultralong RTP (τ=540.4 ms), and shape memory behavior. Notably, there is no apparent decrease in the RTP property upon prolonged immersion in various solvents because the networks are robust. Moreover, the dynamic bonds endow the polymers with superior reprocessablity and recyclability. These novel properties have led to their potential application for information encryption and anti‐counterfeiting.
Incorporating dynamic covalent boronic ester linkages with internal B−N coordination into a commercial epoxy matrix has led to the fabrication of high‐performance polymer‐based materials with ultralong room‐temperature phosphorescence. The polymers show excellent mechanical properties, environmental stability, shape memory, and recyclability, which could be useful for anticounterfeiting, data encryption, and information editing applications.
Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby ...MEC servers and improve the quality of computation experience. In this paper, an MEC enabled multi-user multi-input multi-output (MIMO) system with stochastic wireless channels and task arrivals is considered. In order to minimize long-term average computation cost in terms of power consumption and buffering delay at each user, a deep reinforcement learning (DRL)-based dynamic computation offloading strategy is investigated to build a scalable system with limited feedback. Specifically, a continuous action space-based DRL approach named deep deterministic policy gradient (DDPG) is adopted to learn decentralized computation offloading policies at all users respectively, where local execution and task offloading powers will be adaptively allocated according to each user’s local observation. Numerical results demonstrate that the proposed DDPG-based strategy can help each user learn an efficient dynamic offloading policy and also verify the superiority of its continuous power allocation capability to policies learned by conventional discrete action space-based reinforcement learning approaches like deep Q-network (DQN) as well as some other greedy strategies with reduced computation cost. Besides, power-delay tradeoff for computation offloading is also analyzed for both the DDPG-based and DQN-based strategies.
A comprehensive understanding of multiple Li kinetics in batteries is essential to break the limitations of mechanism study and materials design. Various kinetic processes with specific relaxation ...features can be clearly identified in timescales. Extracting and analyzing the timescale information in batteries will provide insights into investigating kinetic issues such as ionic conductions, charge transfer, diffusions, interfacial evolutions, and other unknown kinetic processes. In this regard, the timescale identification is an important method to combine with the non-destructive impedance characterizations in length scale for online battery monitoring. This perspective introduces and advocates the timescale characterization in the views of the basic timescale property in batteries, employing the concept of distribution of relaxation time (DRT) and presenting successful applications for battery diagnosis. In the future, we suggest that timescale characterizations will become powerful tools for data extraction and dataset building for various battery systems, which can realize data-driven machine learning modeling for practical application situations such as retired battery rapid sorting and battery status estimations.
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Characterizations from different scales are powerful to unravel the hidden mechanisms of working batteries. The timescale diagnosis is an emerging strategy to disassemble the battery “black box” into isolated kinetic information, which not only contributes to a non-destructive practical battery test but also helps to decouple and quantify Li kinetics in-time dimensions such as interfacial properties, ion transportation, and charge transfer processes. This paper introduces the basic scientific knowledge, protocols, applications, and outlooks on the rapidly developing timescale analyses in various battery systems, such as solid-state batteries, metal-S/O2 batteries, and metal-ion batteries. We hope the fresh viewpoint can help to popularize the timescale analyses in both academic study and industry applications.
To comprehensively study the Li kinetics in the “black box” of batteries, timescale identification is indispensable to unravel the hidden information such as interfacial properties, ionic conduction, and charge transfer. The distribution of relaxation time (DRT) is an emerging powerful strategy to realize an accurate battery diagnosis in timescales avoiding subjective errors. DRT is an algorithm-supported solution, which is promising for application in various electrochemistry systems, data-driven analyses, and online monitoring in the battery industry.
The modulation of tropical cyclone (TC) genesis over the western North Pacific (WNP) and the tropical North Atlantic (ATL) by the Madden–Julian Oscillation (MJO) is investigated based on ...observational analysis and numerical simulations. A genesis potential index (GPI) is used to investigate relative contributions of environmental parameters associated with the MJO to TC genesis. It is found that relative humidity plays the most important role in modulating TC genesis in the WNP, the Gulf of Mexico and the western Caribbean Sea (GOM), while vertical wind shear associated with the MJO has the most significant impact on TC activities in the eastern Atlantic (EAT). To further understand the relative importance of the MJO dynamic and thermodynamic impact on TC activities, idealized numerical model experiments are conducted using the Advanced Research version of the Weather Research and Forecasting model (WRF-ARW). The results are consistent with that of observational analysis, indicating that TC activities in the WNP, the GOM and the EAT are modulated by the MJO. Specific humidity anomalies related to the MJO exert the strongest impact on TC development in the WNP and the GOM, while the vertical wind shear is the most critical factor in the EAT.
Organic cathode materials have attracted extensive attention because of their diverse structures, facile synthesis, and environmental friendliness. However, they often suffer from insufficient ...cycling stability caused by the dissolution problem, poor rate performance, and low voltages. An in situ electropolymerization method was developed to stabilize and enhance organic cathodes for lithium batteries. 4,4′,4′′‐Tris(carbazol‐9‐yl)‐triphenylamine (TCTA) was employed because carbazole groups can be polymerized under an electric field and they may serve as high‐voltage redox‐active centers. The electropolymerized TCTA electrodes demonstrated excellent electrochemical performance with a high discharge voltage of 3.95 V, ultrafast rate capability of 20 A g−1, and a long cycle life of 5000 cycles. Our findings provide a new strategy to address the dissolution issue and they explore the molecular design of organic electrode materials for use in rechargeable batteries.
An in situ electropolymerization method was developed to enhance the performance of organic cathodes. 4,4′,4′′‐Tris(carbazol‐9‐yl)‐triphenylamine (TCTA) was employed because carbazole groups can be polymerized under an electric field and they may serve as high‐voltage redox‐active centers. Ultrafast rate performance (20 A g−1), long cycle life (5000 cycles), and high voltage (3.95 V) were demonstrated.
•We coupled a conduit model with a reservoir model.•We simulated a geologically complex alpine karst aquifer system at catchment scale.•Hydrographs from three karst springs were used to calibrate the ...transient model.•The global optimization approach was implemented to achieve efficient calibration.•The model reproduced correctly the observed hydrographs of all three springs.
Karst aquifers are important for freshwater supply, but difficult to manage, due to highly variable water levels and spring discharge rates. Conduits are crucial for groundwater flow in karst aquifers, but their location is often unknown, thus limiting the applicability and validity of numerical models. We have applied a conduit model (SWMM) to simulate highly variable flow in a folded alpine karst aquifer system, where the underground drainage pattern is comparatively well-known from previous tracer studies. The conduit model was coupled with a reservoir model representing recharge, storage and transfer of water in the epikarst and unsaturated zone. The global optimization approach (GA) was applied to achieve an efficient model calibration. It was possible to simultaneously simulate the highly variable discharge characteristics of an estavelle, and overflow spring and a permanent spring draining the conduit system. The model allowed for the collection of spatially differentiated information on recharge, rapid flow and slow flow in four individual sub-catchments. The formation of backwater upgradient from conduit restrictions turned out to be a key process in activating overflow springs. The proposed modeling approach appears to be transferrable to other karst systems with predominant conduit drainage, but requires previous knowledge of the configuration of the conduit system.
Lithium metal batteries (LMBs) are among the most promising candidates of high‐energy‐density devices for advanced energy storage. However, the growth of dendrites greatly hinders the practical ...applications of LMBs in portable electronics and electric vehicles. Constructing stable and efficient solid electrolyte interphase (SEI) is among the most effective strategies to inhibit the dendrite growth and thus to achieve a superior cycling performance. In this review, the mechanisms of SEI formation and models of SEI structure are briefly summarized. The analysis methods to probe the surface chemistry, surface morphology, electrochemical property, dynamic characteristics of SEI layer are emphasized. The critical factors affecting the SEI formation, such as electrolyte component, temperature, current density, are comprehensively debated. The efficient methods to modify SEI layer with the introduction of new electrolyte system and additives, ex‐situ‐formed protective layer, as well as electrode design, are summarized. Although these works afford new insights into SEI research, robust and precise routes for SEI modification with well‐designed structure, as well as understanding of the connection between structure and electrochemical performance, is still inadequate. A multidisciplinary approach is highly required to enable the formation of robust SEI for highly efficient energy storage systems.
Solid electrolyte interphases (SEI) formed on Li metal anodes can inhibit the growth of dendrites, improve Coulombic efficiency, and achieve superior cycling performance in Li metal batteries. Mechanisms of SEI formation and models of SEI structure, as well as progress on the characterization of SEI layers, are summarized. Strategies to achieve stable and robust SEIs in Li metal anodes for cycling efficiency and long cycling life of Li metal batteries are also presented.
COVID-19, caused by SARS-CoV-2, is a virulent pneumonia, with >4,000,000 confirmed cases worldwide and >290,000 deaths as of May 15, 2020. It is critical that vaccines and therapeutics be developed ...very rapidly. Mice, the ideal animal for assessing such interventions, are resistant to SARS-CoV-2. Here, we overcome this difficulty by exogenous delivery of human ACE2 with a replication-deficient adenovirus (Ad5-hACE2). Ad5-hACE2-sensitized mice developed pneumonia characterized by weight loss, severe pulmonary pathology, and high-titer virus replication in lungs. Type I interferon, T cells, and, most importantly, signal transducer and activator of transcription 1 (STAT1) are critical for virus clearance and disease resolution in these mice. Ad5-hACE2-transduced mice enabled rapid assessments of a vaccine candidate, of human convalescent plasma, and of two antiviral therapies (poly I:C and remdesivir). In summary, we describe a murine model of broad and immediate utility to investigate COVID-19 pathogenesis and to evaluate new therapies and vaccines.
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•Mice are sensitized for SARS-CoV-2 infection by Ad5-hACE2 transduction•Genetically deficient strains can be directly assessed without additional breeding•Mice useful for determining host factors necessary for optimal virus clearance•Useful for assessing efficacy of vaccines and therapies such as convalescent plasma
An adenoviral transduction-based mouse model that can be infected with SARS-CoV-2 provides a tool to understand host factors involved in viral infection and clearance as well as potential therapeutic modalities.
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