Advanced Cathodes and Electrolytes for High Energy and Safe of Li-ion Cells
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This paper describes the algorithms and the models that comprise a scanning lidar-based hazard avoidance simulation including a terrain generator, a lidar model, hazard avoidance algorithms and powered landing guidance algorithms.
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Avoiding faradaic side reactions during the operation of electrochemical devices is important to enhance the device stability, to achieve low power consumption, and to prevent the formation of reactive side-products. This is particularly important for bioelectronic devices, which are designed to operate in biological systems. While redox-active materials based on conducting and semiconducting polymers represent an exciting class of materials for bioelectronic devices, they are susceptible to electrochemical side-reactions with molecular oxygen during device operation. In this work, electrochemical side reactions with molecular oxygen are shown to occur during organic electrochemical transistor (OECT) operation using high-performance, state-of-the-art OECT materials. Depending on the choice of the active material, such reactions yield hydrogen peroxide (H 2 O 2 ), a reactive side-product, which may be harmful to the local biological environment and may also accelerate device degradation. A design strategy is reported for the development of redox-active organic semiconductors based on donor–acceptor copolymers that prevents the formation of H 2 O 2 during device operation. This study elucidates the previously overlooked side-reactions between redox-active conjugated polymers and molecular oxygen in electrochemical devices for bioelectronics, which is critical for the operation of electrolyte-gated devices in application-relevant environments.
The separator, an ionic permeable and electronic insulating membrane between cathode and anode, plays a crucial role in the electrochemical and safety performance of batteries. However, the commercial polyolefin separators not only suffer from inevitable thermal shrinkage at elevated temperature, but also fail to inhibit the hidden chemical crosstalk of reactive gases such as O 2 , leading to often reported thermal runaway (TR) and hence preventing large-scale implementation of high-energy-density lithium-ion batteries. Herein, a nanoporous non-shrinkage separator (GS-PI) was fabricated via a novel gel-stretching orientation approach to eliminate TR. In situ synchrotron small angle X-ray scattering during heating clearly showed that the as-prepared thin GS-PI separator exhibits superior mechanical tolerance at high temperature, thus effectively preventing internal short circuit. Meanwhile, the unique nanoporous structure design further blocks chemical crosstalk and the associated exothermic reactions. Accelerating Rate Calorimetry test revealed that the practical 1 Ah LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622)/graphite pouch cell using GS-PI nanoporous separator show a maximum temperature rise (dT/dt max ) of only 3.7 °C s -1 compared to 131.6 °C s -1 in the case of Al 2 O 3 @PE macroporous separator. Furthermore, despite the reduced pore size, the GS-PI separator demonstrates better cycling stability than conventional Al 2 O 3 @PE separator at high temperature without sacrificing specific capacity and rate capability.
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Abstract SARS‐CoV‐2 is still wreaking havoc all over the world with surging morbidity and high mortality. The main protease (M pro ) is essential in the replication of SARS‐CoV‐2, enabling itself an active target for antiviral development. Herein, we reported the design and synthesis of a new class of peptidomimetics‐constrained α, γ‐AA peptides, based on which a series of aldehyde and ketoamide inhibitors of the M pro of SARS‐CoV‐2 were prepared. The lead compounds showed excellent inhibitory activity in the FRET‐based M pro enzymatic assay not only for the M pro of SARS‐CoV‐2 but also for SARS‐CoV and MERS‐CoV, along with HCoVs like HCoV‐OC43, HCoV‐229E, HCoV‐NL63 and HKU1. The X‐ray crystallographic results demonstrated that our compounds form a covalent bond with the catalytic Cys145. They also demonstrated effective antiviral activity against live SARS‐CoV‐2. Overall, the results suggest that α, γ‐AA peptide could be a promising molecular scaffold in designing novel M pro inhibitors of SARS‐CoV‐2 and other coronaviruses.
We present the first next-to-leading order matched and multi-jet merged predictions based on the Alaric parton shower. The components needed for infrared subtraction in the S-Mc@Nlo algorithm are computed analytically for the case of color singlet decays to hadronic final states and validated against existing approaches for up to e$^{−}$e$^{+}$ → 5 jets. Phenomenological results for e$^{−}$e$^{+}$ → hadrons at the Z pole are obtained with up to five jets at next-to-leading order precision, for the first time using an evolution algorithm with NLL-preserving kinematics mapping.
Machine learning-based jet classifiers are able to achieve impressive tagging performance in a variety of applications in high-energy and nuclear physics. However, it remains unclear in many cases which aspects of jets give rise to this discriminating power, and whether jet observables that are tractable in perturbative QCD such as those obeying infrared-collinear (IRC) safety serve as sufficient inputs. In this article, we introduce a new classifier, Jet Flow Networks (JFNs), in an effort to address the question of whether IRC unsafe information provides additional discriminating power in jet classification. JFNs are permutation-invariant neural networks (deep sets) that take as input the kinematic information of reconstructed subjets. The subjet radius and a cut on the subjet’s transverse momenta serve as tunable hyperparameters enabling a controllable sensitivity to soft emissions and nonperturbative effects. We demonstrate the performance of JFNs for quark vs. gluon and Z vs. QCD jet tagging. For small subjet radii and transverse momentum cuts, the performance of JFNs is equivalent to the IRC-unsafe Particle Flow Networks (PFNs), demonstrating that infrared-collinear unsafe information is not necessary to achieve strong discrimination for both cases. As the subjet radius is increased, the performance of the JFNs remains essentially unchanged until physical thresholds that we identify are crossed. For relatively large subjet radii, we show that the JFNs may offer an increased model independence with a modest tradeoff in performance compared to classifiers that use the full particle information of the jet. These results shed new light on how machines learn patterns in high-energy physics data.
We present a case of isolated calyx that was mistaken for a large cyst. A 47-year-old woman was referred for sclerotherapy of a large cystic lesion on her left kidney. Computed tomography (CT) and ultrasound showed that the cystic lesion was a large cyst. We noticed that the cystic lesion was not a typical simple cyst, even after two sessions of catheter-mediated sclerotherapy. Isolated calyx was presumed by medical history review and was confirmed by aspirated fluid analysis and far delayed-phase CT after intravenous contrast injection. We performed meticulous selective arterial embolization for an isolated calyx and inserted a catheter that could be removed without complication.