Accelerated Multi-step Sulfur Redox Reactions in Lithium-Sulfur Batteries Enabled by Dual Defects in Metal-Organic Framework-based Catalysts.

The sluggish sulfur redox kinetics and shuttle effect of lithium polysulfides (LiPSs) are recognized as the main obstacles to the practical applications of the lithium-sulfur (Li-S) batteries. Accelerated conversion by catalysis can mitigate these issues, leading to enhanced Li-S performance. However, a catalyst with single active site cannot simultaneously accelerate multiple LiPSs conversion. Herein, we developed a novel dual-defect (missing linker and missing cluster defects) metal-organic framework (MOF) as a new type of catalyst to achieve synergistic catalysis for the multi-step conversion reaction of LiPSs. Electrochemical tests and first-principle density functional theory (DFT) calculations revealed that different defects can realize targeted acceleration of stepwise reaction kinetics for LiPSs. Specifically, the missing linker defects can selectively accelerate the conversion of S8 →Li2 S4 , while the missing cluster defects can catalyze the reaction of Li2 S4 →Li2 S, so as to effectively inhibit the shuttle effect. Hence, the Li-S battery with an electrolyte to sulfur (E/S) ratio of 8.9 mL g-1 delivers a capacity of 1087 mAh g-1 at 0.2 C after 100 cycles. Even at high sulfur loading of 12.9 mg cm-2 and E/S=3.9 mL g-1 , an areal capacity of 10.4 mAh cm-2 for 45 cycles can still be obtained.

Tuning spin-orbit coupling and realizing inverse persistent spin helix by an extra above-barrier radiation in a GaAs/Al0.3Ga0.7As heterostructure.

A persistent spin helix with equal strength of the Rashba and Dresselhaus spin-orbit coupling (SOC) is expected for future spintronic devices due to the suppression of spin relaxation. In this work we investigate the optical tuning of the Rashba and Dresselhaus SOC by monitoring the spin-galvanic effect (SGE) in a GaAs/Al0.3Ga0.7As two dimensional electron gas. An extra control light above the bandgap of the barrier is introduced to tune the SGE excited by a circularly polarized light below the bandgap of GaAs. We observe different tunability of the Rashba- and Dresselhaus-related SGE currents and extract the ratio of the Rashba and Dresselhaus coefficients. It decreases monotonously with the power of the control light and reaches a particular value of ∼-1, implying the formation of the inverse persistent spin helix state. By analyzing the optical tuning process phenomenologically and microscopically, we reveal greater optical tunability of the Rashba SOC than that of the Dresselhaus SOC.

A Multi-Label Based Physical Activity Recognition via Cascade Classifier.

Physical activity recognition is a field that infers human activities used in machine learning techniques through wearable devices and embedded inertial sensors of smartphones. It has gained much research significance and promising prospects in the fields of medical rehabilitation and fitness management. Generally, datasets with different wearable sensors and activity labels are used to train machine learning models, and most research has achieved satisfactory performance for these datasets. However, most of the methods are incapable of recognizing the complex physical activity of free living. To address the issue, we propose a cascade classifier structure for sensor-based physical activity recognition from a multi-dimensional perspective, with two types of labels that work together to represent an exact type of activity. This approach employed the cascade classifier structure based on a multi-label system (Cascade Classifier on Multi-label, CCM). The labels reflecting the activity intensity would be classified first. Then, the data flow is divided into the corresponding activity type classifier according to the output of the pre-layer prediction. The dataset of 110 participants has been collected for the experiment on PA recognition. Compared with the typical machine learning algorithms of Random Forest (RF), Sequential Minimal Optimization (SMO) and K Nearest Neighbors (KNN), the proposed method greatly improves the overall recognition accuracy of ten physical activities. The results show that the RF-CCM classifier has achieved 93.94% higher accuracy than the 87.93% obtained from the non-CCM system, which could obtain better generalization performance. The comparison results reveal that the novel CCM system proposed is more effective and stable in physical activity recognition than the conventional classification methods.

Interspersing Partially Oxidized V2C Nanosheets and Carbon Nanotubes toward Multifunctional Polysulfide Barriers for High-Performance Lithium-Sulfur Batteries.

Lithium-sulfur (Li-S) batteries have attracted much attention attributed to their high theoretical energy density, whereas the parasitic shuttling behavior of lithium polysulfides (LiPS) hinders this technology from yielding practically competitive performance. Targeting this critical challenge, we develop an advanced polysulfide barrier by modifying the conventional separator with CNTs-interspersed V2C/V2O5 nanosheets to alleviate the shuttle effect. The partial oxidization of V2C MXene constructs the V2C/V2O5 composite with V2O5 nanoparticles uniformly dispersed on few-layered V2C nanosheets, which synergistically and concurrently improves the sulfur confinement and redox reaction kinetics. Moreover, the interstacking between the 1D CNTs and the 2D V2C/V2O5 not only prevents the agglomeration of nanosheets for efficient exposure of active interfaces but also constructs a robust conductive network for fast charge and mass transfers. The Li-S cells with V2C/V2O5/CNTs-modified separator realize a high initial capacity (1240.4 mAh g-1 at 0.2 C), decent capacity retention (82.6% over 500 cycles), and favorable areal capacity (5.9 mAh cm-2) at a raised sulfur loading (6.0 mg cm-2). This work affords a unique multifunctional separator design toward durable and efficient sulfur electrochemistry, holding great promise for improving the electrochemical properties of Li-S batteries.