Design of a Self-Powered System by Wind-Driven Triboelectric Nanogenerator Based on 0.94(Bi0.5 Na0.5 )TiO3 -0.06Ba(Zr0.25 Ti0.75 )O3 /Polyvinylidene Fluoride (BNT-BZT/PVDF) Composites.

The portable power bank as an energy storage device has received tremendous attention while the limited capacity and periodical charging are critical issues. Here, a self-charging power system (SCPS) consisting of a 0.94(Bi0.5 Na0.5 )TiO3 -0.06Ba(Zr0.25 Ti0.75 )O3 /polyvinylidenefluoride (BNT-BZT/PVDF) composite film-based triboelectric nanogenerator (TENG) is designed as a wind energy harvester and an all-solid-state lithium-ion battery (ASSLIB) as the energy storage device. The optimized TENG can provide an output voltage of ≈400 V, a current of ≈45 µA, and a maximum power of ≈10.65 mW, respectively. The ASSLIB assembled by LiNiCoMnO2 as the cathode, NiCo2 S4 as the anode, and Li7 La3 Zr2 O12 as the solid electrolyte can maintain a discharge capacity of 51.3 µAh after 200 cycles with a Coulombic efficiency of 98.5%. Particularly, an ASSLIB can be easily charged up to 3.8 V in 58 min using the wind-driven TENG, which can continuously drive 12 parallel-connected white light-emitting diodes (LEDs) or a pH meter. This work demonstrates the development of low-cost, high-performance and high-safety SCPSs and their large-scale practical application in self-powered microelectronic devices.

Sodium storage mechanism of a GeP5/C composite as a high capacity anode material for sodium-ion batteries.

The sodium storage mechanism of a GeP5/C composite electrode was revealed. Metallic Ge formed during discharge enhances the electronic conductivity of the electrode, while NaxP mitigates the agglomeration and volume change of Ge in the alloying process. The GeP5 phase is regenerated after recharge along with elemental Ge and P, implying a reversible phase transition of GeP5 during cycling.

Triboelectrification-Induced Electricity in Self-Healing Hydrogel for Mechanical Energy Harvesting and Ultra-sensitive Pressure Monitoring.

Triboelectric nanogenerators (TENGs) have shown huge application potential in the fields of micro-nano energy harvesting and multifunctional sensing. However, the damage of triboelectric material is one of the challenges for their practical applications. Herein, we fabricated a flexible TENG employing self-healing hydrogel and fluorinated ethylene propylene film as triboelectric materials for mechanical energy harvesting and pressure monitoring. The prepared hydrogel not only has excellent flexibility, transparency, and self-healing property but also exhibits good mechanical property without plastic deformation and damage under a large stretchable strain of 200%. The output electric signals of TENGs are as high as 33.0 V and 3 µA under a contact frequency of 0.40 Hz and a pressure of 2.9 N, respectively, which can charge a capacitor of 0.22 µF to 24.3 V within 300 s. Note that the voltage retention rate of TENGs after self-healing is up to 88.0%. Moreover, hydrogel-based TENGs can act as a wearable pressure sensor for monitoring human motion, exhibiting a high sensitivity of 105.9 mV/N or 1.73 nA/N under a contact frequency of 0.40 Hz. This research provides a reference roadmap for designing TENGs and self-powered pressure sensors with flexibility, self-healing, and robustness.

Deep Graph Metric Learning for Weakly Supervised Person Re-Identification.

In conventional person re-identification (re-id), the images used for model training in the training probe set and training gallery set are all assumed to be instance-level samples that are manually labeled from raw surveillance video (likely with the assistance of detection) in a frame-by-frame manner. This labeling across multiple non-overlapping camera views from raw video surveillance is expensive and time consuming. To overcome these issues, we consider a weakly supervised person re-id modeling that aims to find the raw video clips where a given target person appears. In our weakly supervised setting, during training, given a sample of a person captured in one camera view, our weakly supervised approach aims to train a re-id model without further instance-level labeling for this person in another camera view. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. The weakly supervised person re-id is challenging since it not only suffers from the difficulties occurring in conventional person re-id (e.g., visual ambiguity and appearance variations caused by occlusions, pose variations, background clutter, etc.), but more importantly, is also challenged by weakly supervised information because the instance-level labels and the ground-truth locations for person instances (i.e., the ground-truth bounding boxes of person instances) are absent. To solve the weakly supervised person re-id problem, we develop deep graph metric learning (DGML). On the one hand, DGML measures the consistency between intra-video spatial graphs of consecutive frames, where the spatial graph captures neighborhood relationship about the detected person instances in each frame. On the other hand, DGML distinguishes the inter-video spatial graphs captured from different camera views at different sites simultaneously. To further explicitly embed weak supervision into the DGML and solve the weakly supervised person re-id problem, we introduce weakly supervised regularization (WSR), which utilizes multiple weak video-level labels to learn discriminative features by means of a weak identity loss and a cross-video alignment loss. We conduct extensive experiments to demonstrate the feasibility of the weakly supervised person re-id approach and its special cases (e.g., its bag-to-bag extension) and show that the proposed DGML is effective.

Tuning P2-Structured Cathode Material by Na-Site Mg Substitution for Na-Ion Batteries.

Most P2-type layered oxides suffer from multiple voltage plateaus, due to Na+/vacancy-order superstructures caused by strong interplay between Na-Na electrostatic interactions and charge ordering in the transition metal layers. Here, Mg ions are successfully introduced into Na sites in addition to the conventional transition metal sites in P2-type Na0.7[Mn0.6Ni0.4]O2 as new cathode materials for sodium-ion batteries. Mg ions in the Na layer serve as "pillars" to stabilize the layered structure, especially for high-voltage charging, meanwhile Mg ions in the transition metal layer can destroy charge ordering. More importantly, Mg ion occupation in both sodium and transition metal layers will be able to create "Na-O-Mg" and "Mg-O-Mg" configurations in layered structures, resulting in ionic O 2p character, which allocates these O 2p states on top of those interacting with transition metals in the O-valence band, thus promoting reversible oxygen redox. This innovative design contributes smooth voltage profiles and high structural stability. Na0.7Mg0.05[Mn0.6Ni0.2Mg0.15]O2 exhibits superior electrochemical performance, especially good capacity retention at high current rate under a high cutoff voltage (4.2 V). A new P2 phase is formed after charge, rather than an O2 phase for the unsubstituted material. Besides, multiple intermediate phases are observed during high-rate charging. Na-ion transport kinetics are mainly affected by elemental-related redox couples and structural reorganization. These findings will open new opportunities for designing and optimizing layer-structured cathodes for sodium-ion batteries.

Synthesis and electrochemical properties of Li1.3Nb0.3Cr0.4O2 as a high-capacity cathode material for rechargeable lithium batteries.

A cation-disordered Li-excess cathode material on the binary system xLi3NbO4-(1 - x)LiCrO2 (x = 0.43) has been successfully prepared by mechanical milling, and delivers a high reversible capacity of ∼362 mA h g-1, which originates from a highly reversible Cr3+/Cr6+ three-electron redox reaction with electrochemically inactive niobium ions.