Engineering catalytic defects via molecular imprinting for high energy Li-S pouch cells.

Heterogeneous catalysis promises to accelerate sulfur-involved conversion reactions in lithium-sulfur batteries. Solid-state Li2S dissociation remains as the rate-limiting step because of the weakly matched solid-solid electrocatalysis interfaces. We propose an electrochemically molecular-imprinting strategy to have a metal sulfide (MS) catalyst with imprinted defects in positions from which the pre-implanted Li2S has been electrochemically removed. Such tailor-made defects enable the catalyst to bind exclusively to Li atoms in Li2S reactant and elongate the Li-S bond, thus decreasing the reaction energy barrier during charging. The imprinted Ni3S2 catalyst shows the best activity due to the highest defect concentration among the MS catalysts examined. The Li2S oxidation potential is substantially reduced to 2.34 V from 2.96 V for the counterpart free of imprinted vacancies, and an Ah-level pouch cell is realized with excellent cycling performance. With a lean electrolyte/sulfur ratio of 1.80 µL mgS -1, the cell achieves a benchmarkedly high energy density beyond 500 Wh kg-1.

A Slightly Expanded Graphite Anode with High Capacity Enabled By Stable Lithium-Ion/Metal Hybrid Storage.

Integrating lithium-ion and metal storage mechanisms to improve the capacity of graphite anode holds the potential to boost the energy density of lithium-ion batteries. However, this approach, typically plating lithium metal onto traditional graphite anodes, faces challenges of safety risks of severe lithium dendrite growth and short circuits due to restricted lithium metal accommodation space and unstable lithium plating in commercial carbonate electrolytes. Herein, a slightly expanded spherical graphite anode is developed with a precisely adjustable expanded structure to accommodate metallic lithium, achieving a well-balanced state of high capacity and stable lithium-ion/metal storage in commercial carbonate electrolytes. This structure also enables fast kinetics of both Li intercalation/de-intercalation and plating/stripping. With a total anode capacity of 1.5 times higher (558 mAh g-1) than graphite, the full cell coupled with a high-loading LiNi0.8Co0.1Mn0.1O2 cathode (13 mg cm-2) under a low N/P ratio (≈1.15) achieves long-term cycling stability (75% of capacity after 200 cycles, in contrast to the fast battery failure after 50 cycles with spherical graphite anode). Furthermore, the capacity of the full cell also reaches a low capacity decay rate of 0.05% per cycle at 0.2 C under the low temperature of -20 °C.

YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation.

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging task due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg. A very essential component of YoloCurvSeg is image synthesis. Specifically, a background generator delivers image backgrounds that closely match the real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.

In situ growth of carbon dots on TiO2 nanotube arrays for PEC enzyme biosensors with visible light response.

Carbon dots (CDs) were grown in situ on secondary anodized TiO2 nanotube arrays (TiO2 NTAs) via a hydrothermal method. The combination of CDs and TiO2 NTAs enhanced the photoelectrochemical performance. Morphology, structure, and elemental composition of the CDs were characterized. No simple physical adsorption was found between the CDs and TiO2, but chemical bonds were formed. UV-vis absorption and fluorescence spectroscopy showed that the CDs could enhance the absorption of TiO2 in the visible and near-infrared regions. Owing to their up-conversion fluorescence properties, the CDs could convert low-energy photon absorption into high-energy photons, which may be used to excite TiO2 to produce a stronger photoelectric response. Moreover, the CDs could effectively transport electrons and accept holes, thus contributing to the effective separation of electrons and holes during photoexcitation. Finally, the PEC biosensor was prepared by immobilizing glucose oxidase (GOx) on the surface of the composite. The PEC biosensor exhibited a broad range of 0.1-18 mM with a detection limit of 0.027 mM under visible irradiation because the composite material reflected strong light absorption for visible light, good conductivity, and good biocompatibility.