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Yuhao Zhang

Researcher at Nanjing University of Information Science and Technology

Publications -  5
Citations -  54

Yuhao Zhang is an academic researcher from Nanjing University of Information Science and Technology. The author has contributed to research in topics: Computer science & Medicine. The author has co-authored 1 publications.

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Porous Heteroatom-Doped Ti3C2Tx MXene Microspheres Enable Strong Adsorption of Sodium Polysulfides for Long-Life Room-Temperature Sodium-Sulfur Batteries.

TL;DR: In this article, a design strategy for encapsulation of sodium polysulfides using Ti3C2Tx MXene has been presented, which has a high reversible capacity (980 mAh g-1 at 0.5 C rate) and extended cycling stability.
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Emerging Green Technologies for Recovery and Reuse of Spent Lithium-ion Batteries - A Review

TL;DR: In this article , the authors proposed rechargeable batteries, as one of the attractive energy storage technologies integrating renewable resources, as a solution to the growing global energy demand and environmental damage are driving the pursuit of sustainable energy and storage technologies.
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Integrated Photovoltaic Charging and Energy Storage Systems: Mechanism, Optimization, and Future.

TL;DR: In this article , a review of photo-electrochemical (PEC) and redox batteries for large-scale solar energy capture, conversion, and storage is presented, where the matching problem of high-performance dye sensitizers, strategies to improve the performance of photoelectrode PEC, and the working mechanism and structure design of multi-energy photoelectronic integrated devices are analyzed.
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Interface Crystallographic Optimization of Crystal Plane for Stable Metallic Lithium Anode.

TL;DR: In this paper , the authors demonstrate a promising metallic Li anode design by introducing a customized magnetron sputtering layer of preferred orientation copper coating on the surface of a current collector.
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A Two-Stage Network Based on Transformer and Physical Model for Single Underwater Image Enhancement

TL;DR: Zhang et al. as mentioned in this paper proposed a two-stage network called WaterFormer to address the issue using deep learning and an underwater physical imaging model, where the first stage uses the Soft Reconstruction Network (SRN) to reconstruct underwater images based on the Jaffe-McGramery model and the second stage uses Hard Enhancement Network (HEN) to estimate the global residual between the original image and the reconstructed result to further enhance the images.