Additives for Aqueous Zinc-Ion Batteries: Recent Progress, Mechanism Analysis, and Future Perspectives

Aqueous zinc-ion batteries (ZIBs) stand out as a promising next-generation electrochemical energy storage technology, offering notable advantages such as high specific capacity, enhanced safety, and cost-effectiveness. However, the application of aqueous electrolytes introduces challenges: Zn dendrite formation and parasitic reactions at the anode, as well as dissolution, electrostatic interaction, and by-product formation at the cathode. In addressing these electrode-centric problems, additive engineering has emerged as an effective strategy. This review delves into the latest advancements in electrolyte additives for ZIBs, emphasizing their role in resolving the existing issues. Key focus areas include improving morphology and reducing side reactions during battery cycling using synergistic effects of modulating anode interface regulation, zinc facet control, and restructuring of hydrogen bonds and solvation sheaths. Special attention is given to the efficacy of amino acids and zwitterions due to their multifunction to improve the cycling performance of batteries concerning cycle stability and lifespan. Additionally, the recent additive advancements are studied for low-temperature and extreme weather applications meticulously. This review concludes with a holistic look at the future of additive engineering, underscoring its critical role in advancing ZIB performance amidst the complexities and challenges of electrolyte additives.

Mott-Schottky MXene@WS2 Heterostructure: Structural and Thermodynamic Insights and Application in Ultra Stable Lithium-Sulfur Batteries

Due to the "shuttle effect" and low conversion kinetics of polysulfides, the cycle stability of lithium sulfur (Li-S) battery is unsatisfactory, which hinders its practical application. The Mott-Schottky heterostructures for Li-S batteries not only provide more catalytic/adsorption active sites, but also facilitate electrons transport by a built-in electric field, which are both beneficial for polysulfides conversion and long-term cycle stability. Here, MXene@WS2 heterostructure was constructed by in-situ hydrothermal growth for separator modification. In-depth ultraviolet photoelectron spectroscopy and ultraviolet visible diffuse reflectance spectroscopy analysis reveals that there is an energy band difference between MXene and WS2 , confirming the heterostructure nature of MXene@WS2 . DFT calculations indicate that the Mott-Schottky MXene@WS2 heterostructure can effectively promote electron transfer, improve the multi-step cathodic reaction kinetics, and further enhance polysulfides conversion. The built-in electric field of the heterostructure plays an important role in reducing the energy barrier of polysulfides conversion. Thermodynamic studies reveal the best stability of MXene@WS2 during polysulfides adsorption. As a result, the Li-S battery with MXene@WS2 modified separator exhibits high specific capacity (1613.7 mAh g-1 at 0.1 C) and excellent cycling stability (2000 cycles with 0.0286 % decay per cycle at 2 C). Even at a high sulfur loading of 6.3 mg cm-2 , the specific capacity could be retained by 60.0 % after 240 cycles at 0.3 C. This work provides deep structural and thermodynamic insights into MXene@WS2 heterostructure and its promising prospect of application in high performance Li-S batteries.

Theoretical Study on B-doped FeN4 Catalyst for Potential-Dependent Oxygen Reduction Reaction

Electrochemical reactions mostly take place at a constant potential, but traditional DFT calculations operate at a neutral charge state. In order to really model experimental conditions, we developed a fixed-potential simulation framework via the iterated optimization and self-consistence of the required Fermi level. The B-doped graphene-based FeN4 sites for oxygen reduction reaction were chosen as the model to evaluate the accuracy of the fixed-potential simulation. The results demonstrate that *OH hydrogenation gets facile while O2 adsorption or hydrogenation becomes thermodynamically unfavorable due to the lower d-band center of Fe atoms in the constant potential state than the neutral charge state. The onset potential of ORR over B-doped FeN4 by performing potential-dependent simulations agree well with experimental findings. This work indicates that the fixed-potential simulation can provide a reasonable and accurate description on electrochemical reactions.

Artificial intelligence-enabled microbiome-based diagnosis models for a broad spectrum of cancer types

Microbiome-based diagnosis of cancer is an increasingly important supplement for the genomics approach in cancer diagnosis, yet current models for microbiome-based diagnosis of cancer face difficulties in generality: not only diagnosis models could not be adapted from one cancer to another, but models built based on microbes from tissues could not be adapted for diagnosis based on microbes from blood. Therefore, a microbiome-based model suitable for a broad spectrum of cancer types is urgently needed. Here we have introduced DeepMicroCancer, a diagnosis model using artificial intelligence techniques for a broad spectrum of cancer types. Built based on the random forest models it has enabled superior performances on more than twenty types of cancers' tissue samples. And by using the transfer learning techniques, improved accuracies could be obtained, especially for cancer types with only a few samples, which could satisfy the requirement in clinical scenarios. Moreover, transfer learning techniques have enabled high diagnosis accuracy that could also be achieved for blood samples. These results indicated that certain sets of microbes could, if excavated using advanced artificial techniques, reveal the intricate differences among cancers and healthy individuals. Collectively, DeepMicroCancer has provided a new venue for accurate diagnosis of cancer based on tissue and blood materials, which could potentially be used in clinics.