Constructing static two-electron lithium-bromide battery.

Despite their potential as conversion-type energy storage technologies, the performance of static lithium-bromide (SLB) batteries has remained stagnant for decades. Progress has been hindered by the intrinsic liquid-liquid redox mode and single-electron transfer of these batteries. Here, we developed a high-performance SLB battery based on the active bromine salt cathode and the two-electron transfer chemistry with a Br-/Br+ redox couple by electrolyte tailoring. The introduction of NO3- improved the reversible single-electron transition of Br-, and more impressively, the coordinated Cl- anions activated the Br+ conversion to provide an additional electron transfer. A voltage plateau was observed at 3.8 V, and the discharge capacity and energy density were increased by 142 and 159% compared to the one-electron reaction benchmark. This two-step conversion mechanism exhibited excellent stability, with the battery functioning for 1000 cycles. These performances already approach the state of the art of currently established Li-halogen batteries. We consider the established two-electron redox mechanism highly exemplary for diversified halogen batteries.

Halogen-powered static conversion chemistry.

Halogen-powered static conversion batteries (HSCBs) thrive in energy storage applications. They fall into the category of secondary non-flow batteries and operate by reversibly changing the chemical valence of halogens in the electrodes or/and electrolytes to transfer electrons, distinguishing them from the classic rocking-chair batteries. The active halide chemicals developed for these purposes include organic halides, halide salts, halogenated inorganics, organic-inorganic halides and the most widely studied elemental halogens. Aside from this, various redox mechanisms have been discovered based on multi-electron transfer and effective reaction pathways, contributing to improved electrochemical performances and stabilities of HSCBs. In this Review, we discuss the status of HSCBs and their electrochemical mechanism-performance correlations. We first provide a detailed exposition of the fundamental redox mechanisms, thermodynamics, conversion and catalysis chemistry, and mass or electron transfer modes involved in HSCBs. We conclude with a perspective on the challenges faced by the community and opportunities towards practical applications of high-energy halogen cathodes in energy-storage devices.

Detecting tea tree pests in complex backgrounds using a hybrid architecture guided by transformers and multi-scale attention mechanism.

BACKGROUND: Tea pests pose a significant threat to tea leaf yield and quality, necessitating fast and accurate detection methods to improve pest control efficiency and reduce economic losses for tea farmers. However, in real tea gardens, some tea pests are small in size and easily camouflaged by complex backgrounds, making it challenging for farmers to promptly and accurately identify them. RESULTS: To address this issue, we propose a real-time detection method based on TP-YOLOX for monitoring tea pests in complex backgrounds. Our approach incorporates the CSBLayer module, which combines convolution and multi-head self-attention mechanisms, to capture global contextual information from images and expand the network's perception field. Additionally, we integrate an efficient multi-scale attention module to enhance the model's ability to perceive fine details in small targets. To expedite model convergence and improve the precision of target localization, we employ the SIOU loss function as the bounding box regression function. Experimental results demonstrate that TP-YOLOX achieves a significant performance improvement with a relatively small additional computational cost (0.98 floating-point operations), resulting in a 4.50% increase in mean average precision (mAP) compared to the original YOLOX-s. When compared with existing object detection algorithms, TP-YOLOX outperforms them in terms of mAP performance. Moreover, the proposed method achieves a frame rate of 82.66 frames per second, meeting real-time requirements. CONCLUSION: TP-YOLOX emerges as a proficient solution, capable of accurately and swiftly identifying tea pests amidst the complex backgrounds of tea gardens. This contribution not only offers valuable insights for tea pest monitoring but also serves as a reference for achieving precise pest control. © 2023 Society of Chemical Industry.

Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities.

Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.

Low follistatin level is a causal risk factor for spontaneous abortion: a two-sample mendelian randomization study.

Background: Recurrent pregnancy loss is a distressing event during pregnancy, and understanding its causal factors is crucial. Follistatin, a glycoprotein involved in folliculogenesis and embryogenesis, has been implicated as a potential contributor to the risk of spontaneous abortion. However, establishing a causal relationship requires rigorous investigation using robust methods. Methods: In this study, we utilized mendelian randomization (MR), a powerful genetic epidemiological approach, to examine the causal relationship between follistatin levels and spontaneous abortion. We obtained instrumental variables strongly associated with follistatin levels from large-scale genome-wide association from the IEU database. The inverse variance weighting (IVW) method was taken as gold standard. We also performed sensitivity test to evaluate the robustness of our result. Results: MR analysis revealed a significant causal relationship between low follistatin levels and spontaneous abortion (p = 0.03). Sensitivity analyses, including pleiotropy test, heterogeneity test, and leave-one-out analysis, all supported the robustness of our findings. Conclusion: Our study provides compelling evidence supporting the causal relationship between low follistatin levels and increased risk of spontaneous abortion. These findings underscore the importance of follistatin in the etiology of spontaneous abortion and suggest potential preventive interventions. Modulating follistatin levels or relevant pathways could hold promise for reducing the incidence of spontaneous abortion and improving reproductive outcomes. The utilization of MRs strengthens the validity of our results by mitigating confounding and reverse causality biases. Further research is needed to elucidate the underlying molecular mechanisms and explore therapeutic strategies targeting follistatin levels.