Design Principle and Development Trends of Silicon-Based Anode Binders for Lithium-ion Batteries: A Mini Review

Abstract: Silicon (Si), recognized as a promising alternative material for the anodes of lithium-ion batteries, boasts a high theoretical specific capacity and abundant natural availability. During the preparation of silicon-based anodes, binders play a pivotal role in ensuring the cohesion of silicon particles, conductive agents, and current collectors. The structure and performance of these binders are critical for the mechanical stability, electrical conductivity, and stress dissipation capacity of the anodes. This review initially outlines the structural characteristics of various binders, including linear, branched, and three-dimensional cross-linked types. It then delves into the relationship between the structure and properties of these binders in the context of their application in high-performance lithium-ion batteries, focusing on their mechanical properties, electrical conductivity, and self-healing capabilities. Particular attention is given to the design strategies for binders that facilitate stress dissipation, with an emphasis on integrating multifunctional polymer binders renowned for their superior conductive and self-healing features. Such binders contribute to the formation of a robust three-dimensional network structure via multiple bonding mechanisms, including chemical, non-covalent, and coordination interactions. This configuration significantly enhances the adhesion between silicon particles, thereby facilitating the efficient dissipation of stress, which is a key aspect for ensuring the long-term cycling stability of lithium-ion batteries. Lastly, the paper explores future development directions for silicon anode binders, advocating for a thorough investigation into the synergy of diverse structural and functional combinations, with the aim of advancing the performance and practical application of silicon-based lithium-ion batteries

PathAsst: Redefining Pathology through Generative Foundation AI Assistant for Pathology

As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, with significant applications in natural image interpretation. However, the field of pathology has largely remained untapped in this regard, despite the growing need for accurate, timely, and personalized diagnostics. To bridge the gap in pathology MLLMs, we present the PathAsst in this study, which is a generative foundation AI assistant to revolutionize diagnostic and predictive analytics in pathology. To develop PathAsst, we collect over 142K high-quality pathology image-text pairs from a variety of reliable sources, including PubMed, comprehensive pathology textbooks, reputable pathology websites, and private data annotated by pathologists. Leveraging the advanced capabilities of ChatGPT/GPT-4, we generate over 180K instruction-following samples. Furthermore, we devise additional instruction-following data, specifically tailored for the invocation of the pathology-specific models, allowing the PathAsst to effectively interact with these models based on the input image and user intent, consequently enhancing the model's diagnostic capabilities. Subsequently, our PathAsst is trained based on Vicuna-13B language model in coordination with the CLIP vision encoder. The results of PathAsst show the potential of harnessing the AI-powered generative foundation model to improve pathology diagnosis and treatment processes. We are committed to open-sourcing our meticulously curated dataset, as well as a comprehensive toolkit designed to aid researchers in the extensive collection and preprocessing of their own datasets. Resources can be obtained at https://github.com/superjamessyx/Generative-Foundation-AI-Assistant-for-Pathology.Comment: 13 pages, 5 figures, conferenc

Suicidal ideation in medical students of Hebei province: prevalence and associated factors

ObjectivesThis study investigated the prevalence of suicidal ideation (SI) among Chinese medical students and its associated risk factors.MethodsA total of 6643 medical students (2383 males/4260 females) were recruited from a medical college in Hebei Province, China. Demographic data were collected via a self-administered questionnaire. The Childhood Trauma Questionnaire Short Form (CTQ-SF) was used to evaluate childhood maltreatment (CM), and the Adolescent Self-Rating Life Events Checklist (ASLEC) was used to evaluate the stressful life events. Suicidal ideation was assessed using the Beck Scale for Suicide Ideation (BSSI). Univariate and multivariate logistic regression models were used to analyze the factors affecting SI.ResultsThe prevalence of SI in medical students was 11.5% (763/6643). Multivariate logistic regression analysis revealed that SI was significantly associated with younger age, a female sex, being lovelorn, being introverted, experiencing CM during childhood, and experiencing stressful life events within the past 12 months. Of the five subtypes of CM, emotional abuse may have the strongest effect on SI (OR=2.76, 95% CI: 1.72–4.42). The joint effects of CM and stressful life events were significantly associated with an increased risk of SI (OR=5.39, 95% CI: 4.15–6.98).ConclusionThe prevalence of SI among medical students is high, and medical students who have experienced CM and stressful life events have a higher tendency towards SI. Screening for both CM and stressful life events may be an effective way of identifying individuals at high risk of SI

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Enhanced Insights into Effluent Prediction in Wastewater Treatment Plants: Comprehensive Deep Learning Model Explanation Based on SHAP

Models are increasingly being utilized to improve the understanding and operation of wastewater treatment plants (WWTPs) in the face of escalating water resource challenges. Abundant operational data provide extensive opportunities for the development of machine learning (ML) and deep learning (DL) models. However, the coupling and time lag among the features exacerbate the black-box nature of such models, hindering their application in WWTPs. In this study, we construct a DL model using a long short-term memory (LSTM) algorithm capable of accurately predicting the effluent quality in a full-scale WWTP with finely tuned hyperparameters and rationally chosen input features. Comprehensive model explanation based on Shapley additive explanations (SHAP) is implemented to clarify the contributions of multivariate time series (MTS) inputs to the predicted results in terms of feature and time dimensions. The LSTM models exhibit excellent accuracy (R2 of 0.96, 0.95, and 0.76 and MAPE of 5.49, 7.17, and 13.37%, respectively) in predicting effluent chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN) better than other baseline ML models. The SHAP results quantify what input features are most important when they exert influence and how they impact results. The analysis from the temporal dimension further explains the time lag characteristics of the wastewater treatment process and justifies the introduction of MTS. Compared to correlation analysis and without feature engineering, the feature selection method by SHAP significantly enhances the predictive accuracy. The combinations of input features are adjusted based on the Shapley values, and features with strong interactions and significant contributions to the model output are identified. This is a novel attempt to construct a WWTP model based on LSTM with both excellent accuracy and explainability and to clarify the influence of MTS inputs on prediction results. This work shows the potential of applying DL to model WWTPs and enhances their performance