Organic-Inorganic Dual-Network Composite Separators for Lithium Metal Batteries.

The suboptimal ionic conductivity of commercial polyolefin separators exacerbates uncontrolled lithium dendrite formation, deteriorating lithium metal battery performance and posing safety hazards. To address this challenge, a novel organic-inorganic composite separator designed is prepared to enhance ion transport and effectively suppress dendrite growth. This separator features a thermally stable, highly porous poly(m-phenylene isophthalamide) (PMIA) electrospun membrane, coated with ultralong hydroxyapatite (HAP) nanowires that promote "ion flow redistribution." The synergistic effects of the nitrogen atoms in PMIA and the hydroxyl groups in HAP hinder anion transport while facilitating efficient Li+ conduction. Meanwhile, the optimized unilateral pore structure ensures uniform ion transport. These results show that the 19 µm-thick HAP/PMIA composite separator achieves remarkable ionic conductivity (0.68 mS cm-1) and a high lithium-ion transference number (0.51). Lithium symmetric cells using HAP/PMIA separators exhibit a lifespan exceeding 1000 h with low polarization, significantly outperforming commercial polypropylene separators. Furthermore, this separator enables LiFePO4||Li cells to achieve an enhanced retention of 97.3% after 200 cycles at 1 C and demonstrates impressive rate capability with a discharge capacity of 72.7 mAh g-1 at 15 C.

Application of surfactants in papermaking industry and future development trend of green surfactants.

In this work, the application of chemical surfactants, including cooking aids, detergents, surface sizing agents, and deinking agents as core components, is introduced in the wet end of pulping and papermaking. This method for the combined application of enzymes and surfactants has expanded, promoting technological updates and improving the effect of surfactants in practical applications. Finally, the potential substitution of green surfactants for chemical surfactants is discussed. The source, classification, and natural functions of green surfactants are introduced, including plant extracts, biobased surfactants, fermentation products, and woody biomass. These green surfactants have advantages over their chemically synthesized counterparts, such as their low toxicity and biodegradability. This article reviews the latest developments in the application of surfactants in different paper industry processes and extends the methods of use. Additionally, the application potential of green surfactants in the field of papermaking is discussed. KEY POINTS: • Surfactants as important chemical additives in papermaking process are reviewed. • Deinking technologies by combined of surfactants and enzymes are reviewed. • Applications of green surfactant in papermaking industry are prospected.

Prediction of sepsis within 24 hours at the triage stage in emergency departments using machine learning.

BACKGROUND: Sepsis is one of the main causes of mortality in intensive care units (ICUs). Early prediction is critical for reducing injury. As approximately 36% of sepsis occur within 24 h after emergency department (ED) admission in Medical Information Mart for Intensive Care (MIMIC-IV), a prediction system for the ED triage stage would be helpful. Previous methods such as the quick Sequential Organ Failure Assessment (qSOFA) are more suitable for screening than for prediction in the ED, and we aimed to find a light-weight, convenient prediction method through machine learning. METHODS: We accessed the MIMIC-IV for sepsis patient data in the EDs. Our dataset comprised demographic information, vital signs, and synthetic features. Extreme Gradient Boosting (XGBoost) was used to predict the risk of developing sepsis within 24 h after ED admission. Additionally, SHapley Additive exPlanations (SHAP) was employed to provide a comprehensive interpretation of the model's results. Ten percent of the patients were randomly selected as the testing set, while the remaining patients were used for training with 10-fold cross-validation. RESULTS: For 10-fold cross-validation on 14,957 samples, we reached an accuracy of 84.1%±0.3% and an area under the receiver operating characteristic (ROC) curve of 0.92±0.02. The model achieved similar performance on the testing set of 1,662 patients. SHAP values showed that the five most important features were acuity, arrival transportation, age, shock index, and respiratory rate. CONCLUSION: Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage. This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

Telomere length and micronuclei trajectories in APP/PS1 mouse model of Alzheimer's disease: Correlating with cognitive impairment and brain amyloidosis in a sexually dimorphic manner.

Although studies have demonstrated that genome instability is accumulated in patients with Alzheimer's disease (AD), the specific types of genome instability linked to AD pathogenesis remain poorly understood. Here, we report the first characterization of the age- and sex-related trajectories of telomere length (TL) and micronuclei in APP/PS1 mice model and wild-type (WT) controls (C57BL/6). TL was measured in brain (prefrontal cortex, cerebellum, pituitary gland, and hippocampus), colon and skin, and MN was measured in bone marrow in 6- to 14-month-old mice. Variation in TL was attributable to tissue type, age, genotype and, to a lesser extent, sex. Compared to WT, APP/PS1 had a significantly shorter baseline TL across all examined tissues. TL was inversely associated with age in both genotypes and TL shortening was accelerated in brain of APP/PS1. Age-related increase of micronuclei was observed in both genotypes but was accelerated in APP/PS1. We integrated TL and micronuclei data with data on cognition performance and brain amyloidosis. TL and micronuclei were linearly correlated with cognition performance or Aß40 and Aß42 levels in both genotypes but to a greater extent in APP/PS1. These associations in APP/PS1 mice were dominantly driven by females. Together, our findings provide foundational knowledge to infer the TL and micronuclei trajectories in APP/PS1 mice during disease progression, and strongly support that TL attrition and micronucleation are tightly associated with AD pathogenesis in a female-biased manner.

High-Safety Lithium-Ion Batteries with Silicon-Based Anodes Enabled by Electrolyte Design.

High-energy-density lithium-ion batteries (LIBs) with high safety have long been pursued for extending the cruise range of electric vehicles. Owing to the high gravimetric capacity, silicon is a promising alternative to the convention graphite anode for high-energy LIBs. However, it suffers from intrinsic poor interfacial stability with liquid electrolytes, inevitably increasing the risk of thermal runaway and posing serious safety challenges. In this review, we will focus on mitigating thermal runaway of silicon anodes-based LIBs from the perspective of electrolyte design. First, the thermal runaway mechanism of LIBs is briefly introduced, while the specific thermal failure reactions associated with silicon anodes and electrolytes are discussed in detail. We then summarize the safety countermeasures (e. g., thermally stable solid electrolyte interphase, nonflammable electrolytes, highly stable lithium salts, mitigating electrode crosstalk, and solid-state electrolytes) enabled by customized electrolyte design to address these triggers of thermal runaway. Finally, the remaining unanswered questions regarding the thermal runaway mechanism are presented, and future directions to achieve intrinsically safe electrolytes for silicon-based anodes are prospected. This review is expected to provide insightful knowledge for improving the safety of LIBs with silicon-based anodes.