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Structural batteries, which integrate mechanical load-bearing with electrochemical energy storage, offer a transformative solution for next-generation electric mobility. In this work, we present a novel and scalable dry- processing strategy for fabricating structural electrodes. Lithium nickel manganese cobalt oxide (NMC111) and graphite are used as the active materials for the positive and negative electrodes, respectively, with carbon fiber fabric serving as structural reinforcement and current collector. A conductive interfacial coating is applied to promote strong adhesion between the dry-processed electrodes and the carbon fiber fabric, ensuring both robust structural integrity and electrochemical performance. This approach enables ultrahigh mass loadings exceeding 30 mg cm− 2 —among the highest reported for structural battery electrodes. The resulting structural pouch cell delivers an energy density of 127 Wh kg− 1 and a Young's modulus of 3.2 GPa, yielding a multifunctional efficiency greater than 1.0. These results outperform conventional designs that separate structural and energy storage functions, offering significant mass savings at the system level. Overall, this work demonstrates the practical feasibility of integrating dry-processed electrodes into multifunctional battery architectures and provides a promising pathway toward lightweight, high-performance structural energy storage systems. 

This paper studies the performance of large language models (LLMs), particularly regarding demographic fairness, in solving real-world healthcare tasks. We evaluate state-of-the-art LLMs with three prevalent learning frameworks across six diverse healthcare tasks and find significant challenges in applying LLMs to real-world healthcare tasks and persistent fairness issues across demographic groups. We also find that explicitly providing demographic information yields mixed results, while LLM`s ability to infer such details raises concerns about biased health predictions. Utilizing LLMs as autonomous agents with access to up-to-date guidelines does not guarantee performance improvement. We believe these findings reveal the critical limitations of LLMs in healthcare fairness and the urgent need for specialized research in this area. 

This paper studies the performance of large language models (LLMs), particularly regarding demographic fairness, in solving real-world healthcare tasks. We evaluate state-of-the-art LLMs with three prevalent learning frameworks across six diverse healthcare tasks and find significant challenges in applying LLMs to real-world healthcare tasks and persistent fairness issues across demographic groups. We also find that explicitly providing demographic information yields mixed results, while LLM’s ability to infer such details raises concerns about biased health predictions. Utilizing LLMs as autonomous agents with access to up-to-date guidelines does not guarantee performance improvement. We believe these findings reveal the critical limitations of LLMs in healthcare fairness and the urgent need for specialized research in this area. 

Cadmium sulfide (CdS) pigments have degraded in several well-known artworks, but the influence of pigment properties and environmental conditions on the degradation process have yet to be fully understood. Traditional non-destructive analysis techniques primarily focus on macroscopic degradation, whereas microscopic information is typically obtained with invasive techniques that require sample removal. Here, we demonstrate the use of pump-probe microscopy to nondestructively visualize the three-dimensional structure and degradation progress of CdS pigments in oil paints. CdS pigments, reproduced following historical synthesis methods, were reproduced as oil paints and artificially aged by exposure to high relative humidity and light. The degradation of CdS to CdSO₄·xH₂O was confirmed by both FTIR (Fourier-transform infrared) and XPS (x-ray photoelectron spectroscopy) experiments. During the degradation process, optical pump-probe microscopy was applied to track the degradation progress in single grains, and volumetric imaging revealed early CdS degradation of small particles and on the surface of large particles. This indicates that the particle dimension influences the extent and evolution of degradation of historical CdS. In addition, the pump-probe signal decrease in degraded CdS is observable before visible changes to the eye, demonstrating that pump-probe microscopy is a promising tool to detect early-stage degradation in artworks. 

Health coaching helps patients achieve personalized and lifestyle-related goals, effectively managing chronic conditions and alleviating mental health issues. It is particularly beneficial, however cost-prohibitive, for low-socioeconomic status populations due to its highly personalized and labor-intensive nature. In this paper, we propose a neuro-symbolic goal summarizer to support health coaches in keeping track of the goals and a text-units-text dialogue generation model that converses with patients and helps them create and accomplish specific goals for physical activities. Our models outperform previous state-of-the-art while eliminating the need for predefined schema and corresponding annotation. We also propose a new health coaching dataset extending previous work and a metric to measure the unconventionality of the patient’s response based on data difficulty, facilitating potential coach alerts during deployment. 

In this study, we present the probe SATE‐G3P‐N3 as a novel tool for metabolic labeling of glycerolipids (GLs) to investigate lipid metabolism in yeast cells. By introducing a clickable azide handle onto the glycerol backbone, this probe enables general labeling of glycerolipids. Additionally, this probe contains a caged phosphate moiety at the glycerol sn‐3 position to not only facilitate probe uptake by masking negative charge but also to bypass the phosphorylation step crucial for initiating phospholipid synthesis, thereby enhancing phospholipid labeling. The metabolic labeling activity of the probe was thoroughly assessed through cellular fluorescence microscopy, mass spectrometry (MS), and thin‐layer chromatography (TLC) experiments. Fluorescence microscopy analysis demonstrated successful incorporation of the probe into yeast cells, with labeling predominantly localized at the plasma membrane. LCMS analysis confirmed metabolic labeling of various phospholipid species (PC, PS, PA, PI, and PG) and neutral lipids (MAG, DAG, and TAG), and GL labeling was corroborated by TLC. These results showcased the potential of the SATE‐G3P‐N3 probe in studying GL metabolism, offering a versatile and valuable approach to explore the intricate dynamics of lipids in yeast cells. 

This review is focused on an attractive class of polymer‐derived high‐temperature ceramics, namely, polymer‐derived nonoxide materials. With a brief introduction of high‐temperature nonoxides, the origin of using polycarbosilane (PCS) polymer melt spinning to synthesize silicon carbide (SiC) fibers is traced back. For SiC formation, the four stages for the conversion from polymer precursors to microcrystalline ceramics are examined first: crosslinking, polymer decomposition, ceramic formation, and crystallization. Also, the important parameters related to PCS pyrolysis are explained, and polymer‐derived SiC microstructures and compositions are evaluated. Solid‐solution carbides and transition metal carbides are further reviewed. For boride materials, the discussion is focused on transition metal borides and boride composites. Similar to PCS conversion to SiC, nitride materials mostly start with polycarbosilazane (PSZ) precursors and form into the final materials through pyrolysis. With different carbide and nitride precursors mixed and pyrolyzed together, high‐temperature nonoxide composites are formed. Such molecular‐level intermixing and versatile capability of forming different shapes enable many exciting properties. Among these are mechanical and thermal properties, along with electrical conductivity, electromagnetic shielding, and charge storage capability. An overview of applications of polymer‐derived nonoxides is provided, followed by a summary and outlook. 

The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results.