bluuebunny/crossref_metadata_2025_split huggingface dataset:
Double‐Edged Dissolving Microneedle Patches Loaded with Zn/Ce Composites and Vancomycin for Treatment of Drug‐Resistant Bacterial Infected Skin Abscess
Authors: Jin Yu , Chu Zhaoyou , Zhu Pengfei , Jiang Yechun , Shen Hui , Wang Yujie , Wu Silong , Yang Miaomiao , Qian Haisheng , Ma Yan 
Published: February 2025

AbstractThe management of abscess wounds induced by antibiotic‐resistant bacterial infections has become increasingly formidable due to the widespread overutilization and misuse of antimicrobial agents. This study presents an innovative dissolvable microneedle (MN) patch incorporating Au@ZnO/Ce nanocomposites and vancomycin (AZC/Van@MN), exhibiting robust antimicrobial and anti‐inflammatory properties, meticulously engineered for the therapeutic intervention of abscess wounds. The developed AZC/Van@MN patch demonstrates exceptional biocompatibility as evidenced by comprehensive histopathological and hematological assessments. It effectively eradicates bacterial colonies through the synergistic action of Van and mild photothermal therapy (PTT, ≤42 °C). Transcriptomic analysis elucidates that the antibacterial mechanism involves the upregulation of riboflavin biosynthesis and the suppression of arginine biosynthesis pathways. Furthermore, AZC/Van@MN significantly reduces abscess dimensions, bacterial load, and inflammatory response, while simultaneously enhancing wound healing via accelerated re‐epithelialization and angiogenesis. This double‐edged MN patch represents a promising strategy for combating skin abscesses instigated by antibiotic‐resistant bacteria.
A Copper‐Manganese Based Nanocomposite Induces Cuproptosis and Potentiates Anti‐Tumor Immune Responses
Authors: Xie Luoyingzi , Gong Jie , He Zhiqiang , Zhang Weinan , Wang Haoyu , Wu Shitao , Wang Xianxing , Sun Pijiang , Cai Lei , Wu Zhongjun , Wang Huaizhi 
Published: February 2025

AbstractCancer is one of the most important challenges worldwide with an increasing incidence. However, most of patients with malignant cancer receiving traditional therapies have tumor recurrence and short‐term 5‐year survival. Herein, a novel Cu2O‐MnO@PEG (CMP) nanomaterial is developed to treat tumors. CMP directly mediates cuproptosis in tumor cells. Meanwhile, CMP potentiates anti‐tumor immune responses in the tumor microenvironment (TME) to induce tumor regression. CMP improves the tumor antigen processing and presentation of dendritic cells and tumor‐associated macrophages, and further promotes CD8+ T cell responses, especially for cytotoxic CD8+ T cells and transitory exhausted CD8+ T cells. Additionally, CMP downregulates the proportion of Treg cells and CTLA‐4 expression on Treg cells. Notably, CMP induces systemic immune responses against distant tumors and long‐term immune memory. Furthermore, CMP synergized with PD‐L1 mAb promotes tumor inhibition and sustains the anti‐tumor efficacy post PD‐L1 mAb treatment. Collectively, this strategy has the clinically therapeutic potential for tumors by facilitating cuproptosis in tumor cells and anti‐tumor immune responses.
Emerging Nitrogen and Sulfur Co‐doped Carbon Materials for Electrochemical Energy Storage and Conversion
Authors: Chen Xia , Feng Ping , Zheng Yong , Li Hui , Zhang Youfang , Shen Yi , Yan Yan , Liu Mingkai , Ye Liqun 
Published: February 2025

AbstractThe growing global energy demands, coupled with the imperative for sustainable environmental challenges, have sparked significant interest in electrochemical energy storage and conversion (EESC) technologies. Metal‐free heteroatom‐doped carbon materials, especially those codoped with nitrogen (N) and sulfur (S), have gained prominence due to their exceptional conductivity, large specific surface area, remarkable chemical stability, and enhanced electrochemical performance. The strategic incorporation of N and S atoms into the carbon framework plays a pivotal role in modulating electron distribution and creating catalytically active sites, thereby significantly enhancing the EESC performance. This review examines the key synthetic strategies for fabricating N, S codoped carbon materials (NSDCMs) and provides a comprehensive overview of recent advancements in NSDCMs for EESC applications. These encompass various electrochemical energy storage systems such as supercapacitors, alkali‐ion batteries, and lithium–sulfur batteries. Energy conversion processes, including hydrogen evolution, oxygen reduction/evolution, and carbon dioxide reduction are also covered. Finally, future research directions for NSDCMs are discussed in the EESC field, aiming to highlight their promising potential and multifunctional capabilities in driving further advancements in electrochemical energy systems.
Helical Soft Robots with Magnetic and Photocatalytic Components for Water Remediation
Authors: Xuan Pengyang , Gong Jiaming , Fu Tongfang , Zhou Yazhou , Qin Jingjing , Chen Haoxiang , Wang Tianlu , Xue Guohao , Peng Xiaoyuan , Qian Yun , Osella Silvio , Zbořil Radek , Hofkens Johan , Müllen Klaus , Lai Feili , Liu Tianxi 
Published: February 2025

AbstractSoft robots have demonstrated exceptional potential in various applications, particularly in biomedicine, which is attributed to their motional agility and machinability. However, their potential applications in water remediation have not been fully explored. The main challenge is to achieve both precise motion and efficient pollutant degradation. Herein, a modular design is reported for fabricating soft robots. These robots are designed with spatially separated components. One is superparamagnetic iron oxide nanoparticles for magnetic actuation and the other is photocatalysts for targeted pollutant degradation (i.e., methyl orange, congo red, rhodamine B, tetracycline, and soybean oil). The helical structure enables diverse programmable motional modes, including high‐speed propulsion up to 3.54 mm s−1. At the same time, the photocatalytic module enables efficient degradation of multiple pollutants with excellent reusability. The modular design combines structural stability with multifunctionality and opens new opportunities for soft robots in environmental remediation.
Silk Nanofibrillar Aerogel as Sustainable Filters for Environmental Purification
Authors: Li Xiufang , Ba Xiaorong , Dai Yunfeng , Feng Yanfei , Yan Shuqin , Zhang Qiang , You Renchuan 
Published: February 2025

AbstractEnvironmental pollution threatens human health and ecosystem sustainability, boosting a huge demand for filters. However, the petroleum‐based filters are likewise environmentally hazardous since their non‐degradability results in disposal in landfills. Although biodegradable synthetic polymers have been contemplated as alternative filtration materials, their utilization in fabricating ultrafine fibers to construct effective 3D filters remains an obstacle. In this study, all‐biomass nanofibrous filters are developed with versatility and biodegradability based on silk nanofibrils (SNFs). Bulk aerogels with tunable structures can be fabricated through solvent‐mediated ice crystal growth by using SNFs as a building block. The SNF‐based aerogel exhibited excellent water purification performance, as evidenced by their ability to remove micro‐nano plastics, organic dyes, and heavy metal ions. The practicability of this filter system is demonstrated by the successful production of purified water from simulated polluted water and dirty river water. Moreover, the versatility of the SNF aerogel is further proven by superior air filtration performance against PM0.3 and oily smoking. Furthermore, the SNF aerogel filter has a low environmental impact as it can be safely biodegraded in the natural environment, with a ratio of 73.8% after one year of landfill. This work provides a path toward sustainable purification treatment of environmental pollution.
Why and How We Combine Multiple Deep Learning Models With Functional Overlaps
Authors: Ma Mingliang , Li Yanhui , Chen Yingxin , Chen Lin , Zhou Yuming 
Published: February 2025

ABSTRACTThe evolution (e.g., development and maintenance) of deep learning (DL) models has attracted much attention. One of the main challenges during the development and maintenance of DL models is model training, which often requires a lot of human resources and computing power (such as labeling costs and parameter training). In recent years, to alleviate this problem, researchers have introduced the idea of software engineering (SE) into DL. Researchers consider the DL model a new type of software, borrowing the practice of traditional software reuse, that is, focusing on the reuse of DL models to improve the quality of DL model development and maintenance. This paper focuses on more complex model reuse scenarios, where developers need to combine multiple models with functional overlaps. We explore whether the model combination technique can meet the requirements for such scenarios. We have conducted an empirical study of the research scenario and found that a model composition approach was needed to meet the requirements. Furthermore, we propose a model combination method based on concatenation‐parallel called MCCP. First, the multiple models' hidden layer features are connected, and then the multiple models are connected in parallel to construct a joint model with all output categories. The joint model is trained to achieve unified requirements under the limited marking cost. Through experiments on data sets in nine domains and five model structures, the following two conclusions are drawn: (1) we observe noticeable differences (38% at most) in the performance of multiple models within overlapping category data, which calls for effective model combination techniques. (2) MCCP is more effective than the baseline, which performs the best in eight of the nine domains. Our research shows that the joint model generated by combining models with overlapping functions can meet the requirements of complex model reuse scenarios.
Succinonitrile‐Rich Electrolyte Solvation Structure Enables Wide‐Temperature‐Range Operation of Lithium‐Metal Batteries
Authors: Kim Boguen , Kim Saehun , Lee Dong Gyu , Lee Donghyun , Son Junsu , Seong Hyeonseok , Kim Bumjoon J. , Lee Tae Kyung , Choi Nam‐Soon 
Published: February 2025

AbstractStable lithium‐metal batteries (LMBs) with wide‐temperature‐range operability can be achieved through the rational design of electrolytes based on their physicochemical and electrochemical characteristics, such as their freezing behavior and functional integrity at battery heterointerfaces. This study demonstrates that succinonitrile (SN)‐dominated solvation chemistry and fluoroethylene carbonate (FEC)‐derived interface engineering can enable the wide‐temperature‐range operation of LMBs while optimally tuning the microstructures of the electrolyte for facile Li‐ion conduction. A mechanically and chemically stable LiF‐rich primary solid–electrolyte interphase (SEI) is constructed using FEC and 1,1,2,2‐tetrafluoroethyl 2,2,3,3‐tetrafluoropropyl ether (TTE). Subsequently, lithium bis(trifluoromethanesulfonyl) imide and SN are utilized to produce ion‐conductive Li3N in the SEI. SN promoted the build‐up of an electron‐ and N‐rich C≡N based cathode–electrolyte interface that could mitigate transition metal‐ion dissolution, microcrack formation, and structural degradation in a LiNi0.8Co0.1Mn0.1O2 (NCM811) cathode. TTE, which exhibits low solvation power, enabled the formation of desirable Li‐ion conduction pathways, including a deep depression of the melting point of the electrolyte and low‐viscosity Li‐ion channels, for low‐temperature operation. The integration of interface engineering and electrolyte chemistry provides an efficient strategy for preparing Li|NCM811 full cells demonstrating stable operation under various temperature conditions.
Battery Cathode Recycling With Superior Dissolution Kinetics by Laser Augmentation
Authors: Wang Zixu , Hu Xin , Zhang Hao , Song Yaduo , Yao Yonggang , Huang Yunhui 
Published: February 2025

AbstractThe application of lithium‐ion batteries challenges environmental sustainability and calls for efficient recycling toward circular economics. Hydrometallurgical recycling, despite being commercialized, still faces challenges such as harsh chemicals, high secondary waste generation, and low efficiencies. Intuitively, higher temperature leads to exponentially higher reaction kinetics (following Arrhenius's law), yet the dissolution temperature is limited to below 100 °C while heating the solution means more energy consumption. This study presents a laser‐assisted wet leaching (Laser‐WL) method that enables decoupled particle/solution temperatures, where the cathode particles are effectively heated by laser adsorption (30 W) to accelerate the dissolution kinetics (7–10 fold) while the solution remains cool for energy saving. Besides, physical laser ablation helps remove the robust solid electrolyte interface and cracks the particles to expose active materials, shortening the diffusion pathways and further enhancing the leaching kinetics. Therefore, Laser‐WL can achieve an extraction rate of 95.6% in 15 min (traditional method >3 h). It reduced the consumption of concentrated HCl by 87%, and water consumption by 27%. The method is applicable to various cathode materials and works for weak acids, thus presenting a sustainable and economically viable solution for metal recycling.
General decay rate of a variable coefficient wave equation with boundary viscoelastic damping
Authors: Liu Yu‐Xiang 
Published: February 2025

AbstractIn this paper, we consider the general decay of wave equation with variable coefficients and boundary viscoelastic damping. Applying the Riemannian geometry method and convex analysis, we establish the energy decay rate which is given by solutions of a first‐order nonlinear, dissipative ordinary differential equation under a wider assumption of the viscoelastic damping and some conditions on the coefficient matrix.
Non‐local wave equation: Time delay with second order terms
Authors: Atanackovic Teodor M. , Kacapor Enes , Djekic Diana Dolicanin , Gilic Ersin 
Published: February 2025

AbstractIn the constitutive equation of nonlocal elasticity with a kernel of general fractional derivative of Riesz type, the time delay of the form is introduced. We call the function the constitutive equation of time delay. Second‐order terms in expansion is taken into account, and the resulting generalized wave equation is derived. The influence of parameters on the solution is studied numerically. The numerical analysis shows that time delay influences both the speed of propagation of maximal displacement and the amplitude of the wave.
Description of chemical systems by means of response functions
Authors: Franco E. , Kepka B. , Velázquez J. J. L. 
Published: February 2025

Abstract
In this paper we introduce a formalism that allows to describe the response of a part of a biochemical system in terms of renewal equations. In particular, we examine under which conditions the interactions between the different parts of a chemical system, described by means of linear ODEs, can be represented in terms of renewal equations. We show also how to apply the formalism developed in this paper to some particular types of linear and non-linear ODEs, modelling some biochemical systems of interest in biology (for instance, some time-dependent versions of the classical Hopfield model of kinetic proofreading). We also analyse some of the properties of the renewal equations that we are interested in, as the long-time behaviour of their solution. Furthermore, we prove that the kernels characterising the renewal equations derived by biochemical system with reactions that satisfy the detail balance condition belong to the class of completely monotone functions.
L-tyrosine inhibits the formation of amyloid fibers of human lysozyme at physiological pH and temperature
Authors: López Santos , Rojo-Domínguez Arturo , López-Simeon Roxana , Sosa-Peinado Alejandro , Nájera Hugo 
Published: February 2025

Abstract
Amyloid fibers are implicated in numerous diseases, making their study crucial for identifying effective therapeutic compounds. This research highlights the ability of L-tyrosine to inhibit the formation of amyloid fibers in human lysozyme. At a 1:1 molar ratio under physiological conditions (pH 7.4, 37 °C), L-tyrosine significantly reduces amyloid fiber formation, as evidenced by a decrease in thioflavin T fluorescence. Differential scanning calorimetry (DSC) shows a major energy requirement for temperature denaturation when the lysozyme is in the presence of L-tyrosine. Additionally, chemical denaturation experiments reveal a shift in the intrinsic fluorescence spectrum of lysozyme in the presence of L-tyrosine, indicating a direct interaction. Computational docking studies with Molecular Operating Environment (MOE) further confirm that L-tyrosine binds effectively, exhibiting similar binding energies to those of the natural substrate. This study underscores L-tyrosine’s potential as a strong inhibitor of amyloid fiber formation, demonstrating its stabilizing effect on lysozyme and its promise in therapeutic applications.
Plastic reduction and firm characteristics: Evidence from (non-) financial information in Japan
Authors: Nomura Kai , Yamamoto Masashi 
Published: February 2025

Abstract
The plastic pollution problem is an international environmental issue of interest. To address the misplacement of used plastics, we need to regulate plastic use; however, such a legislation process typically takes a long time. Thus, there are many examples of companies taking voluntary action ahead of regulation. Why does a profit-maximizing company pay the additional cost of reducing plastics voluntarily? To answer this question, it is important to determine the identity of the active actors. The objective of this study is to understand the characteristics of companies that are active in voluntary reduction efforts; we do so by examining both financial and nonfinancial information. Our empirical results indicate that in addition to the level of awareness and materiality of plastic pollution to their business, larger firms of a specific business type are more likely to introduce voluntary reduction initiatives.
Unveiling the macrosegregation formation mechanism and its impact on properties in dissimilar welding between CoCrFeMnNi high-entropy alloy and 316 stainless steel
Authors: Shen Jiajia , Kim Rae Eon , Mestre Martim , Lopes J. G. , He Jingjing , Yang Jin , Zeng Zhi , Schell N. , Kim Hyoung Seop , Oliveira J. P. 
Published: February 2025

Abstract
High-entropy alloys (HEAs) are increasingly preferred as structural materials in nuclear engineering and aerospace applications. These fields often require the design of dissimilar joints. Here, gas tungsten arc welding (GTAW) was used for the first time to join CoCrFeMnNi HEAs with 316 stainless steel. Microstructural characterization, including electron microscopy, high-energy synchrotron X-ray diffraction, and thermodynamic calculations, along with micro- and macroscale mechanical assessments, was utilized. These methods were instrumental in evaluating and clarifying the effects of the non-equilibrium solidification and weld thermal cycle on the microstructure evolution of the joint. In the fusion zone (FZ), distinctive peninsula-shaped macroscopic segregation area is observed, with its formation being related to the liquidus temperature differences between the base materials (BMs) and the welded metal, compounded by the Marangoni effect. The weld thermal cycle was found to promote multiple solid-state phase transformations in the heat-affected zone (HAZ) adjacent to the CoCrFeMnNi BM, leading to varying degrees of softening. The HAZ near the 316 stainless steel BM maintained its original microstructural and mechanical properties. Fracture predominantly occurred in the FZ, mainly due to the interplay of large columnar grains, macrosegregation effects, and emergence of BCC and σ brittle phases due to the complex chemistry within this region. Thermodynamic modeling validated the formation of these phases. The ultimate tensile strength and elongation at room temperature were approximately ≈493 MPa and ≈10.70%, respectively.
Optimizing Gold Recovery from Witwatersrand-Type Ores Using Alkaline Glycine Leaching and Conditional Simulation
Authors: Nwaila Glen T. , Notole Viwe , Alex Samira , Ghorbani Yousef 
Published: February 2025

Abstract
Witwatersrand-type gold deposits in South Africa are generally amenable to cyanidation due to their free-milling nature. However, the relatively easy-to-process gold ores have been mostly depleted, and the remaining ores are of low-grade combined with semi-refractory properties. Here, we use an integrated approach to understand the mineralogical and textural characteristics of the Witwatersrand-type gold ores and to explore the effectiveness of glycine-leaching gold recovery. Analysis of sulfide minerals using 3D micro-X-ray computed tomography data shows these minerals can be used as predictive indicators for feed gold grade as they either co-exist and/or encapsulate gold. Primary experimental results demonstrate that alkaline glycine can recover > 80% Au in 100 hours at ambient temperatures. Glycine thus holds promise for gold recovery of low-grade free-milling and semi-refractory Witwatersrand-type gold ores. We also note that the presence of carbonaceous matter in ores, such as in the Black Reef orebody, adversely affects gold recovery. Ore blending may therefore be a suitable option to remediate poor gold recovery. Lastly, we demonstrate that stochastic simulations and data analytics can help augment primary experimental data to estimate uncertainty, providing a better understanding of experimental results, and thus providing future research directions.
Diagnosing Human-Object Interaction Detectors
Authors: Zhu Fangrui , Xie Yiming , Xie Weidi , Jiang Huaizu 
Published: February 2025

Abstract
We have witnessed significant progress in human-object interaction (HOI) detection. However, relying solely on mAP (mean Average Precision) scores as a summary metric does not provide sufficient insight into the nuances of model performance (e.g., why one model outperforms another), which can hinder further innovation in this field. To address this issue, we introduce a diagnosis toolbox in this paper to offer a detailed quantitative breakdown of HOI detection models, inspired by the success of object detection diagnosis tools. We first conduct a holistic investigation into the HOI detection pipeline. By defining a set of errors and using oracles to fix each one, we quantitatively analyze the significance of different errors based on the mAP improvement gained from fixing them. Next, we explore the two key sub-tasks of HOI detection: human-object pair localization and interaction classification. For the pair localization task, we compute the coverage of ground-truth human-object pairs and assess the noisiness of the localization results. For the classification task, we measure a model’s ability to distinguish between positive and negative detection results and to classify actual interactions when human-object pairs are correctly localized. We analyze eight state-of-the-art HOI detection models, providing valuable diagnostic insights to guide future research. For instance, our diagnosis reveals that the state-of-the-art model RLIPv2 outperforms others primarily due to its significant improvement in multi-label interaction classification accuracy. Our toolbox is applicable across various methods and datasets and is available at https://neu-vi.github.io/Diag-HOI/.