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Owing to the scarcity of critical metal resources such as lithium and cobalt, as well as the urgent need for environmental protection, the efficient regeneration of cathode materials for lithium-ion batteries (LIBs) has become a core issue in supporting the sustainable development of the electric vehicle industry. At present, the industrial sector mainly relies on pyrometallurgy and hydrometallurgy technologies to recover metals, but the former is limited by high energy consumption and greenhouse gas emissions, while the latter faces the challenges of chemical contamination and high cost. Therefore, there is an urgent need to develop green and economical alternative technologies to avoid the environmental risks caused by landfills of used batteries and achieve resource circulation. In this study, the feasibility of emerging processes such as bio-metallurgy (microbial leaching), eutectic solvents (DES), and direct regeneration was systematically evaluated. Based on this, it is proposed to optimize process efficiency by integrating multiple technologies (such as microwave-assisted enhanced reaction and biomass co-reduction), and promote policy guidance and standardized design, to accelerate the transformation of the LIB industry to a circular economy and contribute to the global carbon neutrality goal and sustainable resource management.

This study aims to reflect the grey information coverage and complex interactions effect in digital economy development. Therefore, a multi-attribute decision making method based on the grey interaction relational degree of the normal cloud matrix (GIRD-NCM) model is proposed. First, the original information coverage grey numbers are transformed into normal cloud matrixes, and then a novel Minkowski distance between normal clouds is proposed by using different information principles. Second, the GIRD-NCM model is established according to the Choquet fuzzy integral and grey relational degree. Finally, the dynamic comprehensive evaluation of digital economy development in China from 2013 to 2020 is conducted. The implementation, availability, and feasibility of the GIRD-NCM model are verified by comparative analysis with three existing evaluation models. The empirical findings reveal a stable growth trend in China’s digital economy, with an annual growth rate of 7.87%, however, there are notable regional development disparities. The change in interaction degree has no effect on the rankings of provinces that are in the lead or have a moderately high level of digital economy development, but has a positive and negative impact on the rankings of these provinces with high and low levels of digital economy development, respectively.

Introduction Heart failure (HF) is a complex clinical condition characterized by impaired cardiac function and progressive structural remodeling. To elucidate the molecular mechanisms driving HF, this study aimed to identify key regulatory hub genes, explore their functional relevance, and assess their diagnostic and therapeutic potential. Methods Four public microarray datasets (GSE161472, GSE147236, GSE116250, and GSE46224) were retrieved from the Gene Expression Omnibus (GEO) database. Differential expression analysis using the limma package in R identified Differentially expressed genes (DEGs), which were further analyzed via Venn diagrams, STRING PPI networks, and Cytoscape’s CytoHubba plugin to determine top hub genes. RT-qPCR and Western blotting were used to validate gene expression in HF and normal cardiomyocyte cell lines. Functional assays (proliferation, colony formation, and wound healing) were conducted following overexpression of COL9A1 and MTIF3. miRNA regulation and immune cell infiltration were analyzed using TargetScan and CIBERSORT, respectively. Enrichment analysis was performed via DAVID, and drug prediction was conducted using DGIdb. Results Four hub genes—COL9A1, MTIF3, MRPS25, and HMGN1—were consistently downregulated in HF and exhibited high diagnostic potential (AUC > 0.8). Overexpression of COL9A1 and MTIF3 significantly reduced cell proliferation, colony formation, and migration in HF cell lines. Immune infiltration analysis revealed strong negative correlations between hub gene expression and various immune cell types. Drug prediction identified Milrinone as a potential therapeutic candidate targeting COL9A1. Conclusion COL9A1, MTIF3, MRPS25, and HMGN1 emerge as critical biomarkers and regulators in HF, offering promising avenues for diagnosis, mechanistic understanding, and targeted therapy development.

A novel composite based on melamine cyanurate (MC) and aluminum hypophosphite (ALHP) was successfully incorporated into high-impact polystyrene (HIPS). The flame retardancy and combustion properties of the composite were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, and thermogravimetric analysis as well as by vertical combustion, limiting oxygen index (LOI), and cone calorimetry tests. When the ratio of MC to ALHP was 1:4 (comprising a total of 20 wt.% of the mass of HIPS), the resulting composite (HIPS-5) reached a UL-94 V-0 classification, and the LOI increased from 19.3 to 26.7%. Relative to pure HIPS, the peak heat release rate of HIPS-5 decreased from 807.64 to 180.71 kw m –2 , and the total heat release decreased from 93.20 to 60.43 MJ m –2 . In addition, the carbon residue of HIPS-5 increased from 6.83 to 21.14%, which was higher than all of the other samples. SEM analysis of the carbon residue after combustion in the cone calorimeter showed that a dense and stable carbon layer was formed on the surface of the HIPS composites. These data indicated that MC and ALHP demonstrated an apparent synergistic role as a flame-retardant system to protect HIPS from further combustion. Graphical abstract

Silica aerogels are considered as the distinguished materials of the future due to their extremely low thermal conductivity, low density, and high surface area. They are widely used in construction engineering, aeronautical domains, environmental protection, heat storage, etc. However, their fragile mechanical properties are the bottleneck restricting the engineering application of silica aerogels. This review briefly introduces the synthesis of silica aerogels, including the processes of sol–gel chemistry, aging, and drying. The effects of different silicon sources on the mechanical properties of silica aerogels are summarized. Moreover, the reaction mechanism of the three stages is also described. Then, five types of polymers that are commonly used to enhance the mechanical properties of silica aerogels are listed, and the current research progress is introduced. Finally, the outlook and prospects of the silica aerogels are proposed, and this paper further summarizes the methods of different polymers to enhance silica aerogels.

Energetic materials (EMs) are the core materials of weapons and equipment. Achieving precise molecular design and efficient green synthesis of EMs has long been one of the primary concerns of researchers around the world. Traditionally, advanced materials were discovered through a trial-and-error processes, which required long research and development (R&D) cycles and high costs. In recent years, the machine learning (ML) method has matured into a tool that compliments and aids experimental studies for predicting and designing advanced EMs. This paper reviews the critical process of ML methods to discover and predict EMs, including data preparation, feature extraction, model construction, and model performance evaluation. The main ideas and basic steps of applying ML methods are analyzed and outlined. The state-of-the-art research about ML applications in property prediction and inverse material design of EMs is further summarized. Finally, the existing challenges and the strategies for coping with challenges in the further applications of the ML methods are proposed.

The lower flammability limit (LFL) is one of the most important parameters for evaluating the fire and explosion hazards of flammable gases or vapors. This study proposed quantitative structure−property relationship (QSPR) models to predict the LFL of binary hydrocarbon gases from their molecular structures. Twelve different mixing rules were employed to derive mixture descriptors for describing the structures characteristics of a series of 181 binary hydrocarbon mixtures. Genetic algorithm (GA)-based multiple linear regression (MLR) was used to select the most statistically effective mixture descriptors on the LFL of binary hydrocarbon gases. A total of 12 multilinear models were obtained based on the different mathematical formulas. The best model, issued from the norm of the molar contribution formula, was achieved as a six-parameter model. The best model was then rigorously validated using multiple strategies and further extensively compared to the previously published model. The results demonstrated the robustness, validity, and satisfactory predictivity of the proposed model. The applicability domain (AD) of the model was defined as well. The proposed best model would be expected to present an alternative to predict the LFL values of existing or new binary hydrocarbon gases, and provide some guidance for prioritizing the design of safer blended gases with desired properties.

A physical model experiment of flume block landslide was used to study the influence of landslide surge impact on underwater pipeline surface under different water depths. The influence of surge impact pressure on pipelines with different water depths and the impact pressure of surge at different angles of underwater pipelines wall were analyzed. And the relationship between the maximum impact pressure of underwater pipelines and the depth of water was obtained. The results indicated that with the decrease of the water depths, the maximum impact pressure at the wall of the underwater pipeline increases approximately linearly, and the slider is easier to form higher first wave height. The maximum impact pressure of the upper surface of the pipeline wall is greater than that of the lower surface of the pipeline wall under the same working conditions. It is also found that the smaller the depth of water, the larger the maximum pressure and average pressure at the measuring point would be and the greater the pressure fluctuation becomes when slider volume and landslide water inlet angle and speed remain the same.

Nowadays, the impact of engineered nanoparticles (NPs) on human health and environment has aroused widespread attention. It is essential to assess and predict the biological activity, toxicity, and physicochemical properties of NPs. Computation-based methods have been developed to be efficient alternatives for understanding the negative effects of nanoparticles on the environment and human health. Here, a classification-based structure-activity relationship model for nanoparticles (nano-SAR) was developed to predict the cellular uptake of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2). The norm index descriptors were employed for describing the structure characteristics of the involved nanoparticles. The Random forest algorithm (RF), combining with the Recursive Feature Elimination (RFE) was employed to develop the nano-SAR model. The resulted model showed satisfactory statistical performance, with the accuracy (ACC) of the test set and the training set of 0.950 and 0.966, respectively, demonstrating that the model had satisfactory classification effect. The model was rigorously verified and further extensively compared with models in the literature. The proposed model could be reasonably expected to predict the cellular uptakes of nanoparticles and provide some guidance for the design and manufacture of safer nanomaterials.

High-energy Ni-rich layered cathodes are critical for next-generation lithium-ion batteries yet remain limited by severe interfacial degradation and thermal vulnerability under high-voltage operation. In this work, a robust spinel-layered heterostructure is constructed by encapsulating LiNi0.8Co0.1Mn0.1O2 (NCM811) with a LiNi0.5Mn1.5O4 (LNMO) spinel shell via a scalable sol–gel route. Structural characterizations confirm that the coating maintains the secondary-particle architecture, while X-ray photoelectron spectroscopy reveals a chemically reconditioned interface, achieved by the scavenging residual lithium species and suppressing of rock-salt-like surface reconstruction. Consequently, the optimized 4 wt% LNMO@NCM811 electrode demonstrates significantly enhanced high-voltage (2.8–4.4 V) stability, maintaining 41.84% of its initial capacity after 200 cycles compared to only 15.75% for the pristine sample. Crucially, thermogravimetric-differential scanning calorimetry (TG-DSC) uncovers the kinetic origin of this safety improvement: the spinel shell alters the thermal decomposition pathway, delaying the 10% mass loss temperature (T10%) from 515.2 °C to 716.6 °C and suppressing the total exothermic heat release from 208.3 J g−1 to 81.5 J g−1. Collectively, these results demonstrate that the co-free spinel encapsulation is a dual-functional strategy to simultaneously stabilize surficial chemistry and intrinsically enhance the thermal safety of Ni-rich cathodes for carbon-neutral energy storage applications.