Search Results (1,062)

The cement industry plays a critical role in infrastructure development, but is a major contributor to CO₂ emissions, driving the search for low-carbon binders that can also valorize industrial wastes. This review examines the engineering performance of red mud (RM)-based binder systems, highlighting the relationships between mixture design, processing, fresh-state behavior, mechanical properties, durability, and microstructural evolution. Special attention is given to how RM’s particle characteristics and mineralogical/chemical composition influence reactivity during geopolymerization, thereby affecting strength development and pore structure. Across the literature, moderate RM incorporation (commonly ≤15–20%) generally preserves workable fresh properties and adequate compressive strength, whereas higher RM contents (≥30%) often increase total porosity and pore connectivity, resulting in reductions in strength and durability. To mitigate these drawbacks, effective strategies such as thermal activation of RM and synergistic blending with supplementary cementitious materials like ground granulated blast-furnace slag and phosphogypsum are consistently reported to enhance reaction extent, densify the gel matrix, refine pore structure, and improve long-term durability. Overall, RM-based cementitious binders demonstrate considerable potential for both structural and non-structural applications; however, further research is needed on long-term performance under realistic exposure conditions, scale-up and quality control to address RM variability, and performance-based mix design guidelines to support reliable field implementation. Full article

In predicting tubing corrosion rates under multi-factor coupling, traditional methods often struggle to effectively analyze the nonlinear interactions among variables such as temperature, pressure, CO₂ partial pressure, and H₂S partial pressure, and they also lack interpretability in the prediction process. To address this, this study first establishes a corrosion dataset covering three typical steels (2205DSS, CT80, N80) through high-temperature and high-pressure weight-loss experiments. A machine learning framework is then proposed, integrating feature coupling analysis with a SHAP-Sobol-based interpretability framework. By incorporating the Context-Aware Sparse Attention (CASA) mechanism into the XGBoost ensemble, a CASA-XGBoost prediction model is constructed to systematically analyze interactions among multiple features and convert them into effective predictive information. Bayesian optimization enables adaptive hyperparameter tuning, while five-fold cross-validation tailored to different materials enhances model generalization and stability. Furthermore, the SHAP-Sobol weighting method systematically evaluates feature contributions and interaction effects across global sensitivity analysis and local sample interpretation, enabling feature coupling reconstruction. Experimental results demonstrate that the proposed framework outperforms benchmark models (Random Forest and Gaussian Process Regression) on three steel corrosion datasets, achieving test set R² values up to 0.98 with a low MAE and RMSE. The SHAP-Sobol-based interpretability framework also reveals material-specific sensitivities: 2205DSS is highly influenced by CO₂-H₂S interaction, CT80 by temperature–pressure coupling, and N80 shows reduced performance at high corrosion rates due to localized mechanisms. This study provides a reference for corrosion prevention and control by delivering high-accuracy and interpretable corrosion rate prediction for tubing under multi-factor coupling conditions, offering practical value for industrial modeling and decision-making. Full article

As new energy vehicles and energy storage systems become more common, safety accidents caused by lithium-ion batteries overheating have become more of a concern. Early detection based on distinctive gases (such as H₂ and CO) can give an earlier warning than typical monitoring methods like temperature, voltage, or impedance. Nonetheless, attaining high-precision identification in intricate mixed-gas settings continues to be difficult because of the considerable cross-sensitivity of metal oxide semiconductor (MOS) gas sensors. This research presents an ISA-LSTM-TCN multi-task learning model utilizing an enhanced spatial attention mechanism for the swift identification and concentration forecasting of distinctive gases during lithium-ion battery thermal runaway. The model improves key feature extraction and anti-noise performance by combining the long-term temporal modeling ability of the Long Short-Term Memory (LSTM) network with the multi-scale feature extraction ability of the Temporal Convolutional Network (TCN). It also adds an Improved Spatial Attention (ISA) module with a residual multiplication structure. Moreover, in a multi-task learning framework, joint optimization of gas categorization and concentration regression is facilitated using a hard parameter-sharing method. Tests using a built MOS sensor array dataset show that the model is 99.23% accurate at classifying gases and that the $R^2$ values for predicting H₂ and CO concentrations are 0.9510 and 0.8400, respectively. Tests on public datasets and in different noisy environments show that the model is even better at generalizing and is more robust. The results show that the suggested method allows for quick, accurate detection of thermal runaway gases. This makes it an effective and smart way to monitor battery safety warning systems. Full article

This study investigates the effect of mass-based replacement of natural coarse aggregate with electric arc furnace (EAF) slag on the performance of ordinary Portland cement (OPC) concrete. Replacement levels of 0%, 30%, 50%, and 100% were examined, with particular attention to the volumetric changes induced by the higher density of EAF slag, which leads to an increase in paste volume. Fresh, mechanical, durability-related, and microstructural properties were evaluated. Results show a continuous reduction in workability with increasing slag content, despite the increase in paste volume, indicating the dominant influence of aggregate morphology on rheological behavior. Mechanical performance exhibited a non-linear response. Within the tested series, the 50% replacement mixture showed the highest mean compressive and splitting tensile strengths; however, the compressive strength difference relative to the control mixture remained small and within typical experimental scatter. In contrast, water absorption decreased progressively, reflecting improved matrix densification. However, this densification did not translate into enhanced mechanical performance, highlighting a decoupling between durability-related indicators and strength. A screening-level CO₂ assessment further showed that reductions in aggregate-related emissions were offset by increased cement content associated with mass-based replacement. The results emphasize the importance of considering volumetric effects when interpreting the behavior and sustainability of slag-based concrete. Note: all strength comparisons are based on mean values from three-specimen sets without formal statistical testing and should be regarded as exploratory observations. Full article

Coal spontaneous combustion in gob areas is a major disaster endangering safe production in underground coal mines, and accurate prediction of carbon monoxide (CO), the core signature gas of coal oxidation, is critical for early warning and targeted prevention of mine fire disasters. However, CO concentration in gob areas is governed by complex gas–solid thermal–chemical multi-field coupling, presenting strong nonlinear characteristics. Traditional numerical methods suffer from prohibitive computational cost, purely data-driven models have inherent black-box defects, and conventional Physics-Informed Neural Networks (PINNs) require explicit full governing equations, which are hard to establish for such complex systems. This paper first proposes a Physics-Informed Modified Kolmogorov–Arnold Network (PIM-KAN), which deeply integrates domain physical knowledge with KAN architecture via a physics encoding layer, a residual-modified KAN layer, a multi-physics attention mechanism, and a multi-term physical consistency constraint framework. Experiments on 3125 real coal mine field samples show that the PIM-KAN achieves R² = 0.9965 and RMSE = 0.9290 ppm, reducing RMSE by 19.5% compared with MLP, and outperforming all baseline models. Ablation studies confirm the significant contribution of each innovation module, and attention weight analysis is highly consistent with Arrhenius reaction kinetics, verifying its superior prediction accuracy, physical consistency and intrinsic interpretability. Full article

COVID-19 remains a challenge to the global healthcare despite the end of the pandemic, including due to the significant involvement of children in the epidemic process. During the pandemic period, an increase in the incidence of Sudden Unexpected Infant Death (SUID) and Sudden Infant Death Syndrome (SIDS) was observed. Currently, their rates remain elevated compared to the prepandemic period. The pathogenetic mechanisms underlying the fulminant course of infection in infants leading to fatal outcomes remain insufficiently understood. In this study, we report for the first time the results of histological and immunohistochemical examination of the lungs in a case of COVID-19-associated SUID in a 2-month-old infant. The absence of similar studies in the available literature limits opportunities for analyzing the pathogenesis of SUID. Our data allow a detailed characterization of the histological changes in the lungs, the localization and range of SARS-CoV-2 nucleocapsid protein expression, the identification of molecular mechanisms underlying apoptosis in the pulmonary microvascular endothelium, and the elucidation of the role of endothelial dysfunction. Particular attention in this article is devoted to the role of cytokines (IL-6, TNF-α, and IFN-γ) in the pathogenesis of hyperacute viral infection. The obtained data demonstrate substantial differences between the observed changes and the classic presentation of COVID-19 in older children. These findings offer prospects for improving prevention strategies and developing targeted therapy for fulminant forms of COVID-19, while also contributing to the understanding of SIDS pathogenesis. Full article

Soils are increasingly affected by microplastic (MP) contamination, mainly coming from industrial activities, agricultural practices, atmospheric or waterborne transport, and improper waste disposal. Despite the increasing attention to the fate of MPs in soil over the last few years, research in this area is still limited compared to aquatic ecosystems. The introduction of MPs into the soil environment can modify not only the soil properties but also the interactions among soil components, plants, and microorganisms, thus affecting the mobility and availability of other contaminants, such as potentially toxic elements (PTEs). This review critically examines the complex dynamics between MPs and PTEs in the soil ecosystem, with a focus on the conditions under which MPs can act as a source or a sink of PTEs. Indeed, on the one hand, MPs can adsorb or complex PTEs on their surfaces (similarly to natural soil colloids), thus reducing their mobility and availability; on the other hand, they can release/mobilize PTEs after MP degradation or act as micro-/nano-vectors of PTEs. Understanding such mechanisms is relevant when evaluating the environmental risks associated with the co-presence of MPs and PTEs in soil, a situation likely to occur in most contaminated sites and in many agricultural soils. Full article

Background/Objectives: Auditory processing disorders (APDs), defined as impaired neural processing of acoustic stimuli despite normal peripheral hearing, often co-occur with neurodevelopmental disorders (NDDs) and may contribute to language, attentional, and learning difficulties. Emerging evidence suggests that shared neurotransmitter systems may represent a common neurobiological substrate underlying these conditions. The aim of this study is to integrate current evidence on glutamatergic, GABAergic, and monoaminergic systems in neurodevelopmental and auditory processing disorders in children and adolescents, and to evaluate the hypothesis that shared neurotransmitter dysregulation may underlie their clinical overlap. Methods: A narrative review of the literature was conducted through electronic searches in PubMed and Embase up to 31 December 2025, using keywords related to neurotransmitters, NDDs and APDs. Results: Available evidence indicates that an imbalance between excitatory glutamatergic and inhibitory GABAergic neurotransmission has been proposed as a central mechanism in NDDs and may also contribute to auditory processing difficulties through altered neural synchrony, sensory gating and temporal auditory coding. Findings collectively suggest the hypothesis of shared neurotransmitter dysregulation across NDDs and APDs. Conclusions: Auditory processing difficulties may represent sensory-level expressions of shared neurochemical vulnerability across neurodevelopmental conditions. Future longitudinal and multimodal studies are needed to clarify causal relationships and to identify clinically useful biomarkers. Full article

Fusarium graminearum infection of maize induces complex transcriptional reprogramming, yet existing differential-expression and local graph convolutional approaches struggle to capture long-range and multi-scale regulatory dependencies. We propose DC-FusionGNN, a dual-channel fusion graph neural network for key resistance-gene identification. Based on the transcriptome dataset GSE174508, we first construct a comprehensive gene interaction network by integrating a WGCNA co-expression network with a STRING-based interaction network. The left channel combines structure-aware propagation with a Transformer-based global self-attention mechanism to model long-range cross-module dependencies, while the right channel couples GraphSAGE with a GCN to capture local topology and neighborhood heterogeneity. Embeddings from the two channels are concatenated to form a unified gene representation, trained via self-supervised link prediction. Compared with baseline graph neural networks, DC-FusionGNN achieves competitive and overall improved performance across multiple metrics, and robustness and independent cross-species (rice, GSE39635) experiments further confirm its stability and generalization ability. GO and KEGG enrichment analyses show that the top-ranked candidate genes are significantly enriched in plant defense responses, hormone signaling, and secondary metabolism, supporting the biological relevance of the model’s predictions. Full article

Social media sentiment analysis demands systems that are simultaneously accurate, scalable, and interpretable. Lexicon-based methods offer transparency but ignore context, while pre-trained language models (PLMs) capture contextual semantics yet encode sentiment only implicitly. Existing integration strategies inject lexicon signals at the input, attention, or feature layer—all outside the recurrent gating mechanism that controls how affective evidence accumulates over a sequence. We propose the SAGU-Net, a framework built around the Sentiment-Aware Gated Unit (SAGU), a gated recurrent unit (GRU) variant with a dedicated sentiment gate conditioned on external lexicon signals. A complementary Context-Adaptive Sentiment Scoring (CASS) module transforms static polarity scalars into context-dependent vectors via learned projections over PLM representations, bridging the gap between discrete lexicon scores and continuous embeddings. The sentiment gate activations provide token-level explainability without post hoc attribution. On a 12,700-sample Chinese social media corpus of intellectual property co-branding reviews (Fleiss’ $\kappa =0.82$) and two public benchmarks, the SAGU-Net achieves 93.62% accuracy and 93.21% Macro-F1, outperforming nine baselines and matching or exceeding LoRA-fine-tuned large language models (GPT-5, Claude Sonnet 4.6, DeepSeek V3.2, Qwen3.5) while requiring three to four orders of magnitude fewer parameters. Ablation confirms the sentiment gate as the single most impactful component. Full article

Ophicarbonates provide an important natural record of mineral carbonation during serpentinization of ultramafic rocks and therefore offer insight into the mechanisms and limits of CO₂ fixation in low-temperature geological environments. This paper presents a synthesis and process-oriented reinterpretation of stable-isotope published and previously unpublished data, petrographic, and mineralogical evidence for carbonate formation under fluid-limited serpentinization conditions. Using mineralogical constraints together with a compiled δ¹³C–δ¹⁸O dataset that includes legacy measurements from the 1980s–1990s, we evaluate how multi-stage carbonate precipitation reflects evolving water–rock ratio, redox state, transport limitation, and deformation-controlled permeability. Particular attention is given to systematic differences between vein-hosted carbonates and dispersed intergranular or scattered-grain ophicarbonates, as these textural–isotopic relationships help identify fluid flux, carbon source, and reaction progress in ultramafic systems. The analysis shows that carbonation does not proceed uniformly but is restricted to overlapping reactive windows controlled by fluid availability, nucleation kinetics, and permeability evolution. These constraints help explain why carbonation may either intensify or stall during progressive serpentinization. The Author further discuss why kinetic barriers and Mg–Ca partitioning may redirect carbonate mineralogy toward calcite or metastable Mg-rich phases even where dolomite or magnesite may be thermodynamically favored. The results highlight the importance of coupling isotopic signatures with petrographic context in reconstructing carbonation pathways and provide a broader framework for understanding natural mineral sequestration of carbon in heterogeneous serpentinite systems. Full article

Conductive hydrogels are functional materials that combine soft, highly hydrated properties with electrical signal transmission capabilities. Their conductivity arises from ionic or electronic pathways, and the key design challenge is achieving good conductivity and long-term stability without compromising mechanical performance and biocompatibility. Among various conductive components, conductive polymers have attracted considerable attention due to their tunable mechanical properties, high electrical conductivity, good biocompatibility, and facile synthesis routes. In this study, a series of conductive hydrogels were rationally designed and fabricated by copolymerizing acrylamide and N,N′-methylenebisacrylamide with functionalized poly(vinyl alcohol) (PVA) and poly(3,4-ethylenedioxythiophene-co-thiophene) [P(EDOT-co-Th)]. The functionalized PVA provided multiple dynamic hydrogen-bonding sites, significantly enhancing the toughness of the hydrogel and its adhesion to various substrates, while the P(EDOT-co-Th) copolymer imparted good and stable electrical conductivity. By systematically adjusting the amount of functionalized PVA, the mechanical strength, adhesiveness, and durability of the conductive hydrogels were effectively optimized. The optimized hydrogel exhibited robust adhesion to a wide range of surfaces, excellent fatigue resistance, and long-term stability under repeated mechanical deformation. Moreover, the combination of mechanical resilience and good conductivity enabled precise and reliable signal transduction, highlighting its strong potential as a next-generation material for wearable strain and pressure sensors. Full article

Chromium slag, a chromium-bearing solid waste characterized by substantial environmental hazards yet with appreciable resource potential, has become a focal topic in solid-waste pollution control and the circular economy. Centered on the overarching logic of “evidence chain–system boundary–scalable and verifiable acceptance,” this review systematically synthesizes recovery technologies, industrial-scale utilization pathways, and the key challenges associated with the detoxification and resource utilization of chromium slag. From the perspective of recovery technologies, we examine pyrometallurgical and hydrometallurgical routes, solidification/stabilization (S/S), and bioelectrochemical coupling approaches, elucidating their fundamental principles, applicability boundaries, and critical nodes where environmental burdens may be transferred across media. We emphasize that process design should concurrently consider detoxification efficiency, resource recovery performance, and whole-process pollution control. Regarding utilization pathways, this review highlights three major routes with strong scale-up relevance—metallurgical process co-treatment (CAP–sintering–blast furnace), bulk utilization in construction materials, and high-value utilization—and analyzes their industrial potential and engineering constraints. Particular attention is given to the lack of long-term leaching and durability evidence, which represents a central bottleneck limiting product-side credibility. Furthermore, we discuss cross-cutting challenges including the long-term stabilization of Cr(VI), the verifiability of “green utilization” concepts, cost and economic feasibility, and standardized acceptance criteria. We propose that future research should shift from single-process optimization toward multi-objective, system-level evaluation, and establish a full-chain evidence system covering “speciation/mineral phases–process mechanisms–environmental behavior–risk assessment–engineering scale-up–standardized acceptance.” This review aims to provide a systematic analytical framework and practical reference for improving comparability across resource-utilization technologies and supporting engineering decision-making for chromium slag management. Full article

Nanobubbles (NBs) are emerging as a promising area of research across multiple scientific and industrial domains due to their unique physicochemical characteristics. NBs exhibit distinctive properties compared to normal bubbles, including high internal pressure, a large specific surface area, high interfacial activity, and long-term stability in liquids. Therefore, NBs have gained increasing attention as a novel enhanced oil recovery (EOR) technique, offering potential advantages over traditional gas flooding and chemical flooding. CO₂-NB specifically represents a particularly promising approach as an intersection of EOR and carbon capture, utilization, and storage (CCUS), as CO₂-NB enables hydrocarbon recovery and in situ CO₂ utilization and storage at reservoir conditions. This paper presents a structured comparative discussion of currently identified experimental EOR studies that employ CO₂-NBs. Based on the observations of these experiments, this paper discusses the proposed mechanisms in those experiments or other studies that could scientifically play a role in achieving incremental recovery. The main mechanisms discussed include interfacial tension reduction, wettability alteration, CO₂ transfer from NBs into the oil liquid phase, and suppression of gravity segregation. Other possible contributors discussed in the literature include buoyancy-assisted mobilization, induced shock waves, and drag force reduction. These mechanisms are examined in relation to the distinctive properties of CO₂-NBs, showing how these properties contribute to the occurrence of the proposed mechanisms, showcasing the potential of CO₂-NBs as an emergent EOR–CCUS technology. A preliminary probabilistic assessment was performed to estimate CO₂ storage potential during CO₂-NBs EOR injection. The results suggest that the majority of the injected CO₂ is dissolved in the saturated liquid phase, while the amount of free NBs is negligible, indicating that CO₂-NB injection may provide secure storage through solubility trapping, but with lower storage capacity compared to conventional geological sequestration in saline aquifers. Full article

In recent years, soil contamination by heavy metals and organic pollutants has become a serious environmental problem. Biochar is a highly carbonaceous, water-insoluble porous material made from biomass feedstock through a thermochemical conversion process, and it has been widely used in the remediation of various soil pollutants. However, previous reviews on the modification of biochar and the remediation reaction mechanism of heavy metals and organic pollutants by biochar in soil were still not sufficiently comprehensive. Based on the current research status of the remediation of heavy metals and organic pollutants by biochar in soil, this review systematically summarized biomass feedstock types, pyrolysis methods and their applicable scenarios, as well as the modification strategies of biochar, including pore structure modification, surface functional group modification, surface charge modification, and magnetic modification. It also comparatively discussed the adsorption of heavy metals by biochar mainly through electrostatic attraction, ion exchange, complexation/precipitation, cation−π interaction, and redox transformation, while the adsorption of organic pollutants via π−π/EDA interactions, electrostatic attraction, hydrogen bonding, hydrophobic partitioning, and pore filling were outlined. The review also discussed competitive effects among pollutants during biochar adsorption under co-contamination scenarios, as well as the synergistic interactions between biochar and soil microorganisms or plants. In addition, the review addressed recent progress in field-scale applications of biochar, as well as the current state of research on aging effects, ecological risks, and economic feasibility. Finally, it identifies key research directions that warrant further attention. This review highlighted the mechanistic differences between heavy metal stabilization and organic pollutant removal in soil by biochar, and provided mechanistic insight and guidance for biochar-based soil remediation. Full article