The rapid expansion of light industries has led to a dramatic increase in dye-contaminated wastewater, creating an urgent need for innovative and scalable remediation technologies. In this work, we report a novel two-step in-situ gel-crosslinking strategy for fabricating montmorillonite/sodium alginate (MMT/SA) dual-network aerogels. Homogeneous internal crosslinking was achieved through the controlled reaction between calcium carbonate and glucono-δ-lactone (GDL), effectively overcoming the nonuniform crosslinking gradients commonly observed in conventional calcium-chloride-mediated methods. The engineered composite system integrates the high adsorption capacity of montmorillonite, a natural molecular sieve for dye contaminants, with the dual functionality of SA in three-dimensional network formation and active adsorption participation. The optimized internal crosslinking architecture enables a pronounced synergistic adsorption effect, yielding an aerogel with excellent performance. The material demonstrated a maximum cationic dye adsorption capacity of 383.75 mg/g. Intraparticle diffusion model and thermodynamic analyses collectively confirmed that the adsorption process occurred spontaneously. The bicontinuous network structure facilitates synergistic enhancement through complementary mechanisms of montmorillonite intercalation and alginate coordination while maintaining favorable biosafety characteristics. These findings provide a promising and practically scalable design strategy for developing high-performance adsorbents for industrial wastewater treatment.

Rare earth elements (REEs), which comprise 15 lanthanides together with scandium and yttrium, are commonly separated by solvent extraction, in which the equilibrium distribution coefficients (lg D) depend on the extractant type, solution pH, and extractant concentration. Owing to the nonlinear interactions among these variables, the quantitative interpretation of REE extraction data remains challenging. A curated dataset of experimentally reported REE solvent-extraction equilibrium data encompassing diverse extractants and operating conditions was compiled from the literature. Two artificial neural network (ANN) models were employed as data-driven tools to simulate and analyze the behavior of lg D based on the solution pH and log-scaled extractant concentration. The extractant identity was represented using one-hot encoding, and the lanthanide atomic number was included as an auxiliary descriptor to represent systematic trends across the rare-earth series. The ANN results showed strong agreement with experimental data across multiple systems, thus demonstrating the internal consistency and analytical value of the compiled dataset for data-centric studies regarding REE solvent-extraction equilibria.

Oil shale semicoke (OSSC) is a naturally occurring organic/inorganic nanocomposite with potential for sustainable functional applications, yet its high-value utilization is limited by an insufficient understanding of component occurrence and structural evolution. Herein, hydrothermal-assisted nitric acid treatment was employed to regulate the full-component evolution of OSSC by coupling acid etching with oxidative reconstruction. Various characterizations revealed that pristine OSSC consisted mainly of organic matter, quartz, kaolinite, magnetite, and amorphous components, in which mineral phases were closely covered or encapsulated by organics, and trace heteroatoms contributed to lattice defects. Under low nitric acid concentration, partial dissolution of metal ions and oxidation of organic matter generated a more developed pore structure, oxidized functional groups, and amorphous ferric sulfate, while the kaolinite framework was partially preserved due to organic-mediated protection. At higher concentration of nitric acid, extensive oxidation decomposed most organics and promoted the transformation of ferric sulfate species into ordered jarosite crystals. However, acid treatment alone caused secondary passivation of active sites by metal–organic complexation and positively charged mineral species. Subsequent alkali impregnation released these blocked sites and markedly enhanced adsorption activity. The optimized OSSC exhibited removal ratios of 97% for methylene blue and 54% for tetracycline, far exceeding those of raw OSSC. This work clarifies the occurrence–evolution–function relationship of OSSC and provides a mechanistic basis for converting mineral-rich solid waste into functional materials.

“Transparent geology” underpins intelligent mine blasting by enabling the precise classification of rock mass blastability and optimal matching of explosive energy. To advance transparency of geological models, we proposed a six-stage workflow comprising data synthesis, classifier-based labeling, stable point selection, machine learning-based probability field prediction, conditional Kriging interpolation, and 3D geological reconstruction. First, we generated multiple synthetic borehole datasets with invariant geological features by applying random skeleton reconstruction fused with generative adversarial networks (GANs) under selected prior constraints. We then used an optimal classifier to predict the lithologies of each synthetic batch and tally the prediction frequencies, retaining only points that recurred across runs as “stable high-precision points.” Next, these stable points were merged with the original borehole logs to form a unified set of “high-confidence points.” For each query location, we computed two neighborhood descriptors: (1) the number of high-confidence points within a predefined radius N_S and (2) the mean Euclidean distance to those points D_A. Following this, these neighborhood descriptions were concatenated with the spatial coordinates to construct a five-dimensional feature vector (x, y, z, N_S, D_A). This vector was inputted into a deep neural network that predicted the lithological class probabilities at each spatial query point. Simultaneously, conditional Kriging was performed on the original logs to obtain a smoothly varying geostatistical probability field. Finally, we fused the machine learning-derived and Kriging-derived probability fields via a weighted scheme and used the fused field to build a 3D lithological model. Cross-sectional slices at specified locations were then extracted to illustrate both thin interbeds and large-scale geological structures. Comparative experiments showed that embedding stable anchor points and spatial neighborhood features enabled the model to capture local heterogeneity more effectively and replace stand-alone Kriging. Even though the addition of spatial features in the machine learning model led to a drop in the training borehole accuracy compared with the pure machine learning model, it substantially improved generalization to unseen boreholes and enhanced thin layer detection.

Fine-grained minerals, characterized by small particle size, large specific surface area, and high surface energy, often exhibit low bubble–particle collision efficiency, extensive nonselective reagent adsorption, and complex pulp rheology during flotation. These factors significantly reduce separation efficiency and mineral recovery. Selective flocculation–flotation relies on the selective adsorption and bridging action of polymer flocculants to aggregate fine particles into larger flocs, thereby enhancing bubble–particle collision and attachment efficiency. It is therefore considered a key technology for the effective recovery of fine-grained minerals. This review systematically summarizes recent advances in the selective flocculation–flotation of fine-grained minerals. It first examines the physicochemical characteristics of these minerals and their influence on flotation behavior, and then elucidates the multiscale mechanisms governing flotation efficiency and selectivity, including interfacial chemistry, particle interactions, and pulp hydrodynamics. Based on this, the theoretical foundations and key mechanisms of selective flocculation–flotation are discussed, followed by an overview of the structural features, functional principles, and application progress of various polymer flocculants in iron ore, copper ore, and tailings treatment systems. Finally, key challenges in current research are identified, future directions are proposed, including the rational design of highly selective flocculants, clarification of interfacial mechanisms, synergistic regulation of composite reagent systems, and industrial-scale validation under complex pulp conditions. These insights aim to support the efficient separation of fine-grained minerals and promote the sustainable utilization of complex mineral resources.

Lithium (Li) is an essential resource for energy storage; however, traditional flotation processes for spodumene are inefficient and environmentally expensive. This study aims to develop an intelligent, low-carbon pre-selection technology based on the photoluminescence properties of spodumene under 365 nm ultraviolet (UV) light. An improved MT-YOLOv11 deep-learning algorithm that integrates wavelet transform convolution and a dynamic detection head is proposed to accurately distinguish spodumene from gangue minerals in UV fluorescence images. Ablation and comparative experiments demonstrated that MT-YOLOv11 achieved superior detection performance, with a precision of 93.5%, recall of 79.2%, and mean Average Precision (mAP) of 88.5%, outperforming other classical detection algorithms. The model was deployed in a semi-industrial UV fluorescence sorting system at the Dahongliutan Mine in Xinjiang. It showed stable operation, increased concentrate grade, and enhanced tailings rejection compared with conventional X-ray sorting. The proposed method is practical and efficient for intelligent, green, and low-carbon Li mining.

Reliable evaluation of rock brittleness is crucial for understanding rock failure behavior and ensuring the stability and safety of underground mining and rock engineering. However, no universally accepted index currently exists, and many proposed indices exhibit limitations in terms of reliability, physical interpretation, and applicability across different rock types and stress conditions. This study introduces a novel brittleness index that integrates both energy dissipation and crack development characteristics, offering a more comprehensive and physically grounded assessment of rock brittleness. The new index is established based on the correlation between energy dissipation, acoustic emission parameters, and brittleness, providing a more robust framework that better reflects the fundamental mechanisms of brittle failure. To validate the new brittleness index, triaxial cyclic loading damage-controlled tests were performed on two representative rock types: siltstone (a porous, weakly cemented rock) and granite (a dense, crystalline rock). These tests generated quantitative data on energy evolution and acoustic emission parameters, which were analyzed to investigate brittleness evolution under varying confining pressures. The results show that the new brittleness index exhibits a nonlinear, monotonic decrease with increasing confining pressure for both siltstone and granite. Furthermore, under identical confining pressure conditions, granite exhibits higher brittleness index values than siltstone. Overall, the new brittleness index demonstrates strong stability and applicability, making it suitable for rocks with different characteristics and stress environments.

Aerogels, an important class of ultra-lightweight porous functional materials, have attracted extensive research attention and have been widely applied in adsorption, catalysis, energy storage, and thermal insulation. However, the widespread adoption of traditional aerogels is often limited by high production costs, complex synthesis procedures, and limited environmental sustainability. In recent years, the incorporation of naturally abundant mineral materials into aerogel structures has emerged as a promising strategy to overcome these limitations. Leveraging the intrinsic advantages of minerals, such as high mechanical strength, excellent stability, and broad availability, mineral integration not only enhances the overall performance of aerogels but also significantly improves their cost-effectiveness. Despite these advancements, research on mineral-based aerogels remains fragmented and lacks systematic consolidation. This review critically examines how different raw materials and fabrication methodologies influence the structural and functional properties of mineral-based aerogels. Furthermore, this review elucidates the reinforcement mechanisms imparted by mineral components and highlights the diverse practical applications enabled by these hybrid systems. Finally, the current technical challenges and bottlenecks hindering the development of mineral-based aerogels are summarized, and future research directions are proposed.

Lead–zinc tailings (LZT), which pose a toxic element pollution risk, represent a significant environmental challenge in mining areas. This study proposes a novel strategy to enhance the Pb²⁺-fixation capacity of LZT by utilizing natural sisal fiber (SF). The key premise of this approach is to improve the Pb²⁺ adsorption capacity of SF to ensure effective performance in engineering applications. We prepared a graft-modified SF (GMSF) with multifunctional groups and investigated its adsorption capacity in Pb²⁺ containing wastewater. The modified fiber achieved an impressive Pb²⁺ adsorption efficiency of 99.60% within just 10 min, with an adsorption capacity of 138.26 mg/g. A segmented model was established for the adsorption curve. During the first 10 min, the adsorption followed a pseudo-first-order kinetic model, indicating that physical adsorption predominated. After this initial period, the desorption process conformed to the ExpAssoc model. Analysis revealed that the desorption phenomenon was due to the excessively high initial concentration of Pb²⁺, in which electrostatic repulsion between the chemically and physically adsorbed Pb²⁺ caused some of the ions to return to the solution. After 2 h, the nitrogen and oxygen atoms in GMSF formed stable metal complexes with Pb²⁺ through their vacant orbitals and electronegativity, effectively fixing the Pb²⁺. Promising results were observed in subsequent cemented paste backfilling experiments, in which the sample strength improved, and most of the Pb²⁺ was effectively immobilized.