The zeolite-containing waste generated during the petroleum fluid catalytic cracking (FCC) process is a high-volume aluminosilicate by-product and a potentially valuable secondary raw material. When these residues lose their catalytic performance, they become environmentally problematic, rendering them unusable and unreusable. This problem creates a clear need for advanced high-temperature mineral processing methods, with plasma technologies offering a particularly promising option. This work investigates the transformation behaviour of zeolite waste under atmospheric-pressure plasma conditions, focusing on melt formation, structural changes, and the development of continuous aluminosilicate microfibers. Dried and sieved zeolite waste particles (60 µm size) were injected into an atmospheric-pressure direct current (DC) air plasma jet. The temperature of the plasma jet exceeded 3500 K and facilitated the rapid melting of zeolite particles. The molten droplets were aerodynamically stretched within the plasma jet to form microfibres with diameters in the 0.1–5 µm range. Scanning electron microscopy (SEM) revealed a smooth fiber morphology without unmelted inclusions, while X-ray diffraction (XRD) confirmed complete vitrification and the absence of characteristic zeolite-Y diffraction peaks. Analysis of the plasma jet parameters showed that plasma enthalpy, gas flow regime, and reactor geometry strongly influence melt homogeneity and fiber formation efficiency. These findings show that atmospheric-pressure plasma processing can effectively convert zeolite waste into high-quality aluminosilicate microfibers, providing an effective way to convert mineral waste into useful products.

This study focuses on the grinding behavior and efficiency optimization of ceramic ball media in vertical stirred mills. By combining experimental methods with discrete element method (DEM) simulations, it systematically investigates the effects of stirring shaft speed, media filling rate, and media size on the grinding performance of specific-sized magnetite. This experimental research employs particle size distribution models, grinding rates, and the t10-Ecs model to analyze and quantitatively evaluate the impact of different parameter conditions on the grinding efficiency and product characteristics of ceramic media from multiple perspectives. Meanwhile, DEM simulations provide a microscopic-level analysis, examining key factors such as the kinetic energy changes of ceramic ball media, particle velocity distribution patterns, motion trajectories and stratification phenomena, collision energy intensity and distribution among different collision types, and the proportion of effective collisions and energy utilization efficiency between grinding media and mineral particles, offering detailed insights into the mechanisms of ceramic ball motion and grinding behavior. The results show that for the specific-sized magnetite tested, higher stirring shaft speeds, filling rates, and larger media sizes generally lead to better grinding performance under the same conditions. However, adjusting parameter ranges can help to improve energy utilization efficiency while maintaining grinding effectiveness, thereby achieving energy-saving and consumption-reducing goals. This research clarifies the dominant grinding mechanisms of ceramic media and provides theoretical and practical strategies for optimizing energy-efficient grinding processes.

Pyrite is the main sulfide mineral responsible for the increased sulfur content in magnetite concentrates at the Golgohar iron ore complex, which negatively affects steel production quality. Reverse flotation using xanthate collectors and methyl isobutyl carbinol (MIBC) as frothers is commonly used for desulfurization. This study investigates the synergistic effects of combining surfactants such as sodium lauryl ether sulfate (SLES) as a filter aid with collectors in flotation. The focus is optimizing reagent dosages to enhance pyrite removal efficiency while minimizing impacts on iron recovery and filtration performance.

Laboratory flotation experiments were conducted on magnetite concentrate samples (64.94% Fe, 24.77% FeO, 0.71% S) using a Central Composite Design (CCD) via Design-Expert software. Key variables included collector (0-164 g/t), frother (0-87 g/t), and surfactant (0-150 g/t) concentrations. Pulp density was 30% solids, with conditioning times of 2 min for collector/surfactant and 1 min for frother. Floated and non-floated fractions were weighed, assayed for Fe, FeO, and S, and evaluated for separation efficiency, recoveries, and concentrate quality. Complementary filtration tests assessed cake moisture.

Adding 75 g/t SLES improved desulfurization, raising sulfur separation efficiency from 52% to 70.1% and recovery from 53% to 72%, while reducing concentrate sulfur from 0.34% to 0.20%. Iron recovery decreased slightly by 0.8%. Optimal dosages: 80 g/t collector, 40 g/t frother, 75 g/t SLES, enabling 40% collector and 62% frother reductions. SLES (HLB=40, MW=496.7 g/mol) showed superior selectivity and power over MIBC, with 2% average moisture reduction in filtration.

The SLES–xanthate synergy enhances pyrite hydrophobicity via co-adsorption, charge screening, and lowered critical micelle concentration, promoting efficient flotation without disruptions. Findings support integrated surfactant use in industrial circuits, with potential for scale-up and alternative surfactants exploration.

Iron impurity is the most common impurity element in sphalerite, and its content has a significant influence on the flotation behavior of sphalerite. To systematically investigate the mechanism by which iron content affects the flotation behavior of sphalerite, this study employed density functional theory (DFT) simulations combined with coordination chemistry principles to analyze the effects of iron content and its spin configuration on the electronic structure and floatability of sphalerite. The results indicate that as the iron content increases, the band gap of sphalerite gradually narrows. Under low iron content conditions, the reduced band gap favors the transfer of electrons, where iron impurities exist as high-spin Fe²⁺. The 3d orbitals of Fe²⁺ can provide a pair of π electrons, enabling the formation of a weak feedback π-bond with xanthate, thereby enhancing the floatability of sphalerite. In contrast, under medium and high iron content conditions, further narrowing of the band gap intensifies electrochemical interactions, promoting the oxidation of Fe²⁺ to Fe³⁺. Since Fe³⁺ lacks π electron pairs in its 3d orbitals, it cannot form a feedback π-bond with xanthate, leading to a significant decrease in the floatability of sphalerite. The theoretical calculations in this study are consistent with experimental flotation results, providing deeper insights from the perspective of coordination chemistry into how iron content influences the flotation behavior of sphalerite, and offering important theoretical support for the flotation of iron-bearing sphalerite.

The Alappuzha district in Kerala, India, has large deposits of high-grade silica sand mainly used for glass manufacturing. In the present study, these mineral deposits were effectively mapped using multispectral satellite data and machine learning algorithms (MLAs). The samples collected from Cherthala show a silica sand content of 93.93-97.94%. Detailed geochemical characterisation using the Energy Dispersive X-ray Fluorescence (ED-XRF) technique reveals a SiO₂ content of 96.93-99.13%. The X-ray diffraction (XRD) analysis reveals diffraction peaks characteristic of quartz, confirming the silica sand's composition. The spectral signatures of silica sand were captured using an ASD Fieldspec® 3 spectroradiometer (spectral range of 400–2500 nm) and compiled as a reference spectrum for mapping using Landsat and ASTER remote sensing datasets. The reference spectra of silica sand were used to generate the potential targets of silica sand occurrences, followed by a comparison of four widely used MLAs, which shows that the Support vector machine (SVM) outperforms other algorithms, such as the Random Forest Classifier (RFC), maximum likelihood classification (MLC), and artificial neural network (ANN), with an overall accuracy of 97.82% and Kappa coefficient of 0.96. The results derived from the satellite data show a good correlation with ground truth verification and laboratory analysis. Integrating remote sensing techniques, mineral characterization, and field data facilitates eco-friendly and sustainable mining of these strategic minerals.

It is no secret that the overexploitation of natural resources such as mineral ores is an unsustainable practice. Not only are these resources finite, but their extraction is often associated with severe environmental consequences during both the extraction and refining processes. Moreover, when a given country is entirely dependent on mineral imports, it is also susceptible to market constraints and geopolitical pressures. Because of this, mineral recycling from secondary sources and their injection back into the economy can prove to be an effective measure to mitigate the problems associated with primary mining. An example of such an approach is the recovery of valuable metals from spent lithium-ion batteries (LIBs). These batteries contain valuable metals such as lithium (Li), manganese (Mn), cobalt (Co), and nickel (Ni), as well as copper (Cu) and iron (Fe). Hydrometallurgical techniques such as solvent extraction have already been used with varying degrees of success to recover these metals from lithium-ion batteries. However, the existing processes can and should be optimized in terms of the extreme acidic conditions and hazardous chemicals that are used. In this work, we propose the use of novel deep eutectic solvents (DES) as alternative solvents in LIB recycling, using less acidic environments at room temperature. Trioctylphosphine oxide (TOPO)-based eutectic solvents were tailored to extract equimolar concentrations of different metals often present in LIBs leachates without the addition of heat and even displayed selective extraction for some metals under specific conditions. Further characterization, including acid partition into the DES, viscosity, and reusability, allowed for a more rigorous assessment of potential applications. Overall, TOPO-based DES exhibited promising behavior in terms of metal extraction from LIB leachates.

The long-term performance of montmorillonite-based engineered barriers in nuclear waste repositories and contaminated groundwater systems depends critically on understanding how sequential heavy-metal sorption alters interlayer chemistry, hydration equilibria, and macroscopic transport properties. Despite decades of research on single-cation systems, the coupled physicochemical processes governing multi-step cation exchange—where incoming divalent species (Co²⁺, Ni²⁺, Mg²⁺, Cu²⁺, Zn²⁺, Cd²⁺, Pb²⁺, Ba²⁺) progressively displace weakly-bound Na⁺ from both external surfaces and interlayer galleries—remain poorly constrained, particularly under conditions that generate coexisting hydration states. This gap is addressed by integrating high-resolution XRD 00l profile modeling of Wyoming-type Na-montmorillonite (SWy-2) across systematic exchange sequences with interpretable machine-learning architectures trained on 600 physics-enriched synthetic records combining crystallographic observables (d₀₀₁ spacing, 0W/1W/2W layer-type abundances, Rietveld Rₘₚ values, coherent domain lengths) and thermochemical descriptors (hydration enthalpy, ionic potential, polarizability). XRD refinements reveal that sequential exchange does not converge toward homogeneous homoionic but instead stabilizes dynamically disordered mixed-layer structures with 15–35% residual Na⁺ occupancy driving non-monotonic d₀₀₁ trajectories that follow Boltzmann-type sigmoid curves rather than linear replacement trends, with effective CEC losses reaching 40% for large, weakly-hydrated cations (Pb²⁺, Ba²⁺) due to incomplete interlayer accessibility. Ensemble machine-learning models (Random Forest, Gradient Boosting, Neural Networks) trained on this augmented feature space achieve robust cross-validated performance (R² = 0.90–0.92 for fractional uptake; 84–86% accuracy for competitive selectivity ranking), with SHAP decomposition demonstrating that hydration free energy and ionic radius dominate early-stage exchange kinetics while XRD-derived structural disorder metrics become critical predictors of late-stage degradation modes including irreversible 2W→1W dehydration, basal-spacing collapse below 12.5 Å, and the emergence of nano-scale permeability pathways. By embedding crystallographic disorder signatures into predictive models, our approach transcends empirical correlation and enables mechanistic forecasting of barrier aging under realistic poly-ionic exposure scenarios, providing quantitative risk assessment tools for repository safety analysis and adaptive monitoring strategies in deep geological disposal environments over millennial timescales.

One of the most persistent challenges in mineral flotation processes is the poor recovery of fine particles, which have limited ability to attach to bubbles due to surface characteristics, low inertia, and weak hydrodynamic interactions within the flotation pulp. These limitations reduce collision and adhesion probabilities, leading to losses of valuable minerals in fine fractions. High‑Intensity Conditioning (HIC) has increasingly been recognized as an effective pretreatment to address poor fine‑particle recovery in flotation. Recent literature demonstrates that mechanical HIC systems, through high‑shear mixing, can substantially enhance reagent dispersion, surface activation, and bubble–particle interactions. Studies collectively indicate that these effects improve mineral selectivity and recovery while decreasing chemical consumption. Despite these promising outcomes, there is still no comprehensive or unified understanding of the fundamental concept and mechanisms occurring within high‑intensity conditioning tanks. This review therefore aims to establish a clearer definition of HIC and to systematically summarize the physical and chemical mechanisms involved in such conditioning systems. It critically examines recent laboratory and industrial advancements in mechanical HIC systems, focusing on the hydrodynamic design and operation of mixing tanks under intensive shear regimes, reviewing key experimental findings and assessing the potential of HIC as a scalable, high‑efficiency pretreatment for enhancing flotation performance in mineral processing and highlighting directions for future research.

A detailed investigation was carried out to characterize garnet and magnesiochromite inclusions in diamonds from the Zapolyarnaya kimberlite pipe, located in the Yakutian diamondiferous province, Russia. Optical microscopy, electron probe microanalysis, scanning electron microscopy, Raman spectroscopy, X-ray diffraction, as well as calculations of compositions and residual stresses in the crystals were applied. Seven garnet inclusions were identified within two diamond crystals (3-3 and 3-4), along with other phases. They vary morphologically and in size (50–200 µm), are well-preserved, and appear as isometric grains or irregularly/slightly elongated crystals. Garnet composition, determined from Raman spectral band positions and crystal lattice parameters, evidences a dominance of the pyrope minal. The identified variations in pyrope and almandine content may reflect changes in the chemical composition of the diamond-forming medium at different stages of crystal growth, but generally correspond to a peridotitic paragenesis. Residual stress values of the inclusions and the surrounding diamond areas were calculated using their dependence on frequency shift of the main diamond peak. Inclusions in crystal 3-3 are characterized by residual compressive stress. Residual stresses of garnet inclusions in diamond 3-4 have negative values that may indicate residual tensile stress. Magnesiochromite inclusions were identified in 4 diamond crystals. One crystal was polished to expose the inclusions, while the remaining were located inside the crystals. Their morphology and chemical composition indicate formation at peridotitic paragenesis conditions. Comparison of central and peripheral inclusions demonstrates a multi-stage diamond growth, which is confirmed by the correlation of #Mg and #Cr parameters with the position of bands in the Raman spectra. The obtained data confirm the important role of garnets and magnesiochromites as indicators of diamond crystallization conditions of the Zapolyarnaya kimberlite pipe. This study is supported by program № FMUF-2022-0001 IEM RAS and project 121061600049-4 MSU.

Cobalt (Co) is an essential metal for sustainable technologies, including electric vehicles and energy storage systems. Its selective recovery from complex leachates remains a challenge due to the co-presence of nickel (Ni) and manganese (Mn). This work examines the influence of the extracting phase composition on the solvent extraction (SX) of Co from highly acidic chloride media (pH ≈ 0.86, 6–12 M LiCl) containing Ni and Mn. Trioctylphosphine oxide (TOPO) was used as the extractant, dissolved in three different media: dodecane, the ionic liquid (IL) 1-methyl-3-octylimidazolium bis(trifluoromethylsulfonyl)imide ([C₈mim][Tf₂N]), and the hydrophobic eutectic solvent (HES) TOPO:decanoic acid (1:1). The extraction of Co increased with LiCl concentration, consistent with the formation of anionic Co–chloride complexes, while Ni extraction remained negligible due to its high hydration energy. Slope analysis confirmed a 2:1 TOPO:Co stoichiometry for all systems, indicating a solvation-controlled extraction mechanism. Both the IL and the HES improved Co recovery compared with the molecular diluent, achieving high selectivity with minimal Mn co-extraction. Volatility tests showed severe solvent loss for dodecane, whereas [C₈mim][Tf₂N] and TOPO:decanoic acid exhibited excellent thermal stability (<2% mass loss) and low water uptake (<2%). These results highlight the potential of IL and HES as sustainable alternatives to conventional organic diluents. Among the systems studied, TOPO:decanoic acid (1:1) displayed the best balance of extraction efficiency, selectivity, and stability, representing a promising medium for Co recovery from chloride solutions.