In this work, the effectiveness of electro-Fenton and photoelectro-Fenton processes for enhancing the oxidative leaching of copper-iron sulfide minerals such as chalcopyrite (CuFeS2) and bornite (Cu5FeS4) in aqueous suspension was investigated. Electro-Fenton experiments were conducted in an electrochemical reactor equipped with a boron-doped diamond anode and a carbon-polytetrafluoroethylene gas-diffusion electrode as a cathode. The results show that, after 3 h, CuFeS₂ and Cu₅FeS₄ reached Cu and Fe leaching rates of 20% and 26%, respectively. These findings demonstrate enhanced performance under mild conditions compared to conventional leaching processes. Additionally, UV–Vis light irradiation was evaluated to assess the contribution of light-induced mechanisms, including photocorrosion and photo-Fenton reactions, establishing that the enhancement in leaching yield was relatively less significant (≈5%). The involvement of radical species such as hydroxyl and sulfate radicals was evaluated using in-situ electron paramagnetic resonance spectroscopy. Furthermore, the role of physisorbed hydroxyl radicals was evaluated by using an active metal mixed oxide anode in place of boron-doped diamond anode. This study demonstrates that in-situ electro-generated H2O2, together with dissolved iron (Fe2+ and Fe3+) and copper ions (Cu2+), can trigger Fenton, and photo-Fenton mechanisms, thereby promoting the oxidative dissolution of sulfide minerals. These findings provide a proof of concept for a more sustainable and alternative approach to mineral resource leaching.

Copper(II) ions in water sources pose serious environmental and health risks due to their toxicity and nonbiodegradability. Their persistent presence, primarily from industrial discharge, necessitates efficient and sustainable removal strategies. In response to this challenge, the present study focuses on developing a green and efficient approach for Cu(II) ion removal through solar-driven photocatalysis, employing solar-assisted synthesized poly(o-phenylenediamine) (POPD)@zinc oxide (ZnO) nanocomposites. POPD was selected for its strong visible light absorption (1.75 eV). At the same time, zinc oxide (ZnO) was selected for its excellent electron mobility and environmental compatibility, enabling the creation of a type-II heterojunction that enhances charge separation and suppresses electron-hole recombination. Thermodynamic studies confirmed the feasibility of Cu(II) reduction within the conduction band. The composites were characterized using FTIR, UV-vis DRS, XRD, SEM-EDX, and TGA. The chosen POPD/ZnO: 50/50 composite exhibited exceptional photocatalytic activity, achieving approximately 99% removal of Cu(II) ions under solar irradiation. A dosage of 2 g L-1 effectively reduced 200 mg L-1 of Cu(II) ions in the presence of 40 mM formic acid as a sacrificial agent. Kinetic studies favored the pseudo-first-order approach. This work presents a dual-functional, energy-efficient approach that harnesses solar energy for both material synthesis and environmental remediation, offering promising potential for real-world water treatment applications.

This study addresses the challenges of real-time spectroscopic sensing in industrial applications, where external factors such as temperature fluctuations, pressure variations, and particle size distribution significantly impact measurement accuracy. Conventional quantitative analytical methods often neglect these dynamic influences, leading to erroneous concentration estimates. To overcome these limitations, we propose an integrated modeling framework that combines a discrete-time process model with a physics-based spectroscopic sensor model, explicitly accounting for the dynamic properties of the system. A key innovation of this work is the development and application of an Adaptive Kalman Filter (AKF) to systematically correct for measurement distortions caused by external disturbances. Unlike conventional filtering techniques, the AKF dynamically adjusts to changing process conditions by leveraging real-time observability analysis, ensuring robustness even in the presence of sensor noise and environmental variability. Furthermore, to address cases where full observability is not achievable, we introduce a reduced-order Adaptive Kalman Filter (rAKF), which optimally estimates concentrations while minimizing computational complexity. A comprehensive series of simulations was conducted to assess the sensitivity of the estimation to variations in external signal type, noise levels, and initial values for parameters and states. The findings of this study demonstrate the superior performance of both AKF and rAKF in comparison to conventional filtering techniques, including the Extended Kalman Filter. The proposed approaches have been shown to enhance the reliability of spectroscopic sensor measurements, enabling more precise real-time estimations that can be used for monitoring and advanced process control strategies in industrial settings.

Copper stands at the forefront of materials driving the global transition to renewable energy and is a valued material for various important applications. For the first time, this paper presents an environmentally sustainable approach for recovering metallic copper through photocatalytic processes on a pilot scale, avoiding the energy-intensive conventional techniques. The study is focused on the selective photocatalytic reduction of copper(ii) to either copper(i) or zerovalent copper (Cu(0)) based on the reaction conditions. This entire process does not involve strong acids or bases or any hazardous chemicals but needs only light and simple photocatalysts such as zinc oxide (ZnO) and poly(o-phenylenediamine)/zinc oxide (POPD/ZnO). A raceway pond reactor (RPR) was used to scale up the process in deionized water (DW), tap water (TW), and seawater (SW) using ZnO. Thermodynamic considerations were used to predict the reduction of Cu(ii) to Cu(i) {Cu(ii)/Cu(i) (+0.153 V)} and Cu(0){Cu(ii)/Cu(0) (+0.337 V), Cu(i)/Cu(0) (+0.521 V)}. Formic acid served as a sacrificial reagent, while chloride ions modulated the reaction pathways and products at pH 6.5. The copper speciation of Cu(ii), Cu(i), and Cu(0) was analyzed using X-ray diffraction (XRD), fluorescence spectroscopy (FS), laser-induced breakdown spectroscopy (LIBS), energy-dispersive X-ray spectroscopy (EDX), and flame atomic absorption spectroscopy (FAAS). The "first copper coin" was produced solely through 100% solar energy-driven photocatalysis. With an 80% recovery rate of Cu(0), our approach demonstrates a proof of concept for efficient copper recovery from wastewater, the mining industry, and e-waste. These findings offer valuable insights for further exploration of solar-driven metal recovery processes, underscoring the potential of solar energy in fostering sustainable industrial practices.

Polyethers-supported catalysts have emerged as a versatile and prominent approach in the field of catalysis which offers improved selectivity, reactivity, and enhanced recyclability. This book chapter provides a perspective of the recent developments, their synthesis, properties, and applications in numerous domains such as organic synthesis, environmental remediations, pharmaceuticals, polymerization, fuel cells, etc. This chapter is deliberated to serve as a valuable resource for students, researchers, academics, and industrial practitioners interested in utilizing the potential of polyether-supported catalysts to drive innovation and sustainability in catalysis.

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The sustainable synthesis of zinc oxide nanoparticles (ZnO-NPs) using plant extracts has gained significant attention in recent years due to its eco-friendly nature and potential applications in numerous fields. This synthetic approach reduces the reliance on non-renewable resources and eliminates the need for hazardous chemicals, minimizing environmental pollution and human health risks. These ZnO-NPs can be used in environmental remediation applications, such as wastewater treatment or soil remediation, effectively removing pollutants and improving overall ecosystem health. These NPs possess a high surface area and band gap of 3.2 eV, can produce both OH° (hydroxide) and O2−° (superoxide) radicals for the generation of holes (h+) and electrons (e−), resulting in oxidation and reduction of the pollutants in their valence band (VB) and conduction band (CB) resulting in degradation of dyes (95–100

Quantification of modal mineralogy in drill-core samples is crucial for understanding the geology and metal deportment in a mining operation. This study assesses conventional procedures to quantify modal mineralogy, that includes an initial drill-core logging, followed by petrographic descriptions and SEM-based automated mineralogy analyses performed in selected regions of interest, against a novel approach using laser-induced breakdown spectroscopy (LIBS). Our proposed methodology aims to quantify the modal mineralogy directly in a drill-core sample, avoiding previous stages of selection and preparation of samples. The novelty of our methodology lies in the simultaneous selection of spectral signals corresponding to a group of elements that are interrelated within a mineral species. The resulting signal combination is strongly correlated with a mineral found in the sample. Our proof of concept combines previously described mineralogy with a detailed spectroscopic and principal component analysis. The selected spectral signals are defined as “mineralogical patterns”, which are processed using supervised chemometrics methods, such as artificial neural networks, to enable an automated mineral classification. We implemented our workflow in three molybdenite-bearing drill-core samples, yielding results comparable to operational characterization, based on petrographic studies, and validated by QEMSCAN analyses, for a suite of ore and gangue minerals, including molybdenite, pyrite, hematite/magnetite, quartz, and aluminosilicates. In brief, we demonstrate how the LIBS-ANN technique can perform automated mineral quantification directly in selected drill-core regions of interest, minimizing previous sample preparation and without expert judgment.

Fourier transform infrared photoacoustic spectroscopic sensing of roxarsone using the Fe2O3/NiO nanocomposite.

Datasets of VIS-NIR (201 bands, 565-805 nm) and SWIR (101 bands, 1066-1386 nm) average spectra of 82 pellets made of copper concentrate mixtures. The dataset are splited into 75 % training and 25 % testing. The copper concentrate mixture used in this dataset have been characterized via QEMSCAN in: F. rivas, et al, "QEMSCAN REPORT OF 82 COPPER CONCENTRATES," figshare (2023) https://opticapublishing.figshare.com/s/52c54f5634ad9fe79878 Classification models available for this dataset can be found in F. rivas, et al, "VISNIR SWIR machine learning models for classification of copper concentrates," figshare (2023) https://opticapublishing.figshare.com/s/0e9c395a492f7ec1abde

Meteor plasmas and impact events are complex, dynamic natural phenomena. Simulating these processes in the laboratory is, however, a challenge. The technique of laser induced dielectric breakdown was first used for this purpose almost 50 years ago. Since then, laser-based experiments have helped to simulate high energy processes in the Tunguska and Chicxulub impact events, heavy bombardment on the early Earth, prebiotic chemical evolution, space weathering of celestial bodies and meteor plasma. This review summarizes the current level of knowledge and outlines possible paths of future development.

A study on the classification of copper concentrates relevant to the copper refining industry is performed by means of reflectance hyperspectral images in the visible and near infrared (VIS-NIR) bands (400-1000 nm) and in the short-wave infrared (SWIR) (900-1700 nm) band. A total of 82 copper concentrate samples were press compacted into 13-mm-diameter pellets, and their mineralogical composition was characterized via quantitative evaluation of minerals and scanning electron microscopy. The most representative minerals contained in these pellets are bornite, chalcopyrite, covelline, enargite, and pyrite. Three databases (VIS-NIR, SWIR, and VIS-NIR-SWIR) containing a collection of average reflectance spectra computed from 9×9p i x e l neighborhoods in each pellet hyperspectral image are compiled to train the classification models. The classification models tested in this work are a linear discriminant classifier and two non-linear classifiers, a quadratic discriminant classifier, and a fine K-nearest neighbor classifier (FKNNC). The results obtained show that the joint use of VIS-NIR and SWIR bands allows for the accurate classification of similar copper concentrates that contain only minor differences in their mineralogical composition. Specifically, among the three tested classification models, the FKNNC performs the best in terms of overall classification accuracy, achieving 93.4% accuracy in the test set when only VIS-NIR data are used to construct the classification model, up to 80.5% using only SWIR data, and up to 97.6% using both VIS-NIR and SWIR bands together.

Herein, novel nanohybrids of poly(o-phenylenediamine)/zinc oxide (POPD/ZnO) were synthesized using an ultrasound-assisted technique via a facile in situ polymerization method for the removal of Cu (II) ions from water. These nanohybrids were characterized using infrared spectra (FTIR), UV-Visible spectra, scanning electron microscopy (SEM), X-ray scattering (XRD), thermogravimetric analysis (TGA), and BET (Brunauer-EmmettTeller surface area). Numerous parameters were optimized to see the performance of the adsorption process like effect of POPD loading, adsorbent dose, initial Cu (II) ion concentration, pH, contact time, and temperature. POPD/ZnO-13/87 was found the best adsorbent based on above analysis. Non-linear equilibrium isotherm (Langmuir and Freundlich), kinetics (pseudo-first and second order, intraparticle diffusion model), and thermodynamic studies were performed to analyze the adsorption process. Langmuir isotherm and pseudo-secondorder kinetic model were found appropriate while thermodynamic parameters Gibbs free energy change (& UDelta;G degrees), entropy change (& UDelta;S degrees) (166.32 J mol-1 K-1), and enthalpy change (& UDelta;H degrees) (34.03 kJ mol-1) were calculated showing an endothermic and spontaneous process for adsorption of Cu (II) ions. According to Langmuir isotherm, the maximum adsorption capacity was found at 2485 mg g-1 while experimental adsorption capacity was found 2360 mg g-1 at adsorbent doses = 0.4 g L-1, Cu (II) ion concentration = 1000 mg L-1, pH = 6.5, temperature = 25 degrees C in 90 min that was higher than that of other reported materials till date to the best of our knowledge. Mechanism studies suggested that the electrostatic interaction and cation-& pi; interaction between nanohybrids and Cu (II) ions was the main driving force for the adsorption of Cu (II) ions.

Heavy metals, minerals, and dyes create water pollution that has become a major environmental concern worldwide. Various methods such as advanced oxidation, adsorption, biodegradation, precipitation, flocculation, ultrafiltration, ion exchange, electrochemical degradation, and coagulation have been proposed for the removal of these types of pollutants from contaminated wastewater. Among all, adsorption is considered the most promising and economically viable. Various conducting polymers are described as superior adsorbents due to the presence of different functional groups on their backbone that can be easily tuned/functionalized and showed excellent feasibility in the removal of various toxic metal ion pollutants from wastewater. Here we review, numerous aspects of water pollution, heavy metal ions, their toxic effects on health, factors affecting adsorption, and the role of conducting polymers-based materials as adsorbents. Future research should focus on real water samples on a large scale and the enhancement of performance via novel functionalization of conducting polymers to fulfill these requirements.

Spectroscopic sensors provide online information about the composition and concentration of species in a sample by analyzing the interaction of light and matter. At the industrial scale, external variables such as temperature, pressure, and particle size distribution affect spectroscopic measurements. Thus, conventional quantitative analytical methods that do not consider these external factors provide poor estimates. Their effects have to be compensated through proper modeling and processing to improve the concentration estimation. This work presents an integrated discrete-time model considering the process dynamic and a physics-based sensor model. Then, we suggest a novel application of an adaptive Kalman filter to provide concentration estimates by correcting external factor effects. The convergence of the Kalman filter requires the fulfillment of uniform observability (persistent excitation) conditions for both inputs and external signals. Simulation results illustrate the modeling methodology and the main characteristics of the proposed Kalman filter approach for performing online correction of the spectroscopic sensor signals. The results show that the proposed adaptive Kalman filter can estimate concentrations with small error under temperature variations and measurement noise.

The prediction of band edge potentials in photocatalytic materials is an important but challenging task. In contrast, bandgaps can be easily determined through absorption spectra. Here, we present two simple theoretical approaches for the determination of band edge potentials which are based on the electron negativity and work function of each constituent atom. We use these approaches to determine band edge potentials in semiconducting metallic oxides and sulfides, such as titanium dioxide (TiO2), chalcopyrite (CuFeS2), pyrite (FeS2), covellite (CuS), and chalcocite (Cu2S) with respect to an absolute scale (eV) and an electrochemical scale (V). Until now, there is little information on iron and copper sulfides referring to these thermodynamic parameters. TiO2 (Titania p25) was used as reference semiconductor to validate the calculation procedures using experimental values by X-ray diffraction analysis (XRD), diffuse reflectance spectrometry (DRS), and electron paramagnetic resonance spectroscopy (EPR). The production of key chemical species such as reactive oxygen species (ROS) and reactive sulfur species (RSS) has been theoretically and experimentally determined by EPR.

Microplastics are widespread in the environment, which generates high concern worldwide. Microplastics have been found in superficial and ocean waters and soils, and consequently can occur in drinking water, urban wastewaters, and living organisms. Here we review microplastics with emphasis on sources, types, toxicity, analysis and removal techniques. For removal, we focus on advanced oxidation processes for plastic degradation. Thes processes involve highly reactive oxygen species such as hydroxyl, •OH, superoxide radical, •O2−, sulfate radical, SO4•-, and hydrogen peroxide H2O2.