This review article discusses the latest advances on molten phase monitoring in metallurgical processes by using Laser-Induced Breakdown Spectroscopy (LIBS). LIBS is an analytical laser-based technique, where a pulsed laser is focused on a sample to create a plasma. The optical emission from the plasma can be transferred through open-path optical configuration or via an optical fiber to a spectrometer to receive analytical information in the form of elemental composition. Thus, a relatively long-distance analysis can be performed using LIBS. Several modern experimental arrangements, patents and industrial notes are assessed, and the literature is reviewed. The review includes applications of LIBS to analyze steel, iron, aluminum, copper, slags, metal melts, and other materials. Temperature, pressure, and atmospheric composition are crucial parameters of any melting process. Hence, past studies on molten phases describing these parameters have been discussed. Finally, the review addresses the last technological advances for these types of applications. It also points out the need of development in some fields and some limitations to overcome. In addition, the review highlights the use of modern machine learning and data processing techniques to increase the effectiveness of calibration and quantification approaches. These developments are expected to improve the performance of LIBS systems already implemented at an industrial scale and ease the development of new applications in pyrometallurgical processes to address the stringent market and environmental regulations.

Effect of reduced graphene oxide on the structural and photocatalytic properties of Fe₂O₃ nanoparticles.

Arsenic (As), a semimetal toxic for humans, is commonly associated with serious health problems. The most common form of massive and chronic exposure to As is through consumption of contaminated drinking water. This study aimed to isolate an As resistant bacterial strain to characterize its ability to oxidize As (III) when immobilized in an activated carbon batch bioreactor and to evaluate its potential to be used in biological treatments to remediate As contaminated waters. The diversity of bacterial communities from sediments of the As-rich Camarones River, Atacama Desert, Chile, was evaluated by Illumina sequencing. Dominant taxonomic groups (>1%) isolated were affiliated with Proteobacteria and Firmicutes. A high As-resistant bacterium was selected (Pseudomonas migulae VC-19 strain) and the presence of aio gene in it was investigated. Arsenite detoxification activity by this bacterial strain was determined by HPLC/HG/AAS. Particularly when immobilized on activated carbon, P. migulae VC-19 showed high rates of As(III) conversion (100% oxidized after 36 h of incubation). To the best of our knowledge, this is the first report of a P. migulae arsenite oxidizing strain that is promising for biotechnological application in the treatment of arsenic contaminated waters.

The direct identification of mineral species in raw rocks was performed using laser induced breakdown spectroscopy (LIBS). A total of 162 sulfide rocks with mineralogical relevance in the copper industry were analyzed. These contained bornite (Cu₅FeS₄), chalcocite (Cu₂S), chalcopyrite (CuFeS₂), covellite (CuS), enargite (Cu₃AsS₄), molybdenite (MoS₂), and pyrite (FeS₂). The samples were collected from different mining locations to account for sample variability. Unsupervised multivariate methods like principal component analysis (PCA) and dendrogram analysis were explored, while supervised pattern recognition techniques, such as soft independent modelling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), K-nearest neighbor (KNN), decision tree analysis and artificial neural networks (ANNs) were compared. The sensitivity test performed on the LIBS data shows that the models KNN, SIMCA, PLS-DA and ANN achieve an average classification accuracy of 96.2, 98.1, 90.6 and 100%, respectively. In contrast, the robustness test of the models SIMCA and PLS-DA yields accuracies of 97.7 and 98.8%, respectively. The correct identification of very similar species in terms of their elemental composition such as bornite/chalcopyrite and chalcocite/covellite is also achieved.