In this study, molten salt electrorefining was used to recover indium metal from In-Sn crude metal sourced from indium tin oxide (ITO) scrap. The electrolyte used was a mixture of eutectic LiF-KF salt and InF3 initiator, melted and operated at 700°C. Voltammetric analysis was performed to optimize InF3 content in the electrolyte, and cyclic voltammetry (CV) was used to determine the redox potentials of In metal and the electrolyte. The optimum initiator concentration was 7 wt% of InF3, at which the diffusion coefficients were saturated. The reduction potential was controlled by applying constant current densities of 5, 10, and 15 mA/cm2 using chronopotentiometry (CP) techniques. In metal from the In-Sn crude melt was deposited on the cathode surface and was collected in an alumina crucible.
Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling