A review on solvent extraction of scandium

Mapping the research landscape of bauxite by-products (red mud): An evolutionary perspective from 1995 to 2022

The global population growth has significantly impacted energy and raw material consumption, unmatched since the Industrial Revolution. Among metals, aluminium ranks second only to steel, with annual production exceeding 69 million tonnes. Due to its high demand, bauxite, the primary ore from which aluminium is extracted, is now classified as a critical material in the EU and the US, given the potential risk of supply shortages for essential applications. Geographical and production challenges surround bauxite, presenting geo-economic and environmental challenges. A critical concern in aluminium production is managing by-products, notably red mud, a bauxite residue, generating over 175 million tonnes annually worldwide. Comprehensive bibliometric research is imperative due to the high amount of bibliographical resources related to this topic, encompassing circular economy, re-valorisation, sustainability, and disposal. This study employs bibliometric methods to assess red mud valorisation, offering insights into research topics, influential authors, and key journals, shedding light on the past, present, and future of red mud research. Such bibliometric analysis not only highlights the current state of the field but also serves as a valuable tool for decision-making, enabling researchers and policymakers to identify trends, gaps, and areas for further exploration, fostering informed and sustainable advancements in the by-products of the aluminium industry.

Scandium Radioisotopes-Toward New Targets and Imaging Modalities

The concept of theranostics uses radioisotopes of the same or chemically similar elements to label biological ligands in a way that allows the use of diagnostic and therapeutic radiation for a combined diagnosis and treatment regimen. For scandium, radioisotopes -43 and -44 can be used as diagnostic markers, while radioisotope scandium-47 can be used in the same configuration for targeted therapy. This work presents the latest achievements in the production and processing of radioisotopes and briefly characterizes solutions aimed at increasing the availability of these radioisotopes for research and clinical practice.

Defining the optimal conditions using FFNNs and NARX neural networks for modelling the extraction of Sc from aqueous solution by Cryptand-2.2.1 and Cryptand-2.1.1

The main aim of this study is to figure out how well cryptand-2.2.1 (C 2.2.1) and cryptand-2.1.1 (C 2.1.1) macrocyclic compounds (MCs) work as novel extractants for scandium (Sc) by using an artificial neural network (ANN) models in MATLAB software. Moreover, C2.2.1 and C2.1.1 have never been evaluated to recover Sc. The independent variables impacting the extraction process (concentration of MC, concentration of Sc, pH, and time), and a nonlinear autoregressive network with exogenous input (NARX) and feed-forward neural network (FFNN) models were used to estimate their optimum values. The greatest obstacle in the selective recovery process of the REEs is the similarity in their physicochemical properties, specifically their ionic radius. The recovery of Sc from the aqueous solution was experimentally evaluated, then the non-linear relationship between those parameters was predictively modeled using (NARX) and (FFNN). To confirm the extraction and stripping efficiency, an atomic absorption spectrophotometer (AAS) was employed. The results of the extraction investigations show that, for the best conditions of 0.008 mol/L MC concentration, 10 min of contact time, pH 2 of the aqueous solution, and 75 mg/L Sc initial concentration, respectively, the C 2.1.1 and C 2.2.1 extractants may reach 99 % of Sc extraction efficiency. Sc was recovered from a multi-element solution of scandium (Sc), yttrium (Y), and lanthanum (La) under these circumstances. Whereas, at a concentration of 0.3 mol/L of hydrochloric acid, the extraction of Sc was 99 %, as opposed to Y 10 % and La 7 %. The Levenberg-Marquardt training algorithm had the best training performance with an mean-squared-error, MSE, of 5.232x10-6 and 6.1387x10-5 for C 2.2.1 and C 2.1.1 respectively. The optimized FFNN architecture of 4-10-1 was constructed for modeling recovery of Sc. The extraction process was well modeled by the FFNN with an R2 of 0.999 for the two MC, indicating that the observed Sc recovery efficiency consistent with the predicted one.

Recovery of copper and silver from industrial e-waste leached solutions using sustainable liquid membrane technology: a review

Waste electrical and electronic equipment or e-waste has recently emerged as a significant global concern. This waste contains various valuable metals, and via recycling, it could become a sustainable resource of metals (viz. copper, silver, gold, and others) while reducing reliance on virgin mining. Copper and silver with their superior electrical and thermal conductivity have been reviewed due to their high demand. Recovering these metals will be beneficial to attain the current needs. Liquid membrane technology has appeared as a viable option for treating e-waste from various industries as a simultaneous extraction and stripping process. It also includes extensive research on biotechnology, chemical and pharmaceutical, environmental engineering, pulp and paper, textile, food processing, and wastewater treatment. The success of this process depends more on the selection of organic and stripping phases. In this review, the use of liquid membrane technology in treating/recovering copper and silver from industrial e-waste leached solutions was highlighted. It also assembles critical information on the organic phase (carrier and diluent) and stripping phase in liquid membrane formulation for selective copper and silver. In addition, the utilization of green diluent, ionic liquids, and synergist carrier was also included since it gained prominence attention latterly. The future prospects and challenges of this technology were also discussed to ensure the industrialization of technology. Herein, a potential process flowchart for the valorization of e-waste is also proposed.