Waste electrical and electronic equipment (WEEE) contains economically significant levels of precious, critical metals and rare earth elements, apart from base metals and other toxic compounds. Recycling and recovery of critical elements from WEEEs using a cost-effective technology are now one of the top priorities in metallurgy due to the rapid depletion of their natural resources. More than 150 publications on WEEE management, leaching and recovery of metals from WEEE were reviewed in this work, with special emphasize on the recent research (2015–2018). This paper summarizes the recent progress regarding various hydrometallurgical processes for the leaching of critical elements from WEEEs. Various methodologies and techniques for critical elements selective recovery (using ionic liquids, solvent extraction, electrowinning, adsorption, and precipitation) from the WEEEs leachates are discussed. Future prospects regarding the use of WEEEs as secondary resources for critical raw materials and its techno-economical and commercial beneficiaries are discussed. GRAPHICAL ABSTRACT Pictorial representation of the hydrometallurgical recovery of critical and precious elements from WEEE

One of the main topics in the development of quantitative structure-property relationship (QSPR) predictive models is the identification of the subset of variables that represent the structure of a molecule and which are predictors for a given property. There are several automated feature selection methods, ranging from backward, forward or stepwise procedures, to further elaborated methodologies such as evolutionary programming. The problem lies in selecting the minimum subset of descriptors that can predict a certain property with a good performance, computationally efficient and in a more robust way, since the presence of irrelevant or redundant features can cause poor generalization capacity. In this paper an alternative selection method, based on Random Forests to determine the variable importance is proposed in the context of QSPR regression problems, with an application to a manually curated dataset for predicting standard enthalpy of formation. The subsequent predictive models are trained with support vector machines introducing the variables sequentially from a ranked list based on the variable importance.