Deep eutectic solvents and ionic liquids in hydrometallurgical recovery of metals - A review of recent advances and challenges
Bibliographic record
Abstract
This review focuses on the hydrometallurgical recovery of metals via deep eutectic solvents (DESs) and ionic liquids (ILs), which are well known for their low toxicity, cost-effectiveness, and eco-friendliness, offering a promising route for sustainable metal extraction through leaching and solvent extraction (SX) processes. The focus is on assessing the efficacy of novel leaching liquids to produce leachates and the use of these unconventional solvents as extractants and diluents in the SX process for metal extraction. This review summarizes and discusses the characteristics of the ILs and DESs used for metal recovery, including their original introduction, synthesis, and classification. The DESs and ILs can be used as lixiviants for metal leaching and have significant potential to replace mineral acids. The selective and efficient leaching of metals from minerals or wastes has been proven in many experimental studies and is surveyed in this review. Solvometallurgy is a new branch of hydrometallurgy that uses DESs and ILs for leaching and SX of metals, and several studies in which both of these solvents are used as mixtures for the recovery and extraction of metals are also included in this review. Furthermore, IL-based and DES-assisted SX processes are discussed in detail, demonstrating that they are a credible alternative to traditional chemical solvents. This review also aims to explore the benefits, challenges, and environmental consequences of using DESs and ILs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".