A review of the application of deep eutectic solvents for metal recovery from diverse secondary sources
Bibliographic record
Abstract
In recent years, the growing demand for critical, strategic, and precious metals across various industries has led to a worldwide shortage. The increasing disposal of metal-containing waste poses significant environmental and health risks while also accelerating the depletion of natural resources. Given the supply risks, environmental hazards, and challenges associated with extracting these metals from primary ores, it is vital to develop sustainable methods for recovering them from secondary sources. Metal recovery has garnered research interest due to its essential role in promoting a circular economy. Metallurgical processes, such as pyrometallurgy, bioleaching, and hydrometallurgy, often lead to secondary pollution and economic challenges. Methods based on solvometallurgy and green chemistry, such as the use of deep eutectic solvents (DESs), offer potential for separating metals from waste. DESs provide a sustainable approach for selective recovery, potentially replacing mineral acids while minimizing wastewater production. This paper provides a literature survey on the extraction of metals from sources including electrical and industrial waste, minerals, biological materials, and environmental samples using DESs. It discusses concepts, specifications, and characterization methods related to DESs. Additionally, the article classifies the techniques used for metal separation and recovery with DESs, along with key parameters that influence efficiency. The review emphasizes the selective and high-efficiency recovery of various metals using DESs. It highlights the necessity of designing DESs with low viscosity, strong coordination, and high reducibility for optimal separation. We hope that the challenges and opportunities for advancing metal recovery through DESs are clearly outlined, providing a roadmap for new applications of DES technology. • Deep eutectic solvents recover metals from electronic, industrial, and mineral waste. • Review covers extraction from batteries, magnets, solar panels, and biological sources. • Key solvent properties drive efficient and selective metal recovery. • Eco-friendly solvents reduce wastewater and replace harmful mineral acids. • Guidance provided for designing deep eutectic solvents for sustainability
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".