Sustainable Recovery of Critical Minerals from Wastes by Green Biosurfactants: A Review
Bibliographic record
Abstract
Biosurfactants have emerged as promising agents for environmental remediation due to their ability to complex, chelate, and remove heavy metals from contaminated environments. This review evaluates their potential for recovering critical minerals from waste materials to support renewable energy production, emphasizing the role of biosurfactant-metal interactions in advancing green recovery technologies and enhancing resource circularity. Among biosurfactants, rhamnolipids demonstrate a high affinity for metals such as lead, cadmium, and copper due to their strong stability constants and functional groups like carboxylates, with recovery efficiencies exceeding 75% under optimized conditions. Analytical techniques, including Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Fourier-Transform Infrared spectroscopy (FTIR), and Scanning Electron Microscopy (SEM), are instrumental in assessing recovery efficiency and interaction mechanisms. The review introduces a Green Chemistry Metrics Framework for evaluating biosurfactant-based recovery processes, revealing 70-85% lower Environmental Factors compared to conventional methods. Significant research gaps exist in applying biosurfactants for extraction of metals like lithium and cobalt from batteries and other waste materials. Advancing biosurfactant-based technologies hold promise for efficient, sustainable metal recovery and resource circularity, addressing both resource scarcity and environmental protection challenges simultaneously.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 0.001 |
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".