Sustainable Recovery of Critical Minerals from Wastes by Green Biosurfactants: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it