Phytomining of valuable metals: status and prospective-a review
Bibliographic record
Abstract
Precious metals accumulated to high concentration spread across tailing dumps, mineralised soil and at many metal-processing facilities across the African continent. Currently, about 71% of the world’s deposits of platinum group metals (PGMs) are found in South Africa. The country produces about 60% of the world’s output of three of these precious metals namely; platinum, palladium and rhodium. Traditional mining technology is grossly insufficient to quantitatively recover all metals economically. Therefore, low concentrations of these valuable metals are often wasted away as leftovers – posing environmental menace to the general ecosystem via dissolved ‘associated-toxic metal leaching’ and run-offs. In the past few decades, the concept of phytomining to recover low concentration precious metals from mine tailings, has been investigated by various researchers inMexico, New Zeeland, Caledonia, Canada, United Kingdom and Australia. This review presents a synopsis of the reported precious metal phytomining studies developed on the laboratory scale, and/or in greenhouse or pilot scale as well as in the field, in the last two and half decades. A summary of work done so far as relating to the techno-economic assessment of precious metal phytomining is also presented. We have also taken the opportunity to specifically discuss the applications of the phytoextracted plant-based metal in catalysis and related fields – an area with great potential to support the catalyst market and its adjacent chemicals and pharmaceutical industries but has been less explored for its possible coupling with phytomining activities so as to augment the economic prospects of this mining method.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".