Extraction of Copper from a Low-Grade Ore by Rhamnolipids
Bibliographic record
Abstract
Mining residues, in general and metal ores, in particular, contain heavy metals within the rock. These heavy metals, including copper (Cu) for which there is an increasing demand around the world, are very harmful to humans and, as such, are a serious problem for the environment. In this study, a rhamnolipid biosurfactant was used to extract copper from an oxide residue with 8,950 mg copper per kg ore. To optimize the conditions for maximum extraction, several batch tests were performed on washed ore samples at 25°C. The best ore particle size for optimal extraction was determined to be between 0.15 and 0.3 mm while the optimal pH of the washing solution was 6. A minimum volume of 10 mL rhamnolipid of 2% concentration for 1 g of ore was required to extract about 28% of copper from the ore. Adding 1% NaOH to the biosurfactant solution dramatically improved the copper extraction from the residue up to 42% in 6 days. Unwashed samples of mixed size particles were tested under the optimized conditions and 24% of copper was extracted. A sequential extraction procedure on the residues was performed to determine the forms of copper in the ore. Although the oxide and hydroxide, residual, and carbonates are the main forms, copper was extracted mainly by the biosurfactant from the oxide and hydroxide portions.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".