Copper Bioleaching in China: Review and Prospect
Bibliographic record
Abstract
The commercial application of copper bioleaching, an environmentally-friendly approach for low-grade and secondary mineral resources recycling, has increased worldwide since the 2000s. As the world’s second-largest economic entity and the largest developing country, China has the largest demand for metal resources, significantly advancing the theory and industrial technology of copper bioleaching. This paper reviews the exploration and application of copper bioleaching in China. Two typical bioleaching applications and technological processes, bioheap leaching at the Zijinshan Copper Mine and bioheap leaching at the Dexing Copper Mine, are introduced. The considerable research completed by researchers is summarized, especially focusing on the isolation and identification of leaching bacteria, the bioleaching mechanism and interface reactions, multistage percolation behavior, bioleaching system reconstruction, the multiphysics coupled model, and enhanced copper bioleaching from waste printed circuit boards (WPCBs). Based on this investigation in China, key trends and prospects in copper bioleaching—such as efficiency improvement, environmental protection, and improved technology applications—are proposed.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".