Bacterial roles, genomic features, and their regulation in the cleaner recovery of low-grade chalcopyrite bioleaching: A critical review and future prospects
Bibliographic record
Abstract
Fluidized bioleaching is an efficient, environmentally friendly, and cost-effective mining method that has been widely explored and utilized for recovering low-grade copper sulfide minerals, such as chalcopyrite. However, the proliferation and apoptosis of dominant leaching bacteria, such as Acidthiobacillus ferrooxidans , within complex pore, void, and fracture structures in deep-earth environments commonly results in a dynamic bacterial community that evolves continuously. This unclear genetic-scale microbial succession often leads to low leaching reaction efficiency, undesirable reaction passivation, and poor bioleaching operations. This review integrates genetic-scale insights with industrial challenges in chalcopyrite bioleaching, proposing novel strategies for regulating microbial communities. A systematic analysis of five critical dimensions is conducted, focusing on: 1) The adaptations of Acidithiobacillus spp. to high Ag + stress. 2) The direct, indirect, and cooperative bioleaching pathways are linked to bacterial extracellular polymer substance (EPS) and Fe/S oxidation genes. 3) The passivation dynamics governed by bacterial genomics, including thiosulfate, polysulfide, and biofilm mechanisms. 4) The microbial succession patterns under genetic control Hi-C sequencing-guided consortia design. 5) Molecular detection methods (16S rDNA, Hi-C) for optimizing leaching efficiency. The following innovations have been identified as being of key significance: A genomic-environmental interaction model has been developed to bridge the gap between bacterial genetics and passivation dynamics. A comprehensive analysis of Ag + catalysis has been conducted, resulting in a 40% reduction in jarosite formation through jar gene suppression. Practical strategies, such as thermophilic consortia engineering, have been validated in pilot trials, achieving a 32% increase in copper recovery. Additionally, this study meticulously reviews and summarizes typical potential stimulations and enhanced bioleaching methods. The genetic sequencing methods, such as 16S rDNA and Hi-C, have been shown to hold promising potential for improving bioleaching reactions and delaying the formation of passivation substances like jarosite.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".