Toward Sustainable Solution for Biooxidation of Waste and Refractory Materials Using Neutrophilic and Alkaliphilic Microorganisms—A Review
Bibliographic record
Abstract
The significant increase in economic concern and environmental restrictions has resulted in increasing interest in biotechnological solutions. The application of acidophilic, sulfur-oxidizing microorganisms in biomining and in the treatment of waste matrices has been extensively explored. However, to surmount the current challenges encountered by the industrial use of acidophiles, there is an opportunity for neutrophilic and alkaliphilic microorganisms to be comprehensively considered for the biooxidation of refractory sulfide materials. This review, for the first time, provides a detailed study of neutrophiles and alkaliphiles that have potential for oxidizing sulfur-containing wastes and sulfide refractory ores to recover entrapped metals especially gold in a sustainable manner. The study illustrates the applicability of neutrophilic and alkaliphilic microorganisms to provide better and sustainable alternatives for the recovery of metals from wastes from various sources as well as refractory materials. The microorganisms summarized in this review have been successfully used in oxidizing different sulfide sources by achieving high oxidizing efficiencies (>80%) in numerous technologies. The fundamentals of biooxidation along with possible mechanisms involved in the biooxidation have been discussed in detail.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".