Cultivation and Use of Acidithiobacillus ferrooxidans in Tellurium Biorecovery
Bibliographic record
Abstract
The use of Acidithiobacillus ferrooxidans, Thiobacillus thiooxidans, and other chemoautotrophic microbes in bioleaching have been implemented in a variety of processes.1 In industry, bioleaching has been studied for its potential to extract valuable metals from low grade ores that would otherwise be cost prohibitive to recover.2 Other applications of bioleaching include the ability of certain microbes to detoxify waste products and even heavy metal contaminated soils.3 Another potential application for such bio-oxidative microbial activity is the extraction of tellurium (Te) from mine tailings, a low-cost abundant resource. Tellurium is one of the least common elements on Earth; it is found in the planetary crust at about 1 µg/kg, a rarity most comparable to that of platinum.4 A major use of Te in the U.S. is in cadmium-telluride (CdTe) solar panels5. Te is primarily imported into the U.S. from Canada, and it is usually recovered as a byproduct of copper refining.6 Since CdTe photovoltaic (PV) cells are the most efficient, cost-effective, and environmentally friendly PV chemistry, the renewables market has seen an increased demand for CdTe PV cells causing some concerns about sustainability and the limited global availability of Te. Acidithiobacillus ferroxidans is a microorganism that can oxidize iron and sulfur to produce ferric iron and sulfuric acid, and it is possible that it could also solubilize Te from sulfidic mine tailings. In this project, different media for growth of A. ferroxidans were evaluated, and a plan for testing the ability of A. ferroxidans to leach Te from mine tailings was developed. Initial characterization of A. ferroxidans cultures grown in the presence of copper tailings suggests that conditions suitable for Te bioleaching can be established.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".