Evaluation of Different Amino Acids on Growth and Cyanide Production by Bacillus megaterium for Gold Recovery
Bibliographic record
Abstract
Bio-cyanidation, as a sustainable and effective method to extract gold from primary and secondary resources, has attracted attention because of its environmental friendliness and economic benefits. The effect of amino acids on bio-cyanide production using Bacillus megaterium (B. megaterium) is a less explored area in this field and is the main interest of this study. Here, the effect of glycine, threonine, and glutamine over a concentration range of 0 to 10 g/L was investigated. The results showed at equal concentration of amino acids (5 g/L), glycine yields (maximum ca. 110 mg/L) a higher concentration of biogenic cyanide (bio-CN), while glutamine and threonine produce less (maximum ca. 74 mg/L and ca. 64 mg/L, respectively). For the first time, optimization of mixing the three amino acids was investigated and revealed more significant roles for glycine and glutamine in stimulation of bio-CN by B. megaterium. The interactions involved in the biosynthesis of bio-CN were explained with a reference to metabolic pathways and the cycle of the bacteria. In mixed amino acids, the optimum medium for bio-CN production was identified to be 2.84 g/L glycine, 3.0 g/L glutamine in the absence of threonine, which could produce a high concentration of ca. 86 mg/L bio-CN, resulting in gold leaching efficiency comparable to chemical cyanide.
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) | 0.001 | 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".