Biomining with bacteriophage: Selectivity of displayed peptides for naturally occurring sphalerite and chalcopyrite
Bibliographic record
Abstract
During mineral processing, concentrates of sulfide minerals of economic interest are formed by froth flotation of fine ore particles. The method works well but recovery and selectivity can be poor for ores with complex mineralogy. There is considerable interest in methods that improve the selectivity of this process while avoiding the high costs of using flotation chemicals. Here we show the first application of phage biotechnology to the processing of economically important minerals in ore slurries. A random heptapeptide library was screened for peptide sequences that bind selectively to the minerals sphalerite (ZnS) and chalcopyrite (CuFeS2). After several rounds of enrichment, cloned phage containing the surface peptide loops KPLLMGS and QPKGPKQ bound specifically to sphalerite. Phage containing the peptide loop TPTTYKV bound to both sphalerite and chalcopyrite. By using an enzyme-linked immunosorbant assay (ELISA), the phage was characterized as strong binders compared to wild-type phage. Specificity of binding was confirmed by immunochemical visualization of phage bound to mineral particles but not to silica (a waste mineral) or pyrite. The current study focused primarily on the isolation of ZnS-specific phage that could be utilized in the separation of sphalerite from silica. At mining sites where sphalerite and chalcopyrite are not found together in natural ores, the separation of sphalerite from silica would be an appropriate enrichment step. At mining sites where sphalerite and chalcopyrite do occur together, more specific phage would be required. This bacteriophage has the potential to be used in a more selective method of mineral separation and to be the basis for advanced methods of mineral processing.
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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) | 0.000 | 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) | 0.001 | 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".