Identification of mineral‐binding peptides that discriminate between chalcopyrite and enargite
Bibliographic record
Abstract
ABSTRACT Innovative approaches to the separation of minerals and subsequent extraction of metals are imperative owing to the increasing mineralogical complexity of ore deposits that are difficult or even impossible to separate into slurries or solutions containing only the minerals or metals of interest. Low recovery of metal is typical for these complex deposits leading to significant losses to tailings. In addition, the minerals often contain impurities, some toxic, which are difficult and costly to control or manage during the processing of a concentrate or other mineral product. One example of this complex situation is the significant economic and environmental costs associated with diluting and processing copper concentrates containing arsenic (in the form of the mineral enargite, Cu 3 AsS 4 ) in the production of pure copper. To overcome these separation problems, we have utilized phage display to identify peptides that demonstrate selective recognition of enargite and the arsenic‐free copper sulfide, chalcopyrite. Screening of two random peptide phage display libraries resulted in the identification of an enargite‐selective peptide with the sequence MHKPTVHIKGPT and a chalcopyrite‐selective peptide with the sequence RKKKCKGNCCYTPQ. Mineral‐binding selectivity was demonstrated by binding studies, zeta potential determination and immunochemistry. Peptides that have the ability to discriminate between enargite and chalcopyrite provide a greener option for the separation of arsenic containing contaminants from copper concentrates. This represents the first step towards a major advance in the replacement or reduction of toxic collectors as well as reducing the level of arsenic‐bearing minerals in the early stages of mineral processing. Biotechnol. Bioeng. 2017;114: 998–1005. © 2016 Wiley Periodicals, Inc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".