Insight into the Binding Mechanisms of Quartz-Selective Peptides: Toward Greener Flotation Processes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Mining practices, chiefly froth flotation, are being critically reassessed to replace their use of biohazardous chemical reagents in favor of biofriendly alternatives as a path toward green processes. In this regard, this study aimed at evaluating the interactions of peptides, as potential floatation collectors, with quartz using phage display and molecular dynamics (MD) simulations. Quartz-selective peptide sequences were initially identified by phage display at pH = 9 and further modeled by a robust simulation scheme combining classical MD, replica exchange MD, and steered MD calculations. Our residue-specific analyses of the peptides revealed that positively charged arginine and lysine residues were favorably attracted by the quartz surface at basic pH. The negatively charged residues at pH 9 (i.e., aspartic acid and glutamic acid) further showed affinity toward the quartz surface through electrostatic interactions with the positively charged surface-bound Na + ions. The best-binding heptapeptide combinations, however, contained both positively and negatively charged residues in their composition. The flexibility of peptide chains was also shown to directly affect the adsorption behavior of the peptide. While attractive intrapeptide interactions were dominated by a weak peptide–quartz binding, the repulsive self-interactions in the peptides improved the binding propensity to the quartz surface. Our results showed that MD simulations are fully capable of revealing mechanistic details of peptide adsorption to inorganic surfaces and are an invaluable tool to accelerate the rational design of peptide sequences for mineral processing applications.
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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) | 0.000 | 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) | 0.001 | 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".