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.
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 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) | 0.001 | 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 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".