Sorption of aqueous amino acid species on sulphidic mineral surfaces—DFT study and insights on biosourced‐reagent mineral flotation
Bibliographic record
Abstract
Abstract The interaction between the 20 natural amino acids and the surfaces of sphalerite, pyrite, and chalcopyrite was examined in the light of density functional theory (DFT) simulations. The electronic properties of the active functional groups and the equilibrium geometries of the sorbed amino acid species were characterized as a function the 65 species resulting from their (de)protonations. Covalent bonds are predicted between the surface metallic atoms and the carboxyl groups and, though to a lesser extent, with the side and main amino groups. Chemisorption as a determining factor in the uptake of amino acid species was assessed in terms of sorption affinities for the mineral surfaces. The largest affinities result from the carboxyl groups of the deprotonated species at intermediate pH, whereas the fully protonated amino acids in acidic media predict the lowest affinities. Affinity of amino acids towards the mineral surfaces followed the trend: pyrite >> chalcopyrite > sphalerite with speciation‐dependent decreasing sequence: ∼2.5 < pH < ∼9.5; pH > ∼9.5; pH < ∼2.5. By targeting the most suitable amino acid building blocks in bio‐sourced collectors, either high collecting powers or superior selectivities can be aimed at depending on pH for improved separation of sulphidic minerals in flotation processes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".