Chemical Functionalization of ZnS: A Perspective from the Ligand–ZnS Bond Character
Bibliographic record
Abstract
Chemical functionalization of metal sulfides plays a critical role in many fields such as materials science and froth flotation. The commonly used thiol-bearing functionalized ligands are generally considered to bind with metal sulfides covalently, and the computational binding energy is widely used to evaluate the functionality of the ligands toward metal sulfides. Herein, we studied the surface chemistry of the model ZnS and its binding with typical S- and O-terminated ligands using density functional theory calculations with an emphasis on the resulting bond character. Surprisingly, it was found that the ligand–ZnS(110) bond is essentially ionic with limited covalency. This very fundamental finding was further extended to the hydrophobization of ZnS in the context of froth flotation and rationalized the previously unresolved phenomenon that the higher the ligand–ZnS(110) binding strength, the lower the hydrophobic functionality of the ligand toward ZnS. Meanwhile, instead of the binding energy, the electronegativity of the ligand was identified as an effective computational descriptor that can accurately predict the relative hydrophobic functionality of the ligand toward ZnS. This work, therefore, further advanced our understanding of the intrinsic ligand–metal sulfide binding mechanism and highlighted the importance of computational parameters, beyond the binding energy, in guiding the first principles design of ligands with enhanced functionalities or optimizing relevant industrial 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".