Origin of the hydrophobicity of sulfur-containing iron surfaces
Bibliographic record
Abstract
Sulfur-containing iron materials such as sulfidized nanoscale zerovalent iron (SNZVI) have shown outstanding water remediation performance in many recent studies, which is largely attributed to its high hydrophobicity compared to that of NZVI. However, the role of sulfur in the reactions, and the origin of the hydrophobicity of SNZVI, were still unclear. In this paper, for the first time, we conducted ab initio molecular dynamics simulation using an explicitly solvated model on both Fe and S-containing Fe surfaces, to explore the hydrophobicity of S-containing Fe materials. We found that the high hydrophobicity of these S-containing Fe surfaces originates from the hydrophobic nature of S: both doping S on top of the Fe surface and inserting S onto an Fe surface can significantly improve the surface hydrophobicity by increasing the distance between the water layer and the Fe surface. This exposes empty Fe sites which do not interact with water and in turn reduces hydrogen evolution. To compare with the theoretical analysis, we experimentally analyzed the hydrophobicity of both NZVI and SNZVI surfaces, leading to a good agreement with our theoretical analysis. We then theoretically show that the doping of other p-block elements (e.g., N and P) to iron surfaces can also create a hydrophobic phenomenon. Most importantly, this study points out that the potential contribution of hydrophobicity to the reactivity on liquid-phase reaction materials should not be ignored in the mechanistic analysis.
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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".