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.
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) | 0.000 | 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 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".