Effects of Thickness and Adsorption of Airborne Hydrocarbons on Wetting Properties of MoS<sub>2</sub>: An Atomistic Simulation Study
Bibliographic record
Abstract
Molybdenum disulfide (MoS2) has attracted great attention due to its distinctive electronic, optical, chemical, and mechanical properties. In almost all of these applications, having a clear understanding about the wetting properties of MoS2 is essential. The basal plane of MoS2 has been generally believed to be hydrophobic with water contact angle (WCA) around 90°. Kozbial et al. have recently suggested that the freshly exfoliated MoS2 was intrinsically relative hydrophilic (WCA = 69.0 ± 3.8°); however, it could become fairly hydrophobic after 1 day exposure to the ambient air (WCA = 89.0 ± 3.1°) (Kozbial et al. Understanding the Intrinsic Water Wettability of Molybdenum Disulfide (MoS2). Langmuir 2015, 31, 8429–8435). They contributed this change in wetting properties to the adsorption of airborne hydrocarbons. The number of layers is another important factor that is believed to affect the wetting properties in ultrathin MoS2 films. For highly crystalline samples grown at high temperature (900°C), Gaur et al. showed that the WCA was a function of number of layers and changed from 98° in monolayer sample to 88° in a sample with 11 layers (Gaur et al. Surface Energy Engineering for Tunable Wettability through controlled synthesis of MoS2. Nano Lett. 2014, 14, 4314–4321). In this work, we study the effects of these two parameters, namely, adsorption of airborne hydrocarbon contaminants and the number of layers, on MoS2 wetting properties using atomistic simulations. Results of our simulations confirm that both of these factors can affect the wetting properties of MoS2 through altering van der Waals interactions between MoS2 and water. We also show that the contributions of both energy and entropy of adhesion should be considered to understand the wetting properties of MoS2. Results of this work improve our understanding about the wetting properties of MoS2 and other transition metal dichalcogenides.
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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".