Microwetting of pH-Sensitive Surface and Anisotropic MoS<sub>2</sub> Surfaces Revealed by Femtoliter Sessile Droplets
Bibliographic record
Abstract
Understanding the microwettability of anisotropic molybdenum disulfide crystal is critically important in separation and processing of this material in liquid. In this work, static microwetting properties of MoS 2 face (MF) and MoS 2 edge (ME) surfaces in water are revealed by the morphology of femtoliter interfacial droplets. The oil droplets with different size distribution were produced from heterogeneous nucleation and growth of nanodroplets during the solvent exchange under controlled flow and solution conditions, and were polymerized for droplet morphology characterization to reveal the relative wettability of the droplets on surfaces. We first demonstrate that the shape of the nanodroplets is responsive to the surface charges on a model pH sensitive substrate of gold coated with a self-assembled monolayer of two types of thiol. The experimental results on MoS 2 substrates indicate that (1) oil contact angle of the droplets on ME surface is much larger than that on MF surface at pH 3.0, suggesting that the ME surface is more hydrophilic than MF; (2) the droplets are pinned by the layered nanostructure on MoS 2 edge. The fundamental understanding of microwettability elucidated in this study may allow for an improved control of the interaction between anisotropic MoS 2 surfaces and the surrounding liquid environment, which is critically important for many industrial applications such as flotation and catalysis systems.
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 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".