Synthesis and characterization of MWCNT-covered stainless steel mesh with Janus-type wetting properties
Bibliographic record
Abstract
Various multi-step methods to fabricate Janus membranes have been reported in literature. However, no article so far reports the durability of the Janus membranes when exposed to liquids. We report on a novel method to fabricate a Janus-type multi-walled carbon nanotubes (MWCNT)-covered stainless steel (SS) mesh, which retains dual-wetting properties even after exposure to water for 540 d. The MWCNTs are grown directly on stainless steel mesh coupons by chemical vapor deposition using acetylene as the carbon source, and are then plasma functionalized using an ammonia-ethylene gas mixture to achieve dual-wettability. We found by x-ray photoelectron spectroscopy that the MWCNTs on the top face of the novel Janus MWCNT-SS mesh, which was directly exposed to the plasma, are coated by a plasma polymer rich in nitrogen-containing functional groups, while the MWCNTs on the bottom face are almost devoid of the plasma polymer coating. Atomic force microscopy studies confirmed that the surface roughness of the bottom face of the mesh is lower than the minimum roughness that allows the capillary ingress of water to sustain its superhydrophobic behavior. In addition, scanning electron microscopy studies also confirmed that the MWCNTs on the bottom face of the treated MWCNT mesh are vertically aligned compared to the MWCNTs on the top face of the mesh. The vertically aligned dense MWCNT forest on the bottom face attributes to its superhydrophobic nature.
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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) | 0.000 | 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 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".