Surface-Induced Patterns from Evaporating Droplets of Aqueous Carbon Nanotube Dispersions
Bibliographic record
Abstract
Evaporation of aqueous droplets of carbon nanotubes (CNTs) coated with a physisorbed layer of humic acid (HA) on a partially hydrophilic substrate induces the formation of a film of CNTs. Here, we investigate the role that the global geometry of the substrate surfaces has on the structure of the CNT film. On a flat mica or silica surface, the evaporation of a convex droplet of the CNT dispersion induces the well-known "coffee ring", while evaporation of a concave droplet (capillary meniscus) of the CNT dispersion in a wedge of two planar mica sheets or between two crossed-cylinder sheets induces a large area (>mm(2)) of textured or patterned films characterized by different short- and long-range orientational and positional ordering of the CNTs. The resulting patterns appear to be determined by two competing or cooperative sedimentation mechanisms: (1) capillary forces between CNTs giving micrometer-sized filaments parallel to the boundary line of the evaporating droplet and (2) fingering instability at the boundary line of the evaporating droplet and subsequent pinning of CNTs on the surface giving micrometer-sized filaments of CNTs perpendicular to this boundary line. The interplay between substrate surface geometry and sedimentation mechanisms gives an extra control parameter for manipulating patterns of self-assembling nanoparticles at substrate surfaces.
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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".