Capillary Rise: A Simple Tool for Simultaneous Determination of Porosity and Thickness of Thin Silica Coatings
Bibliographic record
Abstract
Coating porosity is an important property that supports solid-gas and solid-liquid exchange that can either enhance various science and technological applications or promote damage if not properly controlled. However, non-destructive instrumental techniques for the measurement of porosity on coated walls or surfaces can be quite challenging. Here, a seamless capillary rise technique has been used to determine both the thickness and porosity of a thin silica coating. Uniform coatings were prepared from 5 wt% hydrophobic fumed silica in absolute ethanol and spin-coated at 500–8000 rpm on glass slides. Capillary imbibition of squalane was then controlled into known areas of the resulted hydrophobic nano-porous coatings. The mass of the solid (silica) and the infiltrated oil (squalane) were gravimetrically measured. The porosity of the material was calculated as the percentage fraction of the pore volume while the film thickness was determined as the ratio of the total volume to the area of coverage. Mean values of the porosity and coating thickness calculated from capillary impregnation technique were 86 ± 2% and 3.7 ± 0.2 μm, respectively. The coating thickness obtained was comparable with those revealed by SEM and Dektak profiler measurements. This study highlights the effectiveness of capillary rise as a simple and cost-effective non-destructive technique for assessment of coating thickness and porosity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".