Estimating the viscosity of a highly viscous liquid droplet through the relaxation time of a dry spot
Bibliographic record
Abstract
We discuss in this paper a technique which enables the estimation of the viscosity of microscopic droplets, with application to particles suspended in the atmosphere. The principle of this technique is to deposit a droplet of material approximately 30–100 μm in diameter on a substrate and poke it with a sharp needle hence generating a hole. The amount of sample needed to perform such measurement allows the viscosity of small sample volumes (less than a microliter), such as those generated from atmospheric sampling, to be determined. We show here that the time required for the droplet to relax to its equilibrium shape can be related to the viscosity. We hereby present two mathematical models based on the lubrication approximation which are able to capture the droplet relaxation dynamics. One model is fully transient and resolves the dynamics of the wetting front using a disjoining pressure approach. The other is quasistatic and requires a relationship between the contact line velocity and the contact angle. Comparing the computed relaxation time to that measured experimentally enables the approximate evaluation of the viscosity. The mathematical models are first tested against data from the literature for the closure of a hole in a continuous thin film and then demonstrated for droplets of the polybutene oil N450000 (trade name Cannon Standard Oil), a high-viscosity standard, which serve as a benchmark sample since it is precisely characterized. We also present here viscosity estimates for droplets consisting of secondary organic material and water which are present over forested region yet remain very poorly understood for a lack of adequate characterization technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".