Technical Note: New methodology for measuring viscosities in small volumes characteristic of environmental chamber particle samples
Bibliographic record
Abstract
Abstract. Herein, a method for the determination of viscosities of small sample volumes is introduced, with important implications for the viscosity determination of particle samples from environmental chambers (used to simulate atmospheric conditions). The amount of sample needed is < 1 μl, and the technique is capable of determining viscosities (η) ranging between 10−3 and 103 Pascal seconds (Pa s) in samples that cover a range of chemical properties and with real-time relative humidity and temperature control; hence, the technique should be well-suited for determining the viscosities, under atmospherically relevant conditions, of particles collected from environmental chambers. In this technique, supermicron particles are first deposited on an inert hydrophobic substrate. Then, insoluble beads (~1 μm in diameter) are embedded in the particles. Next, a flow of gas is introduced over the particles, which generates a shear stress on the particle surfaces. The sample responds to this shear stress by generating internal circulations, which are quantified with an optical microscope by monitoring the movement of the beads. The rate of internal circulation is shown to be a function of particle viscosity but independent of the particle material for a wide range of organic and organic-water samples. A calibration curve is constructed from the experimental data that relates the rate of internal circulation to particle viscosity, and this calibration curve is successfully used to predict viscosities in multicomponent organic mixtures.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".