Magnetic microrheometer for <i>in situ</i> characterization of coating viscosity
Bibliographic record
Abstract
A magnetic microrheometer has been designed to characterize the local viscosity of liquid-applied coatings in situ during solidification. The apparatus includes NdFeB magnets mounted on computer-controlled micropositioners for the manipulation of ∼1 μm diameter superparamagnetic particles in the coating. Magnetic field gradients at 20-70 T/m are generated by changing magnet size and the gap distance between the magnets. A specimen stage located between two magnets is outfitted with a heater and channels to control process conditions (temperature and air flow), and a digital optical microscope lens above the stage is used to monitor the probe particle position. Validation studies with glycerol and polyimide precursor solution showed that microrheometry results match traditional bulk rheometry within an error of 5%. The viscosities of polyvinyl alcohol (PVA) solution and polyimide precursor solution coatings were measured at different shear rates (0.01-5 s(-1)) by adjusting the magnetic field gradient. The effect of proximity to the substrate on the particle motion was characterized and compared with theoretical predictions. The magnetic microrheometer was used to characterize the time-viscosity profile of PVA coatings during drying at several temperatures. The viscosity range measured by the apparatus was 0.1-20 Pa s during drying of coatings at temperatures between room temperature and 80 °C.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".