A numerical study on the intrinsic error involved in the use of a shear cell to measure liquid diffusion coefficients
Bibliographic record
Abstract
The shear cell technique has some merits not possessed by the long capillary method for the measurement of diffusion coefficients. However, it also possesses the possibility of an intrinsic error arising from any liquid mixing associated with its shearing operation. A dynamic numerical simulation, with a grid sliding technique, was developed and was used to explore the solute exchange between the two liquid charges in a diffusion couple, the concentration distribution along the liquid charges, and to investigate the error of the shear cell method. This was shown to be associated with the shear rate and annealing time involved. It was found that the intrinsic error was caused mainly by the convection resulting from the shear and strongly depends on the mass Peclet number and the Reynold’s number. The intrinsic error arising from the shearing action in the shear cell required to create the diffusion couple deceases with increasing time of the diffusion anneal. The variation of the maximum intrinsic error with the elapsed diffusion time was calculated for a capillary diameter of 1.5 mm similar to that which was used in diffusion coefficient measurements.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".