Ternary Mutual Diffusion Coefficients from Error-Function Dispersion Profiles: Aqueous Solutions of Triton X-100 Micelles + Poly(ethylene glycol)
Bibliographic record
Abstract
Taylor dispersion has gained widespread popularity for measuring diffusion in liquids. The usual procedure is to inject small volumes of solution containing solute at concentration c̄ + Δ c into carrier streams of composition c̄ . Binary mutual diffusion coefficients D are evaluated from the Gaussian distribution of the dispersed solute measured at the outlet of a long capillary tube. As a result of strong dilution of the injected solute with the carrier solution, obtaining favorable signal-to-noise ratios for the measured profiles can require unacceptably large Δ c values for solutions with strongly composition-dependent diffusion coefficients or broad dispersion profiles produced by slowly diffusing solutes. For these systems, D can be reliably evaluated from error-function profiles generated by changing the solution flowing into dispersion tube from composition c̄ − (Δ c /2) to c̄ + (Δ c /2). There are no dilution factors, so Δ c can be orders of magnitude smaller than the values employed in conventional pulse-injection techniques. In the present study, the error-function dispersion technique is extended to measure coupled diffusion in three-component solutions using small Δ c initial conditions. A least-squares procedure is developed to calculate ternary mutual D ik coefficients from profiles generated by changing the solution flowing into a dispersion tube from composition c̄ 1 − (Δ c 1 /2) and c̄ 2 − (Δ c 2 /2) to c̄ 1 + (Δ c 1 /2) and c̄ 2 + (Δ c 2 /2). D ik coefficients are measured for aqueous solutions of Triton X-100 + poly(ethylene glycol) at 25 °C to study the interactions between nonionic micelles and polymers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".