Taylor dispersion monitored by electrospray mass spectrometry: a novel approach for studying diffusion in solution
Bibliographic record
Abstract
This work describes a novel approach for monitoring analyte diffusion in solution that is based on electrospray ionization mass spectrometry (ESI-MS). A mass spectrometer at the end of a laminar flow tube is used to measure the Taylor dispersion of an initially sharp boundary between two solutions of different analyte concentration. This boundary is dispersed by the laminar flow profile in the tube. However, this effect is diminished by analyte diffusion that continuously changes the radial position, and hence the flow velocity of individual analyte molecules. The steepness of the resulting dispersion profile therefore increases with increasing diffusion coefficient of the analyte. A theoretical framework is developed to adapt the equations governing the dispersion process to the case of mass spectrometric detection. This novel technique is applied to determine the diffusion coefficients of choline and cytochrome c. The measured diffusion coefficients, (11.9 +/- 1.0) x 10(-10) m(2) s(-1) and (1.35 +/- 0.08) x 10(-10) m(2) s(-1), respectively, are in agreement with the results of control experiments where the Taylor dispersion of these two analytes was monitored optically. Due to the inherent selectivity and sensitivity of ESI-MS, it appears that the approach described in this work could become a valuable alternative to existing methods for studying diffusion processes, especially for experiments on multicomponent systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".