Local polydispersity detection in size exclusion chromatography: Method assessment
Bibliographic record
Abstract
Abstract Local polydispersity is the term describing the variety of molecules present at the same retention volume in size exclusion chromatography (SEC) analysis. In the analysis of a linear homopolymer, local polydispersity is generally attributed to the effect of axial dispersion: it can cause molecular size variety (i.e., imperfect resolution) at each retention volume and thus local polydispersity in the molecular weight. In the analysis of polymer blends (copolymers and branched polymers), it is possible to have local polydispersity, even when the resolution is perfect, because molecules of different compositions (or degrees of branching) can have the same molecular size in solution. Conventional SEC interpretation assumes no local polydispersity if the axial dispersion effects are negligible. Three methods are currently available for detecting local polydispersity by using a combination of differential refractive index, light scattering, and viscometer detectors: the chromatogram comparison method, the conventional calibration curve comparison method, and the universal calibration comparison method. Here we experimentally assess these three methods using polymer blends and emphasize the chromatogram comparison method. All three are shown to be useful for assessing triple detector systems, but they are capable of detecting local polydispersity due to molecular heterogeneity only for very large differences in specific refractive index increments in the blend components. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 81: 370–383, 2001
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".