Quantitative analysis of star‐branched polymers by multidetector size‐exclusion chromatography
Bibliographic record
Abstract
Abstract The object of this study is to develop multidetector size‐exclusion chromatography (SEC) methods to determine the number of arms per molecule across the molecular size distribution of star‐branched polymers. An empirical fit between the intrinsic viscosity molecular contraction factor g ‘ and the number of arms f is used as an alternative to converting g ‘ values to root mean square radii ratios used by random walk models. The quantitative analysis of star polymer distributions by SEC is then reduced to understanding factors unique to the accurate measurement of g ‘ across the molecular size distribution. Two methods of analyzing SEC data are then tested: (1) the “conventional method” utilizing values of weight‐average molecular weight and intrinsic viscosity at each retention volume and (2) the method of component chromatograms. The latter is a new method useful when only a few different types of branching are present. It depends on fitting each detector's chromatograms as the sum of component chromatograms. Plotting the intrinsic viscosity of the branched polymer versus that of the linear polymer at the same molecular weight was useful for diagnosing problems. The conventional method was defeated by axial dispersion in the narrow chromatograms and the homogeneity of branching in the samples. The component chromatogram method avoids the axial dispersion problem but its value depends on how accurately the component peaks reflected the true situation. In this study, the method provided the most reasonable values when component peaks were grouped together. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 552–570, 2002
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".