Molar mass distributions of polymers from size exclusion chromatography and matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry: Methods for comparison
Bibliographic record
Abstract
Abstract Equations are presented for calculating molar mass averages and molar mass distributions from matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) data and from size exclusion chromatography (SEC) data. The utility of polydispersity is examined as an indicator of the expectation of MALDI‐TOF MS mass discrimination effects. Cumulative distributions are found to be rich in information for comparing the two techniques and are easily obtained from both SEC and MALDI‐TOF MS data. Analyses of a series of narrow molar mass distribution poly(methyl methacrylate) (PMMA) standards and one polydisperse sample have been performed with both methods. MALDI‐TOF MS did not detect dimer and trimer in the PMMA samples, and it often indicated lower amounts of high‐molar‐mass polymers than did SEC. The results showed that the distribution breadth, as evidenced by the standard deviation of the distribution (calculated from the polydispersity and number‐average molar mass), correlated well with the molar mass range observed in the MALDI‐TOF MS spectra, whereas the polydispersity alone did not. Ratioing the extremes in the molar mass concentrations measured with the SEC differential refractometer, which were necessary to adequately define molar mass distributions, showed that detector dynamic range values as high as approximately 370,000 were required for the polydisperse samples. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 627–639, 2005
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".