Application of an integrated matrix-assisted laser desorption/ionization time-of-flight, electrospray ionization mass spectrometry and tandem mass spectrometry approach to characterizing complex polyol mixtures
Bibliographic record
Abstract
Polyols are being used in a wide range of industrial applications including surfactants and precursors for grafted polymers. The characterization of polyols is of significance in correlating compositions and structures with their properties. We illustrate two real world examples where traditional analytical methods including GPC and NMR failed to reveal compositional differences, but the combination of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF), electrospray ionization mass spectrometry (ESI MS), and MS/MS can produce compositional information required for problem solving. The first example involves failure analysis of four ethylene oxide and propylene oxide (EO/PO) copolymer products. The results from the mass spectrometry analysis unequivocally demonstrate that one of the samples has a small variation in copolymer composition, leading to its abnormal activity. The second example is in the area of deformulation of complex polyol mixtures. Two samples displaying similar properties and activities were found to be two different polyol blends. One of the samples is a more cost-effective product. These examples demonstrate that MALDI, ESI MS, and MS/MS should be seriously considered as an integrated component of an overall polyol characterization program in product failure analysis and deformulation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".