A Comparison of Electrospray Ionization and Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry with Nuclear Magnetic Resonance Spectroscopy for the Characterization of Synthetic Co-Polymers
Bibliographic record
Abstract
Electrospray ionization (ESI-MS) and matrix assisted laser desorption-ionization (MALDI-MS) were used to determine the composition (monomer ratios) and structure (end group analysis) relative to 1H NMR spectroscopy and theoretical predictions for three different copolymers: poly(butyl acrylate/vinyl acetate) (PBA/PVAc), poly(methyl methacrylate/vinyl acetate) (PMMA/PVAc) and poly(butyl acrylate/methyl methacrylate) (PBA/PMMA). We found that the ESI results were in excellent agreement with 1H NMR spectroscopy for PBA/PVAc and PBA/PMMA copolymers whereas there was more divergence in the case of PMMA/PVAc. In the case of PBA/PMMA copolymers similar distributions of products were observe by ESI-MS and MALDI-MS with the two major products classes differing by their end-groups. One class has hydrogen and dodecylthio end groups while in the other the dodecylthio has been replaced by alpha-cyanoisopropyl from the initiator. The relative abundance of these distributions as a function of copolymer conversion for a series of reaction conditions was investigated by both ESI and MALDI. MALDI results consistently underestimated (relative to ESI) the butylacrylate monomer ratio in PBA/PMMA and the abundance of co-polymer oligomers terminated by a dodecylthio group from the chain transfer agent.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".