Temperature-programmed pyrolysis hyphenated with metastable atom bombardment ionization mass spectrometry (TPPy/MAB-MS) for the identification of additives in polymers
Bibliographic record
Abstract
Thermoanalytical techniques are currently used for the analysis of additives contained in polymers that cannot be easily dissolved, extracted, or hydrolyzed. With these techniques, the polymers are heated to liberate the additives trapped in the polymer matrix. If the polymer is heated slowly, up to its thermal degradation, the technique is called temperature-programmed pyrolysis (TPPy). For TPPy experiments, mass spectrometry is generally used as the detection method. The ionization sources commonly used in mass spectrometry, such as CI and EI, can cause fragmentation during the ionization process. Fragmentation decreases the sensitivity of the molecular ions and increases the risks of interferences with the compounds coming from the matrix. An energy-tunable ionization technique, called metastable atom bombardment (MAB), is proposed for TPPy/MS experiments. With this ionization source, the energy of ionization depends on the metastable gas used. With low-energy metastable gases such as Xe or N(2), fragmentation is reduced compared to CI, whereas with medium-energy metastable gases such as Ar or Kr, the fragmentation is similar to that observed with CI. TPPy/MAB-MS was performed on an unknown polyurethane-based car paint. The detection of molecular ions and characteristic fragments with MAB(N(2)) led to the identification of two light stabilizers: Bis(1,2,2,6,6-pentamethyl-4-piperidinyl)sebacate (BPPS) and 2-(2H-benzotriazol-2-yl)-4,6- di-tert-pentylphenol (PTPP). Using MAB(Ar) to simulate CI, the molecular ion and one of the two characteristic fragments of BPPS were not detected, thus confirming the advantage of using MAB(N(2)) ionization for TPPy/MS experiments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".