Application of Microextraction Techniques Including SPME and MESI to the Thermal Degradation of Polymers: A Review
Bibliographic record
Abstract
Here, we discuss the newly developed micro and solventless sample preparation techniques SPME (Solid Phase Microextraction) and MESI (Membrane Extraction with a Sorbent Interface) as applied to the qualitative and quantitative analysis of thermal oxidative degradation products of polymers and their stabilizers. The coupling of these systems to analytical instruments is also described. Our comprehensive literature search revealed that there is no previously published review article on this topic. It is shown that these extraction techniques are valuable sample preparation tools for identifying complex series of degradation products in polymers. In general, the number of products identified by traditional headspace (HS-GC-MS) is much lower than with SPME-GC-MS. MESI is particularly well suited for the detection of non-polar compounds, therefore number of products identified by this technique is not also to the same degree of SPME. Its main advantage, however, is its ability of (semi-) continuous monitoring, but it is more expensive and not yet commercialized.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".