Determination of Trace Elements in Fluoropolymers after Microwave-Induced Combustion
Bibliographic record
Abstract
An effective approach to the digestion of fluoropolymers for the determination of Ag, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, and Ni impurities has been developed using microwave-induced combustion (MIC) in closed quartz vessels pressurized with oxygen. Samples that were examined included the following: polytetrafluorethylene (PTFE); polytetrafluoroethylene with an additional modifier, perfluoropropylvinylether (PTFE-TFM); and fluorinated ethylene propylene (FEP). A quartz device was used as a sample holder, and the influence of the absorber solution was evaluated. Determination of trace elements was performed by inductively coupled plasma-optical emission and mass spectrometry. Neutron activation analysis (NAA) was used for validation purposes. Results were also compared to those obtained using microwave-assisted acid extraction in high-pressure closed systems. Dilute nitric acid (5 mol L(-1)), which was selected as the absorbing medium, was used to reflux the sample for 5 min after the combustion. Using these conditions, agreement for all analytes was better than 98% when compared to values determined by NAA. The residual carbon content in the digests was lower than 1%, illustrating the high efficiency of the method. Up to 8 samples could be digested within 30 min using MIC, providing a suitable throughput, taking into account the inertness of such samples.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".