Use of remote atmospheric mass spectrometry in atmospheric plasma polymerization of hydrophilic and hydrophobic coatings
Bibliographic record
Abstract
Abstract This paper shows that, to a certain extent, remote atmospheric mass spectrometry can be used to identify signature fragments, which are able to predict the final surface chemistry of plasma‐deposited organic hydrophilic or hydrophobic coatings, to propose polymerization mechanisms and to predict coating contamination. Examples are given for the plasma polymerization of anhydrides and organic acids for polar coatings and for the polymerization of fluorinated precursors for hydrophobic coatings. To predict the final surface chemistry of hydrophilic coatings, we show that by tracking the evolution of the CO+ and CO2+ fragments in the plasma phase, one can deduce the relative amount of polar functions on the final coating surface. Similarly, the change in intensities of the various CFx+ fragments during the plasma polymerization of hydrophobic coatings is correlated with the relative amount of such CFx groups in the coating. For the same coatings, when CO2+, COF+, and COF2+ fragments are detected in the gas phase, the final coating will be contaminated. The possibilities, as well as some limits of this approach, are discussed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".