Influence of atmospheric pressure plasma jet processing parameters on the wettability and surface chemistry of polypropylene: relevance for adhesion phenomena
Bibliographic record
Abstract
Atmospheric pressure plasma jet technology has been applied to surface treatment of polypropylene prior to its adhesive bonding to aluminium using structural adhesives. Surface modifications of polypropylene induced duringplasma treatments were investigated using surface free energy measurements, attenuated total reflectance infrared spectroscopy (ATR-IR) analyses, and mechanical evaluation of epoxy and urethane bonded aluminium-polypropylene joints. On the basis of the surface free energy criterion, the influence of parameters describing plasma treatments (i.e. primary-to-secondary gas ratios, output power source, treatment speed, plasma-to-sample distance) was determined for each gas combination employed to generate the plasma (He with O₂, N₂, CO₂, N₂O or air). By submitting polypropylene to the optimised plasma conditions defined for each gas combination, it was found using ATR-IR analyses that a complex mixture of carbonyl functionalities are induced on the surface of processed materials. Using a fitting procedure of Gaussian bands, ATR-IR spectra were resolved into single C=O stretching vibrations. It was then found that regardless of the gas mixture injected in the plasma generator, different extents of amide and COO-based chemical functions (carboxylic acids and/or esters) were introduced onto polypropylene surfaces. From the mechanical evaluation of joint strengths of adhesively bonded hybrid aluminiumpolypropylene assemblies, it was generally observed that the surface chemistry induced by the plasma plays a more important role in adhesion than the surface free energy parameter. Finally, using correlations established between Owens et al. and LWAB surface free energy theories, plasma modified polypropylene surfaces were found to be basic in nature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".