| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Alexandre, Mike Abidine
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (5/5 displayed)
- 2020Effect of PEKK oligomers sizing on the dynamic mechanical behavior of poly(ether ketone ketone)/carbon fiber compositescitations
- 2019Effect of PEKK oligomers sizing on the dynamic mechanical behavior of poly(ether ketone ketone)/carbon fiber compositescitations
- 2019Effect of PEKK oligomers sizing on the dynamic mechanical behavior of poly(ether ketone ketone)/carbon fiber compositescitations
- 2018Formulation of Aqueous Dispersions of PEKK by a Quantitative Structure Property Relationship Approach and Application to Thermoplastic Sizing on Carbon Fiberss
- 2018Formulation of Aqueous Dispersions of PEKK by a Quantitative Structure Property Relationship Approach and Application to Thermoplastic Sizing on Carbon Fiberss
Places of action
| Organizations | Location | People |
|---|
article
Formulation of Aqueous Dispersions of PEKK by a Quantitative Structure Property Relationship Approach and Application to Thermoplastic Sizing on Carbon Fiberss
Abstract
The development of formulations for thermoplastic sizing on carbon fibers requires water dispersions of small polymer particles (< 20 µm). PolyEtherKetoneKetone (PEKK) is a high-performance polymer used as a matrix in Carbon Fiber Reinforced Polymers (CFRP) or as a sizing agent. To limit the formulation steps and the use of organic solvents, the sonofragmentation process can be used to deagglomerate polymers, directly in the final aqueous formulation. The sonofragmentation process is controlled by multiple parameters and, in order to identify the key parameters, a quantitative structure property relationship (QSPR) study was performed using artificial neural networks (ANN). The 40 formulations of this study were characterized with the aim of quantifying the sonofragmentation effect. Various physicochemical techniques were used: Photon Correlation Spectroscopy (PCS), destabilization velocity of the dispersions by analytical centrifugation, and scanning electron microscopy. The results obtained showed that only two parameters (mass concentration of surfactant and duration of sonication) had a notable effect on the sonofragmentation process. By controlling these two parameters, it was possible to define a design space in the stability domain of the formulations and to calculate a sonofragmentation efficiency (ϕ) for four singular zones. Image analysis showed that the sonofragmentation process was accompanied by an increase in the number of particles with Particle size (Ps) < 20 µm. In optimized aqueous formulations, the majority of particles should have Ps < 20 µm.