Institut für Physik und Astronomie
Refine
Has Fulltext
- no (3)
Document Type
- Article (3)
Language
- English (3)
Is part of the Bibliography
- yes (3)
Keywords
- processing (3) (remove)
Institute
- Institut für Physik und Astronomie (3) (remove)
Poly(vinylidenefluoride-trifluoroethylene)-based (P(VDF-TrFE)-based) terpolymers represent a new class of electroactive polymer materials that are relaxor-ferroelectric (RF) polymers and that offer unique and attractive property combinations in comparison with conventional ferroelectric polymers. The RF state is achieved by introducing a fluorine-containing termonomer as a "defect" into the ferroelectric P(VDF-TrFE) copolymer, which reduces the interaction between the VDF/TrFE dipoles. The resulting terpolymer exhibits a low Curie transition temperature and small remanent and coercive fields yielding a slim hysteresis loop that is typical for RF materials. Though the macroscopic behavior is similar to RF ceramics, the mechanisms of relaxor ferroelectricity in semi-crystalline polymers are different and not fully understood yet. Structure-property relationships play an important role in RF terpolymers, as they govern the final RF properties. Hence, a review of important characteristics, previous studies and relevant developments of P(VDF-TrFE)-based terfluoropolymers with either chlorofluoroethylene (CFE) or chlorotrifluoroethylene (CTFE) as the termonomer is deemed useful. The role of the termonomer and of its composition, as well as the effects of the processing conditions on the semi-crystalline structure which in turn affects the final RF properties are discussed in detail. In addition, the presence of noteworthy transition(s) in the mid-temperature range and the influence of preparation conditions on those transitions are reviewed. A better understanding of the fundamental aspects affecting the semi-crystalline structures will help to elucidate the nature of RF activity in VDF-based terpolymers and also help to further improve their applications-relevant electroactive properties.
'Complex systems are information processors' is a statement that is frequently made. Here we argue for the distinction between information processing-in the sense of encoding and transmitting a symbolic representation-and the formation of correlations (pattern formation/self-organisation). The study of both uses tools from information theory, but the purpose is very different in each case: explaining the mechanisms and understanding the purpose or function in the first case, versus data analysis and correlation extraction in the latter. We give examples of both and discuss some open questions. The distinction helps focus research efforts on the relevant questions in each case.
Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.