1:30 pm – 6:00 pm:
T14-Polymer Analysis: Innovative Methods, Morphology, Rheology and Optical Analysis
(Moderator: Greg Kamykowski)-Room S320H

1:30 pm – 2:00 pm:
KEYNOTE: Polyolefin Elastomers: Material Science and End Use Applications

Seema Karande, Application Development Fellow , Dow Chemical

2:00 pm – 2:30 pm:
Rheological Methods for Characterizing the Degree of Long Chain Branching in Polyethylene

Terri Chen, Senior Applications Support Scientist , TA Instruments
Branching in polymers contributes many unique rheological properties in polymer processing. Polymer branching enhances chain entanglements, increases relaxation times, and increases the extensional flow viscosity as evidenced by the strain hardening phenomenon. For many years, researchers have used different rheological methods to quantify the degree of branching in polymer chains. The most commonly used rheological techniques for differentiating linear and long chain branched polymers include traditional small amplitude oscillatory shear testing (SAOS), such as frequency sweeps at multiple temperatures, followed by time-temperature superposition (TTS), extensional viscosity testing, and large amplitude oscillatory shear testing (LAOS). However, polymer chain entanglement and relaxation are not only affected by branching but also by molecular weight (Mw) and molecular weight distribution (MWD). The common rheological methods may not be able to distinguish whether the rheological property contributions are from long chain branching or Mw and MWD effects. In this paper, we describe commonly used melt rheological methods for studying polymer long chain branching and their respective benefits and limitations.

2:30 pm – 3:00 pm:
Crystallization Behavior of Sheared Polyamide 66

Anne Gohn, Penn State University
The application of fast scanning chip calorimetry (FSC) for analysis of sheared polyamide 66 (PA 66) provided quantitative insight of the effect of shear flow and flow-induced formation of crystallization precursors/nuclei on the subsequent crystallization in a wide range of temperatures. In the high-temperature, heterogeneous-nucleation range, there is a direct relationship between the amount of specific work supplied to the melt and the acceleration of crystallization, presumably due in part to increased nucleation density of the sheared samples. This information is directly applicable to polymer engineering applications where the formation of crystalline domains during processing often occurs at rapid-cooling conditions. Analysis of the structure at the micrometer-length scale of sheared PA 66 by polarized-light optical microscopy (POM) revealed large shish-kebab structures.

3:00 pm – 3:30 pm:
Effect of HNTs Dispersion in PVDF on Morphology and its Formation Mechanism ofTensile Fractured Surfaces

Han-xiong Huang, South China University of Technology
Poly (vinylidene fluoride) (PVDF) nanocomposites containing unmodified halloysite nanotubes (HNTs) are prepared by using extrusion with and without water injection. Transmission electron microscopy micrographs show that better HNTs dispersion is obtained in the PVDF matrix with water injection. The Halpin-Tsai equation is employed to quantitatively estimate the HNTs dispersion, indicating that the nanocomposites prepared with water injection possess large fitting aspect ratio of the HNTs owing to improved HNTs dispersion. The tensile fractured surfaces for the neat PVDF, P-Hm, and P-Hm-W samples exhibit different fractured morphologies, as evidenced by scanning electron microscopy, indicating that different fracture mechanism occurs. This is because the crystallization behavior of PVDF and the HNTs dispersion induced by injected water result in the formation of the voids, wedges, and ridges, and so cracks initially form at different locations.

3:30 pm – 4:00 pm:
Determination of Flame Retardant Materials in Plastic Using a Comnbination of Analytical Techniques

Yanika Schneider, EAG
Flame retardant compounds serve an important purpose in society and are particularly critical in plastics, which are more flammable than other materials. To evaluate the efficacy of flame retardants in commercial products, it is important to know both the concentration and composition. However, due to the variability of flame retardants, the appropriate analytical method is not always obvious. In this publication, we analyze an unknown plastic box advertised to have flame retardant properties. We use a series of analytical techniques and evaluate their compatibility with one another.

4:00 pm – 4:30 pm:
Open-cell Foaming of PP/PTFE Fibrillated Composites

Yu Guang Chen, University of Toronto
In the study. PP/PTFE composites with different degree of fibrillation were prepared. Crystallization and rheology behavior was investigated. The presence of PTFE fiber enhanced the kinetics of isothermal crystallization of PP. The second modulus plateau at the low ω and a tan δ peak indicates the existence of a three dimensional networks. Extrusion foaming results shows that addition of PTFE increase a 2 orders increase in cell dencity and 10-fold decrease in expansion ratio due to addition of PTFE compared to that of PP. With PTFE nanofiber, open-cell content of the composites was increased.

4:30 pm – 5:00 pm:
Core/Shell Structure of Electrospun Polycarbonate Nanofibers

Yiyang Xu, University of Wisconsin-Madison
Internal structure is key to tailoring the performance of electrospun (ES) nanofibers. However, it still remains very challenging to characterize the structures inside ES fibers. In this study, ES polycarbonate (PC) nanofibers were successfully cut open along and across the fiber axis by embedding. These sections exhibited a clear core/shell-like structure, where the shell layer remained nearly con¬stant (50 nm or so) with increased fiber diameter, while the core layer showed a linear increase. The reason for this is discussed herein, and a model describing the variation of the core/shell layer thickness is proposed. This model has the potential to enable the production of nano¬fibers with superior properties.

5:00 pm – 5:30 pm:
Effects of Biodegradable Additives on the Nucleation Intensity and Growth Rate of Isotactic Polypropylene Spherulities

Yousef Mubarak, The University of Jordan
The effect of biodegradable additive (Biosphere) on the spherulite growth rates of isotactic polypropylene was studied by means of polarized light microscopy. It has been found that the addition of biodegradable additive to isotactic polypropylene matrix increases the intensity of the spherulites at all covered isothermal crystallization temperature in the range from 125 to 145 oC. In comparison with the neat isotactic polypropylene spherulites, much smaller spherulites were obtained at all crystallization temperatures for the isotactic polypropylene/biodegradable composite. The obtained results show that the presence of the biodegradable additives enhances spherulite growth rate at low crystallization temperatures (below 135 oC) while the effect of these additives is almost negligible at high crystallization temperature (above 135 °C).

5:30 pm – 6:00 pm:
Pellet Shape Classification Using Deep Neutral Networks

Brenda Colegrove, The Dow Chemical Company
In this paper, the task of image-based product classification is considered. This is a supervised learning problem where the input is an image of a polyethylene pellet and the output is a unique label attributed to the image from a finite set of labels corresponding to useful classes. This is a prevalent and highly relevant industrial challenge and recent developments in deep learning have proven to be successful in increasing the image classification accuracy. Thus, in this work, we leverage deep neural networks’ (DNN) ability to automatically learn features from images and test their performance in a real industrial context for describing the pellet shape. Furthermore, other machine learning techniques such as partial least squares discriminant analysis (PLS-DA) and random forests (RF) are also explored in order to assess the benefits of adopting DNN as opposed to current classifiers. PLS-DA, RF, and DNN models were developed for two classification tasks: pellet body shape classification (distinguishing good and bad pellets), and detecting tails in a pellet (distinguishing whether a pellet has tails or not). After developing these models, the results were consistent for both classification objectives: compared to the classification system currently in use, RF was able to better utilize the same pre-defined morphometric features and improve prediction accuracy significantly, while PLS-DA presented slightly better performance. DNN obtained the highest accuracy overall, with the advantage that there is no need to specify a priori which image features to use. Rather, they are directly extracted from the raw images.