RESUMEN
The analysis of 2D scattering maps generated in scatterometry experiments for detection and classification of nanoparticles on surfaces is a cumbersome and slow process. Recently, deep learning techniques have been adopted to avoid manual feature extraction and classification in many research and application areas, including optics. In the present work, we collected experimental datasets of nanoparticles deposited on wafers for four different classes of polystyrene particles (with diameters of 40, 50, 60, and 80 nm) plus a background (no particles) class. We trained a convolutional neural network, including its architecture optimization, and achieved 95% accurate results. We compared the performance of this network to an existing method based on line-by-line search and thresholding, demonstrating up to a twofold enhanced performance in particle classification. The network is extended by a supervisor layer that can reject up to 80% of the fooling images at the cost of rejecting only 10% of original data. The developed Python and PyTorch codes, as well as dataset, are available online.
RESUMEN
The crystallinity of stretched crystallizable rubbers is classically evaluated using x-ray diffraction (XRD). As crystallization is a strongly exothermal phenomenon, quantitative surface calorimetry from infrared thermography offers an interesting alternative to XRD for determining the crystallinity. In this paper, the two measurement techniques have been used for evaluating the strain-induced crystallinity of the same unfilled natural rubber. This study provides the first comparison between the two techniques. The results obtained highlight the very satisfactory agreement between the two measurements, which opens a simple way for evaluating the strain-induced crystallinity from temperature measurements.
RESUMEN
This paper deals with composite structures for biomedical applications. For this purpose, an architectured tubular structure composed of Nickel Titanium (NiTi) Shape Memory Alloy (SMA) and silicone rubber was fabricated. One of the main interests of such structures is to ensure a good adhesion between its two constitutive materials. A previous study of the authors (Rey et al., 2014) has shown that the adhesion between NiTi and silicone rubber can be improved by an adhesion promoter or plasma treatment. However, adhesion promoters are often not biocompatible. Hence, plasma treatment is favored to be used in the present study. Three different gases were tested; air, argon and oxygen. The effects of these treatments on the maximum force required to pull-out a NiTi wire from the silicone rubber matrix were investigated by means of pull-out tests carried out with a self-developed device. Among the three gases, a higher maximum force was obtained for argon gas in the plasma treatment. A tube shaped architectured NiTi/silicone rubber structure was then produced using this treatment. The composite was tested by means of a bulge test. Results open a new way of investigations for architectured NiTi-silicone structures for biomechanical applications.