5/17/2023 0 Comments Supertab 11sd particle size![]() Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. SEM images for each excipient were acquired and divided into training, validation, and test sets. We tested 10 pharmaceutical excipients with widely different particle morphologies. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. It was shown that small grainsĬonvolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. To evaluate the engineering and flow properties of these heterogeneous materials under various conditions, shear tests, particle size analyses, scanning electron microscope observations, and density investigations were conducted. To supplement this, the present study provides comprehensive investigations regarding geotechnical properties of the three base regolith simulant systems: TUBS-M, TUBS-T, and TUBS-I. To overcome this major gap, a unique modular system for flexible adaptable novel lunar regolith simulants was developed and chemically characterized in earlier works. So far, no singular lunar simulant can cover the multitude of use cases that lunar regolith involves, and most available materials are poorly characterized. To minimize mission risks, technologies, such as rovers or regolith processing systems, must be developed and tested on Earth using lunar regolith simulants that closely resemble the properties of real lunar soil. The return to the Moon is an important short-term goal of NASA and other international space agencies. ![]() ![]() This study also revealed that obtained insights on rheological behavior can be used to optimize agitator settings in a tableting machine. The presence of fines between larger particles turned out to reduce the rheological index, which the authors explain by improved particle separation at more dynamic flow fields. Particle size distribution was identified as a main contributing factor to the rheological behavior of powders. A new parameter for rheological behavior was introduced, which is a measure for the change in dynamic cohesive index upon changes in flow field. In the current study, the rheological behavior was investigated for a wide range of excipients with a wide range of material properties. One of the challenges in mimicking the die filling process is the impact of rheological powder behavior as a result of differences in flow field in the feeding frame. Many flow characterization techniques are present, but so far only a few have shown to mimic the die filling process successfully. With the emergence of quality by design in the pharmaceutical industry, it becomes imperative to gain a deeper mechanistic understanding of factors impacting the flow of a formulation into tableting dies.
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