X-ray computed tomography (CT) is a powerful technique for non-destructive volumetric inspection of objects and is widely used for studying internal structures of a large variety of sample types. The raw data obtained through an X-ray CT practice is a gray-scale 3D array of voxels. This data must undergo a geometric feature extraction process before it can be used for interpretation purposes. Such feature extraction process is conventionally done manually, but with the ever-increasing trend of image data sizes and the interest in identifying more miniature features, automated feature extraction methods are sought. Given the fact that conventional computer-vision-based methods, which attempt to segment images into partitions using techniques such as thresholding, are often only useful for aiding the manual feature extraction process, machine-learning based algorithms are becoming popular to develop fully automated feature extraction processes. Nevertheless, the machine-learning algorithms require a huge pool of labeled data for proper training, which is often unavailable. We propose to address this shortage, through a data synthesis procedure. We will do so by fabricating miniature features, with known geometry, position and orientation on thin silicon wafer layers using a femtosecond laser machining system, followed by stacking these layers to construct a 3D object with internal features, and finally obtaining the X-ray CT image of the resulting 3D object. Given that the exact geometry, position and orientation of the fabricated features are known, the X-ray CT image is inherently labeled and is ready to be used for training the machine learning algorithms for automated feature extraction. Through several examples, we will showcase: (1) the capability of synthesizing features of arbitrary geometries and their corresponding labeled images; and (2) use of the synthesized data for training machine-learning based shape classifiers and features parameter extractors.

Developing optimized hydrogel products requires an in-depth understanding of the mechanisms that drive hydrogel tunability. Here, we performed a full 4 × 4 factorial design study investigating the impact of gellan, a naturally derived polysaccharide (1%, 2%, 3%, or 4% w/v) and CaCl 2 concentration (1, 3, 7, or 10 mM) on the viscoelastic, swelling, and drug release behavior of gellan hydrogels containing a model drug, vancomycin. These concentrations were chosen to specifically provide insight into gellan hydrogel behavior for formulations utilizing polymer and salt concentrations expanding beyond those commonly reported by previous studies exploring gellan. With increasing gellan and CaCl 2 concentration, the hydrogel storage moduli (0.1-100 kPa) followed a power-law relationship and on average these hydrogels had higher liquid absorption capability and greater total drug release over 6 days. We suggest that the effects of gellan and CaCl 2 concentration and their interactions on hydrogel properties can be explained by various phenomena that lead to increased swelling and increased resistance to network expansion. K E Y W O R D Sdrug release, factorial design, gellan hydrogel, swelling, viscoelastic response

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A novel method based on correlative microscopy for analyzing metal-rich particulate impurities randomly distributed in large volumes of carbon black powder is described here. Approximately 5 g of carbon black of tap density of 0.075 g cm −3 was used. The powder was encapsulated in epoxy and a combination of 3D X-ray computed tomography (X-CT), laser/ion beam milling and energy dispersive X-ray spectroscopy (EDS) was used to locate and analyze particles of interest. The locations of impurities were identified using X-CT and a focused ion beam (FIB) system with ion and laser beam milling capabilities was then used to analyze regions of interest in the encapsulated sample. The use of a nanosecond pulsed laser for milling allowed for enhanced material removal rates of up to 3 × 10 5 μm 3 s −1 . Finally, scanning electron microscopy (SEM) and EDS were used to image and analyze the composition of the exposed particles. This correlative workflow is shown to be capable of providing detailed spatial, morphological and compositional information about large metal-rich impurities in a carbon black product or a device with carbon black as additives, which may be used for identifying the source of impurities or predicting failure modes in the device.