Application of the Discrete Wavelet Transform to SEM and AFM Micrographs for Quantitative Analysis of Complex Surfaces

Effects of Heat Treatment of Selective Laser Melting Printed Ti-6Al-4V Specimens on Surface Texture Parameters and Cell Attachment

Selective laser melting (SLM) is extensively used for fabricating metallic biomedical products. After 3D printing, it is almost always advisable to apply a heat treatment to release the internal tensions or optimize the mechanical properties of the printed parts. The aim of this paper is to investigate the effects of heat treatment of SLM printed Ti-6Al-4V (Ti64) circular specimens on the areal surface texture parameters and cell attachment. Areal surface texture parameters, including the arithmetic mean height (Sa), root-mean-square height (Sq), skewness (Ssk), and kurtosis (Sku) were characterized. In addition, wavelet-based multi-resolution analysis was applied to investigate the characteristic length scales of untreated and heat-treated Ti64 specimens. In this study, the vertical distance between the highest and lowest position of cell attachment for each sampling area was defined as ΔH. Results showed that an increase in the periodic characteristic length scale was primarily due to the formation of large-scale aggregations of Ti64 metal powder particles on the heat-treated surface. In addition, MG-63 cells preferred lying in concave hollows; in heat-treated specimens, values of ΔH statistically significantly decreased from 31.6 ± 4.2 to 8.8 ± 2.8 μm, while Sku decreased from 3.3 ± 1.4 to 2.6 ± 0.6, indicating a strong influence of Sku on cell attachment.

Elucidation of Differential Nano-Textural Attributes for Normal Oral Mucosa and Pre-Cancer

Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.

Integration of Platinum Group Metal-Free Catalysts and Bilirubin Oxidase into a Hybrid Material for Oxygen Reduction: Interplay of Chemistry and Morphology

Catalytic activity toward the oxygen reduction reaction (ORR) of platinum group metal-free (PGM-free) electrocatalysts integrated with an enzyme (bilirubin oxidase, BOx) in neutral media was studied. The effects of chemical and morphological characteristics of PGM-free materials on the enzyme enhancement of the overall ORR kinetics was investigated. The surface chemistry of the PGM-free catalyst was studied using X-ray Photoelectron Spectroscopy. Catalyst surface morphology was characterized using two independent methods: length-scale specific image analysis and nitrogen adsorption. Good agreement of macroscopic and microscopic morphological properties was found. Enhancement of ORR activity by the enzyme is influenced by chemistry and surface morphology of the catalyst itself. Catalysts with a higher nitrogen content, specifically pyridinic moieties, showed the greatest enhancement. Furthermore, catalysts with a higher fraction of surface roughness in the range of 3-5 nm exhibited greater performance enhancement than catalysts lacking features of this size.

Morphological Characterization of ALD and Doping Effects on Mesoporous SnO2 Aerogels by XPS and Quantitative SEM Image Analysis

Atomic layer deposition (ALD) is unsurpassed in its ability to create thin conformal coatings over very rough and/or porous materials. Yet although the coating thickness on flat surfaces can be measured by ellipsometry, characterization of these coatings on rough surfaces is difficult. Here, two techniques are demonstrated to provide such characterization of ALD-coated TiO2 over mesoporous SnO2 aerogel films on glass substrates, and insights are gained as to the ALD process. First, X-ray photoelectron spectroscopy (XPS) is used to determine the coating thickness over the aerogel, and the results (0.04 nm/cycle) agree well with ellipsometry on flat surfaces up to a coating thickness limit of about 6 nm. Second, quantitative analysis of SEM images of the aerogel cross section is used to determine porosity and roughness, from which coating thickness can be inferred. The analysis reveals increasing porosity from the aerogel/air interface to the aerogel/substrate interface, indicating a thicker ALD coating near the air side, which is consistent with tortuous diffusion through the pores limiting access of ALD precursors to deeper parts of the film. SEM-derived porosity is generally useful in a thin film because bulk methods like nitrogen physisorption or mercury porosimetry are impractical for use with thin-film samples. Therefore, in this study SEM was also used to characterize quantitatively the morphologogical changes in SnO2 aerogel thin films due to doping with Sb. This study can be used as a methodology to understand morphological changes in different types of porous and/or rough materials.

Fractal and Lacunarity Analyses: Quantitative Characterization of Hierarchical Surface Topographies

Biomimetic hierarchical surface structures that exhibit features having multiple length scales have been used in many technological and engineering applications. Their surface topographies are most commonly analyzed using scanning electron microscopy (SEM), which only allows for qualitative visual assessments. Here we introduce fractal and lacunarity analyses as a method of characterizing the SEM images of hierarchical surface structures in a quantitative manner. Taking femtosecond laser-irradiated metals as an example, our results illustrate that, while the fractal dimension is a poor descriptor of surface complexity, lacunarity analysis can successfully quantify the spatial texture of an SEM image; this, in turn, provides a convenient means of reporting changes in surface topography with respect to changes in processing parameters. Furthermore, lacunarity plots are shown to be sensitive to the different length scales present within a hierarchical structure due to the reversal of lacunarity trends at specific magnifications where new features become resolvable. Finally, we have established a consistent method of detecting pattern sizes in an image from the oscillation of lacunarity plots. Therefore, we promote the adoption of lacunarity analysis as a powerful tool for quantitative characterization of, but not limited to, multi-scale hierarchical surface topographies.