Height Fluctuations and Surface Gradients in Topographic Measurements.

Topographic maps are composed of pixels associated with coordinates (x, y, z) on a surface. Each pixel location (x, y) is linked with fluctuations in a measured height sample (z). Fluctuations here are uncertainties in heights estimated from multiple topographic measurements at the same position. Height samples (z) are measured at individual locations (x, y) in topographic measurements and compared with gradients on topographies. Here, gradients are slopes on a surface calculated at the scale of the sampling interval from inclination angles of vectors that are normal to triangular facets formed by adjacent height samples (z = z(x, y)). Similarities between maps of gradients logs and height fluctuations are apparent. This shows that the fluctuations are exponentially dependent on local surface gradients. The highest fluctuations correspond to tool/material interactions for turned surfaces and to regions of maximum plastic deformation for sandblasted surfaces. Finally, for abraded, heterogeneous, multilayer surfaces, fluctuations are dependent on both abrasion and light/sub-layer interactions. It appears that the natures of irregular surface topographies govern fluctuation regimes, and that regions which are indicative of surface functionality, or integrity, can have the highest fluctuations.

Top-down Determination of Fluctuations in Topographic Measurements.

A top-down method is presented and studied for quantifying topographic map height (z) fluctuations directly from measurements on surfaces of interest. Contrary to bottom-up methods used in dimensional metrology, this method does not require knowledge of transfer functions and fluctuations of an instrument. Fluctuations are considered here to be indicative of some kinds of uncertainties. Multiple (n), successive topographic measurements (z = z(x,y)) are made at one location without moving the measurand relative to the measurement instrument. The measured heights (z) at each position (x,y) are analyzed statistically. Fluctuation maps are generated from the calculated variances. Three surfaces were measured with two interferometric measuring microscopes (Bruker ContourGT™ and Zygo NewView™ 7300). These surfaces included an anisotropic, turned surface; an isotropic, sandblasted surface; and an abraded, heterogeneous, multilayer surface having different, complex, multiscale morphologies. In demonstrating the method, it was found that few non-measured points persisted for all 100 measurements at any location. The distributions of uncertainties are similar to those of certain features on topographic maps at the same locations, suggesting that topographic features can augment measurement fluctuations. This was especially observed on the abraded ophthalmic lens; a scratch divides the topographic map into two zones with different uncertainty values. The distributions of fluctuations can be non-Gaussian. Additionally, they can vary between regions within some measurements.

A Multiscale Topographical Surface Analysis of High Entropy Alloys Coatings by Laser Melting.

High Entropy Alloys (HEAs) coatings obtained by laser melting (LM) technique were studied through a multiscale topographical surface analysis using a focus variation microscope. The laser melting creates a multiscale topography from under-powder size (incomplete or complete powder melting) to upper-powder size (process conditions). The surface topography must be optimized because of the significant influence on friction and material transfer during sliding wear. The analyses were shown that different pre-melting zone interactions were present. Statistical analysis based on covariance analyses is allowed to highlight the different process melting scales. The best LM parameter values to minimize Surface Heterogeneity were laser power (Pw) of 55 W, laser exposition time (te) of 1750 µs, and distance between two pulses (dp) of 100 µm.

Surface Inspection and Description in Metrology and Tribology-Vol.1.

The primary goal of this Special Issue was to present recent trends in surface inspection and description, from both metrological and tribological points of view [...].

Optimization of use-wear detection and characterization on stone tool surfaces.

Debates and doubt around the interpretation of use-wear on stone tools called for the development of quantitative analysis of surfaces to complement the qualitative description of traces. Recently, a growing number of studies showed that prehistoric activities can be discriminated thanks to quantitative characterization of stone tools surface alteration due to use. However, stone tool surfaces are microscopically very heterogeneous and the calculated parameters may highly vary depending on the areas selected for measurement. Indeed, it may be impacted by the effects from the raw material topography and not from the altered zones only, if non-altered part of the surface is included in the measurement. We propose here to discuss this issue and present a workflow involving the use of masks to separate worn and unworn parts of the surface. Our results show that this step of extraction, together with suitable filtering, could have a high impact on the optimization of the detection and thus characterization of use traces. This represents the basis for future automatic routines allowing the detection, extraction and characterization of wear on stone tools.

Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms.

High-speed nanoindentation rapidly generates large datasets, opening the door for advanced data analysis methods such as the resources available in artificial intelligence. The present study addresses the problem of differentiating load-displacement curves presenting pop-in, slope changes, or instabilities from curves exhibiting a typical loading path in large nanoindentation datasets. Classification of the curves was achieved with a deep learning model, specifically, a convolutional neural network (CNN) model implemented in Python using TensorFlow and Keras libraries. Load-displacement curves (with pop-in and without pop-in) from various materials were input to train and validate the model. The curves were converted into square matrices (50 × 50) and then used as inputs for the CNN model. The model successfully differentiated between pop-in and non-pop-in curves with approximately 93% accuracy in the training and validation datasets, indicating that the risk of overfitting the model was negligible. These results confirmed that artificial intelligence and computer vision models represent a powerful tool for analyzing nanoindentation data.

A Multiscale Topographical Analysis Based on Morphological Information: The HEVC Multiscale Decomposition.

In this paper, we evaluate the effect of scale analysis as well as the filtering process on the performances of an original compressed-domain classifier in the field of material surface topographies classification. Each surface profile is multiscale analyzed by using a Gaussian Filter analyzing method to be decomposed into three multiscale filtered image types: Low-pass (LP), Band-pass (BP), and High-pass (HP) filtered versions, respectively. The complete set of filtered image data constitutes the collected database. First, the images are lossless compressed using the state-of-the art High-efficiency video coding (HEVC) video coding standard. Then, the Intra-Prediction Modes Histogram (IPHM) feature descriptor is computed directly in the compressed domain from each HEVC compressed image. Finally, we apply the IPHM feature descriptors as an input of a Support Vector Machine (SVM) classifier. SVM is introduced here to strengthen the performances of the proposed classification system thanks to the powerful properties of machine learning tools. We evaluate the proposed solution we called "HEVC Multiscale Decomposition" (HEVC-MD) on a huge database of nearly 42,000 multiscale topographic images. A simple preliminary version of the algorithm reaches an accuracy of 52%. We increase this accuracy to 70% by using the multiscale analysis of the high-frequency range HP filtered image data sets. Finally, we verify that considering only the highest-scale analysis of low-frequency range LP was more appropriate for classifying our six surface topographies with an accuracy of up to 81%. To compare these new topographical descriptors to those conventionally used, SVM is applied on a set of 34 roughness parameters defined on the International Standard GPS ISO 25178 (Geometrical Product Specification), and one obtains accuracies of 38%, 52%, 65%, and 57% respectively for Sa, multiscale Sa, 34 roughness parameters, and multiscale ones. Compared to conventional roughness descriptors, the HEVC-MD descriptors increase surfaces discrimination from 65% to 81%.

Digital Cultural Heritage Preservation in Art Painting: A Surface Roughness Approach to the Brush Strokes.

There is a growing interest in cultural heritage preservation. The notion of HyperHeritage highlights the creation of new means of communication for the perception and data processing in cultural heritage. This article presents the Digital Surface HyperHeritage approach, an academic project to identify the topography of art painting surfaces at the scale at which the elementary information of sensorial rendering is contained. High-resolution roughness and imaging measurement tools are then required. The high-resolution digital model of painted surfaces provides a solid foundation for artwork-related information and is a source of many potential opportunities in the fields of identification, conservation, and restoration. It can facilitate the determination of the operations used by the artist in the creative process and allow art historians to define, for instance, the meaning, provenance, or authorship of a masterpiece. The Digital Surface HyperHeritage approach also includes the development of a database for archiving and sharing the topographic signature of a painting.

Topographical curvature is sufficient to control epithelium elongation.

How biophysical cues can control tissue morphogenesis is a central question in biology and for the development of efficient tissue engineering strategies. Recent data suggest that specific topographies such as grooves and ridges can trigger anisotropic tissue growth. However, the specific contribution of biologically relevant topographical features such as cell-scale curvature is still unclear. Here we engineer a series of grooves and ridges model topographies exhibiting specific curvature at the ridge/groove junctions and monitored the growth of epithelial colonies on these surfaces. We observe a striking proportionality between the maximum convex curvature of the ridges and the elongation of the epithelium. This is accompanied by the anisotropic distribution of F-actin and nuclei with partial exclusion of both in convex regions as well as the curvature-dependent reorientation of pluricellular protrusions and mitotic spindles. This demonstrates that curvature itself is sufficient to trigger and modulate the oriented growth of epithelia through the formation of convex "topographical barriers" and establishes curvature as a powerful tuning parameter for tissue engineering and biomimetic biomaterial design.

Quantification of the Morphological Signature of Roping Based on Multiscale Analysis and Autocorrelation Function Description.

Roping or ridging is a visual defect affecting the surface of ferritic stainless steels, assessed using visual inspection of the surfaces. The aim of this study was to quantify the morphological signature of roping to link roughness results with five levels of roping identified with visual inspection. First, the multiscale analysis of roughness showed that the texture aspect ratio Str computed with a low-pass filter of 32 µm gave a clear separation between the acceptable levels of roping and the non-acceptable levels (rejected sheets). To obtain a gradation description of roping instead of a binary description, a methodology based on the use of the autocorrelation function was created. It consisted of several steps: a low-pass filtering of the autocorrelation function at 150 µm, the segmentation of the autocorrelation into four stabilized portions, and finally, the computation of isotropy and the root-mean-square roughness Sq on the obtained quarters of function. The use of the isotropy combined with the root-mean-square roughness Sq led to a clear separation of the five levels of roping: the acceptable levels of roping corresponded to strong isotropy (values larger than 10%) coupled with low root-mean-square roughness Sq. Both methodologies can be used to quantitatively describe surface morphology of roping in order to improve our understanding of the roping phenomenon.

How to Select 2D and 3D Roughness Parameters at Their Relevant Scales by the Analysis of Covariance.

In this paper, a multi-scale methodology is proposed to model and characterize the effect of two lubricants on changes in surface morphology during a running-in test. The test concerns two steels samples, mounted on a twin-disc tribometer to test each of lubricants A and B for a period of 42 h. The changes are characterized by the standardized roughness parameters given in ISO 25178. A technique involving replication is used to monitor wear during the test. Using all these replication measurements, a multi-scale methodology is applied. These selected models highlighted the relevant parameters for quantifying wear during lifespan, and also showed that lubricant A was better able to preserve surface integrity during wear than lubricant B.

Determination of an Objective Criterion for the Assessment of the Feasibility of an Instrumented Indentation Test on Rough Surfaces.

The influence of roughness on the results of indentation testing was investigated using a semianalytical model. This model used simulated surfaces that were described using three standard roughness parameters: the root-mean-square deviation Sq, the wavelength (or cut-off of Gaussian high-pass filter), and the fractal dimension. It was shown that Sq had the largest effect on the determination of the macrohardness, while the surface wavelength and fractal dimension had negligible effects at the scale of investigation. The error of determination of the macrohardness rose with the increase of the ratio Sq/hmax where hmax was the maximum indentation depth: Sq/hmax ratios lower than 0.02 were required to obtain a systematic error of the macrohardness lower than 5%, whatever the examined material mechanical properties (in elasticity and plasticity).

Surface Reflectance: an Optical Method for Multiscale Curvature Characterization of Wear on Ceramic-Metal Composites.

Surface gradient characterization by light reflectance (SGCLR) is used for the first time for multiscale curvature calculations and discrimination of worn surfaces on six damaged ceramic-metal composites. Measurements are made using reflectance transformation imaging (RTI). Slope and curvature maps, generated from RTI, are analyzed instead of heights. From multiscale decompositions, bootstrapping, and analysis of variance (ANOVA), a strong correlation (R² = 0.90) is found between the density of furrows of Mehlum curvatures, with a band pass filter at 5.4 µm, present in ceramic grains and their mechanical properties. A strong correlation is found between the mean curvatures of the metal and the ceramics, with a high pass filter at 1286 µm.

Surface Texturization of Breast Implants Impacts Extracellular Matrix and Inflammatory Gene Expression in Asymptomatic Capsules.

BACKGROUND: Texturing processes have been designed to improve biocompatibility and mechanical anchoring of breast implants. However, a high degree of texturing has been associated with severe abnormalities. In this study, the authors aimed to determine whether implant surface topography could also affect physiology of asymptomatic capsules. METHODS: The authors collected topographic measurements from 17 different breast implant devices by interferometry and radiographic microtomography. Morphologic structures were analyzed statistically to obtain a robust breast implant surface classification. The authors obtained three topographic categories of textured implants (i.e., "peak and valleys," "open cavities," and "semiopened cavities") based on the cross-sectional aspects. The authors simultaneously collected 31 Baker grade I capsules, sorted them according to the new classification, established their molecular profile, and examined the tissue organization. RESULTS: Each of the categories showed distinct expression patterns of genes associated with the extracellular matrix (Timp and Mmp members) and inflammatory response (Saa1, Tnsf11, and Il8), despite originating from healthy capsules. In addition, slight variations were observed in the organization of capsular tissues at the histologic level. CONCLUSIONS: The authors combined a novel surface implant classification system and gene profiling analysis to show that implant surface topography is a bioactive cue that can trigger gene expression changes in surrounding tissue, even in Baker grade I capsules. The authors' new classification system avoids confusion regarding the word "texture," and could be transposed to implant ranges of every manufacturer. This new classification could prove useful in studies on potential links between specific texturizations and the incidence of certain breast-implant associated complications.

A Biophysical Model for Curvature-Guided Cell Migration.

The latest experiments have shown that adherent cells can migrate according to cell-scale curvature variations via a process called curvotaxis. Despite identification of key cellular factors, a clear understanding of the mechanism is lacking. We employ a mechanical model featuring a detailed description of the cytoskeleton filament networks, the viscous cytosol, the cell adhesion dynamics, and the nucleus. We simulate cell adhesion and migration on sinusoidal substrates. We show that cell adhesion on three-dimensional curvatures induces a gradient of pressure inside the cell that triggers the internal motion of the nucleus. We propose that the resulting out-of-equilibrium position of the nucleus alters cell migration directionality, leading to cell motility toward concave regions of the substrate, resulting in lower potential energy states. Altogether, we propose a simple mechanism explaining how intracellular mechanics enable the cells to react to substratum curvature, induce a deterministic cell polarization, and break down cells basic persistent random walk, which correlates with latest experimental evidences.

Curvotaxis directs cell migration through cell-scale curvature landscapes.

Cells have evolved multiple mechanisms to apprehend and adapt finely to their environment. Here we report a new cellular ability, which we term "curvotaxis" that enables the cells to respond to cell-scale curvature variations, a ubiquitous trait of cellular biotopes. We develop ultra-smooth sinusoidal surfaces presenting modulations of curvature in all directions, and monitor cell behavior on these topographic landscapes. We show that adherent cells avoid convex regions during their migration and position themselves in concave valleys. Live imaging combined with functional analysis shows that curvotaxis relies on a dynamic interplay between the nucleus and the cytoskeleton-the nucleus acting as a mechanical sensor that leads the migrating cell toward concave curvatures. Further analyses show that substratum curvature affects focal adhesions organization and dynamics, nuclear shape, and gene expression. Altogether, this work identifies curvotaxis as a new cellular guiding mechanism and promotes cell-scale curvature as an essential physical cue.

Effect of Substrate Temperature on Pattern Formation of Bidispersed Particles from Volatile Drops.

In this study, pattern formation during evaporation of bidispersed drops (containing 1 and 3.2 µm particles) placed on a smooth substrate at different temperatures is investigated. Five distinctive deposition patterns are observed depending on the substrate temperature: a relatively uniform pattern enclosed by a disk-shaped ring, a nearly nonuniform pattern inside a thick outer ring, a "dual-ring" pattern, a "rose-like" pattern, and a set of concentric rings corresponding to the "stick-slip" pattern. At drops edge, the particle size effect leads to the formation of three rings: an outermost ring formed by the nonvolatile additives smaller than 1 µm, a middle ring built by particles with size of 1 µm, and an innermost ring formed by the mixture of 1 and 3.2 µm. For temperatures between 64 and 99 °C, the depinning of the contact line causes the same particle sorting at the other deposition lines in the interior of the drop. However, the width of the zone between the outermost ring and the middle ring at the initial edge of the drop is found to be smaller than that at the other deposition lines. The size of the width is found to be dependent on the contact angle. Particle velocity is measured by tracking particles during the evaporation. It is shown that particle velocity slightly increases with time, but it rapidly increases at the last stage of the drying process, known as "rush-hour" behavior. The sudden change in the increase of the velocity occurs between the normalized time of 0.7 and 0.8 for temperatures from 22 to 81 °C. The increasing trend of velocity with time matches well with the theoretical model. The tracer particles are also used to measure the distance between the contact line and the nearest turning point of those particles return back toward the top of the drop due to the inward Marangoni flow. It is found that this distance decreases with increasing the substrate temperature.

Evaporation of Binary Sessile Drops: Infrared and Acoustic Methods To Track Alcohol Concentration at the Interface and on the Surface.

Evaporation of droplets of three pure liquids (water, 1-butanol, and ethanol) and four binary solutions (5 wt % 1-butanol-water-based solution and 5, 25, and 50 wt % ethanol-water-based solutions) deposited on hydrophobic silicon was investigated. A drop shape analyzer was used to measure the contact angle, diameter, and volume of the droplets. An infrared camera was used for infrared thermal mapping of the droplet's surface. An acoustic high-frequency echography technique was, for the first time, applied to track the alcohol concentration in a binary-solution droplet. Evaporation of pure alcohol droplets was executed at different values of relative humidity (RH), among which the behavior of pure ethanol evaporation was notably influenced by the ambient humidity as a result of high hygrometry. Evaporation of droplets of water and binary solutions was performed at a temperature of 22 °C and a mean humidity of approximately 50%. The exhaustion times of alcohol in the droplets estimated by the acoustic method and the visual method were similar for the water-1-butanol mixture; however, the time estimated by the acoustic method was longer when compared with that estimated by the visual method for the water-ethanol mixture due to the residual ethanol at the bottom of the droplet.

Characterization of Breast Implant Surfaces, Shapes, and Biomechanics: A Comparison of High Cohesive Anatomically Shaped Textured Silicone, Breast Implants from Three Different Manufacturers.

UNLABELLED: Several companies offer anatomically shaped breast implants but differences among manufacturers are often misunderstood. The shell texture is a crucial parameter for anatomically shaped implants to prevent rotation and to decrease the risk of capsular contracture, even though concerns have recently been raised concerning the complications associated with textured breast implants. The aim of this study was to characterize differences in terms of texture, cell adhesion, shape, and stiffness between some commonly used anatomically shaped implants from three different manufacturers. METHODS: Five commercially available anatomically shaped breast implants from 3 different manufacturers (Allergan, Mentor, and Sebbin) were used. Scanning electron microscopy, X-ray microtomography, and scanning mechanical microscopy were used to characterize the shell texture. Human fibroblast adhesion onto the shells was evaluated. 3D models of the implants were obtained using CT-scan acquisitions to analyze their shape. Implant stiffness was evaluated using a tractiometer. RESULTS: Major differences were observed in the topography of the textures of the shells, but this was not conveyed by a statistically significant fibroblast adhesion difference. However, fibroblasts adhered better on anatomically shaped textured implants than on smooth implants (p < 0.01). Our work pointed out differences in the Biocell® texture in comparison with older studies. The 3D analysis showed significant shape differences between the anatomically shaped implants of the 3 companies, despite similar dimensions. Implant stiffness was comparable among the 3 brands. CONCLUSIONS: Each texture had its specific topography, and this work is the first description of Sebbin anatomic breast implant texturation. Moreover, major discrepancies were found in the analysis of the Biocell® texture when comparing our results with previous reports. These differences may have clinical implications and are discussed. This study also highlighted major shape differences among breast implants from different manufacturers, which is quite counterintuitive. The clinical impact of these differences however needs further investigation. NO LEVEL ASSIGNED: This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.