The Relative Importance of Topography and RGD Ligand Density for Endothelial Cell Adhesion

The morphology and function of endothelial cells depends on the physical and chemical characteristics of the extracellular environment. Here, we designed silicon surfaces on which topographical features and surface densities of the integrin binding peptide arginine-glycine-aspartic acid (RGD) could be independently controlled. We used these surfaces to investigate the relative importance of the surface chemistry of ligand presentation versus surface topography in endothelial cell adhesion. We compared cell adhesion, spreading and migration on surfaces with nano- to micro-scaled pyramids and average densities of 6×102–6×1011 RGD/mm2. We found that fewer cells adhered onto rough than flat surfaces and that the optimal average RGD density for cell adhesion was 6×105 RGD/mm2 on flat surfaces and substrata with nano-scaled roughness. Only on surfaces with micro-scaled pyramids did the topography hinder cell migration and a lower average RGD density was optimal for adhesion. In contrast, cell spreading was greatest on surfaces with 6×108 RGD/mm2 irrespectively of presence of feature and their size. In summary, our data suggest that the size of pyramids predominately control the number of endothelial cells that adhere to the substratum but the average RGD density governs the degree of cell spreading and length of focal adhesion within adherent cells. The data points towards a two-step model of cell adhesion: the initial contact of cells with a substratum may be guided by the topography while the engagement of cell surface receptors is predominately controlled by the surface chemistry

Anti-friction bearing malfunction detection and diagnostics using hybrid approach

Antifriction bearings are widely used in the industries especially in aircraft, machine tool, and construction industry. It holds and guides moving parts of the machine and reduces friction and wear. As they are one of the riskiest components in the rotating machinery, it puts challenges on the bearing health condition monitoring. The defects found in the bearings can lead to malfunctioning of the machinery and impact the level of production. This paper presents detection technique and diagnosis of bearing defects using a novel hybrid approach (continuous wavelet transform, Abbott–Firestone parameter, and artificial neural network). The vibration signals were obtained from Case Western Reserve University. MATLAB is used to analyse the vibration signals, test, and train the required models according to the chosen model structure. Various statistical features are extracted from the time domain namely kurtosis, skewness, root mean square, standard deviation, crest factor, and Abbott parameters to analyse and identify the bearing fault. The results demonstrate that the proposed method is effective in identifying the bearing faults

Wavelet theory and belt finishing process, influence of wavelet shape on the surface roughness parameter values

International audienceThis article presents a multi-scale theory based on wavelet decomposition to characterize the evolution of roughness in relation with a finishing process or an observed surface property. To verify this approach in production conditions, analyses were developed for the finishing process of the hardened steel by abrasive belts. These conditions are described by seven parameters considered in the Tagushi experimental design. The main objective of this work is to identify the most relevant roughness parameter and characteristic length allowing to assess the influence of finishing process, and to test the relevance of the measurement scale. Results show that wavelet approach allows finding this scale

Novel Directional Blanket Covering Method for Surface Curvature Analysis at Different Scales and Directions

The curvature of surface topography is quantified in this study using a newly developed directional blanket covering curvature (DBCC) method. The novel method addresses a long-standing problem of a measurement of a local surface curvature at individual scales and directions. The curvature data are obtained using the first and second derivations of the quadratic polynomial fitted to the local surface profile extracted from the surface height image data at individual scale and direction. The range of scales is set between an instrument spatial resolution and 1/10 of the image shortest size. Using the surface curvature data, three parameters, i.e. curvature, peak and valley dimensions, are calculated as the slopes of lines fitted to data point subsets of log–log plots of surface areas (differences between dilated and eroded surface curvature matrices) against scales of calculation. The dimensions quantify directional changes in the overall curvature and the curvature of peaks and valleys at individual scales. The scale corresponds to the centre of the subset. A flat surface criterion based on the surface areas was also proposed. Using the criterion, a flat surface was identified in computer images of dome, corrugated plate and fractal surface. The DBCC method was applied to computer-generated fractal surfaces with increasing curvature complexity, sine waves with decreasing curvature at single scale and microscope images of isotropic (sandblasted) and anisotropic (ground) surfaces of titanium plates. Results showed that the method is accurate in the measurement of surface curvature and the detection of minute changes occurring in the curvature of real surfaces over a wide range of scales