Quantification Of Surface Roughness Research Articles

Influence of argon-ion bombardment of titanium surfaces on the cell behavior

Several studies in vitro and in vivo accomplished in dental implants have demonstrated that the chemical and physical properties of biomaterials are able to regulate the initial tecidual response around the implant. This tecidual response depends directly on the hydrophily, roughness, texture and chemical composition of the surface. Plasma treatment can be used to improve the surface properties of commercial pure Ti, in order to create different surface topographies, changes in roughness, and modifications in the surface wettability. In this study a surface modification in titanium Cp degree II was carried out, through the plasma treatment and using argon-ion bombardment. A comparison of surface properties and their influence on the cells behavior was also accomplished, besides the quantification of surface roughness, wettability and cellular proliferation. The results showed that the argon-ion bombardment influences the surface energy value, surface roughness and the surface topography of Ti materials. These parameters influence the cellular proliferation process.

Roughness of Hawaiian volcanic terrains

We performed analyses of topographic variation (surface roughness) using a new 2‐D mapping method which shows that understanding the relationship between data resolution, Hurst exponent, y intercept, RMS deviation, and cell size is important for assessing surface processes. We use this new method to assess flows at six field sites in Kilauea caldera, Hawaii, using three data sets at different resolutions, TOPSAR (10 m/pixel), airborne lidar (1 m/pixel), and tripod‐mounted lidar (0.02–0.03 m/pixel). The flows studied include ponded pahoehoe flows, compound pahoehoe flows, and jumbled, slabby pahoehoe. The 2‐D quantification of surface roughness for the Kilauea lava flows indicates that features formed during emplacement and modification of the flows exhibit statistically distinct roughness signatures. The 2‐D method provides a tool for unit mapping based on surface roughness. Key findings indicate that the new 2‐D method provides more robust results than 1‐D methods for surface roughness because of larger 2‐D sample sizes and the removal of 1‐D directional bias leading to a reduction in error. Furthermore, data set resolution relative to the scale of the features under study is important to consider when designing a 2‐D surface roughness study. Future applications to topographic data sets from Mars will provide information on flow emplacement conditions and spatial and temporal evolution of volcanic provinces on Mars.

Lunar topography: Statistical analysis of roughness on a kilometer scale

Quantifying surface roughness may contribute significantly in the stratigraphic study of the Moon. We demonstrate the root mean square (RMS) deviation method as an indicator of topographic roughness on a kilometer scale, using stereo images from an Apollo Mapping Camera in a Digital Terrain Model, and compare three regions in the lunar highlands.

Characterizing arid region alluvial fan surface roughness with airborne laser swath mapping digital topographic data

Range‐front alluvial fan deposition in arid environments is episodic and results in multiple fan surfaces and ages. These distinct landforms are often defined by descriptions of their surface morphology, desert varnish accumulation, clast rubification, desert pavement formation, soil development, and stratigraphy. Although quantifying surface roughness differences between alluvial fan units has proven to be difficult in the past, high‐resolution airborne laser swath mapping (ALSM) digital topographic data are now providing researchers with an opportunity to study topography in unprecedented detail. Here we use ALSM data to calculate surface roughness on two alluvial fans in northern Death Valley, California. We define surface roughness as the standard deviation of slope in a 5‐m by 5‐m moving window. Comparison of surface roughness values between mapped fan surfaces shows that each unit is statistically unique at the 99% confidence level. Furthermore, there is an obvious smoothing trend from the presently active channel to a deposit with cosmogenic 10Be and 36Cl surface exposure ages of ∼70 ka. Beyond 70 ka, alluvial landforms become progressively rougher with age. These data suggest that alluvial fans in arid regions smooth out with time until a threshold is crossed where roughness increases at greater wavelength with age as a result of surface runoff and headward tributary incision into the oldest surfaces.

A Novel Approach to Quantifying Surface Roughness in Grinding

Surface roughness is an important quality characteristic in grinding. Measurement of surface roughness by means of mechanical stylus is widely done in metrology. In this paper, a new machine vision system has been utilized to quantify the surface roughness of machined surfaces (ground and milled). Compared with other measurement methods, it is accurate, quick and credible. This system is mounted on the grinding machine and automates the measurement process by using computer control to automatically position the CCD and capture digital images of machined surfaces between grinding cycles. It was proposed that the proportional formula was used in calibrating this system, and calibration precision meets application requirement. Not only the statistic character of gray image but also which of edge image were calculated out. These characters include the mean value of pixels (Mean), standard deviation (σ), maximal value (Max) and minimal value (Min), the number of pixels on the examine line(Count), etc. It was found out that the standard deviation value σ of the gray image could express the surface roughness most. The correlation between σ and Ra is established by interpolating σ value used Lagrange interpolation law, and the σ value is converted into Ra value through the calculation procedure finally.

A three-dimensional measure of surface roughness based on mathematical morphology

In industrial surface characterization tasks, tactile profile measurement instruments are still the dominating tool for measuring surface roughness. Among the parameters for quantifying surface roughness based on tactile profile sections, R z and R a are the most popular ones. Nevertheless, it is widely recognized that profile parameters in general should be replaced by parameters which use the whole surface information made available by state-of-the-art 3D-measuring devices like white-light interferometry. In this contribution, a natural and easily interpretable extension of the roughness characteristic R z for 3D data, called S z morph , is proposed and its intimate relation to the volume scale function, a fractal characteristic recently proposed for standardization, is shown. The derivation shows that while the slope of the volume scale function gives an indication of local fractal dimension, its absolute value is closely related to traditional definitions of surface roughness. The proposed characteristic ignores surface directionality and as such is applicable to directional and non-directional surfaces. Experimental results for three different technical surfaces demonstrate the very good correlation of S z morph with the original parameter R z .

New HYFLON AD composite membranes and AFM characterization

HYFLON AD membranes are very interesting devices in water treatment and for their impermeability to water and gases and vapour transpirability [1]. In these kinds of processes, such in many large-scale processes, some membrane parameters are particularly important, among them: pore distribution, membrane morphology and their fouling tendency. The aim of the present work is to describe an AFM study of three different composite HYFLON AD 60X membranes characterising three key properties affecting performance: pore size distribution, surface morphology and particle adhesion. The membranes have been imaged at high resolution in NaCl solution, allowing quantification of surface roughness and pore size distribution. Further, the adhesion of polystyrene and silica particles to the membranes was measured in NaCl solution at three different concentrations to quantify their likely fouling tendencies.

Morphological quantification of surface roughness

Morphological granulometric moments have proven useful for quantification and classification of image texture. This paper proposes using granulometries to provide a comprehensive description of surface roughness. To support this proposition it demonstrates that granulometric surface description includes much of the information carried by conventional roughness measures. It does so by using granulometric moments as inputs to a linear system to estimate six conventional surface roughness measures. The analysis is based on simulations in the framework of the Boolean-random-function model for surfaces. Features are obtained from both opening and closing granulometries. The granulometries are generated by computationally efficient one-dimensional linear and triangular structuring elements. This permits fast implementation using special-purpose algorithms. Estimators with granulometric inputs are designed on training data and applied to test data to analyze their effectiveness as estimators of conventional roughness measures. This is done for both simulated and real images.

Relationships between etch rate and roughness of plasma etched surface

Relationships between etch rate and surface roughness are examined as a function of process factors, including source power, bias power, pressure, and O/sub 2/ fraction. Experimental factor ranges are 600-900 W source power, 50-150-W bias power, 4-16-mtorr pressure, and 0%-80%-O/sub 2/ fraction. Atomic force microscopy was used to quantify surface roughness of silicon carbide etched in a C/sub 2/F/sub 6/ inductively coupled plasma. The impact of ion energy was estimated by means of DC bias. The etch rate was inversely related to surface roughness as source power (plasma density) varied. The source power-induced DC bias was strongly correlated to surface roughness. For variations in bias power (ion bombardment energy), the etch rate was almost linearly correlated to both surface roughness and DC bias. For variations in pressure or O/sub 2/ fraction, the etch rate was related to surface roughness in a complex way.

Focus on Sensors

Focus on Sensors

Atomic force microscope studies of stainless steel: Surface morphology and colloidal particle adhesion

An atomic force microscope has been used to image four commercial stainless steel samples of widely differing surface finishes. Analysis of the images allowed quantification of surface roughness over different area scales, 50 × 50 μm, 10 × 10 μm and 1 × 1 μm. The atomic force microscope was also used to measure directly the adhesion of a single polymer latex particle (radius ∼5 μm) to the surface in solution using the colloid probe technique. It was found that the adhesion increased with decreasing roughness, except for the smoothest surface which exhibited very regular surface features on the area scale most relevant to adhesion of the particle (1 × 1 μm). There was a good correlation between the variability of adhesion over each surface and the corresponding variability in surface roughness. Measurements of this type should prove useful in the technical/economic choice of surface finish for a particular purpose. As the colloid probe has dimensions comparable to those of bacteria and yeast cells, such measurements should especially be of value in the selection of surface finish likely to minimise bioadhesion.

A new method for evaluating the surface roughness of concrete cut for repair or strengthening

Deterioration to concrete structures usually starts at the surface and progresses into the structure. The deteriorated concrete has to be removed and replaced by new repair material, which leads to a composite concrete/repair material structure. In order to ensure full structural interaction between the old and new material, it is necessary to have a good bond between them. The bond itself depends on having a clean and rough surface. The importance of surface preparation and the effect of roughness have been emphasised by many researchers. In order to study the effect of surface roughness on the bond between a repair material and the concrete substrate, the roughness of the surface needs to be measured. No standard method for measuring surface roughness of concrete has been adopted. A range of methods are available and are in use for measuring the surface texture of concrete pavements in the highway field, but no method is accepted as standard. A new method of quantifying surface roughness is proposed and compared with the only method reported in the literature. In this new method (referred to as the roughness gradient method) a new method of describing the surface roughness is proposed. The surface of the cut concrete is defined as a series of irregular waves. The roughness parameter is dependent on the peak-to-trough depth and the respective wavelength. The method adopted using the ‘roughness gradient’ (R.G.) gave a better indication of the treated surfaces used in testing concrete repairs. Visual observation of the treated surfaces seemed to be in agreement with the R.G. parameters, while the method reported in the literature failed to define the roughness of the surface of the whole substrate in an accurate and comprehensive way.

Surface roughness quantification of CoCrMo implant alloys.

The articulating surfaces of CoCrMo alloy wear specimens and retrieved femoral components of artificial total knee joints are subject to uneven wear. A repeatable and reliable measurement method is necessary to evaluate the surface damage. In this study, the surface roughness of CoCrMo alloy specimens subjected to in vitro third-body wear, and retrieved femoral components of knee joints were analyzed using a white light interference surface profilometer. Each third-body wear specimen was divided into a 19x19 grid of 1-mm(2) squares (361 squares) and each femoral condyle of retrieved specimens was divided into two 10x10 grids of 1-mm(2) squares (100 squares). The surface roughness average (Ra) and root mean square roughness (RMS) were measured for each of the squares. The average of all points measured was defined as the true surface roughness mean (TSRM). Measurements were then performed on 40-60 (in vitro specimens) or 30 (retrieved specimens) randomly selected points on each surface and a cumulative average was calculated. The cumulative average surface roughness value from only a few (5-15) measurement points generated large deviations (>40%) from the TSRM, but converged to the TSRM as the number of measurements increased. The number of randomly selected points necessary for the cumulative average roughness to be within 10% of the TSRM was defined as the representative measurement number (RMN). The RMN for the third-body wear specimens (surface area of 573 mm(2)) was 40 points, and the RMN for the retrieved femoral components (surface area of 100 mm(2)) was 20 points. To obtain the cumulative surface roughness average within a desired percentage of the TSRM, it is important to define or experimentally determine the critical minimum number of measurements, RMN. Several types of measurements may be necessary to understand wear and damage on metal components of artificial knee joints. The TSRM represents a consistent and reproducible measure of surface damage, and a starting point to develop appropriate measurement protocols to quantify damage on a specific surface.

Quantifying Surface Roughness of Carpets by Fractal Dimension

Surface roughness of carpets refers to the degree of irregularity in the terrain of a carpet surface, and reflects the severity of appearance change with wear, such as pilling, untwisting, tuft matting and crushing. An irregular, rough surface can be represented by a fractal function, and quantified by its fractal dimension. This study used a specially-developed imaging system, including a CCD camera, a frame grabber and a computer, as an analytical tool for capturing and processing carpet images. The study applied the fractal dimension to quantifying surface roughness of carpets, and to analyzing appearance loss in carpets owing to mechanical wear. One algorithm for computing fractal dimensions, "reticular cell counting ", was illustrated. It was found from a trial test that the fractal dimension of a carpet surface corresponds to a visual assessment of the surface wear, and a change in fractal dimensions consistently indicates a change in carpet appearance. Hence, a fractal dimension can be used as an effective descriptor of carpet roughness.

Simulation of RBS spectra for a surface with a periodic roughness

For the purpose of quantifying surface roughness, the geometrical effect on heavy-ion RBS spectra is examined using optical grating bases coated with a thin film of Pb. It is shown that the RBS spectra from such surfaces with a periodic roughness can be well simulated by properly taking account of the system resolution including the detector response and published data of a stopping power of the substrate for the heavy ions. The spectral shape together with the fraction of the surface component reflects the microscopic structure of the sample surface strongly enough for the parameters characterizing the roughness to be determined from the measured spectra. Thus potential applicability of a “Pb coating-RBS” method to quantification of surface roughness is demonstrated.

Quantitative assessment of surface roughness using backscattered ultrasound: The effects of finite surface curvature

We have previously described a technique to quantify surface fibrillatory changes in osteoarthritic articular cartilage. In that study, the angular distribution of the scattered acoustic field from an insonifying source directly related to the distribution of surface fibrillatory changes. In the current study, we demonstrate a more sensitive method to quantify surface roughness, the effect of global surface curvature in estimating surface roughness and the utility of using focused transducers in circumventing this potential problem for in vivo work. Phantoms composed of acrylic rods with and without sandpaper grit (about 15 to 72 μ, mean particle size) applied to the surface were scanned. A more robust angular scattering technique to measure the angle dependent data was employed, in which the integrated squared pressure amplitude over a finite time window (mean power) was measured as a function of incident acoustic angle for varying surface roughnesses and radii of curvature. We show that the potential dynamic range for making roughness discriminations diminishes with decreasing radius of curvature of the acrylic rod phantoms using an unfocused transducer. This effect is minimized with use of a focused transducer. Roughness effects are most evident at sufficiently large angles where incoherent scattering dominates. We conclude that the roughness of cylindrically curved surfaces can be quantitatively assessed using a focused ultrasound beam at sufficiently large incident angles, given that the focal spot size is sufficiently smaller than the radius of curvature of the surface.

Self‐Similarity of solvent‐accessible surfaces of biological and synthetical macromolecules

AbstractThe quantification of surface roughness of globular proteins and synthetical macromolecules in the globular state is discussed using the concept of fractality. The Hausdorff dimension as a measure for local and global fractality of surfaces is applied. To calculate the Hausdorff dimension of any surface at a high level of accuracy, a new algorithm is presented that is based on a triangulated solvent‐accessible molecular surface. It can be demonstrated that protein surfaces (as calculated on the basis of experimentally determined structures) as well as surfaces of globular polyethylene (PE) conformers (calculated on the basis of structural information basing on extensive Monte Carlo and molecular dynamics simulations) in fact show self‐similarity within a reasonable yardstick range, at least in a global statistical sense. The same is true for parts of a protein surface provided that these regions are not too small. The concept of self‐similarity breaks down when individual surface points are considered. The results obtained for the fractal dimension of PE surfaces (average fractal dimension D = 2.23) lead to the conclusion that protein surfaces probably do not exhibit a unique and specific degree of geometrical complexity (or surface roughness) characterized by a fractal dimension of approximately D = 2.2 as was argued in the past. It is clear that the concept of self‐similarity is helpful for the classification of surface roughness of large molecules, but it seems questionable whether this concept is useful for the identification of active sites or other questions related to the field of molecular recognition. © John Wiley & Sons, Inc.

Fractal analysis with scanning probe microscopy

Surface roughness determined qualitatively by direct visualization can be correlated to several physical properties. However, finding a suitable method of quantifying surface roughness, until recently, has been difficult. The concept of Fractal Dimension, recently popularized by Mandelbrot(1982) has been extremely successful in quantifying surface roughness and relating it to such measurable physical properties such as; cleanability, catalytic activity, rate of corrosion, and even flavor.Atomic Force Microscopes permit direct three dimensional measurements of surface microstructure. AFM images are obtained by measuring the motion of a sharp stylus as it is scanned across a surface. Because the AFM directly measures three dimensional topograms, it is ideally suited for two dimensional and three dimensional fractal analysis. Other microscope techniques such as the scanning electron or optical microscope give only two dimensional magnification and fractal measurements are not easily made.The Atomic Force Microscope enables us to obtain the fractal dimension of surface profiles as well as surface areas. For surface profiles we use a box counting method (Mandelbrot 1986, Chesters et al. 1989).

8] Clefts and binding sites in protein receptors

Publisher Summary This chapter discusses the methods for determining clefts and binding sites in protein receptors. Direct methods of cleft determination are aimed at the analysis of a receptor whose three-dimensional structure is known. The meaning of the term “protein surface” is discussed in the chapter so that all the methods can be related to a common set of definitions. The different classes of algorithms that have been used to define and display the surface, to look at mappings of topographic features, to quantify surface roughness and curvature, and to search for packing defects and sites are reviewed in the chapter. All these methods require a set of Cartesian coordinates and produce an essentially static picture of the receptor. Biomolecules are dynamic objects and undergo substantial conformational changes; each static picture should, therefore, be regarded as a snapshot. These stills should be combined into a moving trajectory or a partition function; this is implied whenever a new static method is introduced. Indirect methods of receptor mapping are applicable when the receptor structure is unknown, but the structures of a series of ligands that bind to the receptor at a common site are available. The binding site is deduced from the apparent common properties of the ligands—that is, the pharmacophore. The assumptions behind pharmacophore deduction are discussed in the chapter. The algorithms are illustrated in the chapter by case studies: the method of shape mapping is exemplified by a family of retinal isomers.

Depressional storage for Markov‐Gaussian surfaces

Processes during rain events, such as infiltration, runoff, soil erosion, and crust formation, are influenced in part by depressional storage and surface roughness. If the surface topography is known, its potential depressional storage can be calculated. The objective of this paper was to relate the statistical parameters for quantifying surface roughness to depressional storage. Analysis of topographic data sets digitized at millimeter grids by a laser scanner showed that soil roughness can be quantified by a Markov‐Gaussian (M‐G) type random process. A Monte Carlo simulation procedure was used to find the mean ponding characteristics from simulated M‐G surfaces. Depressional storages were found to be functions of two M‐G parameters, two sample length scales, and the slope steepness. The Markov parameters are the global variance (σ2) and the correlation length scale (L), and the sample length scales are grid spacing (Δx) and side length (Ls). After proper scaling, all storage functions collapsed into two nondimensional relationships: (1) storage at zero slope as a function of relative sample length scale, and (2) storage as a function of scaled slope. When L = 0, simulated surfaces followed the random Gaussian model and the nondimensional storage was only a function of scaled slope. Storages calculated from digitized elevation data sets with M‐G type statistics agreed well with results obtained from simulated surfaces.