Useful surface parameters for biomaterial discrimination

Abstract

Topographical features of biomaterials' surfaces are determinant when addressing their application site. Unfortunately up to date there has not been an agreement regarding which surface parameters are more representative in discriminating between materials. Discs (n = 16) of different currently used materials for implant prostheses fabrication, such as cast cobalt-chrome, direct laser metal soldered (DLMS) cobalt-chrome, titanium grade V, zirconia (Y-TZP), E-glass fiber-reinforced composite and polyetheretherketone (PEEK) were manufactured. Nanoscale topographical surface roughness parameters generated by atomic force microscopy (AFM), microscale surface roughness parameters obtained by white light interferometry (WLI) and water angle values obtained by the sessile-water-drop method were analyzed in order to assess which parameter provides the best optimum surface characterization method. Correlations between nanoroughness, microroughness, and hydrophobicity data were performed to achieve the best parameters giving the highest discriminatory power. A subset of six parameters for surface characterization were proposed. AFM and WLI techniques gave complementary information. Wettability did not correlate with any of the nanoroughness parameters while it however showed a weak correlation with microroughness parameters.

Predictability of Microbial Adhesion to Dental Materials by Roughness Parameters

“…The high positive correlations between most of the roughness parameters and their overlapping confidence intervals explain why none of the parameters were significantly more suitable to predict the initial microbial adhesion than R a . The low discriminatory powers of the surface roughness parameters in the present study are opposed to a study that stated that S a and S z are appropriate parameters to discriminate between the polished surfaces of dental materials [20]. If the profile and surface roughness parameters of a material show more discriminatory powers than in the present study, parameters other than R a will be associated significantly more with initial microbial adhesion.…”

“…Generally, a high positive correlation between the roughness parameters indicates redundancy and low discriminatory powers to characterize surfaces of biomaterials [20]. In the present study, all of the parameters that describe the heights or valleys showed a high positive correlation, except for the roughness parameters that describe distinctive qualities such as the kurtosis (R ku and S ku ), skewness (R sk and S sk ), or material component (R mr ) of a profile or a surface.…”

“…R dc is a functional parameter that describes the profile section height between two material ratios, and is commonly used in the automotive industry, where it excludes the highest peaks that will be worn out, and the deepest valleys that will be filled during the running-in phase of new engines and bearings [31]. While S a is suggested to be a useful parameter for biomaterial discrimination at the microscale [20], to our knowledge, there is no literature available on R dc and its suitability in this regard. Because of the insignificant differences to R a , we consider the high association of R dc with C. albicans adhesion to be coincidental in our experimental setting.…”

“…Attempts to clinically minimize microbial adhesion to dental materials demand smooth surfaces with a low SFE [17]. In the literature, many parameters describe surface roughness, some of which are suitable for discriminating between the polished surfaces of dental materials [18][19][20]. The rapidly increasing number of parameters was called "the parameter rash" by Whitehouse, in 1982 already [21].…”

“…The arithmetical mean roughness value (R a ) is the most commonly used parameter for in vitro and in vivo surface roughness evaluation [22][23][24][25][26][27]. While R a describes the roughness along a two-dimensional profile, S a is the arithmetical mean deviation of surface roughness, and characterizes the surface roughness three-dimensionally [20]. Ideally, a surface parameter will predict microbial adhesion, and indicates the extent of the required surface processing before applying a material into the oral cavity.…”