Trends in Bactericidal Nanostructured Surfaces: An Analytical Perspective

Since the discovery of the bactericidal properties of cicada wing surfaces, there has been a surge in the number of studies involving antibacterial nanostructured surfaces (NSS). Studies show that there are many parameters (and thus, thousands of parameter combinations) that influence the bactericidal efficiency (BE) of these surfaces. Researchers attempted to correlate these parameters to BE but have so far been unsuccessful. This paper presents a meta-analysis and perspective on bactericidal NSS, aiming to identify trends and gaps in the literature and to provide insights for future research. We have attempted to synthesize data from a wide range of published studies and establish trends in the literature on bactericidal NSS. Numerous research gaps and findings based on correlations of various parameters are presented here, which will assist in the design of efficient bactericidal NSS and shape future research. Traditionally, it is accepted that BE of NSS depends on the bacterial Gram-stain type. However, this review found that factors beyond Gram-stain type are also influential. Furthermore, it is found that despite their higher BE, hydrophobic NSS are less commonly studied for their bactericidal effect. Interestingly, the impacts of surface hydrophobicity and roughness on the bactericidal effect were found to be influenced by a Gram-stain type of the tested bacteria. In addition, cell motility and shape influence BE, but research attention into these factors is lacking. It was found that hydrophobic NSS demonstrate more promising results than their hydrophilic counterparts; however, these surfaces have been overlooked. Confirming the common belief of the influence of nanofeature diameter on bactericidal property, this analysis shows the feature aspect ratio is also decisive. NSS fabricated on silicon substrates perform better than their titanium counterparts, and the success of these silicon structures maybe attributed to the fabrication processes. These insights benefit engineers and scientists alike in developing next-generation NSS.