Subtle Variations in Surface Properties of Black Silicon Surfaces Influence the Degree of Bactericidal Efficiency

Chris M. Bhadra¹, Marco Werner², Vladimir A. Baulin², Vi Khanh Truong¹, Mohammad AlKobaisi¹, Song Ha Nguyen¹, Armandas Balcytis^1,3, Saulius Juodkazis^1,3, James Y. Wang¹, David E. Mainwaring¹, Russell J. Crawford⁴, Elena P. Ivanova^{1, *}

Nano-Micro Lett. (2018) 10: 36

DOI: 10.1007/s40820-017-0186-9

Subtle Variations in Surface Properties of Black Silicon Surfaces Influence the Degree of Bactericidal Efficiency

Chris M. Bhadra1, Marco Werner2, Vladimir A. Baulin2, Vi Khanh Truong1, Mohammad AlKobaisi1, Song Ha Nguyen1, Armandas Balcytis1,3, Saulius Juodkazis1,3, James Y. Wang1, David E. Mainwaring1, Russell J. Crawford4, Elena P. Ivanova1, *

Nano-Micro Lett. (2018) 10: 36

DOI: 10.1007/s40820-017-0186-9






Highlights of this article


1 Three types of black silicon (bSi) surfaces have been successfully prepared by deep reactive ion etching. The columnar size range of the surface is high: 652.7–1063.2 nm; density: 8–11 tips per/μm2.

2 When the surface micropillar array height is high (>1000 nm), the density is low (<8 tips per/μm2) and the dispersion is poor, the antibacterial performance of the black silicon surface is significantly reduced.




brief introduction


The development of highly effective antibacterial surfaces is one of the biggest challenges facing the biomedical field. In nature, the wings of insects such as cicadas and dragonflies have wonderful nanostructures, which are very enlightening for the design and preparation of large-scale antibacterial surfaces. The bactericidal activity of the nanostructured surface is related to specific parameters such as geometry and surface wettability, and the related mechanism lacks in-depth research.

Professor Elena P. Ivanova of Swinburne University of Technology in Australia and others have used deep reactive ion etching to prepare a series of black silicon surfaces with different nanoscale parameters (the height and density of nano-pillar arrays) and study the differences in these parameters. The effect on the antibacterial properties of the surface and its mechanism. The research results show that the subtle changes in the nanostructure of the substrate surface will greatly affect the antibacterial performance.



Graphic guide


1 Identification and detection of nano-pillars on black silicon surface




A typical three-layer backpropagation neural network includes three parts: input layer, hidden layer and output layer.

A total of 24 neurons in the hidden layer are used. The output layer is composed of two types of neurons: E (empty area between the pillars) and P (tip of the pillars), where each pixel and its neighborhood are classified. Based on the sample bSi-1, 14 images were created, focusing on seven P areas and seven E areas.

2 Comparative analysis of the nanostructure on the surface of black silicon




Optical, AFM and SEM characterization of the nanostructures on the surface of black silicon. The results show that the nanopillars are randomly distributed on all bSi surfaces.

The FFT analysis of the top-view SEM image of the black silicon surface confirmed the isotropy of the nanopillars, indicating that the change in the average distance between the pillars resulted in a wider halo phenomenon in the FFT image.

3 Antibacterial efficiency of black silicon surface




Studies have shown that the nano-topography parameters of a single surface cannot be directly related to the changes in bactericidal activity, but the highest bactericidal efficiency can be achieved by combining different parameters.



About the Author






The main research areas:

Design, manufacture and operation of planar micro-devices; immobilization of biomolecules and microorganisms in micro/nano/environment; bacterial interaction on the surface of macro/micro/nano structures.

Homepage link:

https://softmat.net/t2t_products/ivanova/

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