Nano-textured surfaces (NTS) are critical to organisms as self-adaptation and survival tools. These NTS have been actively mimicked in the process of developing bactericidal surfaces for diverse biomedical and hygiene applications. To design and fabricate bactericidal topographies effectively for various applications, understanding the bactericidal mechanism of NTS in nature is essential. The current mechanistic explanations on natural bactericidal activity of nanopillars have not utilized recent advances in microscopy to study the natural interaction. This research reveals the natural bactericidal interaction between E.coli and a dragonfly wing's NTS using advanced microscopy techniques and propose a model. Contrary to the existing mechanistic models, this experimental approach demonstrated that the NTS of dragonfly wings has two prominent nanopillar populations and the resolved interface shows membrane damage occurred without direct contact of the bacterial cell membrane with the nanopillars. We propose that the bacterial membrane damage is initiated by a combination of strong adhesion between nanopillars and bacterium EPS layer as well as shear force when immobilised bacterium attempt to move on the NTS. These findings could help guide the design of novel bio-mimetic nanomaterials by maximising the synergies between both biochemical and mechanical bactericidal effects.Nano-textured surfaces (NTS) are critical to organisms as self-adaptation and survival tools. These NTS have been actively mimicked in the process of developing bactericidal surfaces for diverse biomedical and hygiene applications. To design and fabricate bactericidal topographies effectively for various applications, understanding the bactericidal mechanism of NTS in nature is essential. The current mechanistic explanations on natural bactericidal activity of nanopillars have not utilized recent advances in microscopy to study the natural interaction. This research reveals the natural bactericidal interaction between E.coli and a dragonfly wing's NTS using advanced microscopy techniques and propose a model. Contrary to the existing mechanistic models, this experimental approach demonstrated that the NTS of dragonfly wings has two prominent nanopillar populations and the resolved interface shows membrane damage occurred without direct contact of the bacterial cell membrane with the nanopillars. We propose that the bacterial membrane damage is initiated by a combination of strong adhesion between nanopillars and bacterium EPS layer as well as shear force when immobilised bacterium attempt to move on the NTS. These findings could help guide the design of novel bio-mimetic nanomaterials by maximising the synergies between both biochemical and mechanical bactericidal effects.

Bactericidal effects of natural nanotopography of dragonfly wing on Escherichia coli

Wetting is a surface phenomenon that commonly occurs in nature and has an enormous influence on human life. Slippery liquid-infused porous surfaces have recently been developed to support the growing demand for anti-fouling coatings. While short-chain fluorinated compounds, commonly used to reduce the surface energy of substrates, are banned due to environmental toxicity, silane-based compounds are expensive and barely scalable. In this sense, silicone-based chemistry may match the gap as a real alternative. However, the grafting approaches demonstrated so far suffered from either slow binding kinetics or are applied under harsh conditions. Here, it is demonstrated that polydimethylsiloxanes graft to virtually any substrate when illuminated by UV light serving simultaneously as a reducing surface energy agent and infusing lubricant. This procedure is applied on metals, metal oxides, and ceramics of various surface morphologies. The proposed approach is simple, fast, scalable, environmentally friendly, and of low-cost, yet forms stable lubricant-infused slippery surfaces by a one-pot process. Due to the biocompatibility of silicone-based compounds, the process is examined on plain medically applicable substrates such as scalpel blades and glass lenses that display enhanced corrosion resistance, reduced friction through incision, and repel blood staining and bacterial adhesion without deteriorating their mechanical and optical characteristics.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adfm.202101090

A One-Pot Universal Approach to Fabricate Lubricant-Infused Slippery Surfaces on Solid Substrates

Adaptive Coatings with Anticorrosion and Antibiofouling Properties

In this work, the effective electrical conductivity (EEC) and percolation threshold (PT) of a nanocomposite containing spherical particle fillers are predicted by computational homogenization schemes (CHS) with three-dimensional random representative volume elements (RVEs) by random filler removal (RFR). For this prediction, we prepare the RVE having the maximum filler volume fraction (Vf) of 52% with random particle fillers, also termed the master model, by the discrete element method (DEM), and the corresponding finite-element (FE) model is created. Then, 100 RVE samples for each Vf are derived by randomly replacing the material properties of several fillers by those of the matrix from the master model with diverse Vf from 5% to 50%. In addition, the interphase layer is employed by replacing some matrix elements with interphase elements according to the neighboring distance of the fillers. To demonstrate the performance of the proposed scheme, its randomness of RVEs is verified by spatial and physical metrics in terms of autocorrelation analysis, near-neighbor analysis, and directional conductivity ratio. The EEC prediction results with diverse Vf values are compared with those of an analytical model and test results. As a result, the PT at which EEC of the nanocomposites suddenly increases is successfully evaluated, and the effect of the void, interphase thickness, and conductivity, as well as the size of fillers on the EEC and PT is investigated through a sensitivity analysis.

Efficient prediction of the electrical conductivity and percolation threshold of nanocomposite containing spherical particles with three-dimensional random representative volume elements by random filler removal

In this work, we propose a prediction model of the transverse mechanical behavior of unidirectional (UD) composites containing complex microstructure with the help of a convolutional neural network (CNN). For this prediction, a total of 900 representative volume elements (RVE) samples were generated by constructing 300 RVEs for each V f of 40%, 50%, and 60% with the random sequential expansion (RSE) algorithm. The stress-strain (S–S) curves in terms of transverse elastic modulus, transverse tensile strength, and toughness considering interphase debonding were obtained by a finite element (FE) simulation with the RVE samples. After converting FE models with 900 RVE samples to corresponding microstructural binary images, CNN modeling was employed to construct a prediction model on the microstructural images. To demonstrate the performance of the proposed CNN model, we predicted the transverse mechanical behavior in terms of the S–S curves on various test datasets. Prediction accuracy was verified in terms of the loss functions and the error of the S–S curve. The prediction results were in excellent agreement with the test datasets, and the transverse mechanical behavior was quickly predicted for other microstructures. This confirmed that the proposed CNN model is simple and powerful and can efficiently clarify the relationship between the microstructure and transverse mechanical behavior of UD composites.

Prediction and validation of the transverse mechanical behavior of unidirectional composites considering interfacial debonding through convolutional neural networks

In this paper, we propose a simple and efficient generator of random fiber distribution with diverse fiber volume fractions ( V f ) for unidirectional composites by a random fiber removal technique. From the representative volume element (RVE) consisting of 100 fibers that have a maximum V f of about 65% in this work, also termed the master model, we randomly eliminate fibers to match the predefined V f ranging from 60%, 55%, 45%, 35%, 25%, 15%, and 5%, which are lower than that of the master model. Accordingly, 100 RVE samples for each V f can be constructed in a straightforward manner. To demonstrate the performance of the proposed algorithm, its fiber locations are verified in terms of statistical spatial metrics, such as the nearest neighbor orientation, Ripley's K function, and pair distribution function. Furthermore, the elastic properties and the anisotropic ratios of the generated RVEs are investigated and compared to those of other random fiber generation algorithms.

Efficient generator of random fiber distribution with diverse volume fractions by random fiber removal

The biofouling of marine organisms on a surface induces serious economic damage. One of the conventional anti-biofouling strategies is the use of toxic chemicals. In this study, a new eco-friendly oleamide-PDMS copolymer (OPC) is proposed for sustainable anti-biofouling and effective drag reduction. The anti-biofouling characteristics of the OPC are investigated using algal spores and mussels. The proposed OPC is found to inhibit the adhesion of algal spores and mussels. The slippery features of the fabricated OPC surfaces are examined by direct measurement of pressure drops in channel flows. The proposed OPC surface would be utilized in various industrial applications including marine vehicles and biomedical devices.

Myeong Ryun Seong

Papers

Efficient generator of random fiber distribution with diverse volume fractions by random fiber removal

Anti-Biofouling Features of Eco-Friendly Oleamide-PDMS Copolymers.

Eco-friendly erucamide-polydimethylsiloxane coatings for marine anti-biofouling.