Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability

Abstract

Artificial intelligence (AI) has emerged as a powerful set of tools for engineering innovative materials. However, the AI-aided design of materials textures has not yet been researched in depth. In order to explore the potentials of AI for discovering innovative biointerfaces and engineering materials surfaces, especially for biomedical applications, this study focuses on the control of wettability through design-controlled hierarchical surfaces, whose design is supported and its performance predicted thanks to adequately structured and trained artificial neural networks (ANN). The authors explain the creation of a comprehensive library of microtextured surfaces with well-known wettability properties. Such a library is processed and employed for the generation and training of artificial neural networks, which can predict the actual wetting performance of new design biointerfaces. The present research demonstrates that AI can importantly support the engineering of innovative hierarchical or multiscale surfaces when complex-to-model properties and phenomena, such as wettability and wetting, are involved.

Additive manufacturing of micropatterned functional surfaces: a review

“…Another critical aspect of micropatterning is the utilization of data-driven techniques for pattern design [257]. Artificial intelligence has proven instrumental in engineering innovative hierarchical structures and multiscale surfaces with controlled functionality [258,259]. In conjunction with the existing literature, substantial advancements have been made by integrating machine learning and data-driven methodologies to comprehend and achieve diverse micropatterns.…”

Effects of surface patterning and topography on the cellular functions of tissue engineered scaffolds with special reference to 3D bioprinting

“…Artificial intelligence (AI) aided design can be used to fabricate textured surfaces with hierarchical topography and can be able to predict the wettability of the materials. 236 Studies have also indicated that digital tomography-trained AI-based models can predict scaffolds’ mechanical properties with different geometries. 237 Goh et al , in a recent review, highlighted four categories (supervised, unsupervised, semi-supervised, and reinforced) of machine learning and their applicability in 3D printing.…”

Machine Learning Assisted Nanomaterials as Super hydrophobic Coatings for Antiviral Functionalities to Fight COVID-19

“…Controlling wettability through design-controlled hierarchical surfaces or microtextured biointerfaces, whose design is aided by Artificial Neural Networks and whose performance is predicted (ANN). To build design maps for super hydrophobic polymer topographies, [26] used a hybrid approach that included experimental methodologies, numerical simulations, and machine learning (ML) algorithms. The maximum water contact angle (WCA) and Laplace pressure were two super hydrophobic properties studied.…”