Computational Intelligence-Assisted Understanding of Nature-Inspired Superhydrophobic Behavior

Abstract

In recent years, state-of-the-art computational modeling of physical and chemical systems has shown itself to be an invaluable resource in the prediction of the properties and behavior of functional materials. However, construction of a useful computational model for novel systems in both academic and industrial contexts often requires a great depth of physicochemical theory and/or a wealth of empirical data, and a shortage in the availability of either frustrates the modeling process. In this work, computational intelligence is instead used, including artificial neural networks and evolutionary computation, to enhance our understanding of nature-inspired superhydrophobic behavior. The relationships between experimental parameters (water droplet volume, weight percentage of nanoparticles used in the synthesis of the polymer composite, and distance separating the superhydrophobic surface and the pendant water droplet in adhesive force measurements) and multiple objectives (water droplet contact angle, sliding angle, and adhesive force) are built and weighted. The obtained optimal parameters are consistent with the experimental observations. This new approach to materials modeling has great potential to be applied more generally to aid design, fabrication, and optimization for myriad functional materials.

Keywords: artificial neural networks; computational intelligence; evolutionary computation; superhydrophobic behavior.

Figure 1

a) Juice, b) milk, and c) coffee droplets formed spheres on hydrophobic SiO₂ powder. d) SiO₂ powder dispersed in n‐hexane solution. e,f) Sessile water droplet on superhydrophobic SiO₂/PVC surfaces: e) Sample S1; f) Sample S2, with inset contact angle (upper right) and sliding angle (lower right) measurements.

Figure 2

FESEM images of a,b) the obtained PDMS surface with negative biomimetic lotus topography and SiO₂/PVC surface: c,d) Sample S1, e,f) Sample S3, and g,h) Sample S5.

Figure 3

The relationship between water droplet volume and the a) contact angle and b) sliding angle for Samples S1–S5.

Figure 4

Adhesive force−distance curves with various water droplet volumes for Samples a) S1, b) S2, c) S3, d) S4, and e) S5. f) Adhesive force of water droplet on surface as a function of droplet volume.

Figure 5

The functional mapping landscapes of a) f ₁ and b) f ₂ obtained by the surrogate models built by artificial neural networks. [X ₁, percentage of SiO₂ in SiO₂/PVC mixture (wt%); X _2, water droplet volume (µL); Y ₁, water contact angle (°); Y ₂, sliding angle (°)].

Figure 6

The final solution set obtained by optimising the surrogate models from Section 3.2 by using the state‐of‐the‐art RVEA. [Y ₁, water contact angle (°); Y ₂, sliding angle (°); Y ₃, adhesive force (µN)].