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.

Durable fire retardant, superhydrophobic, abrasive resistant and air/UV stable coatings

“…The adhesive forces between mud droplet (droplet size 5 μL) and SiO2@PDMS/APP/PER-coated samples were also measured using a high-sensitivity micro electromechanical balance system that was reported in our previous work. [18] However, this -OH band was notably weakened in the SiO2@PDMS/APP/PER-coated cotton fabric, compared with the untreated articles and it is observed that new peaks appeared around 1261, 860, 798 cm -1 (Figure 1b), which correspond to the P-O-P, P=O (from APP) and Si-C stretches from SiO2/PDMS, respectively. [5,19] The FT-IR spectra show that the obtained surfaces were efficiently covered with APP, SiO2 and PDMS.…”

Bioinspired Designs of Superhydrophobic and Superhydrophilic Materials

“…Then, WCA, SA, advancing/receding contact angle, adhesive force, and integrity of microtopography should all be taken into evaluation systems. 69 The best solution is to develop a highly integrated testing system which can test all-around surface wettability. With the aid of an external humidity/temperature control system, gas circuit system, and super-high-speed camera, liquid volatilization and icing processes can be studied via this integrated system in different atmospheres or in vacuum.…”

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

“…In recent times, machine learningbased nano-coatings are in the rise, DOI: 10.30726/esij/v8.i4.2021.84026 owing to their ability to make experimentation easier. Machine learning models can be used to characterise surface attributes such as adhesion forces, water contact and sliding angles, and features of the morphology that determine how water droplets bounce over a surface during self-cleaning [24]. The researchers also established some specific goals for themselves, stating that a self-cleaning surface's water contact angle must be more than 150 degrees.…”