Data-driven analysis and machine learning (ML) algorithms can offer novel insights into tribological phenomena by establishing correlations between material and tribological properties. We developed ML algorithms using tribological data available in the literature for predicting the coefficient of friction (COF) and wear-rate of self-lubricating aluminum graphite (Al/Gr) composites. We collected data on effects of material variables (graphite content, hardness, ductility, yield strength, silicon carbide content, and tensile strength), processing procedure, heat treatment and tribological test variables (normal load, sliding speed, and sliding distance) on tribological properties and established two-parameter relationships. These data are analyzed using several ML algorithms: artificial neural network (ANN), K nearest neighbor (KNN), support vector machine (SVM), gradient boosting machine (GBM), and random forest (RF). The trained ML models can predict the tribological behavior from material variables and test conditions, beyond what is possible from two-parameter correlations. GBM outperformed other ML algorithms in predicting friction behavior, while RF had the best prediction of the wear behavior. ML analysis identified graphite content and hardness and as the most significant variables in predicting the COF, while graphite content and sliding speed were the most dominant variables for wear-rates.

Lotus-effect-based superhydrophobicity is one of the most celebrated applications of biomimetics in materials science. Due to a combination of controlled surface roughness (surface patterns) and low-surface energy coatings, superhydrophobic surfaces repel water and, to some extent, other liquids. However, many applications require surfaces which are water-repellent but provide high friction. An example would be highway or runway pavements, which should support high wheel–pavement traction. Despite a common perception that making a surface non-wet also makes it slippery, the correlation between non-wetting and low friction is not always direct. This is because friction and wetting involve many mechanisms and because adhesion cannot be characterized by a single factor. We review relevant adhesion mechanisms and parameters (the interfacial energy, contact angle, contact angle hysteresis, and specific fracture energy) and discuss the complex interrelation between friction and wetting, which is crucial for the design of biomimetic functional surfaces.

Airborne and deposited water microdroplets can carry viruses including coronaviruses. The evaporation of microdroplets containing virus particles often leads to virus inactivation. Microdroplet evaporation involves various mechanisms such as diffusion, the Kelvin effect, infrared irradiation, and the role of solutes. For the evaporation of airborne water microdroplets, temperature and relative humidity of ambient air are important factors. However, for sessile droplets deposited on solid surfaces, wetting and porosity become important factors that control evaporation on regular, superhydrophobic, and photocatalytic surfaces. An experimental study of the effect of wetting properties and porosity on the evaporation rates on the polypropylene non-woven medical fabric was consistent with the theoretical models. Highly porous tile surfaces exhibited 2.2–4 times faster evaporation rates than nonporous surfaces.

Hydrophobic and self-cleaning photocatalytic ceramics and concretes with potential for the superhydrophobicity are a promising novel material for civil engineering applications including buildings, bridges, road pavements, and airport runways. Due to embedded liquid-repellent properties, such materials have low water and salt absorption and enhanced durability. However, in applications requiring high tractions (e.g., tire and pavement) there is a concern that reduced adhesion may compromise the friction. Friction has adhesion and roughness-related components. Wetting, dry friction, and roughness properties of TiO₂ coated (hydrophilic) and polymethylhydrogen siloxane (PMHS) coated (hydrophobic) self-cleaning ceramic tiles were studied. The values of the tile-rubber coefficient of friction do not change significantly with the applications of the coatings (0.45 for uncoated reference, up to 0.67 for hydrophilic (TiO₂ based) and up to 0.46 for hydrophobic (TiO₂+PMHS) coatings). This response is attributed to the roughness component of friction due to TiO₂ coating. The challenges related to hydrophobic coatings, including the durability and future research are also discussed.

Nonlinear small-amplitude high-frequency vibrations affect mechanical systems leading to such effects as the stabilization of an inverted pendulum on a vibrating foundation and size separation of particles of a granular material. These effects can be studied using the mathematical technique called “the method of separation of fast and slow motions.” When applied to fluid mechanics applications, the method predicts various unusual effects including jamming holes in a vibrating tank with liquid, vibrational propulsion, and multiphase flow separation. Many of these effects have been confirmed experimentally. Here we discuss the possibility of similar microfluidic effects in applications where capillary forces dominate over viscosity and inertia, such as vibrational aeration, vibration-induced superhydrophobicity (the elimination of the contact angle hysteresis), and vibrational two-phase flow separation for microparticle extraction.