The study of friction is traditionally a data-driven area with many experimental data and phenomenological models governing structure-property relationships. Triboinformatics is a new area combining Tribology with Machine Learning (ML) and Artificial Intelligence (AI) methods, which can help to establish correlations in data on friction and wear. This is particularly relevant to unstable motion, where deterministic models are difficult to build. There are several types of friction-induced instabilities including those caused by the velocity dependency of dry friction, coupling of friction with another process (wear, heat generation, etc.), the elastic Adams instabilities, and others. The onset of sliding is also an unstable process. ML/AI methods, such as Topological Data Analysis and various ML algorithms, which have been already used for various aspects of data analysis on friction, can be applied also to the frictional instabilities.

A concept of piezo-responsive hydrogen-bonded π-π-stacked organic frameworks made from Knoevenagel-condensed vanillin–barbiturate conjugates was proposed. Replacement of the substituent at the ether oxygen atom of the vanillin moiety from methyl (compound 3a) to ethyl (compound 3b) changed the appearance of the products from rigid rods to porous structures according to optical microscopy and scanning electron microscopy (SEM), and led to a decrease in the degree of crystallinity of corresponding powders according to X-ray diffractometry (XRD). Quantum chemical calculations of possible dimer models of vanillin–barbiturate conjugates using density functional theory (DFT) revealed that π-π stacking between aryl rings of the vanillin moiety stabilized the dimer to a greater extent than hydrogen bonding between carbonyl oxygen atoms and amide hydrogen atoms. According to piezoresponse force microscopy (PFM), there was a notable decrease in the vertical piezo-coefficient upon transition from rigid rods of compound 3a to irregular-shaped aggregates of compound 3b (average values of d₃₃ coefficient corresponded to 2.74 ± 0.54 pm/V and 0.57 ± 0.11 pm/V), which is comparable to that of lithium niobate (d₃₃ coefficient was 7 pm/V).

It is demonstrated that a reduction in the defect concentration in ZnO films formed by high-frequency magnetron sputtering allows effective doping with acceptor impurities both in the cation (Li) and anion (N⁺) sublattices and p-type conductivity with reproducible charge-carrier parameters (concentration and mobility) to be obtained. ZnO films are doped with nitrogen by annealing in a high-frequency gas discharge atmosphere. Hall measurements by the Van der Pauw technique show that the deposition of thin Eu layers on the ZnO film surface increases the concentration and mobility of majority charge carriers. The embedding of metal impurities with different ionic radii (Ag and Au) in the cation sublattice of the ZnO films to compensate misfit stresses makes it possible to enhance the concentration of radiative-recombination centers.

Use of inorganic oxides as transport layer material is a promising way to increase the efficiency of perovskite solar cells. Results of the studies of the influence of the gas mix composition...