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Wetting of multiphase alloys and their composites depends on multiple parameters, and these relationships are difficult to predict from first principles only. We study correlations between the composition, surface finish, and microstructure of Al–Si alloys (Si content 7–50%) and Al metal matrix composites (MMCs) with graphite (Gr), NiAl3, and SiC and the water contact angle (CA) experimentally, theoretically, and with machine learning (ML) techniques. Their surface properties were modified by mechanical abrasion, etching, and addition of alloying elements. An ML approach was developed to investigate correlations between the predictor variables (properties of the materials) and the CA. Theoretical models of wetting of rough surfaces (Wenzel, Cassie–Baxter, and their modifications) do not fully capture the CA, while ML models follow the experimental values. A full factorial design is utilized with combinations of all levels of the predictor factors (grit size, silicon percentage, droplet size, elapsed time, etching, reinforcing particles). To map the predictor variables to the response variables, 409 experimental data points were applied to train and test various supervised ML models, namely, regression, artificial neural network (ANN), chi-square automatic interaction detection (CHAID), extreme gradient boosting (XGBoost), and random forest. The correlations between the most significant factors and CA are explored through visualization techniques. The most accurately trained model shows a strong positive linear correlation (r > 0.9) between predicted and observed CA values in the test set, indicating the robustness of the model. The experimental measurements and artificial intelligence results demonstrate that CA increases following mechanically abrading the surface, etching, and adding Gr to the surface. The ML methods are promising to predict wetting properties and to provide a deeper understanding of the physical phenomena associated with the wettability of metallic alloys and their metal matrix composites.

The transient nature of RNA has rendered it one of the more difficult biological targets for imaging. This difficulty stems both from the physical properties of RNA as well as the temporal constraints associated therewith. These concerns are further complicated by the difficulty in imaging endogenous RNA within a cell that has been transfected with a target sequence. These concerns, combined with traditional concerns associated with super-resolution light microscopy has made the imaging of this critical target difficult. Recent advances have provided researchers the tools to image endogenous RNA in live cells at both the cellular and single-molecule level. Here, we review techniques used for labeling and imaging RNA with special emphases on various labeling methods and a virtual 3D super-resolution imaging technique.

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SYNOPSISThe surface energy of the human nail plate is expected to influence the adhesion of microorganisms (and subsequent colonisation and infections) as well as that of medicines (and subsequent drug permeation) and of cosmetics. The aim of the study was therefore to measure the surface energy of nail plates in vivo. The surface energy of healthy human fingernails (untreated, hydrated and abraded) and of hoof membranes (often used as a model for the nailplate) was estimated from contact angle measurements of liquids (water, formamide, diiodomethane and glycerol) on the nail plate and subsequent computation using the Lifshitz-van der Waals/acid-base (LW-AB) approach. The surface energy of untreated fingernail plates was found to be 34 mJ/m 2 . Most of this total energy was from the apolar Lifshitz-van der Waals component. When the polar component of the surface energy was analysed, the electron-donor component was considerably larger than the electron-acceptor one.Hydrating the nail plate had no significant influence on the surface energy. In contrast, abrasion caused a small, but statistically significant increase in the apolar surface energy component. The surface energy of bovine hoof membrane was similar to that of the fingernail plate. We conclude that the human fingernail plate is a low-energy surface, and that bovine hoof membranes may be used as a substitute for the nail plate in certain experiments.