Wetting of Superhydrophobic Polylactic Acid Micropillared Patterns.

Superhydrophobic (SH) polylactic acid (PLA) surfaces were previously produced by various methods and used especially in biomedical applications and oil/water separation processes after 2008. However, the wettability of SH-PLA patterns containing micropillars has not been investigated before. In this study, PLA patterns having regular microstructured pillars with 12 different pillar diameters and pillar-to-pillar distances were prepared by hot pressing pre-flattened PLA sheets onto preformed polydimethylsiloxane (PDMS) soft molds having micro-sized pits. PDMS templates were previously prepared by photolithography using SU-8 molds. Apparent, advancing, and receding water contact angle measurements were carried out on the PLA patterns containing micropillars, and the morphology of the patterns was examined by optical and SEM microscopy. The largest contact angle obtained without the surface modification of the pure PLA pattern was 139°. Then, PLA micropatterns were hydrophobized using three types of silanes via chemical vapor deposition method, and SH-PLA patterns were obtained having θs of up to 167°. It was found that the highest θ values could be obtained when PLA pattern samples were coated with a silane containing a fluorine atom in its chemical structure. Washing and service life stability tests were also performed on the coated pattern samples and all of the silane coatings on the PLA patterns were found to be resistant over a 6 month period.

A pilot study for fragment identification using 2D NMR and deep learning.

This paper presents a proof of concept of a method to identify substructures in 2D NMR spectra of mixtures using a bespoke image-based convolutional neural network application. This is done using HSQC and HMBC spectra separately and in combination. The application can reliably detect substructures in pure compounds, using a simple network. Results indicate that it can work for mixtures when trained on pure compounds only. HMBC data and the combination of HMBC and HSQC show better results than HSQC alone in this pilot study.