Glycolipid and Lipopeptide Biosurfactants: Structural Classes and Characterization-Rhamnolipids as a Model.

In recent years, biosurfactants (BS) produced by various bacteria, fungi and yeast strains have attracted much interest because of their unique properties and potential applications in many industries ranging from bioremediation to agriculture and biomedical to cosmetics. Glycolipids are a popular group of BS that include rhamnolipids, sophorolipids, mannosylerythritol, trehalose lipids, xylolipids and cellobiose lipids. Lipopeptides e.

Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns.

Ultrafast laser irradiation can induce spontaneous self-organization of surfaces into dissipative structures with nanoscale reliefs. These surface patterns emerge from symmetry-breaking dynamical processes that occur in Rayleigh-Bénard-like instabilities. In this study, we demonstrate that the coexistence and competition between surface patterns of different symmetries in two dimensions can be numerically unraveled using the stochastic generalized Swift-Hohenberg model.

High pressure promoted dearomatization of nitroarenes by [4+2] cycloadditions with silyloxydienes.

Simple nitroarenes such as nitronaphthalenes and nitroquinolines smoothly undergo dearomatizing [4+2] cycloadditions with silyloxydienes under 16 kbar. Highly functionalized 3-dimensional polycyclic adducts bearing a tetrasubstituted carbon centre at the ring junction are obtained in one step from simple raw materials. This unprecedented dearomative Diels-Alder process is performed at room temperature without any chemical promoter, illustrating the exceptional role of high pressure as a physical promoter.

Learning PDE to Model Self-Organization of Matter.

A self-organization hydrodynamic process has recently been proposed to partially explain the formation of femtosecond laser-induced nanopatterns on Nickel, which have important applications in optics, microbiology, medicine, etc. Exploring laser pattern space is difficult, however, which simultaneously (i) motivates using machine learning (ML) to search for novel patterns and (ii) hinders it, because of the few data available from costly and time-consuming experiments. In this paper, we use ML to predict novel patterns by integrating partial physical knowledge in the form of the Swift-Hohenberg (SH) partial differential equation (PDE).

Predicting Laser-Induced Colors of Random Plasmonic Metasurfaces and Optimizing Image Multiplexing Using Deep Learning.

Structural colors of plasmonic metasurfaces have been promised to a strong technological impact thanks to their high brightness, durability, and dichroic properties. However, fabricating metasurfaces whose spatial distribution must be customized at each implementation and over large areas is still a challenge. Since the demonstration of printed image multiplexing on quasi-random plasmonic metasurfaces, laser processing appears as a promising technology to reach the right level of accuracy and versatility.