Machine Learning Aided Design and Optimization of Antifouling Surfaces.

Antifouling surfaces, renowned for their strong surface resistance to proteins, cells, or tissues in various biological and environmental conditions, have broad applications in implanted devices, antibacterial coatings, biosensors, responsive materials, water treatment, and lab-on-a-chip. While extensive experimental research exists on antifouling surfaces, machine learning studies on this topic are relatively few. This perspective specifically focuses on exploring the complex relationships between the composition, structure, and properties of antifouling surfaces, examining how these factors correlate with surface hydration and protein adsorption.

Synthesis of 9,9-bis(4-hydroxyphenyl) fluorene catalyzed by bifunctional ionic liquids.

Through structural design, a series of bifunctional ionic liquids (BFILs) containing sulfonic acid (-SOH) and sulfhydryl groups (-SH) were synthesized and characterized by NMR and MS. The acidity of these BFILs was measured by the Hammett acidity ( ) and the effective sulfhydryl molar content of BFILs was determined by Ellman's method. Moreover, BFIL's catalytic properties in the condensation reaction of 9-fluorenone and phenol were studied.