Gelation properties of various long chain amidoamines: Prediction of solvent gelation via machine learning using Hansen solubility parameters.

Four new amphiphilic long chain amidoamine derivatives displaying different structure variations are synthesized and tested in 27 liquids and compared to the study of two similar molecules already reported in the literature. In many cases, these compounds can act as low molecular weight gelators to form a three-dimensional network in organic liquids or water, which can be confirmed by FE-SEM observations and rheology measurements. For each sample, XRD diffraction of the corresponding xerogel and FT-IR analysis of native supramolecular gels reveal that they can self-assemble into lamellar-like aggregates or in pseudo-cubic structures, depending on the alkyl chain length and the steric hindrance of the polar head. The number of amide bonds and their positions inside gelator structures are determinant for the nature of the packing. For each gelator, we perform a series of gelation tests in each of the solvents and show that Hansen parameters, which are known characteristics of each liquid, can be used to successfully predict their gelation properties via machine learning in the vast majority of liquids at a concentration of 4 wt%. Unlabelled Image • Six different amphiphilic amidoamines were tested in 28 liquids for their gelation abilities. • These compounds could assemble into pseudo-cubic or lamellar-like structures. • The phenomena were dependent on both their alkyl chain length and polar head nature. • A machine learning study was also efficient to predict gelation of various liquids. [ABSTRACT FROM AUTHOR]