화학공학소재연구정보센터
Chemical Engineering Journal, Vol.315, 262-273, 2017
A gemini-type superspreader: Synthesis, spreading behavior and superspreading mechanism
This paper describes the facile microwave-assisted synthesis of a series of trisiloxane gemini super spreaders, as well as their surface and aggregation properties and superspreading behavior on plant leaf surfaces. The molecular structures of the trisiloxane gemini surfactants were characterized by Fourier transform infrared spectroscopy (FTIR) and H-1 nuclear magnetic resonance spectroscopy (H-1 NMR). The obtained thermodynamic parameters showed that an increase in the spacer group (CH2) resulted in decreases in the critical aggregation concentration (CAC), corresponding surface tension (gamma(CAC)), and surface excess concentration (T-max) but increases in the occupied area per surfactant molecule (AcAc) and absolute values of the standard free energies of aggregation(Delta G(mic)(circle)) and adsorption (Delta G(ads)(circle)). An increase in ethoxy units (CH2-CH2-0-) resulted in increases in the CAC, gamma(CAC), and A(cAc) but decreases in l(max) and the absolute values of Delta G(mic)(circle) and Delta G(ads)(circle). The transmission electron microscopy and dynamic light scattering results showed that the average sizes of the aggregates of superspreader solutions increased with an increasing number of spacer units (CH2) but decreased with an increasing number of ethoxy units (CH2-CH2-O-). The dynamic spreading behavior results demonstrated that the average spreading velocity increased with increasing spacer chain length, and the dependence of the maximum spreading velocity on the ethoxy chain length was nonmonotonous with a maximum at n(EO) = 8.68. The optimal HLB value was essential to obtaining good superspreading behavior, and the substrate wettability (hydrophobic riceplant and hydrophilic mango plant surfaces) greatly influenced the superspreading. The synergistic effects from the precursor film and Marangoni effect existed in the proposed superspreading model. (C) 2017 Elsevier B.V. All rights reserved.