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使用耗散粒子动力学建模和人工神经网络对油-极性溶剂界面处表面活性剂单层弯曲趋势进行定量研究。

Quantitative investigation of surfactant monolayer bending tendency at an oil-polar solvent interface using DPD modeling and artificial neural networks.

作者信息

Ren Hua, Zhang Baoliang, Li Haonan, Zhang Qiuyu

机构信息

School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, 127 West Youyi Road, 710072 Xi'an, Shaanxi, China.

出版信息

Soft Matter. 2023 Oct 18;19(40):7815-7827. doi: 10.1039/d3sm00825h.

Abstract

The bending tendency of a surfactant monolayer at an interface is critical in determining the type of emulsion formed and the proximity of the emulsion system to its equilibrium state. Despite its importance, the influence of interaction and surfactant structure on the bending tendency has not been quantitatively investigated. In this study, we develop and validate an artificial neural network (ANN) model based on the torque densities from dissipative particle dynamics (DPD) simulations to address this gap. With the validated ANN model, the relationship between surfactant monolayer bending tendency and all the interaction parameters, oil size, and surfactant structure (size and tail branching) was derived, from which the significance of each factor was ranked. With this ANN model, both the relationship and factor analysis can be instantly investigated without further DPD modeling. Furthermore, we expand the study to surfactant-oil-polar solvent (SOP) systems by varying the interaction parameters between polar solvents (PP). Our finding indicates that the interaction between polar solvents plays an important role in determining the bending tendency of surfactant monolayers; weaker intermolecular attraction between polar solvents makes surfactants tend to bend toward the oil phase (tend to form oil in polar solvent emulsion). Factor analysis reveals that increasing the repulsion between head-head (HH) or head-oil (HO) makes the model surfactants more polar-solvophilic, while increasing the repulsion between polar solvent-head (PH), tail-tail (TT) or oil-oil (OO) makes the model surfactants more lipophilic. The ANN model effectively reproduces the dependence of surfactant monolayer bending tendency on oil size, consistent with experimental observations, the larger the oil size, the higher the bending tendency toward the oil phase. The most intriguing insight derived from the ANN model here is that the effect of branching in the lipophilic tail will be enhanced by factors that make surfactants behave more lipophilic in a surfactant-oil-polar solvent (SOP) system, for rather polar-solvophilic surfactants, the effect of tail branching is negligible.

摘要

表面活性剂单分子层在界面处的弯曲倾向对于确定形成的乳液类型以及乳液体系与其平衡状态的接近程度至关重要。尽管其很重要,但相互作用和表面活性剂结构对弯曲倾向的影响尚未得到定量研究。在本研究中,我们基于耗散粒子动力学(DPD)模拟的扭矩密度开发并验证了一个人工神经网络(ANN)模型,以填补这一空白。利用经过验证的ANN模型,得出了表面活性剂单分子层弯曲倾向与所有相互作用参数、油滴大小和表面活性剂结构(大小和尾部支化)之间的关系,并对每个因素的重要性进行了排序。借助这个ANN模型,无需进一步的DPD建模就能立即研究这种关系和因素分析。此外,我们通过改变极性溶剂之间的相互作用参数(PP),将研究扩展到表面活性剂 - 油 - 极性溶剂(SOP)体系。我们的研究结果表明,极性溶剂之间的相互作用在决定表面活性剂单分子层的弯曲倾向方面起着重要作用;极性溶剂之间较弱的分子间吸引力使表面活性剂倾向于向油相弯曲(倾向于形成极性溶剂包油乳液)。因素分析表明,增加头 - 头(HH)或头 - 油(HO)之间的排斥力会使模型表面活性剂更亲极性溶剂,而增加极性溶剂 - 头(PH)、尾 - 尾(TT)或油 - 油(OO)之间的排斥力会使模型表面活性剂更亲脂。ANN模型有效地再现了表面活性剂单分子层弯曲倾向对油滴大小的依赖性,这与实验观察结果一致,油滴越大,向油相的弯曲倾向越高。这里从ANN模型得出的最有趣的见解是,在表面活性剂 - 油 - 极性溶剂(SOP)体系中,使表面活性剂表现得更亲脂的因素会增强亲脂尾部支化的效果,对于相当亲极性溶剂的表面活性剂,尾部支化的效果可以忽略不计。

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