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Updated: Sep 24, 2025

Preparation of Functional Silica Using a Bioinspired Method
Published on: August 1, 2018
Bio-based alternatives to volatile silicones: Relationships between chemical structure, physicochemical properties
Valentin Goussard1, Jean-Marie Aubry1, Véronique Nardello-Rataj1
1Univ. Lille, CNRS, Centrale Lille, Univ. Artois, UMR 8181, UCCS Unité de Catalyse et de Chimie du Solide, F-59000 Lille, France.
Abstract:
Emollient oils are ubiquitous ingredients of personal care products, especially skin care and hair care formulations. They offer excellent spreading properties and give end-use products a soft, pleasant and non-sticky after-feel. Emollients belong to various petro- or bio-based chemical families among which silicone oils, hydrocarbons and esters are the most prominent. Silicones have exceptional physicochemical and sensory properties but their high chemical stability results in very low biodegradability and a high bioaccumulation potential. Nowadays, consumers are increasingly responsive to environmental issues and demand more environmentally friendly products. This awareness strongly encourages cosmetics industries to develop bio-based alternatives to silicone oils. Finding effective silicon-free emollients requires understanding the molecular origin of emollience. This review details the relationships between the molecular structures of emollients and their physicochemical properties as well as the resulting functional performances in order to facilitate the design of alternative oils with suitable physicochemical and sensory properties. The molecular profile of an ideal emollient in terms of chemical function (alkane, ether, ester, carbonate, alcohol), optimal number of carbons and branching is established to obtain an odourless oil with good spreading on the skin. Since none of the carbon-based emollients alone can imitate the non-sticky and dry feel of silicone oils, it is judicious to blend alkanes and esters to significantly improve both the sensory properties and the solubilizing properties of the synergistic mixture towards polar ingredients (sun filters, antioxidants, fragrances). Finally, it is shown how modelling tools (QSPR, COSMO-RS and neural networks) can predict in silico the key properties of hundreds of virtual candidate molecules in order to synthesize only the most promising whose predicted properties are close to the specifications.
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