Medinger Joelle, Nedyalkova Miroslava, Furlan Marco, Lüthi Thomas, Hofmann Jürgen, Neels Antonia, Lattuada Marco
Department of Chemistry, University of Fribourg, Chemin du Musée 9, CH-1700 Fribourg, Switzerland.
eCO2 SA, Via Brüsighell 6, 6807 Taverne, Switzerland.
ACS Appl Mater Interfaces. 2021 Oct 13;13(40):48040-48052. doi: 10.1021/acsami.1c13324. Epub 2021 Oct 1.
Composite materials designed by nature, such as nacre, can display unique mechanical properties and have therefore been often mimicked by scientists. In this work, we prepared composite materials mimicking the nacre structure in two steps. First, we synthesized a silica gel skeleton with a layered structure using a bottom-up approach by modifying a sol-gel synthesis. Magnetic colloids were added to the sol solution, and a rotating magnetic field was applied during the sol-gel transition. When exposed to a rotating magnetic field, magnetic colloids organize in layers parallel to the plane of rotation of the field and template the growing silica phase, resulting in a layered anisotropic silica network mimicking the nacre's inorganic phase. Heat treatment has been applied to further harden the silica monoliths. The final nacre-inspired composite is created by filling the porous structure with a monomer, leading to a soft elastomer upon polymerization. Compression tests of the platelet-structured composite show that the mechanical properties of the nacre-like composite material far exceed those of nonstructured composite materials with an identical chemical composition. Increased toughness and a nearly 10-fold increase in Young's modulus were achieved. The natural brittleness and low elastic deformation of silica monoliths could be overcome by mimicking the natural architecture of nacre. Pattern recognition obtained with a classification of machine learning algorithms was applied to achieve a better understanding of the physical and chemical parameters that have the highest impact on the mechanical properties of the monoliths. Multivariate statistical analysis was performed to show that the structural control and the heat treatment have a very strong influence on the mechanical properties of the monoliths.
自然界设计的复合材料，如珍珠母，能展现出独特的机械性能，因此常被科学家模仿。在这项工作中，我们分两步制备了模仿珍珠母结构的复合材料。首先，我们通过改进溶胶 - 凝胶合成法，采用自下而上的方法合成了具有层状结构的硅胶骨架。将磁性胶体添加到溶胶溶液中，并在溶胶 - 凝胶转变过程中施加旋转磁场。当暴露于旋转磁场时，磁性胶体沿平行于磁场旋转平面的方向分层排列，并为生长的二氧化硅相提供模板，从而形成模仿珍珠母无机相的层状各向异性二氧化硅网络。已进行热处理以进一步硬化二氧化硅整体材料。通过用单体填充多孔结构来制备最终的受珍珠母启发的复合材料，聚合后形成软弹性体。对片状结构复合材料的压缩测试表明，类珍珠母复合材料的机械性能远远超过具有相同化学成分的非结构化复合材料。实现了韧性增加以及杨氏模量近10倍的提高。通过模仿珍珠母的自然结构，可以克服二氧化硅整体材料天然的脆性和低弹性变形。应用机器学习算法分类获得的模式识别，以更好地理解对整体材料机械性能影响最大的物理和化学参数。进行多变量统计分析以表明结构控制和热处理对整体材料的机械性能有非常强烈的影响。
