Staniewicz Lech, Vaudey Thomas, Degrandcourt Christophe, Couty Marc, Gaboriaud Fabien, Midgley Paul
Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge, CB3 0FS, United Kingdom.
Manufacture Française des Pneumatiques Michelin, 23 Place des Carmes Déchaux, 63040 Clermont Ferrand Cedex 9, France.
Sci Rep. 2014 Dec 9;4:7389. doi: 10.1038/srep07389.
Rubber-filler composites are a key component in the manufacture of tyres. The filler provides mechanical reinforcement and additional wear resistance to the rubber, but it in turn introduces non-linear mechanical behaviour to the material which most likely arises from interactions between the filler particles, mediated by the rubber matrix. While various studies have been made on the bulk mechanical properties and of the filler network structure (both imaging and by simulations), there presently does not exist any work directly linking filler particle spacing and mechanical properties. Here we show that using STEM tomography, aided by a machine learning image analysis procedure, to measure silica particle spacings provides a direct link between the inter-particle spacing and the reduction in shear modulus as a function of strain (the Payne effect), measured using dynamic mechanical analysis. Simulations of filler network formation using attractive, repulsive and non-interacting potentials were processed using the same method and compared with the experimental data, with the net result being that an attractive inter-particle potential is the most accurate way of modelling styrene-butadiene rubber-silica composite formation.
橡胶-填料复合材料是轮胎制造中的关键部件。填料为橡胶提供机械增强作用和额外的耐磨性，但它反过来又给材料引入了非线性力学行为，这很可能源于由橡胶基体介导的填料颗粒之间的相互作用。虽然已经对整体力学性能和填料网络结构（通过成像和模拟）进行了各种研究，但目前还没有任何工作直接将填料颗粒间距与力学性能联系起来。在这里，我们表明，使用扫描透射电子显微镜断层扫描技术，并借助机器学习图像分析程序来测量二氧化硅颗粒间距，能够在颗粒间距与通过动态力学分析测量的作为应变函数的剪切模量降低（佩恩效应）之间建立直接联系。使用吸引、排斥和非相互作用势对填料网络形成进行的模拟采用相同方法处理，并与实验数据进行比较，最终结果是，吸引性的颗粒间势是模拟丁苯橡胶-二氧化硅复合材料形成的最准确方法。
