A variety of applications, spanning from structural or biomedical engineering to flexible electronics, require the development of materials able to withstand high load and, at the same time, accommodate high strain before failure. While strength and toughness are often self-excluding properties in man-made materials, they can be efficiently combined by nature, which provides source of inspiration for novel materials design. Herein this paper, we pursue a bio-inspired approach, based on the introduction of a mechanical sink, such as a running knot, to improve the toughness modulus of high-performance polymeric microfibres. These are then enriched with additional smart features, such as a viscoelastic coating, surface roughening or a combination of those, to amplify the beneficial effect of the knot introduction. The role played by all such features on the mechanical performances of the prepared fibre samples, namely load at failure and toughness modulus increase, is then evaluated through a statistical technique, known as correspondence analysis (CA). While this exploratory analysis is widely adopted in biology, ecology, neuroscience or genetics, applications in structural or mechanical engineering are still rare. Here, we show that CA can be a powerful tool for the design of materials provided with enhanced toughness without losing strength.

各种应用，从结构或生物医学工程到柔性电子产品，都需要开发能够承受高负载，同时在失效前适应高应变的材料。虽然强度和韧性在人造材料中通常是自排除的特性，但它们可以在自然界中有效地结合，这为新型材料设计提供了灵感来源。在本文中，我们寻求一种仿生方法，基于引入机械水槽，例如运行结，以提高高性能聚合物微纤维的韧性模量。然后通过附加的智能功能丰富这些功能，例如粘弹性涂层、表面粗糙化或这些功能的组合，以放大引入结的有益效果。然后通过称为对应分析 (CA) 的统计技术评估所有这些特征对制备的纤维样品的机械性能所起的作用，即破坏载荷和韧性模量增加。虽然这种探索性分析在生物学、生态学、神经科学或遗传学中被广泛采用，但在结构或机械工程中的应用仍然很少。在这里，我们展示了 CA 可以成为设计具有增强韧性而不损失强度的材料的强大工具。在结构或机械工程中的应用仍然很少见。在这里，我们展示了 CA 可以成为设计具有增强韧性而不损失强度的材料的强大工具。在结构或机械工程中的应用仍然很少见。在这里，我们展示了 CA 可以成为设计具有增强韧性而不损失强度的材料的强大工具。