Abstract Shape memory polymers (SMPs) are a polymeric smart material that can register two or more temporary shapes and transform to one another through an external stimulus. Despite their compactness and customizability, SMPs haven't been able to be adopted for mainstream applications. Since the majority of SMPs are triggered by heat, and SMPs have a very poor thermal conductivity, large thermal gradients within the polymer appear which cause slow response, inhomogeneous heat distribution and thus non-uniform transformation of shapes and cracks. Many have attempted to improve their thermal performance through the incorporation of filler-based nanomaterials. However, the outcome is ineffective as the spatial dispersion of fillers within the SMP is inhomogeneous and leads to performance loss. Contrastingly, the herein presented new class of nanocomposite-SMP, composed by 3D-foam fillers, showcase a much more efficient SMP adaptable to larger area with faster transformation speed and without any performance loss. Furthermore, the improved thermal properties lead to a decrease in required input energy, as well as render the SMP a self-heating capability which can be further designed into timed multi-step SMP behavior.

中文翻译：

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用于大面积和节能应用的新型定时和自阻加热形状记忆聚合物混合物

摘要 形状记忆聚合物 (SMP) 是一种聚合智能材料，可以记录两个或多个临时形状并通过外部刺激相互转换。尽管它们具有紧凑性和可定制性，但 SMP 尚未能够被主流应用采用。由于大多数 SMP 是由热量引发的，并且 SMP 的导热性非常差，因此聚合物内会出现大的热梯度，导致响应缓慢、热量分布不均匀，从而导致形状和裂纹的不均匀转变。许多人试图通过加入基于填料的纳米材料来提高它们的热性能。然而，结果是无效的，因为 SMP 中填料的空间分散不均匀并导致性能损失。相比之下，本文介绍的新型纳米复合材料-SMP，由 3D 泡沫填料组成，展示了一种更高效的 SMP，适用于更大的区域，转换速度更快，并且没有任何性能损失。此外，改进的热性能导致所需输入能量的降低，并使 SMP 具有自热能力，可以进一步设计为定时多步 SMP 行为。