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arXiv 2108.08747physics.app-ph
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可印刷、可浇铸的纳米晶纤维素-环氧树脂复合材料,表现出分级的珠光状增韧

Printable, castable, nanocrystalline cellulose-epoxy composites exhibiting hierarchical nacre-like toughening

Abhinav Rao, Thibaut Divoux, Crystal Owens, A. John Hart

摘要:

由于其特殊的机械和化学性能及其天然丰度,纤维素纳米晶体(CNC)是可持续聚合物复合材料的有前途的构建块。然而,CNC分散体的快速凝胶化通常将基于CNC的复合材料限制为低CNC分数,其中聚合物仍然是主要相。在这里,我们报告的配方和加工的交联CNC-环氧树脂复合材料的CNC分数超过50重量%。微观结构包括交联到聚合物的CNC的亚微米聚集体,其类似于珍珠层的层状结构,并且促进与本体延展性行为相关的增韧机制,尽管聚集体在纳米级下的脆性行为。在63重量%复合材料的硬度为0.66 GPa,断裂韧性为5.2 MPa·m $^{1/2}$。这种全有机材料的硬度与铝合金相当,厘米级的断裂韧性与木材细胞壁相当。我们表明,CNC-环氧树脂复合材料物体可以从凝胶前体通过直写印刷和铸造成型,而固化的复合材料可以加工成复杂的3D形状。从我们的多尺度方法中获得的配方,加工路线和增韧机制的见解可以广泛应用于高负载的纳米复合材料。

英文摘要:

Due to their exceptional mechanical and chemical properties and their natural abundance, cellulose nanocrystals (CNCs) are promising building blocks of sustainable polymer composites. However, the rapid gelation of CNC dispersions has generally limited CNC-based composites to low CNC fractions, in which polymer remains the dominant phase. Here we report on the formulation and processing of crosslinked CNC-epoxy composites with a CNC fraction exceeding 50 wt.%. The microstructure comprises sub-micrometer aggregates of CNCs crosslinked to polymer, which are analogous to the lamellar structure of nacre and promotes toughening mechanisms associated with bulk ductile behavior, despite the brittle behavior of the aggregates at the nanoscale. At 63 wt.% CNCs, the composites exhibit a hardness of 0.66 GPa and a fracture toughness of 5.2 MPa.m$^{1/2}$. The hardness of this all-organic material is comparable to aluminum alloys, and the fracture toughness at the centimeter scale is comparable to that of wood cell wall. We show that CNC-epoxy composite objects can be shaped from the gel precursors by direct-write printing and by casting, while the cured composites can be machined into complex 3D shapes. The formulation, processing route, and the insights on toughening mechanisms gained from our multiscale approach can be applied broadly to highly loaded nanocomposites.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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