Monomer diffusion into static and evolving polymer networks during frontal photopolymerisation.
Monomer diffusion into static and evolving polymer networks during frontal photopolymerisation.
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DOI：
10.1039/c7sm01279a
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发表时间：
2017-12
期刊：
影响因子：
3.4
通讯作者：
M. Hennessy;A. Vitale;O. Matar;J. Cabral
中科院分区：
文献类型：
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作者：
M. Hennessy;A. Vitale;O. Matar;J. Cabral
正面光聚合（FPP）是一种定向固化过程，其通过曝光将富含单体的液体转化为交联聚合物固体，并且应用范围从光刻到3D打印。该过程的固有特征是形成暴露于单体浴中的不断演变的聚合物网络。结合理论和实验研究进行，以确定条件下，单体从这个浴可以扩散到传播的聚合物网络，并导致它溶胀。首先，通过将预制的聚合物网络浸入保持在不同温度下的单体浴中，使生长和溶胀过程解耦。网络厚度的实验测量被发现是在良好的协议与从非线性多孔弹性模型获得的理论预测。然后在导致溶胀的条件下进行FPP传播实验。出乎意料的是，对于固定的曝光时间，发现溶胀随着入射光强度而增加。实验数据很好地描述了一种新的FPP模型占质量传输和聚合物网络的机械响应，提供了关键的见解单体扩散如何影响聚合物固体的转化率曲线和在其生长过程中产生的应力。该模型的预测能力将使梯度材料的制造与调谐的机械性能和控制的应力发展。
Frontal photopolymerisation (FPP) is a directional solidification process that converts monomer-rich liquid into crosslinked polymer solid by light exposure and finds applications ranging from lithography to 3D printing. Inherent to this process is the creation of an evolving polymer network that is exposed to a monomer bath. A combined theoretical and experimental investigation is performed to determine the conditions under which monomer from this bath can diffuse into the propagating polymer network and cause it to swell. First, the growth and swelling processes are decoupled by immersing pre-made polymer networks into monomer baths held at various temperatures. The experimental measurements of the network thickness are found to be in good agreement with theoretical predictions obtained from a nonlinear poroelastic model. FPP propagation experiments are then carried out under conditions that lead to swelling. Unexpectedly, for a fixed exposure time, swelling is found to increase with incident light intensity. The experimental data is well described by a novel FPP model accounting for mass transport and the mechanical response of the polymer network, providing key insights into how monomer diffusion affects the conversion profile of the polymer solid and the stresses that are generated during its growth. The predictive capability of the model will enable the fabrication of gradient materials with tuned mechanical properties and controlled stress development.