Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK.
Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK.
Acta Biomater. 2023 Sep 1;167:361-373. doi: 10.1016/j.actbio.2023.06.021. Epub 2023 Jun 19.
Hydrolytic degradation of polymers involves the scission of long chain molecules, leading to molecular weight reduction and mass loss. The precise degradation response however depends on the scission probability of individual bonds along the polymer backbone. In particular, bonds near the chain ends are considered to be more susceptible to hydrolysis than inner bonds. In this paper, we incorporate a discrete chain scission model that can handle arbitrary bond scission probabilities within a continuum reaction-diffusion framework. Overall hydrolysis kinetics (including autocatalysis) is described independently of the chain scission model. By decoupling the description of the chain scission mechanism from kinetics, our framework enables the identification of the chain scission mechanism from molecular weight reduction and mass loss curves commonly reported in experimental degradation studies. We further propose a reduced continuum model which is better suited for large-scale simulations while retaining the predictive capability of the full discrete-continuum model. The model capability is illustrated in representative case studies based on experimental data from the literature for different materials and geometries. STATEMENT OF SIGNIFICANCE: Many models have been proposed to predict the evolution of molecular weight and mass loss in biodegradable polymers undergoing hydrolytic degradation. However, existing models remain limited in their ability to describe the degradation mechanism, autocatalytic kinetics and short chains diffusion simultaneously. Moreover, existing models often rely on empirical relations and a large number of fitting parameters. Here, we propose a conceptually simple discrete-continuum mathematical framework with a small number of parameters which all have a clear physical meaning. Model calibration against experimental data is simplified, and further provides insights into the degradation mechanisms at play, namely random scission, chain-end scission, or a combination of both. The framework can serve as a basis for future generalisations, including a description of evolving crystallinity, or other degradation mechanisms, such as thermal oxidation or photo-degradation.
聚合物的水解降解涉及长链分子的断裂，导致分子量降低和质量损失。然而，精确的降解响应取决于聚合物主链上各个键的断裂概率。特别是，靠近链末端的键被认为比内部键更容易受到水解的影响。在本文中，我们引入了一种离散链断裂模型，可以在连续反应-扩散框架内处理任意键断裂概率。整体水解动力学（包括自催化）与链断裂模型独立描述。通过将链断裂机制的描述与动力学解耦，我们的框架能够从通常在实验降解研究中报告的分子量降低和质量损失曲线中识别链断裂机制。我们进一步提出了一种简化的连续模型，该模型更适合大规模模拟，同时保留全离散-连续模型的预测能力。该模型的能力通过基于文献中不同材料和几何形状的实验数据的代表性案例研究进行了说明。
许多模型已被提出用于预测经历水解降解的可生物降解聚合物的分子量和质量损失的演变。然而，现有的模型在描述降解机制、自催化动力学和短链扩散方面仍然存在局限性。此外，现有的模型通常依赖于经验关系和大量的拟合参数。在这里，我们提出了一个概念上简单的离散-连续数学框架，具有少量参数，所有参数都具有明确的物理意义。通过将模型与实验数据进行校准，简化了模型校准过程，并进一步深入了解了起作用的降解机制，即随机断裂、链末端断裂或两者的组合。该框架可以作为未来推广的基础，包括对演变结晶度的描述，或其他降解机制，如热氧化或光降解。
