摘要
Computational modeling is a new approach to optimize Young’s modulus of scaffolds by performing a minimal number of experiments.However,presenting a modeling algorithm to predict Young’s modulus and characterize the governing parameters is a challenging task.Here,a novel modeling approach has been proposed to estimate Young’s modulus of scaffolds,considering particle agglomeration and interphase interactions.Employing the characteristic parameters of these two phenomena,we modified the Maxwell model using a simple three-step algorithm to determine the optimal value of these parameters and predict Young’s modulus.Interestingly,the model provides a precision of more than 95%for all the studied cases and presents a remarkably better performance compared to the two other models.For instance,the proposed model has reduced the average absolute relative error of Young’s modulus of poly(3-hydroxybutyrate)-keratin/hydroxyapatite nanorods from 25.1%to 0.08%,demonstrating the high efficiency of this model in predicting Young’s modulus of scaffolds.The results of this study could lighten the way of fabricating nanobiocomposites with optimal mechanical properties,spending lower cost and energy.
计算建模是一种通过进行最少数量的实验来优化支架杨氏模量的新方法.然而,提出一种建模算法来预测杨氏模量并表征控制参数是一项具有挑战性的任务.在这里,考虑到颗粒团聚和相间相互作用,提出了一种新的建模方法来估计支架的杨氏模量.利用这两种现象的特征参数,我们使用简单的三步算法来修改麦克斯韦模型,以确定这些参数的最优值并预测杨氏模量.有趣的是,与其他两个模型相比,该模型在所有研究案例中的精度都超过95%,并且表现出明显更好的性能.例如,该模型将聚(3-羟基丁酸酯)-角蛋白/羟基磷灰石纳米棒的杨氏模量的平均绝对相对误差从25.1%降低到0.08%,证明了该模型在预测支架杨氏模量方面的高效性.这项研究结果可以简化制备具有最佳机械性能的纳米生物复合材料的方法,降低成本和能耗.
基金
supported by Isfahan University of Medical Sciences and the Student Research Committee of Isfahan University of Medical Sciences in Iran(Grant No.1400124).
