Replication Data for "Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes."

Poly[(butylene succinate)-co-adipate] (PBSA)-based materials are gathering much attention in the packaging industry, agriculture, and other fields owed to their biocompatibility and biodegradability. Nonetheless, poor thermal and mechanical properties of biodegradable polymers, such as PBSA, have hampered their wide-spread use. Herein, a simple, cost-effective and scalable solution to improve the mechanical properties of PBSA is reported by using functionalized single-walled carbon nanotubes (SWCNTs). Different SWCNT loadings have been incorporated in the PBSA matrix via simple solution casting, and the ultrasonication conditions have been optimized to attain a homogenous SWCNT dispersion. The nanocomposites have been characterized in detail by scanning electron microscopy (SEM), Infrared spectroscopy, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), tensile and impact strength tests. Unprecedented increments in stiffness were found at low nanotube loadings. Further, four machine learning (ML) algorithms, Polynomial Regression (PR), Support Vector Machines for Regression (SVR), Gradient Boosting (GB) and Artificial Neural Network (ANN), were applied to predict their mechanical properties. The algorithm´s performance was assessed using analytical parameters such as the coefficient of determination (R2), the mean square error (MSE) and the mean absolute error (MAE). The developed models exhibited strong performance, achieving R2 values ranging from 0.69 to 0.99 across the evaluated properties. The results corroborate that even when the same prediction model is used, its performance varies depending on the physical property to be predicted. Thus, SVR, GB, PR, and ANN were found to be the most effective for estimating the Young’s modulus, tensile strength, elongation at break and impact strength, respectively. This research holds great potential for advancing the field of modelling the mechanical properties of polymeric nanocomposites and their practical applications in various industries such as food, pharmaceutical and biomedicine. The development of accurate models for predicting nanocomposite properties would cheapen, simplify and systematize their design and production processes, resulting in improved final products and more efficient development procedures

以聚（丁二酸丁二醇酯-共聚己二酸丁二醇酯）（PBSA）为基底的材料因优异的生物相容性与可生物降解性，在包装工业、农业及其他领域受到广泛关注。然而，可生物降解聚合物（如PBSA）较差的热学与力学性能，阻碍了其大规模推广应用。本文报道了一种简单、低成本且可规模化的改性方案，通过使用功能化单壁碳纳米管（SWCNTs）改善PBSA的力学性能。研究采用简单溶液浇筑法将不同负载量的SWCNTs掺入PBSA基体中，并优化超声处理条件以实现SWCNTs的均匀分散。通过扫描电子显微镜（SEM）、红外光谱、热重分析（TGA）、差示扫描量热法（DSC）以及拉伸与冲击强度测试，对该纳米复合材料进行了详细表征。结果发现，在低碳纳米管负载量下，材料的刚度实现了前所未有的提升。此外，本研究采用四种机器学习（ML）算法，即多项式回归（PR）、回归型支持向量机（SVR）、梯度提升（GB）与人工神经网络（ANN），对复合材料的力学性能进行预测。通过决定系数（R²）、均方误差（MSE）及平均绝对误差（MAE）等分析参数评估各算法的预测性能。所构建的模型均表现出优异的预测性能，针对所评估的力学性能，其R²值介于0.69至0.99之间。研究结果证实，即便使用同一预测模型，其性能也会因待预测的物理性质不同而存在差异。最终确定，SVR、GB、PR与ANN分别是估算杨氏模量、拉伸强度、断裂伸长率及冲击强度的最优算法。本研究在聚合物纳米复合材料力学性能建模领域以及其在食品、制药与生物医学等多行业的实际应用中均具有重要的推进价值。精准预测纳米复合材料性能的模型开发，能够降低其设计与生产的成本、简化流程并实现系统化管控，最终获得性能更优异的终端产品，并推动开发流程更高效地进行。