Gómez-Bacab Maya T, Quezada-Campos Aldo L, Patiño-Arévalo Carlos D, Zepeda-Rodríguez Zenen, Romero-Cano Luis A, Zárate-Navarro Marco A
Laboratorio de Ingeniería Química, Departamento de Biotecnológicas y Ambientales, Universidad Autónoma de Guadalajara, Av. Patria 1201, Zapopan CP. 45129, Jalisco, Mexico.
Grupo de Investigación en Materiales y Fenómenos de Superficie, Departamento de Biotecnológicas y Ambientales, Universidad Autónoma de Guadalajara, Av. Patria 1201, Zapopan CP. 45129, Jalisco, Mexico.
Polymers (Basel). 2025 Aug 29;17(17):2349. doi: 10.3390/polym17172349.
Polymer recycling is challenging due to practical classification difficulties. Even when the polymer matrix is identified, the presence of various polymeric composites complicates their accurate classification. In this study, Fourier-transform infrared spectroscopy (ATR-FTIR) was used in combination with artificial neural networks (ANNs) to quantitatively predict the mineral filler content in polypropylene (PP) composites. Calibration curves were developed to correlate ATR-FTIR spectral features (600-1700 cm) with the concentration (wt.%) of three mineral fillers: talc (PP-Talc), calcium carbonate (PP-CaCO), and glass fiber (PP-GF). ANN models developed in MATLAB 2024a achieved prediction errors below 7.5% and regression coefficients (R) above 0.98 for all filler types. The method was successfully applied to analyze a commercial recycled pellet, and its predictions were validated by X-ray fluorescence (XRF) and energy-dispersive X-ray spectroscopy (EDX). This approach provides a simple, rapid, and non-destructive tool for non-expert users to identify both the type and amount of mineral filler in recycled polymer materials, thereby reducing misclassification in their commercialization or quality control in industrial formulations.
由于实际分类困难，聚合物回收具有挑战性。即使确定了聚合物基体，各种聚合物复合材料的存在也会使它们的准确分类变得复杂。在本研究中，傅里叶变换红外光谱（ATR-FTIR）与人工神经网络（ANNs）结合使用，以定量预测聚丙烯（PP）复合材料中的矿物填料含量。绘制了校准曲线，将ATR-FTIR光谱特征（600-1700 cm）与三种矿物填料的浓度（重量%）相关联：滑石粉（PP-滑石粉）、碳酸钙（PP-CaCO）和玻璃纤维（PP-GF）。在MATLAB 2024a中开发的ANN模型对所有填料类型的预测误差均低于7.5%，回归系数（R）均高于0.98。该方法成功应用于分析一种商业回收颗粒，其预测结果通过X射线荧光（XRF）和能量色散X射线光谱（EDX）进行了验证。这种方法为非专业用户提供了一种简单、快速且无损的工具，用于识别回收聚合物材料中矿物填料的类型和数量，从而减少其商业化过程中的错误分类或工业配方中的质量控制问题。
