Wu Jiawei, Wang Ruobing, Tan Yan, Liu Lulu, Chen Zhihong, Zhang Songhong, Lou Xiaoling, Yun Junxian
State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Chaowang Road 18, Hangzhou 310032, PR China.
State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Chaowang Road 18, Hangzhou 310032, PR China.
J Chromatogr A. 2024 Jul 19;1727:464996. doi: 10.1016/j.chroma.2024.464996. Epub 2024 May 19.
Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and drug delivery. However, the variations in their components significantly impactfinal properties. This study presents a two-step hybrid machine learning approach for predicting the properties of innovative poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose (pHEMA-PVA-BC) based on their compositions. By considering the ratios of HEMA (1.0-22.0 wt%), PVA (0.2-4.0 wt%), poly(ethylene glycol) diacrylate (1.0-4.5 wt%), BC (0.1-1.5 wt%), and water (68.0-96.0 wt%) as investigational variables, overlay sampling uniform design (OSUD) was employed to construct a high-quality dataset for model development. The random forest (RF) model was used to classify the preparation conditions. Then four models of artificial neural network, RF, gradient boosted regression trees (GBRT), and XGBoost were developed to predict the basic properties of the composite cryogels. The results showed that the RF model achieved an accurate three-class classification of preparation conditions. Among the four models, the GBRT model exhibited the best predictive performance of the basic properties, with the mean absolute percentage error of 16.04 %, 0.85 %, and 2.44 % for permeability, effective porosity, and height of theoretical plate (1.0 cm/min), respectively. Characterization results of the representative pHEMA-PVA-BC composite cryogel showed an effective porosity of 81.01 %, a permeability of 1.20 × 10 m, and a range of height of theoretical plate between 0.40-0.49 cm at flow velocities of 0.5-3.0 cm/min. These indicate that the pHEMA-PVA-BC cryogel was an excellent material with supermacropores, low flow resistance and high mass transfer efficiency. Furthermore, the model output demonstrates that the alteration of the proportions of PVA (0.2-3.5 wt%) and BC (0.1-1.5 wt%) components in composite cryogels resulted in significant changes in the material basic properties. This work represents an attempt to efficiently design and prepare target composite cryogels using machine learning and providing valuable insights for the efficient development of polymers.
具有增强可调功能的超大孔复合冷冻凝胶在生物分离、组织工程和药物递送领域受到了广泛关注。然而，其成分的变化会显著影响最终性能。本研究提出了一种两步混合机器学习方法，用于根据其组成预测嵌入细菌纤维素的新型聚（甲基丙烯酸2-羟乙酯）-聚（乙烯醇）复合冷冻凝胶（pHEMA-PVA-BC）的性能。通过将甲基丙烯酸2-羟乙酯（HEMA）（1.0-22.0 wt%）、聚乙烯醇（PVA）（0.2-4.0 wt%）、聚乙二醇二丙烯酸酯（1.0-4.5 wt%）、细菌纤维素（BC）（0.1-1.5 wt%）和水（68.0-96.0 wt%）的比例作为研究变量，采用重叠采样均匀设计（OSUD）构建用于模型开发的高质量数据集。随机森林（RF）模型用于对制备条件进行分类。然后开发了人工神经网络、RF、梯度提升回归树（GBRT）和XGBoost四种模型来预测复合冷冻凝胶的基本性能。结果表明，RF模型实现了对制备条件的准确三类分类。在这四种模型中，GBRT模型对基本性能的预测性能最佳，渗透率、有效孔隙率和理论塔板高度（1.0 cm/min）的平均绝对百分比误差分别为16.04%、0.85%和2.44%。代表性pHEMA-PVA-BC复合冷冻凝胶的表征结果表明，在流速为0.5-3.0 cm/min时，有效孔隙率为81.01%，渗透率为1.20×10 m，理论塔板高度范围在0.40-0.49 cm之间。这些表明pHEMA-PVA-BC冷冻凝胶是一种具有超大孔、低流动阻力和高传质效率的优异材料。此外，模型输出表明，复合冷冻凝胶中PVA（0.2-3.5 wt%）和BC（0.1-1.5 wt%）成分比例的改变导致材料基本性能发生显著变化。这项工作代表了一种尝试，即利用机器学习高效设计和制备目标复合冷冻凝胶，并为聚合物的高效开发提供有价值的见解。
