人们越来越担心人类暴露于纳米粒子(NPs)的可能风险。一些研究已经报道了在不同条件下NPs在多孔介质中的传输行为。因此,在预测模型中可以使用这些信息,以便可以预测任何未探索的NP的运输行为。他研究的主要重点,因此,是应用不同的基于机器学习(ML)的模型来预测各种NP的传输效率并识别重要特征。为了实现目标,首先,通过从选定的NP的已发表论文中提取数据来准备数据集[即,银(nAg),二氧化钛(nTiO2),氧化锌(nZnO),氧化石墨烯(nGO),等。].然后,随机森林,XGBoost,和CatBoost算法结合合成少数过采样技术(SMOTE)被应用,其中保留分数(RF)被视为目标特征和粒子特征(即,表面电荷,尺寸,concentration),溶液化学[pH,离子强度(IS),多孔介质特性(晶粒尺寸,孔隙率)和流速被认为是训练特征。研究结果表明,CatBoost联合SMOTE在预测整个NP范围(R2>0.89和MSE<0.007)以及单个NP的RF方面表现最好。特征重要性分析表明四个特征,即zeta电位,IS,pH值,和粒径(NPs的整个范围,nGO,nZnO)或晶粒尺寸(nAg,nTiO2)具有显著的权重(>75%)。结果表明,这些特征超过了运输行为的预测,而不是单个NP的类型。特征的相对重要性取决于所使用的参数的范围。确定的重要特征与基础物理过程一致,这使得预测模型更加可靠。
There is a rising concern related to the possible risk of human exposure to nanoparticles (NPs). Several studies have reported on the transport behavior of NPs in the porous media under varying conditions. Thus, there is a scope to use this information in a predictive model so that the transport behavior of any un-explored NPs could be predicted. The main focus of his study, therefore, is to apply different machine learning (ML) based models to predict the transport efficiency of a wide range of NPs and to identify the important features. To achieve the objective, first, the dataset is prepared by extracting data from published papers for selected NPs [i.e., silver (nAg), titanium dioxide (nTiO2), zinc oxide (nZnO), graphene oxide (nGO), and etc.]. Then, random forest, XGBoost, and CatBoost algorithms combined with synthetic minority oversampling technique (SMOTE) were applied where retention fraction (RF) is considered as the target feature and particle characteristics (i.e., surface charge, size, concentration), solution chemistry [pH, ionic strength (IS]), porous media properties (grain size, porosity) and flow rate are considered as the training features. The outcome of the study indicates that CatBoost combined with SMOTE performed the best in predicting RF for the entire range of NPs (R2 > 0.89 and MSE < 0.007) as well as for individual NPs. Feature importance analysis indicates four features, namely zeta potential, IS, pH, and particle diameter (the entire range of NPs, nGO, nZnO) or grain size (nAg, nTiO2) have significant weightage (>75%). The result suggests that the features overrule the prediction of transport behavior rather than the types of individual NPs. The relative importance of the features depends on the range of the parameter used. The identified important features are in accordance with the underlying physical process, which makes the prediction model more reliable.