Multivariate Calibration for Carbon Nanotubes in the Environment Using the Microwave Induced Heating Method.

The goal of the present paper is to develop chemometrics-based multivariate calibration approaches for simultaneously determining quantity of individual carbon nanotubes (CNTs) in a multicomponent environmental matrix using a microwave induced heating method. A multifactor and multilevel experiment design was used to create 4 separate calibration datasets. Each calibration dataset contained 25 orthogonal CNTs with 2 or 3 factors (CNTs: single-walled CNTs (SWCNTs)/multi-walled CNTs (MWCNTs)/carboxylated MWCNTs (MWCNT-COOH)) and 5 levels (CNTs mass). The temperature rise (ΔT) spectral information was obtained for each sample by exposing to varying microwave conditions. This study showed the potential and applicability of partial least square regression (PLS), least square-support vector machine (LS-SVM) and artificial neural networks (ANN) in predicting quantities of SWCNTs, MWCNTs and MWCNT-COOH in environmental matrices with microwave induced temperature rises data. Our results revealed that the developed LS-SVM model presented higher R and lower root mean square error of prediction (RMSEP) (R = 0.74-0.93, RMSEP =0.0251 mg to 0.0328 mg in 2-component systems and R = 0.64-0.95, RMSEP = 0.0243 mg to 0.0410 mg in 3-component systems), while the ANN model was only accurate in estimating mass of SWCNT and MWCNT in a 2-component mixture (R = 0.77-0.89, RMSEP = 0.0322 mg to 0.0503 mg). The PLS model was found not effectively interpret relationship between microwave induced temperature rises data and mass of CNTs, indicated by small R (0.20-0.87) and large RMSEP (0.0209 mg -0.1021 mg).