Detection of carbon nanotubes in environmental matrices using programmed thermal analysis

Carbon nanotube (CNT) production is rapidly growing, and there is a need for robust analytical methods to quantify CNTs at environmentally relevant concentrations in complex organic matrices. Because physical and thermal properties vary among different types of CNTs, we studied 14 single-walled (SWCNTs) and multiwalled CNTs (MWCNTs). Our aim was to apply a classic analytical air pollution method for separating organic (OC) and elemental carbon (EC) (thermal optical transmittance/reflectance, TOT/R) to environmental and biological matrices and CNTs. The TOT/R method required significant modification for this analysis, which required a better understanding of the thermal properties of CNTs. An evaluation of the thermal properties of CNTs revealed two classes that could be differentiated using Raman spectroscopy: thermally "weak" and "strong." Using the programmed thermal analysis (PTA) method, we optimized temperature programs and instituted a set of rules for defining the separation of OC and EC to quantify a broad range of CNTs. The combined Raman/PTA method was demonstrated using two environmentally relevant matrices (cyanobacteria (CB) and urban air). Thermal evaluation of CB revealed it to be a complex matrix with interference occurring for both weak and strong CNTs, and thus a pretreatment method was necessary. Strong CNT masses of 0.51, 2.7, and 11 μg, corresponding to concentrations of 10, 54, and 220 μg CNT/g CB, yielded recoveries of 160 ± 29%, 99 ± 1.9%, and 96 ± 3.0%, respectively. Urban air was also a complex matrix and contained a significant amount (12%) of background EC that interfered greatly with weak CNTs and minimally with strong CNTs. The current detection limit at 99% confidence for urban air samples and strong CNTs is 55 ng/m³ (0.33 μg). Overall, the PTA method presented here provides an initial approach for quantifying a wide range of CNTs, and we identify specific future research needs to eliminate potential interferences and lower detection limits.