Library based identification and characterisation of polymers with nano-FTIR and IR-sSNOM imaging

摘要

AFM is a technique widely applied in the nanoscale characterisation of polymers and their surface properties. With nano-FTIR and IR-sSNOM imaging an optical dimension is added to this technique that allows for straightforward high resolution characterisation and spectroscopy of polymers. As the volume sampled by these near-field techniques depends mostly on the radius of the cantilever tip, typically 10 nm, it is orders of magnitude smaller than in conventional techniques. Nevertheless, comparability of nano-FTIR near-field spectra and data from macroscopic methods has been shown. Some relevant polymers such as polystyrene however, prove to be more difficult to detect than others. Furthermore, the small sampled volume suggests lower signal quality of nano-FTIR data and proof of its suitability for a reliable library search identification is lacking. To evaluate the techniques especially towards automatic and higher throughput identification of nanoscale polymers, for example in blends or environmental samples, we examined domain distributions in a PS-LDPE film and spectral responses of foils of the most relevant commercial polymers. We demonstrate the successful library search identification of all samples with nano-FTIR data measured in less than seven minutes per spectrum with a free IR spectra database in combination with established commercial OPUS 7.5 software and the recently released freeware siMPle. We discuss aspects affecting the accuracy of the identification for different polymers and show that the small spectral range of 1700-1300 cm(-1) already leads to similar success in differentiating between polymer types with near-field data as with conventional far-field FTIR spectroscopy. Even a polymer sample weathered in the environment can be identified without prior cleaning, proving wide fields of applications for characterisation and identification of diverse polymer samples. Finally, we propose measurement and analysis strategies for known and unknown samples with this novel technique.