Abstract
Single-walled carbon nanotubes (SWCNTs) possess unique optical and electronic properties that depend strongly on their exact chiral structure. Since synthesis methods cannot produce samples with just one structure this project studied in detail the role of surfactants in the structure sorting of SWCNTs. The project explored advanced methodologies for sorting and characterization of SWCNTs, with a particular focus on chiral separation, hyperspectral imaging and deep learning-based segmentation techniques. First, a systematic study of the competition between different surfactants for the structure sorting of SWCNTs through aqueous-two phase extraction was investigated, providing key information for predictive sorting. Secondly, hyperspectral imaging combined with automated segmentation of the SWCNTs was employed to investigate the effect of a chiral surfactant on the adsorption of SWCNT enantiomers. The latter technique was then employed to calibrate chiroptical responses of SWCNT enantiomers.
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