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Evaluation of the nanoscale spectroscopic and chemical mapping results of carbon nanotube composite materials (Poster)

Abstract

Modern technique combining infra-red spectroscopy and atomic force microscopy (AFM) allows the chemical mapping with the spatial resolution down to 30 nm. However, due to specific detection of the infrared signal special attention must be paid to data-processing stage. In this contribution evaluation of the nanoscale chemical mapping results of carbon nanotube composite materials will be presented. Nanostructural composites reinforced with carbon nanotube assemblies, such as CNT fibres or mats are promising materials for the numerous applications from avionics to high-tech industry. Ability to gain insight into the chemical composition of such composite materials may provide a way for their further improvement.

Date
2017-09-23 00:00
Location
Worcester College, Oxford, UK

Modern technique combining infra-red spectroscopy and atomic force microscopy (AFM) allows the chemical mapping with the spatial resolution down to 30 nm. However, due to specific detection of the infrared signal special attention must be paid to data-processing stage. In this contribution evaluation of the nanoscale chemical mapping results of carbon nanotube composite materials will be presented. Nanostructural composites reinforced with carbon nanotube assemblies, such as CNT fibres or mats are promising materials for the numerous applications from avionics to high-tech industry 1. Ability to gain insight into the chemical composition of such composite materials may provide a way for their further improvement.


  1. Y. L. Li, I. A. Kinloch, and A. H. Windle. Direct spinning of carbon nanotube fibers from chemical vapor deposition synthesis. Science, 304 (5668): 276–278, 2004. ↩︎

Krzysztof Banas
Krzysztof Banas
Principal Research Fellow

I work as beam-line scientist at Singapore Synchrotron Light Source. My research interests include application of advanced statistical methods for hyperspectral data processing (dimension reduction, clustering and identification).