Abstract
Nowadays, the development of new technology is strongly based on nanomaterials studies, such as vertically aligned TiO2 nanotubes (TONTs), which are widely used in different fields of industry. Physical properties of these materials depend on their size (inner and outer diameters) and shape, for this reason they must be measured accurately. Inner and outer diameter measurement is performed manually on images obtained by means of scanning electron microscopy. Time-consuming image analysis, subjective and low-representative readings are some disadvantages found in this process. This paper proposes a model to predict the average outer diameter of TONTs using Data Mining and Ellipsometry, because they have the potential to overcome disadvantages mentioned above. The diameter with measurements of light reflection intensity and ellipsometric parameters is modelled using different techniques. A model that shows a very low prediction error using linear support vector machines for regression is reported.
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