Abstract
In this work, we report a simple method to direct identify nanometer sized textures in composite materials by means of AFM spectroscopy, aiming at recognizing structured region to be further investigated. It consists in acquiring a set of dynamic data organized in spectroscopy maps and subsequently extracting most valuable information by means of the principal component analysis (PCA) method. This algorithm projects the information of D spectroscopy curves, each containing P data, acquired at each point of an LxC grid into a subset of LxC maps without any assumption on the sample structure, filtering out redundancies and noise. As a consequence, a huge amount of 3D data is condensed into few 2D maps, easy to be examined. Results of this algorithm allow to find and locate regions of interest within the map, allowing a further reduction of data series to be extensively analyzed or modeled. In this work, we explain the main features of the method and show its application on a nanocomposite sample. Microsc. Res. Tech. 73:973-981, 2010.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 973-981 |
| Number of pages | 9 |
| Journal | Microscopy Research and Technique |
| Volume | 73 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2010 |
| Externally published | Yes |
Bibliographical note
Funding Information:This work was supported by grants to R.J. and P.B. from RGS Genome Inc., the National Alliance for Research on Schizophrenia and Depression (NARSAD) and the Canadian Institutes of Health Research (CIHR). We thank Ying Zhang and Julie Zhu for their expert and careful technical assistance.
Keywords
- Dimensionality reduction
- Dynamic spectroscopy
- Functional materials
- Functional recognition
- Microscopy
- Nanotechnology
- Principal component analysis
ASJC Scopus subject areas
- Anatomy
- Histology
- Instrumentation
- Medical Laboratory Technology