A very important goal for chemical imaging is to understand and control complex chemical processes. This ultimately requires the ability to perform multimodal or multitechnique imaging across all length and time scales. Complete characterization of a complex material requires information not only on the surface or in bulk chemical components, but also on stereometric features such as size, distance, and homogeneity in three-dimensional space. In chemical imaging, it is frequently difficult to uniquely distinguish between alternative surface morphologies using a single analytical method and routine data acquisition and analysis. Multitechnique image correlation allows for extending lateral and vertical spatial characterization of chemical phases. This approach improves spatial resolution by utilizing techniques with nanometer resolution to enhance data from techniques with micrometer resolution—such as atomic force microscopy (AFM) or scanning electron microscopy (SEM) combined with X-ray photoemission spectroscopy (XPS) or Fourier transform infrared (FTIR) spectroscopy. Multi-modal imaging also facilitates correlation of different physical properties such as phase information in AFM with chemical information in XPS. By combining techniques that use different physical principles and record different properties of the object space, complementary and better-quality information becomes available.
As in most cases of systems integration, multimodal imaging requires more than simply networking different imaging techniques. Advances in computational capabilities, for example, are fundamental to effective integration of imaging techniques. Data fusion is the name for the techniques used to combine data from multiple techniques to perform inferences that may not be possible from a single technique by itself. The goal is to combine image data to form a new image that contains more interpretable information than could be gained using the original information. Combining images to form a multimodal image requires—beyond the usual image processing for a single image—a compensation for changes in image alignment from one instrument to another due to slight movements of the specimens, slight differences in magnification, or imperfect centering of the sample.
There is a need to develop multitechnique correlations for various combinations of imaging techniques.
Two examples for techniques that may be combined are listed below, but they are by no means meant to be comprehensive.
Combining Surface Enhanced Raman Spectroscopy and Nanoscale Scanning Probe Techniques
Surface enhanced Raman spectroscopy (SERS) experiments on silver and gold nanoclusters have demonstrated large enhancement levels and field confinement of 5 nm or less for various samples such as single-walled carbon nanotubes.1 However, the locations of these conditions cannot be controlled but are instead determined by the specific nanostructures used. That is, the target molecules have