Quantitative Chemical Mapping is an electron microscopic technique capable of revealing compositional variations in crystalline materials. It combines chemical lattice imaging which maps the sample composition, with vector pattern recognition, which quantifies the local information content of the image to measure the local sample composition. Here we briefly address the spatial resolution of this technique, assuming complete familiarity with its theoretical underpinnings.In chemical imaging, we are concerned with the way that a compositional inhomogeneity is imaged under conditions appropriate for chemical sensitivity, and how the pattern recognition algorithm extracts information from a chemical lattice image. The problem can be formulated as follows. Given a “chemical impulse” of a specific shape, such as a column of Al atoms imbedded in GaAs (approximating a δ-function), an abrupt interface (a θ-function), or a diffuse interface (e.g., with an error function profile), what is the shape of the impulse on the analyzed chemical image? Or, alternatively, what region of the sample contributes to the information content of an image unit cell? By reciprocity, these two formulations are equivalent.
AES and ToF-SIMS combination for single cell chemical imaging of gold nanoparticle-labeled Escherichia coli