Quantitative Surface Analysis

Surface analysis techniques, such as XPS, provide a wealth of information about surface properties. But for most samples, no single technique can provide the complete characterization of all the relevant properties, so combining data from several different techniques results in a more reliable, comprehensive, and quantitative interpretation.

XPS and NEXAFS techniques use incident x-rays to probe the core levels of nitrogen atoms (blue) in thymine. FTIR spectroscopy adds vibrational fingerprints of submolecular structures such as the C=O groups. NEXAFS and FTIR use linearly polarized incident photons and thus are sensitive, via the dipole selection rule, to the orientation of nitrogen π* orbitals (yellow) and C=O ligands (green), respectively.

For realistic samples, which always have some degree of disorder and some level of impurities, interpretation of data from individual surface analysis techniques necessarily includes uncertainties. Accordingly, it is particularly advantageous to use several complementary techniques, whereby the results can be used to cross-validate the assumptions used to interpret each of the individual datasets. For example, XPS is largely insensitive to orientation of atomic orbitals, which means that elemental concentrations measured by XPS can be used to normalize NEXAFS and FTIR data. In turn, data from polarization-dependent techniques such as NEXAFS and FTIR in many cases can be used to cross-validate interpretation of the respective orientational information.

Multi-technique characterization is particularly important in cases when a priori assumptions about the composition and structure of a sample can be very uncertain. For example, it relatively easy to prepare a series of silicon oxide samples with consistent composition and thickness of the oxide layer, but it is nearly impossible to prepare a series of biomolecular films having a similar degree of uniformity or reproducibility. Most of the systems in nanotechnology and nanobiotechnology, in fact, inherently possess a degree of uncertainty about their chemical composition, structure, and stability. The ability to characterize such interesting and difficult samples is currently in the early development stages, so the associated challenges will provide continuous motivation for advances in quantitative surface analysis.


These papers provide examples of quantitative analysis for biointerfaces.