Students who have worked in my laboratory have had two significant opportunities. First, they have published a lot. Second, and of great significance, they have had the opportunity to learn many new things. Most of our work is focused on surface modification and patterning of materials like silicon, polymers, and diamond. To do these surface modifications my students learn and perform bioconjugate chemistry, as well as organic and polymer chemistry.
Once my students have made these new materials, they need to characterize them, which gives them the opportunity to learn how to use a series of analytical instruments. These include X-ray photoelectron spectroscopy (XPS), which gives surface elemental composition and oxidation state information, optical ellipsometry, which gives surface thicknesses to better than Ångstrom precision, time-of-flight secondary ion mass spectrometry (ToF-SIMS), which is a powerful form of surface mass spectrometry, atomic force microscopy (AFM), which gives surface morphology information, wetting, which gives information about surface free energies, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). We are fortunate at BYU to have all of this equipment. These instruments are widely used in industry by polymer, materials, pharmaceutical, and semiconductor companies.
Not only do my students learn the fundamentals of these instrumental techniques and learn how to take data with them, but they also learn how to use advanced data processing methods to analyze their data. Indeed, it is becoming increasingly critical for analytical chemists to possess strong data analysis skills because of the enormous amounts of data that can be collected with modern instrumentation. That is, it is often nearly impossible for large data sets to be analyzed in a traditional (univariate) fashion. In my group we frequently use principal component analysis (PCA) and multivariate curve resolution (MCR) to analyze ToF-SIMS data. These methods allow us to quickly find the variation in complex data sets, categorize samples, and relate complex spectra to physical properties. In addition, we employ the statistical methods of experimental design to efficiently optimize new surface chemistries that we develop.