Surface chemistry plays a key role in science and technology because materials interact with their environments through their surfaces. Understanding surface chemistry can help alter/improve the properties of materials. However, surface characterization and modification often require multiple characterization and synthesis techniques. Silicon/silica-based materials are technologically important, so studying their surface properties can enable future advancements. In this dissertation, I explore surface modification and characterization of different types of Si/SiO2 thin films, including silicon wafers, fused silica capillary columns, and oblique angle sputtered Si/SiO2 thin films. In Chapters 2-5, I first present a method to rapidly silanize silica surfaces using a gas-phase synthesis that employs a small aminosilane that passivates/deactivates silicon wafers and the inner surfaces of capillary columns. This deposition takes place in a flow-through, atmospheric pressure, gas-phase reactor. This surface modification results in a significant decrease in the number of free surface silanols, which was confirmed by high-sensitivity low energy ion scattering (HS-LEIS), X-ray photoelectron spectroscopy (XPS), and spectroscopic ellipsometry (SE). I then show that this silanization inhibits atomic layer deposition (ALD) of zinc oxide (ZnO), which is an important optical thin film material. Finally, I performed in-depth characterization of thin films of oblique angle deposited porous Si/SiO2. These films have been used as the active coatings in solid phase microextraction (SPME) devices. The characterization and analysis in this study were mainly by scanning transmission electron microscopy (STEM) and various computational microstructural characterization techniques, e.g., two-point statistics. The rest of my dissertation focuses on XPS data analysis and interpretation. I first show box plots as a simple graphical tool for determining overfitting in XPS peak fitting. I next present a series of chemometrics/informatics analyses of an XPS image dataset from a patterned silicon surface with different oxide thicknesses. This dataset was probed via an initial, graphical analysis of the data, summary statistics with a focus on pattern recognition entropy (PRE), principal component analysis (PCA), multivariate curve resolution (MCR), and cluster analysis (CA).