10.1021/ac200641y.s001 Jonathan D. Lam Jonathan D. Lam Michael J. Culbertson Michael J. Culbertson Nathan P. Skinner Nathan P. Skinner Zachary J. Barton Zachary J. Barton Daniel L. Burden Daniel L. Burden Information Content in Fluorescence Correlation Spectroscopy: Binary Mixtures and Detection Volume Distortion American Chemical Society 2011 fluorescence correlation spectroscopy detection measurement diffusion constants Fluorescence Correlation Spectroscopy species mole fraction Gaussian geometry ubiquitously FCS solution Detection Volume DistortionWhen 2011-07-01 00:00:00 Journal contribution https://acs.figshare.com/articles/journal_contribution/Information_Content_in_Fluorescence_Correlation_Spectroscopy_Binary_Mixtures_and_Detection_Volume_Distortion/2634313 When properly implemented, fluorescence correlation spectroscopy (FCS) reveals numerous static and dynamic properties of molecules in solution. However, complications arise whenever the measurement scenario is complex. Specific limitations occur when the detection region does not match the ideal Gaussian geometry ubiquitously assumed by FCS theory, or when properties of multiple fluorescent species are assessed simultaneously. A simple binary solution of diffusers, where both mole fraction and diffusion constants are sought, can face interpretive difficulty. In order to better understand the limits of FCS, this study systematically explores the relationship between detection–volume distortion, diffusion constants, species mole fraction, and fitting methodology in analyses that utilize a two-component autocorrelation model. FCS measurements from solution mixtures of dye-labeled protein and free dye are compared to simulations, which predict the performance of FCS under a variety of experimental circumstances. The results reveal a range of conditions necessary for performing accurate measurements and describe experimental scenarios that should be avoided. The findings also provide guidelines for obtaining meaningful measurements when grossly distorted detection volumes are utilized and generally assess the latent information contained in FCS datasets.