Without his knowledge, guidance, and vision, this project would not have culminated to what it is today. I would also like to thank all of my professors, who helped me cultivate a curiosity for learning and aided me in converging on my academic goals. Finally, I would like to thank my family and friends, whose continuous support and care helped me to flourish throughout my education.

By removing the effects of ensemble averaging and molecular aggregation, we untangle the factors that govern the dispersive electron transfer kinetics of eosin-sensitized TiO 2 , focusing on the impact of environmental heterogeneity versus injection from multiple excited states. The blinking dynamics of single eosin Y chromophores on nanocrystalline TiO 2 films are analyzed using a change point detection algorithm for binned data. Robust statistical analysis based on maximum likelihood estimation, Kolmogorov−Smirnov tests, and log likelihood ratio tests is used to determine the functional form that best fits the resulting on-and off-time distributions and to distinguish between mechanisms for dispersive electron transfer. Using this approach, we find that the on-and off-time distributions for eosin Y on TiO 2 are best fit to lognormal distributions corresponding to μ on = −0.64 ± 0.04, σ on = 1.52 ± 0.02, μ off = −0.23 ± 0.04, and σ off = 1.96 ± 0.03, respectively. Monte Carlo simulations based on the Albery model for dispersive electron transfer (i.e., where the median rate constant κ is modified by the exponential of a parameter, x, that is normally distributed, k = κ e −γx ) successfully reproduce this behavior using a median rate constant for injection and back electron transfer of ∼10 10 and ∼10 4 s −1 , respectively, and a corresponding energetic dispersion, γ, of ∼200−350 meV. To examine how injection from both the singlet and triplet excited states contributes to this dispersion, we studied two rhodamine sensitizers, R123 and 5ROX, that inject only from their singlet excited state. Surprisingly, when access to the T 1 state is minimized in going from EY to R123 and 5ROX, kinetic dispersion actually increases. Collectively, these observations support the interpretation that static and dynamic inhomogeneities at the EY−TiO 2 interface govern kinetic dispersion, with dynamic fluctuations in binding configuration and/or vibrational motion playing a decisive role.

Air turbulence ensures that in a natural environment insects tend to encounter odor stimuli in a pulsatile fashion. The frequency and duration of odor pulses varies with distance from the source, and hence successful mid-flight odor tracking requires resolution of spatiotemporal pulse dynamics. This requires both olfactory and mechanosensory input (from wind speed), a form of sensory integration observed within the antennal lobe (AL). In this work, we employ a model of the moth AL to study the effect of mechanosensory input on AL responses to pulsatile stimuli; in particular, we examine the ability of model neurons to: (1) encode the temporal length of a stimulus pulse; (2) resolve the temporal dynamics of a high frequency train of brief stimulus pulses. We find that AL glomeruli receiving olfactory input are adept at encoding the temporal length of a stimulus pulse but less effective at tracking the temporal dynamics of a pulse train, while glomeruli receiving mechanosensory input but little olfactory input can efficiently track the temporal dynamics of high frequency pulse delivery but poorly encode the duration of an individual pulse. Furthermore, we show that stronger intrinsic small-conductance calcium-dependent potassium (SK) currents tend to skew cells toward being better trackers of pulse frequency, while weaker SK currents tend to entail better encoding of the temporal length of individual pulses. We speculate a possible functional division of labor within the AL, wherein, for a particular odor, glomeruli receiving strong olfactory input exhibit prolonged spiking responses that facilitate detailed discrimination of odor features, while glomeruli receiving mechanosensory input (but little olfactory input) serve to resolve the temporal dynamics of brief, pulsatile odor encounters. Finally, we discuss how this hypothesis extends to explaining the functional significance of intraglomerular variability in observed phase II response patterns of AL neurons.