EFFECTS OF ION LIMITING CONDITIONS ON THE BEHAVIOR OF MICROFLUIDIC DEVICES

Finite numbers of ions are present in microfluidic devices. This leads to ion limiting effects in microfluidic channels and electrode surfaces. These effects include electrode surface changes and ion concentration gradient formation across microfluidic channels, and can influence microfluidic device behavior. A literature survey on the use of electrochemical analysis techniques in micro- and nanofluidic devices was carried out, which puts into perspective the importance of electrode surface changes with regards to analytical microfluidic applications. Surface changes in Pt wire electrodes under various physiological buffer and electric field conditions were investigated using cyclic voltammetry (CV), SEM-EDS and XPS. Effects of surface changes on electrochemical analysis performance of Pt wire and thin film electrodes were investigated. Electrode surfaces were subjected to varying phosphate buffer and electric field conditions, and their CV performance was monitored. Electrode surfaces were also studied with SEM-EDS. Two studies of ion concentration gradient formation in microfluidic channels were conducted. In the first, concentration gradients of H+ and OH- ions generated on electrode surfaces were found to cause significant pH decreases in certain buffer and electric field conditions, which was also found to play a key role in iDEP manipulation of proteins. The role of electrode surface reactions in this case shows the importance of understanding electrode surface changes in microfluidic devices. In the second study of ion concentration gradient formation, Cl- ion concentration gradient formation was attempted to be quantified upon electric field application across a KCl solution. Electrokinetic transport of the Cl- indicating fluorophore MQAE contributed significantly to the fluorescence microscopy signals collected, complicating Cl- quantification as a function of position and time. It was shown that a dielectric coating on electrode surfaces is effective at preventing MQAE electrokinetic transport.

A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence.

Modeling contaminant behavior in Lake Superior : a comparison of PCBs, PBDEs, and mercury

A mass‐balance model for Lake Superior was applied to polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and mercury to determine the major routes of entry and the major mechanisms of loss from this ecosystem as well as the time required for each contaminant class to approach steady state. A two‐box model (water column, surface sediments) incorporating seasonally adjusted environmental parameters was used. Both numerical (forward Euler) and analytical solutions were employed and compared. For validation, the model was compared with current and historical concentrations and fluxes in the lake and sediments. Results for PCBs were similar to prior work showing that air‐water exchange is the most rapid input and loss process. The model indicates that mercury behaves similarly to a moderately‐chlorinated PCB, with air‐water exchange being a relatively rapid input and loss process. Modeled accumulation fluxes of PBDEs in sediments agreed with measured values reported in the literature. Wet deposition rates were about three times greater than dry particulate deposition rates for PBDEs. Gas deposition was an important process for tri‐ and tetra‐BDEs (BDEs 28 and 47), but not for higher‐brominated BDEs. Sediment burial was the dominant loss mechanism for most of the PBDE congeners while volatilization was still significant for tri‐ and tetra‐BDEs. Because volatilization is a relatively rapid loss process for both mercury and the most abundant PCBs (tri‐ through penta‐), the model predicts that similar times (from 2 ‐ 10 yr) are required for the compounds to approach steady state in the lake. The model predicts that if inputs of Hg(II) to the lake decrease in the future then concentrations of mercury in the lake will decrease at a rate similar to the historical decline in PCB concentrations following the ban on production and most uses in the U.S. In contrast, PBDEs are likely to respond more slowly if atmospheric concentrations are reduced in the future because loss by volatilization is a much slower process for PBDEs, leading to lesser overall loss rates for PBDEs in comparison to PCBs and mercury. Uncertainties in the chemical degradation rates and partitioning constants of PBDEs are the largest source of uncertainty in the modeled times to steady‐state for this class of chemicals. The modeled organic PBT loading rates are sensitive to uncertainties in scavenging efficiencies by rain and snow, dry deposition velocity, watershed runoff concentrations, and uncertainties in air‐water exchange such as the effect of atmospheric stability.