Physical Sciences and Mathematics Commons^™

Electrochemical Behavior Of Dense Electrodes For Impedancemetric Nox Sensors, Nabamita Pal

Doctoral Dissertations

NOx (NO and NO2) exhaust gas sensors for diesel powered vehicles have traditionally consisted of porous platinum (Pt) electrodes along with a dense ZrO2 based electrolyte. Advancement in diesel engine technology results in lower NOx emissions. Although Pt is chemically and mechanically tolerant to the extreme exhaust gas environment, it is also a strong catalyst for oxygen reduction, which can interfere with the detection of NOx at concentrations below 100 ppm. Countering this behavior can add to the complexity and cost of the conventional NO x sensor design. Recent studies have shown that dense electrodes are less prone to heterogeneous …

Understanding The Surface Fouling Mechanism Of Ultrananocrystalline Diamond Microelectrodes Using Microfluidics For Neurochemical Detection, An-Yi Chang

Doctoral Dissertations

Electrochemical methods are widely used for chronic neurochemical sensing, but thus far, the organic solution redox reactions fouled the electrodes' surface. It caused the reduction of sensitivity and the electrodes' lifetime.

Here, we present the boron-doped nanocrystalline diamond microelectrodes (BDUNCD) as the next generation electrode material for neurochemical sensor development. To aid in long-term chronic monitoring of neurochemicals, they have a wide window of electrochemical potential, extremely low background current, and excellent chemical inertness. The main research goal is to reduce the rate of electrode fouling due to the reaction by-products, and significantly extend their useful lifetime.

We systematically characterize …

Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan

Doctoral Dissertations

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined …