Functionalized carbon nanotube nanoelectrodes for biomolecular recognition

The objective of this research is to develop nanoscale ultrasensitive transducers for detection of biological species at molecular level using carbon nanotubes as nanoelectrodes. Rapid detection of ultra low concentration or even single DNA molecules are essential for medical diagnosis and treatment, pharmaceutical applications, gene sequencing as well as forensic analysis. Here the use of functionalized single walled carbon nanotubes (SWNT) as nanoscale detection platform for rapid detection of single DNA molecules is demonstrated. The detection principle is based on obtaining electrical signal from a single amine terminated DNA molecule which is covalently bridged between two ends of an SWNT separated by a nanoscale gap. The synthesis, fabrication, chemical functionalization of nanoelectrodes and DNA attachment were optimized to perform reliable electrical characterization these molecules. Using this detection system fundamental study on charge transport in DNA molecule of both genomic and non genomic sequences is performed. We measured an electrical signal of about 30 pA through a hybridized DNA molecule of 80 base pair in length which encodes a portion of sequence of H5N1 gene of avian Influenza A virus. Due the dynamic nature of the DNA molecules the local environment such as ion concentration, pH and temperature significantly influence its physical properties. We observed a decrease in DNA conductance of about 33% in high vacuum conditions. The counterion variation was analyzed by changing the buffer from sodium acetate to tris(hydroxymethyl) aminomethane, which resulted in a two orders of magnitude increase in the conductivity of the DNA. The fabrication of large array of identical SWNT nanoelectrodes was achieved by using ultralong SWNTs. Using these nanoelectrode array we have investigated the sequence dependent charge transport in DNA. A systematic study performed on PolyG - PolyC sequence with varying number of intervening PolyA - PolyT pairs showed a decrease in electrical signal from 180 pA (PolyG - PolyC) to 30 pA with increasing number of the PolyA - PolyT pairs. This work also led to the development of ultrasensitive nanoelectrodes based on enzyme functionalized vertically aligned high density multiwalled CNTs for electrochemical detection of cholesterol. The nanoelectrodes exhibited selectively detection of cholesterol in the presence of common interferents found in human blood.

Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.