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Description
In the search for chemical biosensors designed for patient-based physiological applications, non-invasive diagnostic approaches continue to have value. The work described in this thesis builds upon previous breath analysis studies. In particular, it seeks to assess the adsorptive mechanisms active in both acetone and ethanol biosensors designed for breath analysis. The thermoelectric biosensors under investigation were constructed using a thermopile for transduction and four different materials for biorecognition. The analytes, acetone and ethanol, were evaluated under dry-air and humidified-air conditions. The biosensor response to acetone concentration was found to be both repeatable and linear, while the sensor response to ethanol presence was also found to be repeatable. The different biorecognition materials produced discernible thermoelectric responses that were characteristic for each analyte. The sensor output data is presented in this report. Additionally, the results were evaluated against a mathematical model for further analysis. Ultimately, a thermoelectric biosensor based upon adsorption chemistry was developed and characterized. Additional work is needed to characterize the physicochemical action mechanism.
ContributorsWilson, Kimberly (Author) / Guilbeau, Eric (Thesis advisor) / Pizziconi, Vincent (Thesis advisor) / LaBelle, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2011
Description
The American Diabetes Association reports that diabetes costs $322 billion annually and affects 29.1 million Americans. The high out-of-pocket cost of managing diabetes can lead to noncompliance causing serious and expensive complications. There is a large market potential for a more cost-effective alternative to the current market standard of screen-printed self-monitoring blood glucose (SMBG) strips. Additive manufacturing, specifically 3D printing, is a developing field that is growing in popularity and functionality. 3D printers are now being used in a variety of applications from consumer goods to medical devices. Healthcare delivery will change as the availability of 3D printers expands into patient homes, which will create alternative and more cost-effective methods of monitoring and managing diseases, such as diabetes. 3D printing technology could transform this expensive industry. A 3D printed sensor was designed to have similar dimensions and features to the SMBG strips to comply with current manufacturing standards. To make the sensor electrically active, various conductive filaments were tested and the conductive graphene filament was determined to be the best material for the sensor. Experiments were conducted to determine the optimal print settings for printing this filament onto a mylar substrate, the industry standard. The reagents used include a mixture of a ferricyanide redox mediator and flavin adenine dinucleotide dependent glucose dehydrogenase. With these materials, each sensor only costs $0.40 to print and use. Before testing the 3D printed sensor, a suitable design, voltage range, and redox probe concentration were determined. Experiments demonstrated that this novel 3D printed sensor can accurately correlate current output to glucose concentration. It was verified that the sensor can accurately detect glucose levels from 25 mg/dL to 400 mg/dL, with an R2 correlation value as high as 0.97, which was critical as it covered hypoglycemic to hyperglycemic levels. This demonstrated that a 3D-printed sensor was created that had characteristics that are suitable for clinical use. This will allow diabetics to print their own test strips at home at a much lower cost compared to SMBG strips, which will reduce noncompliance due to the high cost of testing. In the future, this technology could be applied to additional biomarkers to measure and monitor other diseases.
ContributorsAdams, Anngela (Author) / LaBelle, Jeffrey (Thesis advisor) / Pizziconi, Vincent (Committee member) / Abbas, James (Committee member) / Arizona State University (Publisher)
Created2017
Description
Aortic pathologies such as coarctation, dissection, and aneurysm represent a
particularly emergent class of cardiovascular diseases and account for significant cardiovascular morbidity and mortality worldwide. Computational simulations of aortic flows are growing increasingly important as tools for gaining understanding of these pathologies and for planning their surgical repair. In vitro experiments are required to validate these simulations against real world data, and a pulsatile flow pump system can provide physiologic flow conditions characteristic of the aorta.
This dissertation presents improved experimental techniques for in vitro aortic blood flow and the increasingly larger parts of the human cardiovascular system. Specifically, this work develops new flow management and measurement techniques for cardiovascular flow experiments with the aim to improve clinical evaluation and treatment planning of aortic diseases.
The hypothesis of this research is that transient flow driven by a step change in volume flux in a piston-based pulsatile flow pump system behaves differently from transient flow driven by a step change in pressure gradient, the development time being substantially reduced in the former. Due to this difference in behavior, the response to a piston-driven pump can be predicted in order to establish inlet velocity and flow waveforms at a downstream phantom model.
The main objectives of this dissertation were: 1) to design, construct, and validate a piston-based flow pump system for aortic flow experiments, 2) to characterize temporal and spatial development of start-up flows driven by a piston pump that produces a step change from zero flow to a constant volume flux in realistic (finite) tube geometries for physiologic Reynolds numbers, and 3) to develop a method to predict downstream velocity and flow waveforms at the inlet of an aortic phantom model and determine the input waveform needed to achieve the intended waveform at the test section. Application of these newly improved flow management tools and measurement techniques were then demonstrated through in vitro experiments in patient-specific coarctation of aorta flow phantom models manufactured in-house and compared to computational simulations to inform and execute future experiments and simulations.
particularly emergent class of cardiovascular diseases and account for significant cardiovascular morbidity and mortality worldwide. Computational simulations of aortic flows are growing increasingly important as tools for gaining understanding of these pathologies and for planning their surgical repair. In vitro experiments are required to validate these simulations against real world data, and a pulsatile flow pump system can provide physiologic flow conditions characteristic of the aorta.
This dissertation presents improved experimental techniques for in vitro aortic blood flow and the increasingly larger parts of the human cardiovascular system. Specifically, this work develops new flow management and measurement techniques for cardiovascular flow experiments with the aim to improve clinical evaluation and treatment planning of aortic diseases.
The hypothesis of this research is that transient flow driven by a step change in volume flux in a piston-based pulsatile flow pump system behaves differently from transient flow driven by a step change in pressure gradient, the development time being substantially reduced in the former. Due to this difference in behavior, the response to a piston-driven pump can be predicted in order to establish inlet velocity and flow waveforms at a downstream phantom model.
The main objectives of this dissertation were: 1) to design, construct, and validate a piston-based flow pump system for aortic flow experiments, 2) to characterize temporal and spatial development of start-up flows driven by a piston pump that produces a step change from zero flow to a constant volume flux in realistic (finite) tube geometries for physiologic Reynolds numbers, and 3) to develop a method to predict downstream velocity and flow waveforms at the inlet of an aortic phantom model and determine the input waveform needed to achieve the intended waveform at the test section. Application of these newly improved flow management tools and measurement techniques were then demonstrated through in vitro experiments in patient-specific coarctation of aorta flow phantom models manufactured in-house and compared to computational simulations to inform and execute future experiments and simulations.
ContributorsChaudhury, Rafeed Ahmed (Author) / Frakes, David (Thesis advisor) / Adrian, Ronald J (Thesis advisor) / Vernon, Brent (Committee member) / Pizziconi, Vincent (Committee member) / Caplan, Michael (Committee member) / Arizona State University (Publisher)
Created2015