Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical Engineering

First Advisor's Name

Shekhar Bhansali

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Ernesto A. Pretto Jr.

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Kingsley Lau

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Nezih Pala

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Bilal El-Zahab

Fifth Advisor's Committee Title

Committee Member

Sixth Advisor's Name

James N. Hall

Sixth Advisor's Committee Title

Committee Member

Seventh Advisor's Name

Kang K. Yen

Seventh Advisor's Committee Title

Committee Member

Keywords

Proton exchange membrane fuel cell (PEMFC), sensor, volatile organic compound (VOC), alcohol, isoflurane, calibration, multivariate, principal component regression (PCR)

Date of Defense

11-5-2018

Abstract

In this research, a proton exchange membrane fuel cell (PEMFC) sensor was investigated for specific detection of volatile organic compounds (VOCs) for point-of-care (POC) diagnosis of the physiological conditions of humans. A PEMFC is an electrochemical transducer that converts chemical energy into electrical energy. A Redox reaction takes place at its electrodes whereas the volatile biomolecules (e.g. ethanol) are oxidized at the anode and ambient oxygen is reduced at the cathode. The compounds which were the focus of this investigation were ethanol (C2H5OH) and isoflurane (C3H2ClF5O), but theoretically, the sensor is not limited to only those VOCs given proper calibration.

Detection in biosensing, which needs to be carried out in a controlled system, becomes complex in a multivariate environment. Major limitations of all types of biosensors would include poor selectivity, drifting, overlapping, and degradation of signals. Specific detection of VOCs in multi-dimensional environments is also a challenge in fuel cell sensing. Humidity, temperature, and the presence of other analytes interfere with the functionality of the fuel cell and provide false readings. Hence, accurate and precise quantification of VOC(s) and calibration are the major challenges when using PEMFC biosensor.

To resolve this problem, a statistical model was derived for the calibration of PEMFC employing multivariate analysis, such as the “Principal Component Regression (PCR)” method for the sensing of VOC(s). PCR can correlate larger data sets and provides an accurate fitting between a known and an unknown data set. PCR improves calibration for multivariate conditions as compared to the overlapping signals obtained when using linear (univariate) regression models.

Results show that this biosensor investigated has a 75% accuracy improvement over the commercial alcohol breathalyzer used in this study when detecting ethanol. When detecting isoflurane, this sensor has an average deviation in the steady-state response of ~14.29% from the gold-standard infrared spectroscopy system used in hospital operating theaters.

The significance of this research lies in its versatility in dealing with the existing challenge of the accuracy and precision of the calibration of the PEMFC sensor. Also, this research may improve the diagnosis of several diseases through the detection of concerned biomarkers.

Identifier

FIDC007021

ORCID

https://orcid.org/0000-0003-1857-1621

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Saturday, October 24, 2020

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