The transition from fossil fuels to renewable resources has created more demand for energy storage devices. Lithium-oxygen (Li-O2) batteries have attracted much attention due to their high theoretical energy densities. They, however, are still in their infancy and several fundamental challenges remain to be addressed. Advanced analytical techniques have revealed that all components of a Li-O2 battery undergo undesirable degradation during discharge/charge cycling, contributing to reduced cyclability. Despite many attempts to minimize the anode and cathode degradation, the electrolyte remains as the leading cause for rapid capacity fading and poor cyclability in Li-O2 batteries. In this dissertation, composite gel polymer electrolytes (cGPEs) consisting of a UV-curable polymer, tetragylme based electrolyte, and glass microfibers with a diameter of ~1 µm and an aspect ratio of >100 have been developed for their use in Li-O2 battery application. The Li-O2 batteries containing cGPEs showed superior charge/discharge cycling for 500 mAh.g-1 cycle capacity with as high as 400% increase in cycles for cGPE over gel polymer electrolytes (GPEs). Results using in-situ electrochemical impedance spectroscopy (EIS), Raman spectroscopy, and scanning electron microscopy revealed that the source of the improvement was the reduction of the rate of lithium carbonates formation on the surface of the cathode. This decrease in formation rate afforded by cGPE-containing batteries was possible due to the decrease of the rate of electrolyte decomposition. The increase in solvated to the paired Li+ ratio at the cathode, afforded by increased lithium transference number, helped lessen the probability of superoxide radicals reacting with the tetraglyme solvent. This stabilization during cycling helped prolong the cycling life of the batteries. The effect of ion complexes on the stability of liquid glyme based electrolytes with various lithium salt concentrations has also been investigated for Li-O2 batteries. Charge/discharge cycling with a cycle capacity of 500 mAh·g-1 showed an improvement as high as 300% for electrolytes containing higher lithium salt concentrations. Analysis of the Raman spectroscopy data of the electrolytes suggested that the increase in lithium salt concentration afforded the formation of cation-solvent complexes, which in turn, mitigated the tetragylme degradation.

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.