Computational modeling of intermediate temperature proton exchange membrane (PEM) fuel cells

A two-phase three-dimensional computational model of an intermediate temperature (120--190°C) proton exchange membrane (PEM) fuel cell is presented. This represents the first attempt to model PEM fuel cells employing intermediate temperature membranes, in this case, phosphoric acid doped polybenzimidazole (PBI). To date, mathematical modeling of PEM fuel cells has been restricted to low temperature operation, especially to those employing Nafion ® membranes; while research on PBI as an intermediate temperature membrane has been solely at the experimental level. This work is an advancement in the state of the art of both these fields of research. With a growing trend toward higher temperature operation of PEM fuel cells, mathematical modeling of such systems is necessary to help hasten the development of the technology and highlight areas where research should be focused.^ This mathematical model accounted for all the major transport and polarization processes occurring inside the fuel cell, including the two phase phenomenon of gas dissolution in the polymer electrolyte. Results were presented for polarization performance, flux distributions, concentration variations in both the gaseous and aqueous phases, and temperature variations for various heat management strategies. The model predictions matched well with published experimental data, and were self-consistent.^ The major finding of this research was that, due to the transport limitations imposed by the use of phosphoric acid as a doping agent, namely low solubility and diffusivity of dissolved gases and anion adsorption onto catalyst sites, the catalyst utilization is very low (∼1--2%). Significant cost savings were predicted with the use of advanced catalyst deposition techniques that would greatly reduce the eventual thickness of the catalyst layer, and subsequently improve catalyst utilization. The model also predicted that an increase in power output in the order of 50% is expected if alternative doping agents to phosphoric acid can be found, which afford better transport properties of dissolved gases, reduced anion adsorption onto catalyst sites, and which maintain stability and conductive properties at elevated temperatures.^

High pressure studies on group VI metal hexacarbonyl molecular solids

Group VI metal hexacarbonyls, M(CO)6 (M = Cr, Mo and W), are of extreme importance as catalysts in industry and also of fundamental interest due to the established charge transfer mechanism between the carbon monoxide and the metal. They condense to molecular solids at ambient conditions retaining the octahedral (Oh) symmetry of gas phase and have been extensively investigated by previous workers to understand their fundamental chemical bonding and possible industrial applications. However little is known about their behavior at high pressures which is the focus of this dissertation. Metal hexacarbonyls were subjected to high pressures in Diamond-Anvil cells to understand the pressure effect on chemical bonding using Raman scattering in situ. The high-pressure results on each of the three metal hexacarbonyls are presented and are followed by a critical analysis of the entire family. The Raman study was conducted at pressures up to 45 GPa and X-ray up to 58 GPa. This is followed by a discussion on infra red spectra in conjunction with Raman and X-ray analysis to provide a rationale for polymerization. Finally the probable synthesis of extremely reactive species under high-pressures and as identified via Raman is discussed. The high-pressure Raman scattering, up to 30 GPa, demonstrated the absence of Π-backbonding. The disappearance of parental Raman spectra for (M = Cr, Mo and W) at 29.6, 23.3 and 22.2 GPa respectively was attributed to the total collapse of the Oh symmetry. This collapse under high-pressure lead to metal-mediated polymeric phase characterized by Raman active δ(OCO) feature, originating from intermolecular vibrational coupling in the parent sample. Further increase in pressures up to 45 GPa, did not affect this feature. The pressure quenched Raman spectra, revealed various chemical groups non-characteristic of the parent sample and adsorption of CO in addition to the characteristic δ(OCO) feature. The thus recorded Raman, complemented with the far and mid-infrared pressure quenched spectra, reveal the formation of novel metal-mediated polymers. The X-ray diffraction on W(CO)6 up to 58 GPa revealed the generation of amorphous polymeric pattern which was retained back to ambient conditions.

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.