Modification of n-type and p-type metal oxide semiconductor systems for gas sensing applications

This thesis investigates the modification of three metal oxide semiconductor gas sensors with zeolite materials for the purposes of detecting trace concentrations of gases that have an effect on health, security, safety and the environment. SnO2, Cr2O3 and Fe2O3 were chosen as the base materials of interest. Zeolites HZSM- 5, Na-A and H-Y were incorporated into the sensing system either as admixtures with the base material or as coatings on top of it. The aim of introducing zeolites into the sensing system was to improve the performance of the otherwise unmodified sensors. Twenty-two novel zeolite-modified sensor systems are presented for the detection of a range of hydrocarbons and inorganic gases. Whilst sensors based on SnO2 systems were more responsive to gases, some sensors were also found to provide a greater degree of variability among repeat tests, particularly at lower operating temperatures i.e. 300 °C. Cr2O3 sensors modified by admixture with zeolite H-ZSM- 5 were seen to be poorly sensitive to most analytes. Cr2O3 sensors modified by admixture with zeolite Na-A and by overlayer of zeolite H-Y provided very promising sensitive and selective results towards toluene gas. Sensors based on the zeolite modification of Fe2O3 were not found to be promising candidates as gas sensors at this stage. Sensors were purposely exposed to gases that had similar molecular structures or kinetic diameters to assess the true capability of the sensors to discriminate among analytes. An array of four sensors based on n-type and p-type systems was subsequently chosen to see whether machine learning classifiers could be used to accurately discriminate among nine analytes. Using an SVM SMO classifier with a polykernel function, the model was 94.1% accurate in correctly classifying nine analytes of interest just after five seconds into the gas injection. Using an RBF kernel function, the model was 90.2% accurate in correctly classifying the data into gas type. These are very encouraging results, which highlight the importance of furthering research in this field; a sensing array based on zeolite-modified metal oxide semiconductor sensors may benefit a number of research domains by providing accurate results in a very fast and inexpensive manner

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used