This thesis describes the research conducted by t he author in completion of a Doctor of Philosophy in the Centre for Advanced Macromolecular Design(CAMD), Univcrsity of New South Wales (UNSW) , Sydney, Australia; under the supervision of Professor Christopher Barner-Kowollik and Doctor Michelle L. Coote (Australian National University). The research has led to the creation of new knowledge in the fields of free radical polymerisation and chemical kinetics. Research was conducted in two main thrusts: (1) investigation into the governing kinetic processes behind star polymer synthesis via what has become known as a reversible addition fragmentation chain transfer (RAFT), R-group approach and (2) an entirely new mode of living free radical (LFR) polymerisation which has been named thioketone mediated polymerisation (TKMP). In the first broad area of the described research, a novel kinetic modelling scheme has been developed in which only the reactions of a single arm star are simulated explicitly. Subsequently, the molecular weight distributions (MWDs) arising from the single arm star simulation are convolved, using probabilistic calculations, to generate the MWD appropriate to a multi-arm star polymerisat ion bearing t he same kinetic parameters as the single arm one. This model is validated against experimental data, enabling, for the first time, the use of rigorous theoretical reasoning to distill a set of synthetic guidelines for star polymer synthesis via a RAFT, R-group approach. Subsequently, the product spectra resulting from RAFT, R-group approach polymerisations of para-acetoxystyrene have been analysed via mass spectrometry. This has led to direct evidence for many of the complex species whose existence had, up until this point, been inferred from gel permeation chromatography (GPC) measured MWDs. The menagerie of species identified includes, but is not limited to, star-star couples, initiator fragment terminated stars, initiator fragment terminated star-star couples and linear chains -- both living and terminated. Using a kinetic model devised specifically for application in mass spectrometry analysis, the experimentally observed abundances of each of the above species have been compared to t hose predicted by simulation. The qualitative agreement between the predicted and observed abundances has provided additional evidence that t he proposed mechanism for RAFT, R-group approach polymerisations is correct and operative. Further, it seems unlikely that significant, undiscovered kinetic phenomena exist. Due to (a) long simulation times encountered using the state of the art, commercial partial differential equation solver for polymerisation kinetics (i.e. PREDICI, Computing in Technology (CiT), GmbH; see http://www.cit-wulkow.de) and (b) the limited flexibility this software provides with respect to the types of chemical species that can be simulated, fundamental research has been conducted into the kinetic Monte Carlo method to (i) examine fundamental aspects of this simulation approach; (ii) determine the maximum speed attainable through a combination of optimisations including run-time generation of problem specific code and parallelisation; and, therefore (iii) find out what the potential of this method may be as a replacement for t he existing methods. In terms of speed, the developed code outperforms previous Monte Carlo benchmarks in the literature by a factor of 2.6 and the latest developments in the commercial tool, PREDICI that took place during the author's Ph.D. candidature give it similar performance to the herein described Monte Carlo code; however, the latter is required to run on multiple processors in order to compete with the serial algorithm implemented in PREDICI. The Monte Carlo method does, however, provide complete freedom with respect to the chemical species whose kinetics can be simulated, allowing for complex species with many chain lengths and, in principal copolymer compositions and branched structures. The Monte Carlo approach is the method of choice for these types of simulations and for the first time competes with the commercial tool in terms of speed. In the second broad area of the described research, an experimental investigation has been conducted into the applicability of thioketones, S=C (R1) (R2), as mediating agents for free radical polymerisations. The compound di-tert-butyl thioketone (DTBT), S=C-(C(CH3)3)2, has been chosen as a model reagent and this, when incorporated into a free radical polymerisation of styrene has led to a linear increase of the average molecular weight as conversion of monomer into polymer takes place - demonstrating control. A reversible radical trapping mechanism has been proposed and evidence for this has been provided in the form of an ab initio calculation of the equilibrium constant for the trapping of a styryl dimer radical by DTBT. This equilibrium constant was approximately K = 105 L mol-1 and is close to the value which is expected on the basis of the experimental results. To aid future experimental investigations intoTKMP, a quantum chemical survey has, been conducted with the aim of discovering the radical affinities of a large range of thioketones. It has been demonstrated that there is ample scope within this class of compound for potent radical trapping - far above that of DTBT. The affinities of various thioketone substrates for radicals have been understood in terms of the radical stabilising and thioketone destabilising effect of the two substituents R1 and R2 on, respectively, the adduct radical, R-S-C???(R1) (R2), and the parent thioketone. All results appearing in this thesis have been published previously in peer-reviewed scientific journals.

In an emerging energy landscape that increasingly discourages the use of traditional fossil fuels, there remain applications for which the continued use of high energy density liquid fuels is required, such as aviation and other uses where space and weight are critical design factors, or long term energy storage where cost and long term availability are required. To achieve this while transitioning to green sources of energy requires the design of next-generation combustion engines that can burn alternative fuels such as bio-derived or synthetic fuels; this process will be heavily dependent on design tools such as computational fluid dynamics packages, underpinned by accurate chemical kinetic models for the fuels in question. These kinetic models often contain thermodynamic information about hundreds of unique chemical species and thousands of chemical reactions forming an interconnected network between species governing their rates of production and destruction. Historically, generation of such high-fidelity kinetic models has required decades of research---too long for the engines that will require advanced fuels. Development of a kinetic model that is predictive of certain quantities of interest (ignition delay times, flame speeds, etc) can broadly be broken into four distinct stages: 1) initial ``crude'' model generation, 2) experimental design, 3) experiments and ab-initio theory calculations, and 4) kinetic model optimization. Advances in data-enabled science and ever-increasing computing power have offered pathways towards eventually automating this process. This work aims to introduce a collection of tools and building blocks that will assist in the overall aim of automatic kinetic model development, and in doing so fill important gaps in the current capabilities available in the literature. In particular, the work here touches on aspects of all four of the stages in the model development process described above. With regard to 1), while there are tools available in the literature for automatic generation of kinetic models for an increasingly large library of fuels, these models remain subject to the constraints imposed by current chemical kinetic model structures and combustion codes. Here, automatic screening procedures are introduced that investigate the impact on kinetic model prediction errors due to two distinct issues related to pressure-dependent chemistry: the lack of a new class of chemical reaction type in current chemical kinetic models, and effects due to how species-specific energy transfer parameters are represented in pressure-dependent stabilization reactions within kinetic models. With regard to 2) and 3), a Bayesian optimal experimental design algorithm is paired with computer-controllable perfectly-stirred reactor experiments with unique capability to both explore a combinatorically complex experiment parameter space (including flowing up to ten unique gas mixtures simultaneously) and measure dozens of chemical species using rapid, on-line diagnostics. This setup allows for key reaction pathways to be carefully "sensitized'' with the addition of trace quantities of key chemical species, a capability that has not been used elsewhere in literature. Generally speaking, other experimental design algorithms in literature have not explored experimental design spaces that are radically different from those used by experienced researchers in their manual experimental design processes, and the complexity of the mixtures explored by most traditional combustion experiments is limited to two or three different chemical species at most. The sensitization of key reaction pathways unlocks the ability to perform truly transformational parameter inferences with minimal amounts of experimental data. With regard to joining step 3) to 4) in the above process, semi-automated post-processing codes allow for rapid optimizations to be performed for a prior kinetic model on the basis of experiments chosen by our experimental design algorithm. Critically, a combination of the experimental design algorithm developed here and the jet-stirred reactor experiments described was tested on the kinetic model for N₂O decomposition, which has uncertainties for key reaction rates that have persisted for decades (indeed, researchers suggest kinetic rate constants for N₂O+O=N₂+O₂ that differ by at least four orders of magnitude!). Optimizations using the Multi-Scale Informatics (MSI) tool developed by our research group were run on the basis of experimental data obtained in the aforementioned experiments, and used to gain insights about the rate constant for a key reaction in N₂O decomposition chemistry, N₂O+O=N₂+O₂ , serving as a proof-of-concept for key portions of what will form the backbone of an automatic kinetic model development pipeline.