Pd/C catalysts based on synthetic carbons with bi- and tri-modal pore-size distribution:applications in flow chemistry

Two new types of phenolic resin-derived synthetic carbons with bi-modal and tri-modal pore-size distributions were used as supports for Pd catalysts. The catalysts were tested in chemoselective hydrogenation and hydrodehalogenation reactions in a compact multichannel flow reactor. Bi-modal and tri-modal micro-mesoporous structures of the synthetic carbons were characterised by N2 adsorption. HR-TEM, PXRD and XPS analyses were performed for characterising the synthesised catalysts. N2 adsorption revealed that tri-modal synthetic carbon possesses a well-developed hierarchical mesoporous structure (with 6.5 nm and 42 nm pores), contributing to a larger mesopore volume than the bi-modal carbon (1.57 cm3 g-1versus 1.23 cm3 g-1). It was found that the tri-modal carbon promotes a better size distribution of Pd nanoparticles than the bi-modal carbon due to presence of hierarchical mesopore limitting the growth of Pd nanoparticles. For all the model reactions investigated, the Pd catalyst based on tri-modal synthetic carbon (Pd/triC) show high activity as well as high stability and reproducibility. The trend in reactivities of different functional groups over the Pd/triC catalyst follows a general order alkyne ≫ nitro > bromo ≫ aldehyde.

A pragmatic Bayesian approach to wind field retrieval

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about 800 km, carrying a C-band scatterometer. A scatterometer measures the amount of backscatter microwave radiation reflected by small ripples on the ocean surface induced by sea-surface winds, and so provides instantaneous snap-shots of wind flow over large areas of the ocean surface, known as wind fields. Inherent in the physics of the observation process is an ambiguity in wind direction; the scatterometer cannot distinguish if the wind is blowing toward or away from the sensor device. This ambiguity implies that there is a one-to-many mapping between scatterometer data and wind direction. Current operational methods for wind field retrieval are based on the retrieval of wind vectors from satellite scatterometer data, followed by a disambiguation and filtering process that is reliant on numerical weather prediction models. The wind vectors are retrieved by the local inversion of a forward model, mapping scatterometer observations to wind vectors, and minimising a cost function in scatterometer measurement space. This thesis applies a pragmatic Bayesian solution to the problem. The likelihood is a combination of conditional probability distributions for the local wind vectors given the scatterometer data. The prior distribution is a vector Gaussian process that provides the geophysical consistency for the wind field. The wind vectors are retrieved directly from the scatterometer data by using mixture density networks, a principled method to model multi-modal conditional probability density functions. The complexity of the mapping and the structure of the conditional probability density function are investigated. A hybrid mixture density network, that incorporates the knowledge that the conditional probability distribution of the observation process is predominantly bi-modal, is developed. The optimal model, which generalises across a swathe of scatterometer readings, is better on key performance measures than the current operational model. Wind field retrieval is approached from three perspectives. The first is a non-autonomous method that confirms the validity of the model by retrieving the correct wind field 99% of the time from a test set of 575 wind fields. The second technique takes the maximum a posteriori probability wind field retrieved from the posterior distribution as the prediction. For the third technique, Markov Chain Monte Carlo (MCMC) techniques were employed to estimate the mass associated with significant modes of the posterior distribution, and make predictions based on the mode with the greatest mass associated with it. General methods for sampling from multi-modal distributions were benchmarked against a specific MCMC transition kernel designed for this problem. It was shown that the general methods were unsuitable for this application due to computational expense. On a test set of 100 wind fields the MAP estimate correctly retrieved 72 wind fields, whilst the sampling method correctly retrieved 73 wind fields.

Hierarchical macroporous-mesoporous SBA-15 sulfonic acid catalysts for biodiesel synthesis

Hierarchical macroporous-mesoporous SBA-15 silicas have been synthesised via dual-templating routes employing liquid crystalline surfactants and polystyrene beads. These offer high surface areas and well-defined, interconnecting macro- and mesopore networks with respective narrow size distributions around 300 nm and 3-5 nm for polystyrene:tetraethoxysilane ratios ≥2:1. Subsequent functionalisation with propylsulfonic acid yields the first organized, macro-mesoporous solid acid catalyst. The enhanced mass transport properties of these new bi-modal solid acid architectures confer significant rate enhancements in the transesterification of bulky glyceryl trioctanoate, and esterification of long chain palmitic acid, over pure mesoporous analogues. This paves the way to the wider application of hierarchical catalysts in biofuel synthesis and biomass conversion. © 2010 The Royal Society of Chemistry.

Knowledge transfer in website design:exploring the processes and benefits of design collaboration for non-creative Micros

This thesis explores the interaction between Micros (<10 employees) from non-creative sectors and website designers ("Creatives") that occurred when creating a website of a higher order than a basic template site. The research used Straussian Grounded Theory Method with a longitudinal design, in order to identify what knowledge transferred to the Micros during the collaboration, how it transferred, what factors affected the transfer and outcomes of the transfer including behavioural additionality. To identify whether the research could be extended beyond this, five other design areas were also examined, as well as five Small to Medium Enterprises (SMEs) engaged in website and branding projects. The findings were that, at the start of the design process, many Micros could not articulate their customer knowledge, and had poor marketing and visual language skills, knowledge core to web design, enabling targeted communication to customers through images. Despite these gaps, most Micros still tried to lead the process. To overcome this disjoint, the majority of the designers used a knowledge transfer strategy termed in this thesis as ‘Bi-Modal Knowledge Transfer’, where the Creative was aware of the transfer but the Micro was unaware, both for drawing out customer knowledge from the Micro and for transferring visual language skills to the Micro. Two models were developed to represent this process. Two models were also created to map changes in the knowledge landscapes of customer knowledge and visual language – the Knowledge Placement Model and the Visual Language Scale. The Knowledge Placement model was used to map the placement of customer knowledge within the consciousness, extending the known Automatic-Unconscious -Conscious model, adding two more locations – Peripheral Consciousness and Occasional Consciousness. Peripheral Consciousness is where potential knowledge is held, but not used. Occasional Consciousness is where potential knowledge is held but used only for specific tasks. The Visual Language Scale was created to measure visual language ability from visually responsive, where the participant only responds personally to visual symbols, to visually multi-lingual, where the participant can use visual symbols to communicate with multiple thought-worlds. With successful Bi-Modal Knowledge Transfer, the outcome included not only an effective website but also changes in the knowledge landscape for the Micros and ongoing behavioural changes, especially in marketing. These effects were not seen in the other design projects, and only in two of the SME projects. The key factors for this difference between SMEs and Micros appeared to be an expectation of knowledge by the Creatives and failure by the SMEs to transfer knowledge within the company.