Encoding Interactions and Printability into Hairy Colloidal Biomaterials. Printing mixtures of live cells and biomaterials (or 'BioInks') to make bespoke engineered tissues has the potential to enable personalised platforms for therapeutic discovery and organ replacement. Using a novel high throughput approach to materials synthesis, BioInk design and process optimisation, this project aims to discover new biomaterials and printing nozzles to help realise this potential. It will produce new insi ....Encoding Interactions and Printability into Hairy Colloidal Biomaterials. Printing mixtures of live cells and biomaterials (or 'BioInks') to make bespoke engineered tissues has the potential to enable personalised platforms for therapeutic discovery and organ replacement. Using a novel high throughput approach to materials synthesis, BioInk design and process optimisation, this project aims to discover new biomaterials and printing nozzles to help realise this potential. It will produce new insights in colloid science, cell-laden biomaterials design, and BioInk processing. Structure-property-function guides for colloid-based BioInks and quality-assured bioprinting as outcomes represent significant benefits for researchers and industries alike engaged in biofabrication, cell therapy and biotherapeutics.Read moreRead less
Data-based Control of Process Feature Dynamics through Latent Behaviours. This project aims to develop a novel data-based approach to control the feature dynamics of complex industrial processes. The dynamic features of desired process operations (leading to high energy and material efficiencies and good product quality) are often not directly measured but can be distilled from high-dimensional big process data. However, little effort has been made to develop process control approaches to achiev ....Data-based Control of Process Feature Dynamics through Latent Behaviours. This project aims to develop a novel data-based approach to control the feature dynamics of complex industrial processes. The dynamic features of desired process operations (leading to high energy and material efficiencies and good product quality) are often not directly measured but can be distilled from high-dimensional big process data. However, little effort has been made to develop process control approaches to achieve desired dynamic features. This project aims to develop such a data-based approach by controlling latent variable dynamics, using the behavioural systems framework integrated with big data analytics and artificial neural networks. The outcomes are expected to help build a cornerstone for future smart manufacturing.Read moreRead less