A cardiovascular occlusion method based on the use of a smart hydrogel

Smart hydrogels for biomedical applications are highly researched materials. However, integrating them into a device for implantation is difficult. This paper investigates an integrated delivery device designed to deliver an electro-responsive hydrogel to a target location inside a blood vessel with the purpose of creating an occlusion. The paper describes the synthesis and characterization of a Pluronic/methacrylic acid sodium salt electro-responsive hydrogel. Application of an electrical bias decelerates the expansion of the hydrogel. An integrated delivery system was manufactured to deliver the hydrogel to the target location in the body. Ex vivo and in vivo experiments in the carotid artery of sheep were used to validate the concept. The hydrogel was able to completely occlude the blood vessel reducing the blood flow from 245 to 0 ml/min after implantation. Ex vivo experiments showed that the hydrogel was able to withstand physiological blood pressures of > 270 mm·Hg without dislodgement. The results showed that the electro-responsive hydrogel used in this paper can be used to create a long-term occlusion in a blood vessel without any apparent side effects. The delivery system developed is a promising device for the delivery of electro-responsive hydrogels.

Occupant location prediction in smart buildings using association rule mining

Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.

Embedding colloidal nanoparticles inside mesoporous silica using gas expanded liquids for high loading recyclable catalysts

The ability to tune the structural and chemical properties of colloidal nanoparticles (NPs), make them highly advantageous for studying activity and selectivity dependent catalytic behaviour. Incorporating pre-synthesized colloidal NPs into porous supports materials remains a challenge due to poor wetting and pore permeability. In this report monodisperse, composition controlled AgPd alloy NPs were synthesised and embedded into SBA-15 using supercritical carbon dioxide and hexane. Supercritical fluid impregnation resulted in high metal loading without the requirement for surface pre-treatments. The catalytic activity, reaction profiles and recyclability of the alloy NPs embedded in SBA-15 and immobilised on non-porous SiO2 are evaluated. The NPs incorporated within the SBA-15 porous network showed significantly greater recyclability performance compared to non-porous SiO2.