Beeswax organogels: Influence of gelator concentration and oil type in the gelation process

This work focused on how different types of oil phase, MCT (medium chain triglycerides) and LCT (long chain triglycerides), exert influence on the gelation process of beeswax and thus properties of the organogel produced thereof. Organogels were produced at different temperatures and qualitative phase diagrams were constructed to identify and classify the type of structure formed at various compositions. The microstructure of gelator crystals was studied by polarized light microscopy. Melting and crystallization were characterized by differential scanning calorimetry and rheology (flow and small amplitude oscillatory measurements) to understand organogels' behaviour under different mechanical and thermal conditions. FTIR analysis was employed for a further understanding of oil-gelator chemical interactions. Results showed that the increase of beeswax concentration led to higher values of storage and loss moduli (G, G) and complex modulus (G*) of organogels, which is associated to the strong network formed between the crystalline gelator structure and the oil phase. Crystallization occurred in two steps (well evidenced for higher concentrations of gelator) during temperature decreasing. Thermal analysis showed the occurrence of hysteresis between melting and crystallization. Small angle X-ray scattering (SAXS) analysis allowed a better understanding in terms of how crystal conformations were disposed for each type of organogel. The structuring process supported by medium or long-chain triglycerides oils was an important exploit to apprehend the impact of different carbon chain-size on the gelation process and on gels' properties.

A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.