Biomimetic mineralization: encapsulation in calcium carbonate shells

Calcium carbonate biomineralization is a self-assembly process that has been studied to be applied in the biomedical field to encapsulate biomolecules. Advantages of engineering mineral capsules include improved drug loading efficiencies and protection against external environment. However, common production methods result in heterogeneous capsules and subject biomolecules to heat and vibration which cause irreversible damage. To overcome these issues, a microfluidic device was designed, manufactured and tested in terms of selectivity for water and oil to produce a W/O/W emulsion. During the development of this work there was one critical challenge: the selective functionalization in closed microfluidic channels. Wet chemical oxidation of PDMS with 1M NaOH, confirmed by FTIR, followed by adsorption of polyelectrolytes - PDADMAC/PSS - confirmed by UV-Vis and AFM results, render the surface of PDMS hydrophilic. UV-Vis spectroscopy also confirmed that this modification did not affect PDMS optical properties, making possible to monitor fluids and droplets. More important, with this approach PDMS remains hydrophilic over time. However, due to equipment constrains selectivity in microchannels was not achieved. Therefore, emulsion studies took place with conventional methods. Several systems were tried, with promising results achieved with CaCO3 in-situ precipitation, without the use of polymers or magnesium. This mineral stabilizes oil droplets in water, but not in air due to incomplete capsule formation.

Assessment of NDVI, land surface temperature and precipitation anomalies for drought monitoring in Bayankhongor province, Mongolia

During the last decade Mongolia’s region was characterized by a rapid increase of both severity and frequency of drought events, leading to pasture reduction. Drought monitoring and assessment plays an important role in the region’s early warning systems as a way to mitigate the negative impacts in social, economic and environmental sectors. Nowadays it is possible to access information related to the hydrologic cycle through remote sensing, which provides a continuous monitoring of variables over very large areas where the weather stations are sparse. The present thesis aimed to explore the possibility of using NDVI as a potential drought indicator by studying anomaly patterns and correlations with other two climate variables, LST and precipitation. The study covered the growing season (March to September) of a fifteen year period, between 2000 and 2014, for Bayankhongor province in southwest Mongolia. The datasets used were MODIS NDVI, LST and TRMM Precipitation, which processing and analysis was supported by QGIS software and Python programming language. Monthly anomaly correlations between NDVI-LST and NDVI-Precipitation were generated as well as temporal correlations for the growing season for known drought years (2001, 2002 and 2009). The results show that the three variables follow a seasonal pattern expected for a northern hemisphere region, with occurrence of the rainy season in the summer months. The values of both NDVI and precipitation are remarkably low while LST values are high, which is explained by the region’s climate and ecosystems. The NDVI average, generally, reached higher values with high precipitation values and low LST values. The year of 2001 was the driest year of the time-series, while 2003 was the wet year with healthier vegetation. Monthly correlations registered weak results with low significance, with exception of NDVI-LST and NDVI-Precipitation correlations for June, July and August of 2002. The temporal correlations for the growing season also revealed weak results. The overall relationship between the variables anomalies showed weak correlation results with low significance, which suggests that an accurate answer for predicting drought using the relation between NDVI, LST and Precipitation cannot be given. Additional research should take place in order to achieve more conclusive results. However the NDVI anomaly images show that NDVI is a suitable drought index for Bayankhongor province.