Piezoelectric Silicon Micropump for Drug Delivery Applications

Subcutaneous injection is crucial for the treatment of many diseases. Especially for regular or continuous injections, automated dosing is beneficial. However, existing devices are large, uncomfortable, visible under clothing, or interfere with physical activity. Thus, the development of small, energy efficient and reliable patch pumps or implantable systems is necessary and research on microelectromechanical system (MEMS) based drug delivery devices has gained increasing interest. However, the requirements of medical applications are challenging and especially the dosing precision and reliability of MEMS pumps are not yet sufficiently evaluated. To enable further miniaturization, we propose a precise 5 × 5 mm2 silicon micropump. Detailed experimental evaluation of ten pumps proves a backpressure capability with air of 12.5 ± 0.8 kPa, which indicates the ability to transport bubbles. The maximal water flow rate is 74 ± 6 µL/min and the pumps’ average blocking pressure is 51 kPa. The evaluation of the dosing precision for bolus deliveries with water and insulin shows a high repeatability of dosed package volumes. The pumps show a mean standard deviation of only 0.02 mg for 0.5 mg packages, and therefore, stay below the generally accepted 5% deviation, even for this extremely small amount. The high precision enables the combination with higher concentrated medication and is the foundation for the development of an extremely miniaturized patch pump

High methane production in drained lake basin wetlands in northern Alaska

Wetlands in drained lake basins are important elements of the Arctic carbon budget. They may store large amounts of carbon while also producing substantial amounts of greenhouse gasses. After lake drainage the former lake bottom is colonized by pioneer graminoids, succeeded by mosssedge-dwarf shrub vegetation, producing a typical peat sequence. However, post-drainage organic matter dynamics are not well studied. We hypothesize that vegetation composition reflects both succession and surface wetness, which in turn determine soil organic matter content and methane production. We propose that vegetation types detected by remote sensing-based landcover classification may be used to extrapolate methane production and organic matter composition across drained lake basin landscapes. We investigated (i) plots along a temporal drainage gradient, surveying vegetation, surface sediment, and pond water. We then used (ii) landcover classification of main eco-hydrological classes to (iii) upscale from plot to basin scale. We found that vegetation and organic matter changed markedly between recently drained basins and older age classes. Overall, vegetation composition differed more between eco-hydrological classes than between age classes. Surface sediments had very high water contents (>80 %), suggesting largely anaerobic conditions favouring methane production. Methane concentrations were indeed relatively constant throughout, and particularly high in sediments beneath few centimetres of water (“wet patches”, up to 200 μmol/L) and in pond water (up to 22 μmol/L). Landcover classification yielded seven classes including five classes we also identified using statistical clustering of vegetation data plus a water class and a bare ground class. We found that 67 % of basin areas were occupied by wet patches with especially high methane production. Our study shows that remote sensing-based landcover classifications are useful for quantifying wet-vs-moist patches and high-vs-moderate methane production in Arctic drained lake basins. The study highlights the potential for future upscaling of methane emissions from these abundant wetland environments