Early season ozone uptake is important for determining ozone tolerance in two trembling aspen clones

There is substantial genetic variability in response to ozone amongst and within tree species. Aspen is a highly variable species with a wide range of responses to ozone. Aspen response to elevated O3 levels is being investigated at the Aspen FACE site near Rhinelander, WI where five aspen clones of varying O3 tolerance have been fumigated with elevated O3 over the past decade. In this study, we examined the physiological differences in two of the aspen clones that differed significantly in their O3 tolerance with 8L being tolerant and 42E being sensitive. Throughout the 2007 and 2008 growing seasons we periodically estimated instantaneous photosynthetic rates, ACi responses and light response curves. The results of our study suggest that aspen clone 8L’s tolerance is due in part to decreased stomatal conductance early in the season, which lowered ozone uptake. Later during the season O3 uptake was comparable for the two clones. Our results also suggest the response of Vcmax, TPU, Rd, Gm, light compensation point and quantum flux to elevated O3 did not differ significantly between the two clones. Ozone uptake is important for ozone tolerance in clone 8L early in the season but cannot explain late season tolerance. Photosynthetic parameters for the two clones were similar, so clone 8L’s ozone tolerance is not due to a more efficient photosynthetic system.

Control design and genetic algorithm optimization for electrostatic MEMS

This dissertation discusses structural-electrostatic modeling techniques, genetic algorithm based optimization and control design for electrostatic micro devices. First, an alternative modeling technique, the interpolated force model, for electrostatic micro devices is discussed. The method provides improved computational efficiency relative to a benchmark model, as well as improved accuracy for irregular electrode configurations relative to a common approximate model, the parallel plate approximation model. For the configuration most similar to two parallel plates, expected to be the best case scenario for the approximate model, both the parallel plate approximation model and the interpolated force model maintained less than 2.2% error in static deflection compared to the benchmark model. For the configuration expected to be the worst case scenario for the parallel plate approximation model, the interpolated force model maintained less than 2.9% error in static deflection while the parallel plate approximation model is incapable of handling the configuration. Second, genetic algorithm based optimization is shown to improve the design of an electrostatic micro sensor. The design space is enlarged from published design spaces to include the configuration of both sensing and actuation electrodes, material distribution, actuation voltage and other geometric dimensions. For a small population, the design was improved by approximately a factor of 6 over 15 generations to a fitness value of 3.2 fF. For a larger population seeded with the best configurations of the previous optimization, the design was improved by another 7% in 5 generations to a fitness value of 3.0 fF. Third, a learning control algorithm is presented that reduces the closing time of a radiofrequency microelectromechanical systems switch by minimizing bounce while maintaining robustness to fabrication variability. Electrostatic actuation of the plate causes pull-in with high impact velocities, which are difficult to control due to parameter variations from part to part. A single degree-of-freedom model was utilized to design a learning control algorithm that shapes the actuation voltage based on the open/closed state of the switch. Experiments on 3 test switches show that after 5-10 iterations, the learning algorithm lands the switch with an impact velocity not exceeding 0.2 m/s, eliminating bounce.