Regional comparisons of nano-mechanical properties of the human meniscus; structure and function

Osteoarthritis (OA) is a debilitating disease that is becoming more prevalent in today’s society. OA affects approximately 28 million adults in the United States alone and when present in the knee joint, usually leads to a total knee replacement. Numerous studies have been conducted to determine possible methods to halt the initiation of OA, but the structural integrity of the menisci has been shown have a direct effect on the progression of OA. Menisci are two C-shaped structures that are attached to the tibial plateau and aid in facilitating proper load transmission within the knee. The meniscal cross-section is wedge-like to fit the contour of the femoral condyles and help attenuate stresses on the tibial plateau. While meniscal tears are common, only the outer 1/3 of the meniscus is vascularized and has the capacity to heal, hence tears of the inner 2/3rds are generally treated via meniscectomy, leading to OA. To help combat this OA epidemic, an effective biomimetric meniscal replacement is needed. Numerous mechanical and biochemical studies have been conducted on the human meniscus, but very little is known about the mechanical properties on the nano-scale and how meniscal constituents are distributed in the meniscal cross-section. The regional (anterior, central and posterior) nano-mechanical properties of the meniscal superficial layers (both tibial and femoral contacting) and meniscal deep zone were investigated via nanoindentation to examine the regional inhomogeneity of both the lateral and medial menisci. Additionally, these results were compared to quantitative histological values to better formulate a structure-function relationship on the nano-scale. These data will prove imperative for further advancements of a tissue engineered meniscal replacement.

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.