Photolithography based patterning of bacteriorhodopsin films

The patterning of photoactive purple membrane (PM) films onto electronic substrates to create a biologically based light detection device was investigated. This research is part of a larger collaborative effort to develop a miniaturized toxin detection platform. This platform will utilize PM films containing the photoactive protein bacteriorhodopsin to convert light energy to electrical energy. Following an effort to pattern PM films using focused ion beam machining, the photolithography based bacteriorhodopsin patterning technique (PBBPT) was developed. This technique utilizes conventional photolithography techniques to pattern oriented PM films onto flat substrates. After the basic patterning process was developed, studies were conducted that confirmed the photoelectric functionality of the PM films after patterning. Several process variables were studied and optimized in order to increase the pattern quality of the PM films. Optical microscopy, scanning electron microscopy, and interferometric microscopy were used to evaluate the PM films produced by the patterning technique. Patterned PM films with lateral dimensions of 15 μm have been demonstrated using this technique. Unlike other patterning techniques, the PBBPT uses standard photolithographic processes that make its integration with conventional semiconductor fabrication feasible. The final effort of this research involved integrating PM films patterned using the PBBPT with PMOS transistors. An indirect integration of PM films with PMOS transistors was successfully demonstrated. This indirect integration used the voltage produced by a patterned PM film under light exposure to modulate the gate of a PMOS transistor, activating the transistor. Following this success, a study investigating how this PM based light detection system responded to variations in light intensity supplied to the PM film. This work provides a successful proof of concept for a portion of the toxin detection platform currently under development.

MODEL UPDATING AND STRUCTURAL HEALTH MONITORING OF HORIZONTAL AXIS WIND TURBINES VIA ADVANCED SPINNING FINITE ELEMENTS AND STOCHASTIC SUBSPACE IDENTIFICATION METHODS

Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.