Browsing by Subject "Python"

Now showing 1 - 4 of 4
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    AI Implementation in Digital Microfluidics
    (2021) Hein, Henry R
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    The Expansion of Digital Microfluidic Systems
    (2021) Anderson, Chase
    Digital microfluidics (DMF) is a technology that allows for movement and manipulation of liquid using electricity. By charging and discharging conductive pads upon a grid, one is able translate, split, and mix droplets to perform conventional wet lab operations. By minimizing handling time, chemical dangers, and laboratory wastes (e.g. pipette tips); DMF stands at the forefront of modern bio-chemical implementations. Most DMF prototypes/platforms utilize a single grid space, and this should change. In hopes to connect 4 ,16 , 64, or n-many grids for that matter; current DMF designs do not have required infrastructure. Each pad must be charged to upwards of 300 volts, so hardware, separate from the brains of the system, is required to facilitate normal operation. Like the heart, DMF platforms operate on beats and must be synchronous. Often, a microcontroller (Arduino, PIC, STM32) is used; however, microcontrollers operate sequentially, meaning each instruction given to the machine must execute one after the other. If one wished to parallelize the process of DMF, a separate computing paradigm must be used. Enter the FPGA (Field Programmable Gate Array). Being able to process multiple lightweight channels of data at a time, the FPGA enables the end user to have thousands if not millions of droplets moving synchronously across hundreds of grid systems. The research task was to find an elegant solution to the problem of parallelizing DMF systems using FPGAs.
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    Python Attitude Heading and Reference System
    (2014-07-23) Taylor, Brian
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    A Python Implementation of a Drift-Diffusion Model to Capture Ion Migration in Perovskite Solar Cells
    (2021-06) Anderson, Nathan
    Charge carrier dynamics and ion migration are attributed to the current-voltage hysteresis in perovskite solar cells (PSCs). This study implements a drift-diffusion model in Python to simulate the characteristic current-voltage scans for realistic device architectures. The novel work in this research involve the integration of a transfer-matrix optical model to the drift-diffusion model with ion migration, the implementation of a Radau 5th order solver to the method of lines, and a demonstration that standard Python libraries can handle stiff systems of differential algebraic equations. A comparative analysis with published works was conducted to validate the algorithm. It was found that the simulation was able to capture fast carrier dynamics under a variety of experimental conditions. Lastly, it is shown that the model captures physically relevant trends in PSCs.