ALGAE DETECTION WITH A GLASS MICROCHIP
For my PhD research, I developed a glass-based optofluidic microsystem for the automated classification of phytoplankton (small floating algae). This project combined work in microfabrication methods, optics, microfludics, system integration, and signal processing/pattern recognition.
Our primary motivation for this project was environmental monitoring. The amount and type of algae present in a body of water can change dramatically when the local environment is changed. We wanted to develop a fast, low-cost method for continuously monitoring those changes in the algae population, whether for early detection of a toxin-producing cyanobacteria bloom, or to study how the algae species dynamics vary in response to the introduction of pollutants.
The device I worked on was fabricated using the technology under development by the Femtoprint project, which was led by my supervisor, Dr. Yves Bellouard. We used an ultrashort pulsed laser to modify glass to produce useful optical and microfluidic structures in the material.
To identify the algae, we used a glass chip with a microfluidic channel and waveguide embedded in the material, and used a laser and photodetector to obtain characteristic optical "fingerprints" of phytoplankton as they flow along the channel. These optical signals can be used to extract information about the phytoplankton size and shape, and to classify the phytoplankton species using automated pattern recognition methods.
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