UC Berkeley

Today, the development of eco-friendly and renewable energy sources is imperative because we are facing a severe global warming and energy depletion. In this regard, the microalgae that absorb carbon dioxide from the atmosphere and convert it into a high-energy carrier which can be used as biofuel are in the spotlight. Microalgae have been considered as a next-generation energy source in that it is not only extremely renewable and reducing carbon dioxide but also highly productive even with non-arable lands, seawater or wastewater. However, its applicability to next-generation energy sources still faces practical limitations, such as the higher unit cost of producing microalgae-based biofuel compared to fossil fuels. The reason is that technologies essential to efficient and economically competitive production of microalgae-based biofuel have not yet been fully developed. The representative bottlenecks include the lack of technologies as follows; 1) methods for screening the most productive microalgae strain from the hundreds of thousands of natural and genetically engineered strains rapidly and efficiently. 2) on-site, real-time, and non-invasive methods for monitoring of mass production of microalgae over the extensive area. In this respect, this thesis discusses the novel methods for superior microalgal species selection and real-time microalgal cultivation monitoring utilizing various technologies of BioMEMS, optics, and electronics.

First, we demonstrate a buoyancy-based cell separation method that can easily sort out the best microalgal strain accumulating the largest amount of lipids using BioMEMS technology. Our simple microfluidic channel structure inducing hydraulic jumps of incoming cells allows the selection of only the lightest species (i.e., superior one which accumulates the largest amount of lipid) among numerous microalgae cells having various densities depending on their lipid amount. This method has the advantage of being able to select superior species easily and rapidly without additional labeling process and observation of the entire microalgal growth process.

Second, we demonstrate an integrated microalgae analysis photobioreactor for rapid strain selection using optics. Our photobioreactor, hemispherical cavity embedded with metal nanostructures, scatters specific wavelengths favored by microalgae, which will increase the photosynthetic efficiency of microalgae, resulting in shorter screening times through rapid growth. Besides, the additional optical property (i.e., light focusing) provided by the geometry of a hemispherical cavity helps rapid screening even with naked-eye by facilitating rapid growth through cell gathering and cell-cell interactions.

Lastly, we demonstrate a novel electrical sensor that can effectively monitor microalgae in the real field. Our approach utilizes electric signal, photocurrent, derived from the channel-rhodopsin, a unique feature of microalgae. This new method overcomes the limitations in terms of the field application that other existing approaches have not solved in that it can monitor microalgae in real-time and in-situ even with low cost without the need for additional sample processing steps.

We believe this series of new approaches can elevate the productivity in real fields of microalgae-based biofuel production by contributing to the superior strain selection and production management and are expected to provide an opportunity to step closer to the realization of microalgae as a next-generation energy source.