Bioinspired hydrogels for improved cultivation of microalgal biomass

Theme: Agriculture and Food Security

Summary: Microalgae are an essential contributor to the biosphere, producing ~75% of the global oxygen demand as well as playing an invaluable role in the food chain among many other ecological functions. As well as their ecological significance, they are also important in many industries, such as biofuels and human nutrition. However, despite their industrial potential, current cultivation methods prevent microalgal biotechnology from being economically and environmentally sustainable, mainly due to the prevalence of suspended cultivation approaches, such as raceway ponds. By converting to an immobilised cultivation technique, higher productivity can be achieved with lower energy and water demands, also in a smaller space footprint. Here agarose hydrogels are used as a scaffold to grow Chlorella vulgaris as a biofilm. Biofilms are naturally more productive than suspended cultivation, have lower water and energy demands, while also being easier to harvest. Despite the advantages of immobilised cultivation there are still many efficiency improvements required to make algal based technology sustainable. Here the focus is optimising the photosynthetic rate of the culture. Through the addition of scattering particles, such as cellulose nanocrystals, light attenuation is increased throughout the culture, creating more even illumination and reducing cell self-shading, which in turn improves the photosynthetic rate. If the current cultivation issues, such as poor productivity, can be overcome, microalgae can be exploited to achieve their biotechnological potential in a cost-effective and scalable manner.

Additional to this a machine learning approach is being taken to understand the correlation between the optical response of a single cell and its physiology. Measuring biomass quantity and quality is an important procedure both in industry and research, such as lipid content or growth stage. Quantifying the biochemical compound content of the biomass, for example pigment or lipid content, at present is mainly done by chemical extraction which is an invasive process and destroys the sample. By using machine learning, a non-invasive optical based assessment method to evaluate biomass qualities from pigment content to cell size distribution and culture age can be developed. Once this has been understood at a single cell level, it will be expanded to bulk culture optical measurements to act as an in-situ, non-invasive measurement tool for characterising a culture's physiological properties.