John A. McGowen

Strain, biochemistry, and cultivation-dependent measurement variability of algal biomass composition.

Accurate compositional analysis in biofuel feedstocks is imperative; the yields of individual components can define the economics of an entire process. In the nascent industry of algal biofuels and bioproducts, analytical methods that have been deemed acceptable for decades are suddenly critical for commercialization. We tackled the question of how the strain and biochemical makeup of algal cells affect chemical measurements. We selected a set of six procedures (two each for lipids, protein, and carbohydrates): three rapid fingerprinting methods and three advanced chromatography-based methods. All methods were used to measure the composition of 100 samples from three strains: Scenedesmus sp., Chlorella sp., and Nannochloropsis sp. The data presented point not only to species-specific discrepancies but also to cell biochemistry-related discrepancies. There are cases where two respective methods agree but the differences are often significant with over- or underestimation of up to 90%, likely due to chemical interferences with the rapid spectrophotometric measurements. We provide background on the chemistry of interfering reactions for the fingerprinting methods and conclude that for accurate compositional analysis of algae and process and mass balance closure, emphasis should be placed on unambiguous characterization using methods where individual components are measured independently.

From benchtop to raceway : spectroscopic signatures of dynamic biological processes in algal communities.

The search is on for new renewable energy and algal-derived biofuel is a critical piece in the multi-faceted renewable energy puzzle. It has 30x more oil than any terrestrial oilseed crop, ideal composition for biodiesel, no competition with food crops, can be grown in waste water, and is cleaner than petroleum based fuels. This project discusses these three goals: (1) Conduct fundamental research into the effects that dynamic biotic and abiotic stressors have on algal growth and lipid production - Genomics/Transcriptomics, Bioanalytical spectroscopy/Chemical imaging; (2) Discover spectral signatures for algal health at the benchtop and greenhouse scale - Remote sensing, Bioanalytical spectroscopy; and (3) Develop computational model for algal growth and productivity at the raceway scale - Computational modeling.