Monday, April 19, 2010
2-14

Application of an integrated high-throughput pretreatment and enzymatic hydrolysis (HTPPH) screening tool to identify key biomass features and processing conditions

Jaclyn D. DeMartini, Chemical & Environmental Engineering, Center for Environmental Research and Technology, University of California, Riverside, 1084 Columbia Avenue, Riverside, CA 92507, Michael H. Studer, Institute of Process Engineering, Swiss Federal Institute of Technology, Sonneggstrasse 3, CH-8092, Zurich, Switzerland, and Charles E. Wyman, Center for Environmental Research and Technology and Chemical and Environmental Engineering Department, University of California, Riverside, 1084 Columbia Avenue, Riverside, CA 92521.

A high throughput pretreatment and enzymatic hydrolysis (HTPPH) method has been developed to screen natural and genetically modified biomass types to identify those with lower recalcitrance to sugar release, define pretreatment conditions, and screen enzyme formulations.  The high throughput system has been shown to mimic conventional pretreatment and enzymatic hydrolysis laboratory methods, but with the advantage of being able to quickly screen hundreds of samples.  Thus far, the HTPPH system has been successfully applied to a set of 47 natural Populus trichocarpa samples in the BioEnergy Science Center’s Poplar Association Study to define trends in sugar release behavior and to identify outliers that warrant further study.  In addition, the HTPPH system has enabled screening of individual annual rings from a cross section of Populus tremuloides to investigate radial variation of sugar release and the importance of sampling technique.  Finally, the HTPPH system has facilitated the comparison of different biomass species on a common basis to determine how changes in pretreatment severity, enzyme formulation, and biomass features impact performance in pretreatment and enzymatic hydrolysis.  Results will be shown from these studies to demonstrate the power of the HTPPH system for screening biomass samples for sugar release and identifying those with reduced recalcitrance.  In support of the system, a scaled-down method was developed to determine biomass composition that speeds compositional analysis, increases accuracy, and reduces labor demands.