Browsing by Subject "Biofuels."
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Item Chemometric modeling of UV-visible and LC-UV data for prediction of hydrolysate fermentability and identification of inhibitory degradation products.(2011-12-19) Hedayatifar, Negar.; Chambliss, C. Kevin.; Chemistry and Biochemistry.; Baylor University. Dept. of Chemistry and Biochemistry.Production of ethanol from lignocellulosic biomass requires a pretreatment step to liberate fermentable sugars trapped within the plant. During pretreatment, lignin and some sugars undergo degradation to form compounds which have shown inhibitory effects to fermentative microorganisms. Accordingly, development of a rapid and accurate method for assessment of microbial inhibition and identification of inhibitory compounds is essential for gaining a better understanding of pretreatment and its downstream effects on fermentation processes. Traditional methods for identification of inhibitory compounds involve a “bottom-up” approach. Using this approach, one or more known degradation compounds are added to fermentation media and their effects on batch fermentation of ethanol are observed. These methods are extremely time-consuming and labor-intensive which makes them unattractive to researchers. Furthermore, they are carried out on degradation compounds that have already been identified. Given that biomass hydrolysates contain many unidentified constituents, identification of inhibitory compounds by traditional means is unlikely to occur on a timescale that is consistent with current mandates for commercial production of cellulosic ethanol. To address these limitations, we have developed a chemometric model that correlates ultraviolet (UV)-visible spectroscopic data of 21 different biomass hydrolysates with their fermentability (percent inhibition of ethanol production). This novel approach enables rapid prediction of hydrolysate fermentability using UV-visible spectroscopic data alone and offers significant improvements in throughput and labor when compared to traditional batch fermentation methods. The model was subsequently used to predict percent inhibition for five hydrolysate samples, with a root-mean-square error of prediction of 6%. To evaluate the use of chemometric modeling for identification of inhibitory compounds in biomass hydrolysate, a second model was developed to correlate HPLC-UV chromatographic data of the 21 hydrolysates with their percent inhibition. Detection was monitored at four specific wavelengths identified by the UV-visible model as significant spectral regions. Once constructed, the HPLC-UV model was used to identify retention times that had the highest correlation with inhibition. To determine whether better resolution or more universal detectability of sample constituents may lead to identification of additional retention times, a third chemometric model was developed with chromatographic data of hydrolysates obtained via ion chromatography with conductivity detection.Item Improved analytical methods for carbohydrate analysis in biofuel research.(2013-09-16) Sevcik, Richard Scott.; Chambliss, C. Kevin.; Chemistry and Biochemistry.; National Renewable Energy Lab (NREL).; Thermofisher Scientific.; Baylor University. Dept. of Chemistry and Biochemistry.Essential for continued growth of the biofuels industry is the need for continued development of rapid, robust, and accurate carbohydrate quantitation methods. As new feedstocks are identified and developed, understanding how to optimize the total amount of energy obtainable will be of primary consideration for biorefineries. Conditions needed to obtain the optimal energy yield will require the testing and monitoring of different chemical and biological treatment technologies. Monitoring of carbohydrates, specifically monosaccharides and sucrose, is required to evaluate the effectiveness of the applied bioprocessing conditions. However, due to the increasing number of possible treatment technologies and potential combinations, the number of samples to be analyzed for optimization becomes the rate limiting step. Therefore rapid, accurate, and robust analytical methods for carbohydrate analysis are critical for researchers as they investigate potential feedstocks. Currently, high performance liquid chromatography with refractive index detection (HPLC-RI) and a ligand-exchange column is the standard method the biofuels industry utilizes for interrogation of carbohydrates in research samples. Overall, the HPLC-RI methods have proven to be both robust and easy to use, providing a large detection range and requiring little to no sample dilution. However, the caveats of this approach include long analysis times (45 - 60 minutes), limited resolution of select carbohydrates and an inability to separate sucrose from cellobiose. In addition, the use of a universal detector creates the possibility of false positives due to interferences caused by co-eluting compounds. An alternative method is the application of high-performance anion-exchange chromatography with pulsed amperometric detection (HPAE-PAD) for carbohydrate analysis. The primary advantage of the HPAE-PAD approach is the selectivity of PAD for carbohydrates, while a reduction of analysis time is also feasible. A series of experiments was conducted to improve the resolution of monosaccharides and sucrose while reducing analysis time. Initial experiments utilized the addition of carbonate to a commercially available column to improve analyte resolution and reduce overall analysis time to ~5 min and employed for the analysis of several sorghum types and process streams. Additionally, a commercially produced column, inspired by the carbonate-modified column was tested in an interlaboratory collaboration involving government, industry, and academic labs.