When titanium dioxide (TiO2) is irradiated with near-UV light, this semiconductor exhibits strong bactericidal activity. In this paper, we present the first evidence that the lipid peroxidation reaction is the underlying mechanism of death of Escherichia coli K-12 cells that are irradiated in the presence of the TiO2 photocatalyst. Using production of malondialdehyde (MDA) as an index to assess cell membrane damage by lipid peroxidation, we observed that there was an exponential increase in the production of MDA, whose concentration reached 1.1 to 2.4 nmol · mg (dry weight) of cells−1 after 30 min of illumination, and that the kinetics of this process paralleled cell death. Under these conditions, concomitant losses of 77 to 93% of the cell respiratory activity were also detected, as measured by both oxygen uptake and reduction of 2,3,5-triphenyltetrazolium chloride from succinate as the electron donor. The occurrence of lipid peroxidation and the simultaneous losses of both membrane-dependent respiratory activity and cell viability depended strictly on the presence of both light and TiO2. We concluded that TiO2 photocatalysis promoted peroxidation of the polyunsaturated phospholipid component of the lipid membrane initially and induced major disorder in the E. coli cell membrane. Subsequently, essential functions that rely on intact cell membrane architecture, such as respiratory activity, were lost, and cell death was inevitable.

We report carbon mass balance and kinetic data for the total oxidation of cells, spores, and biomolecules deposited on illuminated titanium dioxide surfaces in contact with air. Carbon dioxide formation by photocatalytic oxidation of methanol, glucose, Escherichia coli, Micrococcus luteus, Bacillus subtilis (cells and spores), Aspergillus niger spores, phosphatidylethanolamine, bovine serum albumin, and gum xanthan was determined as a function of time. The quantitative data provide mass balance and rate information for removal of these materials from a photocatalytic surface. This kind of information is importantfor applications of photocatalytic chemistry in air and water purification and disinfection, self-cleaning surfaces, and the development of self-cleaning air filters.

Switchgrass (Panicum virgatum L.) and giant miscanthus (Miscanthus x giganteus Greef & Deuter ex Hodkinson & Renvoize) are productive on marginal lands in the eastern USA, but their productivity and composition have not been compared on mine lands. Our objectives were to compare biomass production, composition, and theoretical ethanol yield (TEY) and production (TEP) of these grasses on a reclaimed mined site. Following 25 years of herbaceous cover, vegetation was killed and plots of switchgrass cultivars Kanlow and BoMaster and miscanthus lines Illinois and MBX-002 were planted in five replications. Annual switchgrass and miscanthus yields averaged 5.8 and 8.9 Mg dry matter ha, respectively, during 2011 to 2015. Cell wall carbohydrate composition was analyzed via near-infrared reflectance spectroscopy with models based on switchgrass or mixed herbaceous samples including switchgrass and miscanthus. Concentrations were higher for glucan and lower for xylan in miscanthus than in switchgrass but TEY did not differ (453 and 450 L Mg , respectively). In response to biomass production, total ethanol production was greater for miscanthus than for switchgrass (5594 vs 3699 L ha ). Relative to the mixed feedstocks model, the switchgrass model slightly underpredicted glucan and slightly overpredicted xylan concentrations. Estimated TEY was slightly lower from the switchgrass model but both models distinguished genotype, year, and interaction effects similarly. Biomass productivity and TEP were similar to those from agricultural sites with marginal soils.Keywords Cellulosic bioenergy feedstock . Mine reclamation . Near-infrared reflectance spectroscopy . Theoretical ethanol production . Theoretical ethanol yield Abbreviations ADLAcid detergent lignin ARA Arabinan GAL Galactan GLC1

BackgroundCost-effective production of lignocellulosic biofuels remains a major financial and technical challenge at the industrial scale. A critical tool in biofuels process development is the techno-economic (TE) model, which calculates biofuel production costs using a process model and an economic model. The process model solves mass and energy balances for each unit, and the economic model estimates capital and operating costs from the process model based on economic assumptions. The process model inputs include experimental data on the feedstock composition and intermediate product yields for each unit. These experimental yield data are calculated from primary measurements. Uncertainty in these primary measurements is propagated to the calculated yields, to the process model, and ultimately to the economic model. Thus, outputs of the TE model have a minimum uncertainty associated with the uncertainty in the primary measurements.ResultsWe calculate the uncertainty in the Minimum Ethanol Selling Price (MESP) estimate for lignocellulosic ethanol production via a biochemical conversion process: dilute sulfuric acid pretreatment of corn stover followed by enzymatic hydrolysis and co-fermentation of the resulting sugars to ethanol. We perform a sensitivity analysis on the TE model and identify the feedstock composition and conversion yields from three unit operations (xylose from pretreatment, glucose from enzymatic hydrolysis, and ethanol from fermentation) as the most important variables. The uncertainty in the pretreatment xylose yield arises from multiple measurements, whereas the glucose and ethanol yields from enzymatic hydrolysis and fermentation, respectively, are dominated by a single measurement: the fraction of insoluble solids (fIS) in the biomass slurries.ConclusionsWe calculate a $0.15/gal uncertainty in MESP from the TE model due to uncertainties in primary measurements. This result sets a lower bound on the error bars of the TE model predictions. This analysis highlights the primary measurements that merit further development to reduce the uncertainty associated with their use in TE models. While we develop and apply this mathematical framework to a specific biorefinery scenario here, this analysis can be readily adapted to other types of biorefining processes and provides a general framework for propagating uncertainty due to analytical measurements through a TE model.

Summary Harnessing stem carbohydrate dynamics in grasses offers an opportunity to help meet future demands for plant‐based food, fiber and fuel production, but requires a greater understanding of the genetic controls that govern the synthesis, interconversion and transport of such energy reserves.We map out a blueprint of the genetic architecture of rice (Oryza sativa) stem nonstructural carbohydrates (NSC) at two critical developmental time‐points using a subpopulation‐specific genome‐wide association approach on two diverse germplasm panels followed by quantitative trait loci (QTL) mapping in a biparental population.Overall, 26 QTL are identified; three are detected in multiple panels and are associated with starch‐at‐maturity, sucrose‐at‐maturity and NSC‐at‐heading. They tag OsHXK6 (rice hexokinase), ISA2 (rice isoamylase) and a tandem array of sugar transporters.This study provides the foundation for more in‐depth molecular investigation to validate candidate genes underlying rice stem NSC and informs future comparative studies in other agronomically vital grass species.

Production of biofuels, bioproducts, and bioenergy requires a well-characterized, stable, and reasonably uniform biomass supply and well-established supply chains for shipping biomass from farm fields to biorefineries, while achieving year-round production targets. Preserving and stabilizing biomass feedstock during storage is a necessity for cost-effective and sustainable biofuel production. Ensiling is a common storage method used to preserve and even improve forage quality; however, the impact of ensiling on biomass physical and chemical properties that influence bioconversion processes has been variable. Our objective in this work was to determine the effects of ensiling on lignocellulosic feedstock physicochemical properties and how that influences bioconversion requirements. We observed statistically significant decreases (p < 0.05) in the content of two major structural carbohydrates (glucan and xylan) of 5 and 8%, respectively, between the ensiled and non-ensiled materials. We were unable to detect differences in sugar yields from structural carbohydrates after pretreatment and enzymatic hydrolysis of the ensiled materials compared to non-ensiled controls. Based on this work, we conclude that ensiling the corn stover did not change the bioconversion requirements compared to the control samples and incurred losses of structural carbohydrates. At the light microscopy level, ensiled corn stover exhibited little structural change or relocation of cell wall components as detected by immunocytochemistry. However, more subtle structural changes were revealed by electron microscopy, as ensiled cell walls exhibit ultrastructural characteristics such as wall delimitation intermediate between non-ensiled and dilute-acid-pretreated cell walls. These findings suggest that alternative methods of conversion, such as deacetylation and mechanical refining, could take advantage of lamellar defects and may be more effective than dilute acid or hot water pretreatment for biomass conversion of ensiled materials.

A simple and highly selective analytical procedure is presented for the determination of cellulosic glucan content in samples that contain both cellulose and starch. This method eliminates the unacceptably large compounding errors of current two-measurement methods. If both starch and cellulose are present before analytical hydrolysis, both will be hydrolyzed to glucose causing bias and inaccuracy in the method. To prevent this interference, the removal of starch prior to cellulosic quantification is crucial. The method presented here is a concise in-series procedure with minimal measurements, eliminating large compounding errors. Sample preparation consists of a starch extraction employing enzymatic hydrolysis followed by a simple filtration and wash. The samples are then subjected to a two-stage acid hydrolysis. The concentration of glucose is determined by ion exchange high-performance liquid chromatography with a Pb2+ column and a refractive index detector. The cellulosic glucan content is calculated based on the initial dry weight of the starting material. Data for the native biomass materials studied show excellent reproducibility, with coefficients of variance of 3.0% or less associated with the method. This selectivity for cellulosic glucan by the procedure was validated with several analytical techniques such as liquid chromatography coupled with mass spectrometry (LC–MS), Raman spectroscopy, and nuclear magnetic resonance.

Cool-season (C3) perennial grasses have a long history of cultivation and use as animal forage. This study evaluated 15 cultivars of C3 grasses, when harvested in late June for increased biomass yield, as biofuel feedstocks using near-infrared spectroscopy (NIR) based partial least square (PLS) analysis. These grasses were grown near Iliff, CO, for three growing seasons (2009)(2010)(2011). The carbohydrate composition and released carbohydrates (total glucose and xylose released from dilute acid pretreatment and enzymatic hydrolysis [EH]) were predicted for samples from the study using NIR/PLS. The results were analyzed from a biofuels perspective, where composition combined with harvest yield provided information on the carbohydrate yield available for biomass conversion processes, and released carbohydrate yield provided information on the accessibility of those carbohydrates to conversion methods. The range in harvest yields varied more among cultivars (2900 kg ha -1 ) than did the range in carbohydrate composition (56.0 g kg -1 ) or released carbohydrates (60.0 g kg -1 ). When comparing carbohydrate yield to released carbohydrate yield between cultivars, an efficiency as high as 87% release of available carbohydrates was obtained for pubescent wheatgrass [Thinopyrum intermedium (Host) Barkworth & D.R. Dewey 'Mansaka'], with a low of 71% for hybrid wheatgrass [Elytrigia repens (L.) nevski ´ pseudoroegneria spicata (PURSH) A. Love 'Newhy']. Though hybrid wheatgrass had the lowest release efficiency, its high harvest yield resulted in release of more total carbohydrates than half the other cultivars analyzed. This suggested that harvest yield, carbohydrate release, and carbohydrate composition, togetherplay significant roles in biofuel feedstock evaluation.