All energy scenarios show a shift toward an increased percentage of renewable energy sources, including biomass. This study gives an overview of water footprints (WFs) of bioenergy from 12 crops that currently contribute the most to global agricultural production: barley, cassava, maize, potato, rapeseed, rice, rye, sorghum, soybean, sugar beet, sugar cane, and wheat. In addition, this study includes jatropha, a suitable energy crop. Since climate and production circumstances differ among regions, calculations have been performed by country. The WF of bioelectricity is smaller than that of biofuels because it is more efficient to use total biomass (e.g., for electricity or heat) than a fraction of the crop (its sugar, starch, or oil content) for biofuel. The WF of bioethanol appears to be smaller than that of biodiesel. For electricity, sugar beet, maize, and sugar cane are the most favorable crops [50 m 3 /gigajoule (GJ)]. Rapeseed and jatropha, typical energy crops, are disadvantageous (400 m 3 /GJ). For ethanol, sugar beet, and potato (60 and 100 m 3 /GJ) are the most advantageous, followed by sugar cane (110 m 3 /GJ); sorghum (400 m 3 /GJ) is the most unfavorable. For biodiesel, soybean and rapeseed show to be the most favorable WF (400 m 3 /GJ); jatropha has an adverse WF (600 m 3 /GJ). When expressed per L, the WF ranges from 1,400 to 20,000 L of water per L of biofuel. If a shift toward a greater contribution of bioenergy to energy supply takes place, the results of this study can be used to select the crops and countries that produce bioenergy in the most water-efficient way.sustainability ͉ climate change ͉ energy ͉ biomass ͉ natural resource use

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We report a series of Raman-Rayleigh-LIF measurements in two turbulent natural-gas jet diffusion flames produced by the Delft piloted jet diffusion flame burner. The main objective of the Raman-Rayleigh-LIF measurements was to obtain detailed information on the major species concentrations in the flames. The measurements provide simultaneous data on temperature, the concentrations of the major species and the radicals OH and NO and mixture fraction. The application of the Raman technique in the undiluted naturalgas flames proves to be very challenging because of the high fluorescence interference levels. The interference contributions to the recorded Raman signals are identified and subtracted using empirical correlations between the Raman signals and the signals on fluorescence interference monitor channels. The calibration and data reduction of the Raman-Rayleigh and LIF signals are discussed in detail. The resulting dataset compares excellently with data from previous experiments. Because the Raman-Rayleigh-LIF data provide quantitative concentrations and accordingly quantitative mixture fractions, they form a valuable and useful extension of the existing database for the Delft piloted jet diffusion flame burner.

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A mathematical model for low‐pressure chemical vapor deposition (LPCVD) in a single wafer reactor of the impinging jet type has been developed. The model includes the partial differential equations describing the balance of mass, momentum, heat, and species concentration, Stefan‐Maxwell equations for multicomponent diffusion, multicomponent thermodiffusion, multiple surface reactions, and variable gas properties. Gas‐phase chemistry is neglected. The equations are solved numerically in two‐dimensional, axisymmetric form, using a control‐volume‐based finite difference method. The model is applied to silicon LPCVD from silane. It is shown that in single wafer LPCVD modeling, the coupling of the flow equations to the species concentrations is very important, as are thermodiffusion effects. Multicomponent diffusion phenomena can be modeled accurately using Wilke's approximation in many cases. In some situations, however, the Stefan‐Maxwell equations should be used. The model is used to optimize both reactor geometry and process conditions, in order to obtain uniform deposition on large wafers at high growth rates. A series of parameter variations is presented, illustrating the power of the model as an aid in such an optimization study.