The main problem with agricultural straw is its relatively low density in its original or baled forms. The bulk density of loose and standard baled straw is approximately 40 kg/m3 and 100 kg/ m3, respectively, compared with the bulk density of unprocessed wood residue, which is approximately 250 kg/ m3 (Demirba§, 2001; Tripathi et al., 1998). The relative low density of straw makes it more expensive to transport compared to wood and coal because a lower mass of straw can be transported per unit volume. Additionally, a larger storage area/ volume is required for baled straw compared to wood chip. Densification into pellets increases the bulk density of biomass (McMullen et al., 2005; Obernberger and Thek,2004) and as a result, the net calorific content per unit volume is increased (Bhattacharya et al., 1989) and the storage, transport and handling of the material is easier and cheaper (Balatinecz, 1983; Bhattacharya et al., 1989; Kaliyan and Morey, 2006).

The quality of fuel pellet is usually assessed based on its density and durability. High bulk density increases storage and transport capacity of pellets (Adapa et al., 2007; Mani et al., 2003). Since feeding of boilers and gasifiers generally is volume-dependent, variations in bulk density should be avoided (Larsson et al., 2008). A bulk density of 650 kg/ m3 is stated as design value for wood pellet producers (Obernberger and Thek 2004). Low durability of pellets results in problems like disturbance within pellet feeding systems, dust emissions,

and increased risk of fire and explosions during pellet handling and storage (Temmerman et al. 2006).

Densification of straw and determining the optimal parameters involved is an art in itself. The entire process involves securing of baled straw from agricultural fields, size reduction (chopping and grinding), application of pre-treatment (chemical, physico-chemical, and biological), determining the physical and frictional properties of straw grinds, lignocellulosic characterization of straw, lab-scale and pilot-scale densification of grinds into pellets to determine the effect of various independent parameters on quality (density and durability), and energy analysis/ balance (Fig. 1). This chapter will only address the effect and need of lignocellulose characterization, pre-treatment and size reduction, and physical properties on densification of agricultural straw.

Sultana et al. (2010) performed a techno-economic analysis and developed a model for a plant that can produce agricultural straw (barley, oat and wheat) pellets for 30 years. They have included the cost of obtaining the straw, transporting straw to the pellet plant, and producing pellets. Costs incurred by the plant for the production of pellets included capital cost, energy cost, labor cost, and consumable cost. The biomass procurement area was determined to estimate the transportation cost. The scale factors for all the equipment related to pellet production were determined based on the data of previous studies (Sultana et al., 2010). To develop the model, minimum, average and maximum yields of wheat, barley and oats straw in Western Canada were considered. They have determined that the cost of pellets does not change much for capacities over 70,000 tonnes per year (cost of production per tonne is $170.89). Therefore, the optimum size is the same for both average and maximum yield cases. In addition, it was observed that the total cost of pellet production is most sensitive to field cost followed by the transportation cost.

Life cycle assessment (LCA) study was performed on wheat straw production system and densification system in the Canadian Prairies using the LCA modelling software tool SimaPro 7.2 to determine the environmental burdens of manufacturing the wheat straw bale and wheat straw pellet (Li et al., 2011). The factors taken into consideration were greenhouse gas emission, acidification, eutrophication, ozone layer depletion, abiotic depletion, human toxicity, and photochemical oxidation. Li et al. (2011) reported that the production of biomass pellet has higher global warming effect than biomass bale, especially in CO2 and CH4 emissions from fossil fuel consumption, which is very high in densification system due to machinery usage. It was also reported that the production of wheat straw pellet has higher environmental impact on acidification, eutrophication, human toxicity and other categories than biomass bale. The dominant factors determining most environmental impacts in agricultural system are fertilizer use and production, while machinery use, manufacturing and energy consumption are main contributors to greenhouse gas emission and other environmental burdens in the densification system (Li et al., 2011).

*NT — Non-Treated; SE — Steam Exploded

£ Total Specific Energy = Specific Energy (Chopping Biomass + Operating Chopper + Grinding Biomass + Operating Hammer Mill + Pilot-Scale Pelleting)

YNet Energy = HHV — Total

Table 6. Overall specific energy analysis to show net energy available for production of biofuels after postharvest processing and densification of agricultural straw.

Energy crops are those developed only for fuel. These crops include fast growing trees, shrubs and grasses. These can be grown in agricultural land not needed neither for food, nor pasture nor fibers. In addition, farmers can grow energy crops along the banks of rivers, around lakes or in farms areas including, natural forests or swamps, for creating habitat for wildlife, renewing and improving soil biodiversity. Trees can be grown for a decade and then being cut down for energy.

Thus, bioenergy covers all forms of energy derived from organic fuels (biofuels) form biological origin used for producing energy. It includes both crops intended to produce energy which are particularly grown and multipurpose crops and by-products (residues and wastes). The term By-products includes solid, liquid and gaseous byproducts derived from human activities. It can be considered biomass as a sort of converted solar energy.

It can be said that biodiesel production tends to come mainly from oils extracted from oilseeds plants, but any material containing triglycerides can be used for biodiesel production (sunflower, rapeseed, soybean, oil palm, castor oil, used cooking oils, animal fat). Here are the main raw materials for biodiesel production (Mesa, 2006).

Conventional vegetable oils: raw materials traditionally used for biodiesel production have been: oils from oilseeds such as sunflower and rapeseed (in Europe), soybeans (in The United States) and coconut (in The Philippines), and oils from oilseeds fruits such as oil palm (in Malaysia, Indonesia and Colombia).

Alternative vegetable oils: in addition to traditional vegetable oils, there are other species adapted to the conditions from the country where they are developed and better positioned within the field of energy crops: Jatropha curcas oil (Ministerio de Minas y Energia, 2007). Biofuels have become very important because of the variety of crops from which they can be derived, but this energy supply demands a high production of them. This would have harmful effects because of the destruction of forests and jungles and replacement of crops that are essential to human diet; besides the drawbacks shown in the following fields: climatic, geographical and physical. The main supply sources of raw materials for biofuels production are shown in Table 1 and Figures 2, 3 and 4.

But not all the questions are clear and therefore the UN declares: if growing fields for biofuels production increase disproportionately, food and the environment could be at risk. Increased logging. Also food prices could increase.

For major producing countries, costs of ethanol production range between 32 and 87 USD/barrel (International Energy Agency, 2006). According to the available information, about 47% and 58% of this cost is raw materials, about 13% and 24% for inputs, about 6% and 18% for operation and maintenance costs and, about 11% and 23% to capital costs. It can be said that production costs widely vary between countries due to agro-climatic factors, land availability and labor cost that affect the kind of biomass used as raw material; this factor affects transformation technologies selection.

Figure 2 shows sources of raw materials sources for alcohol and biodiesel production and the corresponding efficiency. Figure 3 and 4 show ethanol efficiency from biomass sources in countries outstanding in their production. There is higher ethanol efficiency from sugar beet, in comparison with sugar cane and corn.

For every ton of cassava, 200 liters of ethanol can be obtained, when making the cassava calculations as a yield base of 25 ton/ha it can be obtained a yield of 5000 liters/ha can be obtained which is lower in comparison to sugar beet but higher compared to corn and sugar cane. With fertilization programs and cassava crops mechanization, yields can be increased to values of 70 ton/ha, which will triple cassava yield in liters/ha (Altin et al., 2001 ). Another important factor is that biofuels do not work as well as petroleum fuels. In order to increase their production most of the fertile lands would have to be assigned for farming them, which could be counterproductive in a world where hungry and desertification are two problems with difficult solution.

Fig. 4. Ethanol yields in liters per Tone of Feedstock. (Source: FAO, 2007)

In the LCA concept of this paper, the direct factors and the indirect ones have to be considered. In our definition, fossil fuel energy inputs (primary energy basis) and the electricity of fossil fuel origin are included in the direct factors. Also, chemical fertilizers are included in the indirect ones. Here, note that another greenhouse gases such as N2O and CH4 are not taken into consideration.

So far, in the biomass LCA analyses, the pre-processing process of chipping, transportation and drying of biomass materials, and the energy conversion process of a production energy of electricity and/or heat, through an energy system are included. This time, the paprika harvesting process has to be added to the entire life cycle stage. Using the chemical experimental data, the design of BT plant with SOFC units would be extremely significant in the biomass LCA. A target is to estimate a life cycle inventory of the entire system with BT gasifier and SOFC.

Here, we describe on the system boundary in this study. Following ISO 14041 guidelines, we define the system boundary in the biomass energy system (see Fig. 10) (Dowaki et al., 2010b).

Paprika greenhouse
(4.6 ha)

Energy conversion

Fig. 10. System boundary of a paprika production system.

The system boundary includes the entire life cycle of each energy input (electricity/thermal energy), including the pre-processing process, the energy conversion process and the paprika harvesting process. In the pre-processing process, there are sub-processes of chipping, transportation by trucks, and drying. In the energy conversion process, there are sub-processes of the gasification through the BT plant (19 t/d) with the four units of SOFC (200 kW / unit) process. In the paprika harvesting process, it is assumed that the exhausted gas of CO2 is available as a growth agent. Here, the target product is a paprika. Thus, the functional unit is assumed to be the unit per a produced paprika (Dowaki et al., 2011c).

Next, in the pre-processing process, there are sub-processes of chipping, transportation, and drying of biomass materials. In particular, within the sub-processes of transportation and drying, we have to consider uncertainties (see section 3.3.1). To date, there are a few studies considering these uncertainties. CO2 emissions in the biomass LCA would be affected by the moisture content of biomass materials, and the transportation distance from the cultivation site, or the site of accumulating waste materials, to the energy plant. Hence, it would be extremely significant to consider these factors. Table 7 shows the specific CO2 emissions, for each fuel with biomass materials, respectively.

On the energy conversion process, assuming that the 19 t/d BT plant and 4×200 kW SOFC (BT-SOFC system) were installed, we estimated the CO2 emission in the paprika production system. Here, the operational condition of SOFC unit is assumed to be almost full load operation. Also, the specification of SOFC unit is shown in Table 8.

Table 8. Specification of SOFC unit.

Due to the specification data in each system, the performance of BT-SOFC system is obtained as Table 9. Also, the thermal energy supply to the facility is assumed to be due to the heat pump (COP: 5.5).

The earliest discovery between biology and electrical energy was demonstrated by Galvani in 1791 showing the frog leg twitching from an electric current (Galvani, 1791). The first fuel

cell, which involved electrolysis of water, was discovered by Grove in 1839. An electrical stimulation can induce a biological reaction and vice-versa a biological process can also generate electricity. The first half-cell using microorganism (E. coli) was demonstrated by Potter at University of Durham (Potter, 1910). Further development of half-cell by Cohen from University of Cambridge led to one of the major types of biofuel cells, i. e., microbial fuel cells. Cohen applied a number of microbial half-cells connected in series, which generated over 35 volts (Cohen, 1931). In the late 1950s and early 1960s, the interest in development of biofuel cells received a boost by the USA space program, which led to the application of microbial fuel cells as an advanced technology for waste disposal treatment in space flights. Also, in the late 1960s, a biofuel cell using cell-free enzyme systems was discovered aiming to permanently power medical implants by utilizing specific body fluids as fuel (Yahiro et al., 1964). The concept of using microorganism as a biocatalyst in microbial fuel cells was widely applied since the 1970s (Suzuki, 1976 and Roller et al., 1984) and in the 1980s it was found out that power output could be greatly improved by using electron mediators (Vega & Fernandez, 1987; Habermann & Pommer, 1991 and Allen & Bennetto, 1993). However, the toxicity and instability of mediators limited the cell performance. A breakthrough was made when some microorganisms were found to transfer electrons directly to the electrode which led to the mediator-less microbial fuel cells first used in wastewater treatment and electricity generation (Kim et al., 1999; Chaudhuri & Lovley, 2003). These microorganisms are stable and yield a high Coulombic efficiency which facilitates the direct electron transfer (Scholz & Schroder, 2003). Shewanella putrefaciens (Kim et al., 2002), Geobacteraceae sulferreducens (Bond & Lovley, 2003), Geobacter metallireducens (Min et al., 2005) and Rhodoferax ferrireducens (Chaudhuri & Lovley, 2003) are all bioelectrochemically active microbes and can transfer electrons directly through the membrane. On the other hand, since the first enzymatic biofuel cell was reported in 1964, noticeable developments have been made in the terms of the power density, cell lifetime, operational stability (Bockris & Srinivasan, 1969; Govil & Saran, 1982 and Palmore & Whitesides, 1994). However, the output potential generated from enzymatic biofuel cells was still far beyond the demand of commercial application. Therefore, instead of considering enzymatic biofuel cells as a conventional power source, most of the researches on enzymatic biofuel cells have been aimed toward special applications such as implantable medical devices (Katz & Willner, 2003; Barton et al., 2004 and Heller, 2004). In the past ten years, cell performances on both types of biofuel cells have been improved significantly and we will discuss the detailed development in the following sessions.

The EU standard specifies individual limit values for mono-, di — and triglyceride as well as a maximum value for total glycerol. The standards for Brazil and the USA do not provide explicit limits for the contents of partial acylglycerides. In common with the concentration of free glycerol, the amount of glycerides depends on the production process. Fuels out of specification with respect to these parameters are prone to deposit formation on injection nozzles, pistons and valves (Mittelbach et al. 1983).

1.10 Free glycerol

The content of free glycerol in biodiesel is dependent on the production process, and high values may stem from insufficient separation or washing of the ester product. The glycerol may separate in storage once its solvent methanol has evaporated. Free glycerol separates from the biodiesel and falls to the bottom of the storage or vehicle fuel tank, attracting other polar components such as water, monoglycerides and soaps. These can lodge in the vehicle fuel filter and can result in damage to the vehicle fuel injection system (Mittelbach 1996). High free glycerol levels can also cause injector coking. For these reasons free glycerol is limited in the specifications.

1.11 Total glycerol

Total glycerol is the sum of the concentrations of free glycerol and glycerol bound in the form of mono-, di — and triglycerides. The concentration depends on the production process.

Fuels out of specifications with respect to these parameters are prone to coking and may thus cause the formation of deposits on injector nozzles, pistons and valves (Mittelbach et al. 1983). For this reason total glycerol is limited in the specifications of the three regions.

Owing to the attractiveness of D. salina for biotechnology, there is a renewed interest in engineering this organism. Publications have reported the genetic transformation of D. salina by both microparticle bombardment and electroporation (Geng et al., 2003; Tan et al., 2005). Some of the most impressive progress in the field has come from the Xue group at Zhengzhou University in China. With research covering optimization of transformation techniques, gene characterization, and enhanced gene expressing utilizing matrix attachment regions, their work provides important information and an exemplary research path to follow toward genetic engineering of D. salina (Wang et al., 2009; Lu et al., 2009; Jia et al., 2009a; Feng et al., 2009; Wang et al., 2007; Jia et al., 2009b; Liu et al., 2005; Jiang et al., 2003). The down-regulation of specific genes using RNAi in D. salina has also been reported (Jia et al., 2009a; Sun et al., 2008). These advances, however, are not readily reproducible and represent solitary accomplishments with an alga that has otherwise been difficult to transform.

Today, the majority of the methanol is synthesised from syngas produced by steam reforming of natural gas (SMR). The synthesis can be done either with heat provided by a furnace where the tubular reactor is located, or by auto-thermal reforming (ATR) combined with steam reforming. Once the natural gas is reformed the resulting synthesis gas can be shift adjusted for its H2/CO ratio and the CO2 decreased to a few percentages as previously specified. The syngas is then fed to a reactor vessel in the presence of a catalyst to produce methanol and water vapor. This crude methanol, which usually contains up to 18% water, plus ethanol and higher alcohols is fed to a distillation plant that consists of a unit that removes the volatiles and a unit that removes the water and higher alcohols. The unreacted syngas is recirculated back to the methanol converter resulting in an overall conversion efficiency of 99%. A generic methanol synthesis process flow diagram (PFD) is shown in the Figure 3.

As is the case with Fisher Tropsch synthesis, one of the challenges associated with commercial methanol synthesis is removing the large excess heat of reaction. Methanol synthesis increases at higher temperatures so does the chance for competing side reactions. Controlling and dissipating the heat of reaction and overcoming the equilibrium constraint to maximise the per-pass conversion efficiency are the two main process features that are considered when designing the methanol synthesis reactor, commonly referred to as a methanol converter. Numerous methanol converter designs have been commercialised over the years and these can be roughly separated into two categories: adiabatic or isothermal reactors.

• Adiabatic reactors often imply multiple catalysts beds separated by gas cooling devices, either direct heat exchange or injection of cooled, fresh or recycled syngas.

• The isothermal reactors are designed to continuously remove the heat of the reaction so they operate essentially like a heat exchanger.

Haldor Topsoe low-pressure methanol synthesis process. This process is designed to produce methanol from natural gas or associated gas feedstocks, utilizing a two-step reforming process to generate a syngas mixture feed for the methanol synthesis (Sunggyu, 2007). Associated gas is natural gas produced with crude oil from the same reservoir. It is claimed that the total investment for this process is lower than with the conventional flow scheme based on straight steam reforming of natural gas by approximately 10%, even after considering an oxygen plant. As shown in figure 4, the two stage reforming is conducted by primary reforming in which a preheated mixture of natural gas and steam is reacted, followed by a secondary reforming which further converts the exit gas from the primary reformer with the aid of oxygen that is feed separately. The amount of oxygen required as well as the balance of conversion between the primary and the secondary reformers need to be properly adjusted so that a balanced syngas (in a stoichiometric ratio (2:1) of H2/ CO) is obtained with a low inert content.

Liquid-Phase methanol process. The liquid phase methanol process was originally developed by Chem. Systems Inc in 1975 (Cybulski, 1994). The R&D of this process was sponsored by the U. S. Department of Energy and Electric Power Research Institute. Commercialised by Air Products and Chemicals Inc and Eastman Chemical Co. in the 1990s, the process is based on the low-pressure methanol synthesis using coal as the source of syngas. Recently, in Quebec (Canada) Enerkem Inc. has developed a liquid phase methanol process using syngas produced from biomass. The chemical reaction is carried out in a slurry reactor using a Cu/ ZnO/ Al2O3 catalyst at temperature ranging from 230 to 260 °C and 50 to 100 atm. The commercial reactor used a liquid entrained reactor in which fine grains of catalyst are slurried in an inert high-boiling oil typically white mineral oil. Pressurized gaseous reactants are dissolved in the oil and the dissolved molecular species are reacted on the catalytic surfaces of the grains present in a slurry. The figure 5 shows the schematic of liquid phase methanol process of Enerkem Inc.

Typically, hammer mills are used in forage processing industry as they are relatively inexpensive, easy to operate and produces wide range of particles (Lopo, 2002). Hammer mills have achieved merit because of their ability to finely grind a greater variety of materials than any other machines (Scholten et al., 1985). The performance of a hammer mill is measured in terms of energy consumption and geometric mean diameter and particle size distribution of the ground product (Adapa et al., 2011a; Mani et al., 2004).

Screen Size: Hammer mill screen opening size was the most significant factor affecting mill performance (Fang et al., 1997) and also has significant effect on mean particle size (Pfost and Headley, 1971). The specific energy required to grind agricultural biomass significantly increases with a decrease in hammer mill screen size and shows a negative power correlation (Arthur et al., 1982; Soucek et al., 2003). Similarly, Adapa et al. (2011a) reported negative correlation between specific energy and particle size of biomass as affected by hammer mill screen sizes. However, two other studies reported a second-order polynomial relationship between the specific energy requirements for grinding biomass (Mani et al. 2004; Sitkei, 1986). Usually, the mean geometric particle size for any particular biomass decreases with a decrease in hammer mill screen size (Adapa et al., 2011a). It has been reported that wider particle size distribution is suitable for compaction (pelleting/ briquetting) process (Adapa et al., 2011a; Mani et al., 2004). During compaction, smaller (fine) particles rearrange and fill in the void space of larger (coarse) particles producing denser and durable compacts (Tabil, 1996).

Operating Speed (Peripheral Velocity): The speed has a significant effect on mean particle size (Pfost and Headley, 1971). The total specific energy of hammer mill grinding has direct correlation to an increase in hammer tip speed (Bitra et al., 2009; Vigneault et al., 1992). High speed hammer mills with smaller diameter rotors are good for fine or hard-to-grind material. However, at high tip speeds, the material moves around the mill parallel to the screen surface making the openings only partially effective. At slower speeds, the material impinges on the screen at a greater angle causing greater amounts of coarser feed to pass through (Balk, 1964).

Hammer Angles and Thickness: The direct energy input for grinding also depends on hammer angles. In general, the specific energy for grinding decreases with an increase in hammer degrees (Bitra et al., 2009). In addition, the specific energy for grinding increases with an increase in hammer thickness (Vigneault et al., 1992).

Material Moisture Content and Feed Rate: A positive correlation has been reported between moisture content and specific energy consumption for grinding of agricultural biomass (Balk, 1964; Mani et al., 2004; Soucek et al., 2003). Feeding rate also has significant effect on specific energy consumption during hammer mill grinding and has positive correlation (O’Dogherty, 1982).

Bulk and Particle Densities, and Geometric Mean Particle Size: Usually, the bulk and particle density of agricultural straw significantly increases with a decrease in hammer mill screen size (Adapa et al., 2011a). The geometric mean particle size of pre-treated straw is usually smaller than that of the non-treated straw. This could be due to the fact that application of pre-treatment disrupts/ disintegrates the lignocellulosic structure of the biomass (Sokhansanj et al., 2005) leading to lower shear strength (easier to grind the straw).

This part aims at showing the importance to realize a correlation between the structural properties of the catalysts and their electrocatalytic activities towards glucose oxidation. The use of nanocatalysts indeed involves a deep structural characterization of the nanoparticles to fully understand the whole of the catalytic process. Therefore, in order to show the presence and the proportion of gold and platinum at the surface of the catalysts, electrochemical investigations have been carried out (Burke et al., 2003). It is indeed possible to quantify surface compositions of the catalysts by using cyclic voltammetry and by calculating the amount of charge associated with both reduction of platinum and gold oxides (Woods, 1971). The charge calculated for pure metals was 493 pC cm-2 and 543 pC cm-2 for Au and Pt, respectively, for an upper potential value of 250 mV vs. MSE (Habrioux et al., 2007) in a NaOH (0.1 M) solution. The atomic ratio between gold and platinum can be thus determined according to Eq. 7 and Eq. 8 assuming that for all bimetallic compositions, the oxidation takes place only on the first atomic monolayer.

Pt

Both voltammograms used and results of the quantification are shown in Fig. 7. Mean diameter of the different nanoparticles weighted to their volume (obtained from
transmission electron microscopy measurements) as well as their mean coherent domain size weighted to the volume of the particles (obtained from X-ray diffraction measurements) are also presented in Fig. 7.

% At. Pt %At. Au DV (nm) Lv (nm)

Fig. 7. Voltammograms (after 19 cycles) of gold-platinum nanoparticles recorded at 25 °C in alkaline media (0.1 M NaOH). Scan rate = 20 mV s-1. The surface composition of the used catalyst is given on the right of the corresponding voltammogram.

In Fig. 7 it is noticed that for all compositions, desorption of oxygen species occurs in two peaks. The reduction of the gold surface takes place at -0.38 V vs. MSE whereas the potential for which platinum surface is reduced depends on the amount of gold in the alloy. Indeed, for pure platinum nanoparticles this potential is ca. -0.8 V vs. MSE (reduction of platinum oxides). The potential at which oxygen species desorption occurs, shifts to lower potentials when the atomic ratio of gold increases in the composition of alloys. The deformation of this peak increases with the amount of gold probably because of the formation of more complex platinum oxides. The quantification realized on the different bimetallic compositions, clearly shows a platinum enrichment of nanoparticles surfaces. Desorption of gold oxides is indeed invisible for low gold containing samples (i. e. with gold content lower than 40%). These nanoparticles exhibit a typical core-shell structure composed of a gold core and a platinum shell (Habrioux et al., 2009b), while high gold content samples (i. e. with gold content higher than 80%) possess a surface composition that is close to the nominal one. This results in a purely kinetic effect. Actually, reduction of gold precursor is considerably faster than reduction of platinum cation. Consequently, there is firstly formation of a gold seed on which platinum reduction occurs. So, the natural tendency of these systems is to form core­shell particles. Furthermore, let’s notice that both mean diameter of nanoparticles weighted to their volume and their mean coherent domain size weighted to their volume increase with gold content but ever stay in the nanometer range. That is only the result of differences in reduction kinetics of the particles since the ratio water to surfactant remains constant whatever the synthesized sample. To correlate surface composition with efficiency to
oxidize glucose for all gold-platinum catalysts compositions, voltammograms were first recorded in alkaline medium. Results are shown in Fig. 8.

In Fig. 8, different oxidation peaks appear during the oxidation process on gold-platinum nanocatalysts. When platinum content decreases in the bimetallic surface composition, intensity of peak A, located at ca. -0.7 V vs. SCE, diminishes. For pure gold catalyst, this peak is furthermore invisible. It is thus related to the oxidation phenomenon on platinum. It has already been attributed to dehydrogenation of anomeric carbon of glucose molecule (Ernst et al., 1979). Peaks B and C correspond to the direct oxidation of glucose molecule (Habrioux et al., 2007) and are located both in gold and platinum oxides region. In the case of catalysts with nominal compositions such as Au70Pt30 or Au80Pt20, the different oxidation peaks located between -0.3 V vs. SCE and 0.4 V vs. SCE are not well-defined. For these catalysts, the presence of platinum at their surface allows a low potential oxidation of glucose molecule, which starts earlier than on pure gold. Moreover, on these catalysts, after the dehydrogenation step, current densities raise rapidly. Furthermore, in the potential region where formation of both gold hydroxides and platinum oxides occurs, current densities are very high (i. e. 12 mA mg-1 at 0.2 V vs. SCE). This is the result of a synergistic effect between the two oxidized metals at the bimetallic catalyst surface (Habrioux et al., 2007). Such effect between gold and platinum has already been observed for CO oxidation (Mott et al., 2007). On these catalysts, during the negative going scan, two oxidation peaks, E and F, are visible. During the reduction of both oxidized gold and platinum clusters, oxygenated species are desorbed from the surface and stay at its vicinity. Subsequently, there is desorption of adsorbed lactone from the electrode surface what implies the formation of both peak E and

peak F (Beden et al., 1996). Fig. 9 shows the reactions involving in the oxidation of glucose on the catalyst surface.

Fig. 10. a) Experimental and simulated diffractograms obtained with Au, Au70Pt30 and Pt nanoparticles (from top to bottom), b) Experimental (•) and simulated (o) Williamson-Hall diagrams obtained with Au30Pt70 and Au nanoparticles (from top to bottom).

Each experimental diffractogram has been fitted with five Pearson VII functions what gives two important parameters: the accurate peak position b (b = IsinO/X) and the integral line width db. The value of db is plotted versus b in Fig.10b. As a result of best fits, it can be assumed that line profiles of diffractograms are lorentzian. This implies that all contributions to the integral line width can be added linearly and can be expressed as follows:

where Lv is the mean coherent domain size weighted to the volume of the particles, a the stacking fault probability, Vhki a parameter depending on the miller indexes, a the mean internal stress and Ehkl the young modulus. The fit of Williamson-Hall diagrams with the expression given by Eq.7 leads to the determination of Lv, a and a for each catalyst. It has been concluded that for catalysts with nominal compositions of Au7oPt3o and AusoPt2o, both a and a values were high (Habrioux et al., 2009b). For AusoPt2o, these values were indeed of 510 N. mm-2 and 8.2%, respectively for a and a. In the case of Au7oPt3o, these values were of 49o N. mm-2 and 7.4%. HRTEM observations have confirmed the results of the fit since the observed particles present numerous twins and stacking faults, as shown in Fig. 11.

As a result of the high internal mean strain existing in these particles, there is an important strain energy which leads to the formation of twins and stacking faults. Consequently the equilibrium shape of the particles is modified and the interaction between the different surface atoms is changed. Accordingly, the catalytic behaviour of these particles is greatly affected. This can also explain the remarkable activity of these particles towards glucose oxidation both in alkaline medium as shown in Fig. 8, and in physiological type medium, as shown in Fig. 12. Let’s notice that at low potential values, current densities obtained with Au7oPt3o and Pt catalysts are similar. Competitive adsorption between phosphate species and glucose molecules can be involved to explain this phenomenon. Actually, de Mele et al. (de Mele et al., 1982) showed that phosphate species are capable of creating oxygen-metal bonds with platinum surfaces and thus inhibiting glucose oxidation. This engenders the low current density observed at low potentials on pure platinum. On Au7oPt3o catalyst, it is possible that modification of 5d band center of platinum due to the presence of gold allows discriminating the adsorption of phosphate species. Furthermore, the oxidation of glucose on high gold content catalysts starts at a very low potential value (i. e. — o.5 V vs. SCE), which can easily be compared with values observed for catalysts such as Pt-Bi, Pt-Sn (Becerik & Kadirgan, 2001) or Pt-Pd (Becerik et al., 1999).