Optimization of the yield of dark microaerobic production of hydrogen from lactate by Rhodopseudomonas palustris

Accepted Manuscript Optimization of the yield of dark microaerobic production of hydrogen from lactate by Rhodospeudomonas palustris Carolina Zampol Lazaro, Zeynep Yilmazer Hitit, Patrick C. Hallenbeck PII: DOI: Reference:

S0960-8524(17)31521-3 http://dx.doi.org/10.1016/j.biortech.2017.08.207 BITE 18816

To appear in:

Bioresource Technology

Received Date: Revised Date: Accepted Date:

2 August 2017 30 August 2017 31 August 2017

Please cite this article as: Lazaro, C.Z., Hitit, Z.Y., Hallenbeck, P.C., Optimization of the yield of dark microaerobic production of hydrogen from lactate by Rhodospeudomonas palustris, Bioresource Technology (2017), doi: http:// dx.doi.org/10.1016/j.biortech.2017.08.207

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Optimization of the yield of dark microaerobic production of hydrogen from lactate by Rhodospeudomonas palustris

Carolina Zampol Lazaro1, Zeynep Yilmazer Hitit1,2 and Patrick C. Hallenbeck1,3

1

Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal,

CP6128 Succursale Centre-ville, Montréal, Québec, Canada H3C 3J7 2

Faculty of Engineering, Department of Chemical Engineering, Ankara University,

Tandogan, 06100 Ankara, Turkey 3

Life Sciences Research Center, Department of Biology

United States Air Force Academy 2355 Faculty Drive, USAF Academy, Colorado 80840

*

Corresponding author; Tel : (514)343-6278 Fax : (514)343-5701

E-mail address : [email protected]

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Abstract: Hydrogen yields of dark fermentation are limited due to the need to also produce reduced side products, and photofermentation, an alternative, is limited by the need for light. A relatively new strategy, dark microaerobic fermentation, could potentially overcome both these constraints. Here, application of this strategy demonstrated for the first time significant hydrogen production from lactate by a single organism in the dark. Response surface methodology (RSM) was used to optimize substrate and oxygen concentration as well as inoculum using both (1) regular batch and (2) O2 fed batch cultures. The highest hydrogen yield (HY) was observed under regular batch (1.4±0.1 mol H2/mol lactate) and the highest hydrogen production (HP) (173.5 µmol H2) was achieved using O2 fed batch. This study has provided proof of principal for the ability of microaerobic fermentation to drive thermodynamically difficult reactions, such as the conversion of lactate to hydrogen.

Keywords: photosynthetic bacterium; dark hydrogen production; lactate to hydrogen; microaerobic fermentation; fed batch; RSM

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1. Introduction Two main worldwide concerns, the increasing demand for energy linked to the limitation of fossil fuel sources, and environmental degradation caused by fossil fuel combustion, drive studies on alternative and sustainable sources of energy. Hydrogen has long been proposed as a potential energy carrier for the future due to its high energy content, the fact it is carbon neutral/negative, and that it can be produced biologically by a variety of microorganisms, including algae and bacteria, using wastes as substrates (Das and Veziroglu, 2008; Hallenbeck and Benemann, 2002; Hallenbeck and Liu, 2016). In the absence of light, hydrogen can be produced through the metabolism of a variety of bacteria, including anaerobic and facultative microorganisms (Hung et al., 2011; Kalia and Purohit, 2008). The fermentation of simple sugars (i.e. glucose, fructose, lactose, xylose) occurs along with a high rate production of H2 and organic acids. This process has been studied using psychrophilic, mesophilic, thermophilic and hyper thermophilic pure cultures and microbial consortia (pre-treated or not) growing in batch and continuous mode (Alvarez-Guzmán et al., 2016; Cakir et al., 2010; Gadow et al., 2013; O-Thong et al., 2008). Furthermore, hydrogen production through dark fermentation can use complex substrates, such as domestic and industrial residues, coupling alternative energy production with waste mitigation (Chong et al., 2009). However, even though hydrogen production through dark fermentation is attractive since it is a relatively simple method, the remaining untreated organic acids in the reactor´s effluent limits the hydrogen yield from glucose to 33% of the theoretical value (Hallenbeck, 2009).

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The inability to carry out complete substrate conversion not only makes dark hydrogen fermentation too inefficient to be practical, the process as presently carried out creates a very significant waste stream that would have to undergo treatment before discharge. Moreover, the inability to effectively use organic acids in this process eliminates from consideration many available waste streams (Keskin et al., 2011). The reason for this is purely thermodynamic, as shown by the following considerations for lactate (Thauer et al., 1977).

Lactate- + 2H2O

Acetate- + HCO3- + H+ + 2H2

ΔG0’= -4.2 kJ

(1)

Acetate- + 4H2O

2 HCO3-+ H+ + 4H2

ΔG0’= +104.6 kJ

(2)

Lactate- + 6H2O

3 HCO3-+ 2H+ + 6H2

ΔG0’= +100.4 kJ

(3)

Lactate could be taken up and converted to acetate, yielding two hydrogens (equation 1). However, the free energy available is essentially zero since it is well below the amount required to generate a single ATP. Moreover, this would still leave two thirds of the carbon as organic carbon that would need to be further treated. Conversion of acetate to hydrogen (equation 2) is impossible under standard conditions given the large positive free energy required. Thus, coupling these reactions to give complete conversion to hydrogen will not occur except under special conditions. One example of this is coupled syntrophic metabolism requiring two or more organisms. For example, some organic acid conversions can take place in the presence of methanogens, whose avid uptake of hydrogen sharply reduces the hydrogen partial pressure, creating a Lavoisier driving force (Stams and Plugge, 2009).

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Another possibility to attempt to force the complete conversion of organic acids would be to supply extrinsic energy to the microbe to enable it to carry out thermodynamically difficult or forbidden reactions. For example, purple non-sulfur photosynthetic bacteria are capable of converting organic acids to hydrogen since they can use captured light energy in a type of metabolism called either photoheterotrophic growth, or more simply, photofermentation. Photofermentation has been extensively investigated as it holds promise as an environmentally friendly way to produce hydrogen biologically (Hallenbeck and Liu, 2016). Diverse genera of photosynthetic purple non-sulfur bacteria can, using captured light energy, extract the hydrogen stored in organic acids for further hydrogen production, potentially improving the hydrogen yield up to 12 moles of hydrogen per mole of hexose (Hallenbeck and Benemann, 2002). Therefore, from this point of view, photofermentation could be considered an advantageous process over dark fermentation because of the versatility of PNSB (purple non-sulfur bacteria) which consume a variety of carbon sources, including both sugars and organic acids (Abo-Hashesh et al., 2011). However, one of the key issues of photofermentative hydrogen production that impacts its economic viability is the high costs related to the need for specialized hydrogen impermeable photobioreactors as well as the large surface areas that would be required. Thus, at present, there is no practical way to convert organic acids, such as lactate to hydrogen, or to boost the dark fermentation of carbohydrates past the 4 H2 / glucose limit. In a relatively novel alternative approach, microaerobic dark fermentation, the required energy for nitrogenase activity is provided under microaerobic conditions through supplying small amounts of oxygen for the cells to produce energy via oxidative

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phosphorylation instead of photophosphorylation (Abo-Hashesh and Hallenbeck, 2012). That study investigated the effects of different carbon sources (glucose and organic acids) and nitrogen sources (glutamate and ammonium) sources on hydrogen production by Rhodobacter capsulatus JP91. Under dark microaerobic conditions a positive correlation between cell growth and the O2 concentration was found. However, increasing oxygen concentrations negatively affected hydrogen production. This suggests that in order to optimize microaerobic dark fermentative hydrogen production, process parameters should be carefully regulated. This is especially true for the supply of oxygen, which, although required for energy generation under these conditions, id

inhibitory at high levels which interfere with the synthesis and enzymatic activity of nitrogenase. Another environmental stimulus that affects nitrogenase and, consequently, hydrogen production in photosynthetic bacteria, is the fixed nitrogen concentration, which regulates the synthesis and activity of nitrogenase at multiple transcriptional and post-translational levels (Masepohl, 2017). Nevertheless, since the cells need nitrogen for growth, the assessment of which nitrogen source and the concentration that promotes the highest yields is necessary (Gabrielyan et al., 2010; Hakobyan et al., 2012). Thus, the aim of the present study is to evaluate the possibility of using a photosynthetic bacterium, Rhodopseudomonas palustris GCA009, under microaerobic dark conditions, to produce hydrogen from lactate. Initial screening of the different important bioprocess variables; nitrogen source and its concentration, carbon source concentration, and inoculum size, which could influence hydrogen production, was performed using Plackett-Burman design. In a second round, Box-Behnken designs,

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using both regular and oxygen fed-batch experiments, were performed to optimize the process responses; hydrogen production and yields.

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2. Materials and Methods

2.1.Microorganism and inoculum preculture

Rhodopseudomonas palustris GCA009 was obtained from Dr. Caroline Harwood (University of Washington) and cultivated for 4 days under illumination at 30ºC without agitation in RCV medium according to Abo-Hashesh and Hallenbeck, (2012). The carbon and nitrogen sources were lactate (30 mM) and sodium glutamate (2 mM), respectively. Pre-culturing was performed in 0.5 L glass bottles using a 0.4 L working volumes. Argon was flushed through the headspace for 10 minutes after inoculation. When a suitable cell density had been reached, the culture was centrifuged at 3,600 rpm for 10 minutes at room temperature, and the pellet was used as inoculum for the microaerobic dark fermentation tests.

2.2. Microaerobic dark hydrogen production experiments

Hydrogen production experiments were performed in glass bottles (160 ml) using a 38 ml liquid working volume. The concentration of the carbon source, sodium lactate, was varied from 2 to 10 mM according to the experimental design. The medium was supplemented with 5 ml/L RCV super salts solution (Weaver et al., 1975). The concentration of the nitrogen source, sodium glutamate, was also varied according to the experimental design (Table 1). The following procedure was used for making the microaerobic atmosphere: (1) after inoculation, the headspace of the bottles was flushed with Argon for 10 minutes and then (2) different volumes of pure oxygen were

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introduced into the headspace using a sterile syringe to create the desired microaerobic conditions accordingly to the experimental design (Table 1, 2 and 3). The oxygen concentration was determined as volume percentage. In the O2 fed batch experiments, the addition of oxygen up to the initial concentration was made when its concentration had decreased by ~20 %. All the bottles were kept in a temperature-controlled incubator at 30ºC without and with agitation (120 rpm) accordingly to the experimental conditions.

2.3.Chemical and chromatographic analysis

Hydrogen and oxygen were measured by a gas chromatograph equipped with a TCD (Shimadzu GC-8A) using a 1 m molecular sieve 5A column with argon as carrier gas; the temperature of the injector and column were 110ºC and 60ºC, respectively and current was 70 mA. Carbon dioxide production was measured in the same gas chromatograph equipped with 1 m Porapak Q column using helium as a gas carrier, the temperature of injector and column were 100ºC and 40ºC, respectively, and current was 100 mA. The consumption of lactate was quantified by the spectrophotometric method described by Figenschou and Marais (1991). Cell dry weights were obtained by converting the absorbance (660 nm) into cell dry weight using the following standard curve developed in this laboratory (y=2.3732x-0.0027; R² = 0.9939) where y is the absorbance value and x is the cell dry weight (g/L). The absorbance values (660 nm) for inoculum sizes 50, 100 and 150 % were 0.29±0.02, 0.53±0.03, 0.88±0.03, respectively.

2.4.Experimental Design

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First, a screen for the important parameters that could influence microaerobic dark hydrogen production by R. palustris GCA009 was carried out using a Plackett– Burman experimental design (Table 1). Each analysed variable, at its lowest and highest levels, was as follows: lactate (2-10mM), glutamate (0-5mM), yeast extract (0-0.5 g/L), ammonium sulphate (0-5mM), oxygen concentration (3-10 % v/v) and inoculum size (50-150 % v/v). Total hydrogen production was analyzed at the end of the experimental period, when the biogas production ceased, as the response. Secondly, to optimize the key parameters for enhanced H2 yield of photofermentation process, a 3k Box-Behnken Design (Annadurai et al., 1999) was employed using Design-Expert software (StatEase, Inc., USA). H2 yield and total hydrogen production were chosen as the response variables, while the lactate concentration (X1), oxygen concentration (X2), and inoculum size (X3) were chosen as three independent variables (Tables 2 and 3).

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3. Results and discussion:

The production of hydrogen under microaerobic dark conditions is a new approach, and previous initial studies only carried out a partial survey of the effects of process variables on performance metrics such as rates of hydrogen production and yields. Here we use R. palustris GCA009 in a series of designed experiments to examine the effects of medium composition on process performance.

3.1. Plackett-Burman experimental design

A Plackett-Burman design was used to evaluate the parameters that effect either positively or negatively microaerobic hydrogen production. The experimental design is presented in Table 1 along with total hydrogen production, the response used for analysing the model terms effects. ANOVA analysis showed that the model is significant (p-value > F 0.0491). In general, the presence of fixed nitrogen, glutamate, ammonium or yeast extract negatively affected microaerobic dark hydrogen production (Table 1). Therefore, based on these results, further experiments were done without the addition of a nitrogen source.

3.2. Box-Behnken experimental design for hydrogen production

A better understanding of how different factors could affect the hydrogen production can be obtained by using DOE (design of experiments) along with a statistical modeling (Ghosh et al., 2012). Thus, after identifying the parameters that

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influence the response using Plackett-Burman, which demonstrated that fixed nitrogen negatively affects microaerobic hydrogen production, a Box-Behnken model was used to evaluate the importance and interaction of three different parameters, lactate concentration, oxygen concentration, and inoculum size on hydrogen production (HP) and hydrogen yield (HY). The three independent factors and the responses, hydrogen yield (HY) and hydrogen production (HP) are given in Tables 2 and 3 for regular and O2 fed batch experiments, respectively.

3.2.1. MADF Hydrogen Production and Hydrogen Yields with regular batch O2

In one set of experiments, the parameters; lactate concentration (2-10 mM), oxygen concentration (v/v 3-15 %), and the inoculum size (50-150 %), were examined using regular batch cultures with oxygen added only at the beginning of the experiment. Hydrogen production was observed in all the experimental runs (1-16); however, as expected, it varied. As can be seen from Table 2, the highest hydrogen production (71.8 µmoles H2) was observed with run 16, in which had the highest substrate concentration (10 mM) and the greatest inoculum size (150 %). In contrast, the highest hydrogen yields were achieved at the central points (runs 7-10) with less substrate. This pattern, with higher HYs achieved at lower substrate concentration, and the greatest cumulative hydrogen production achieved at higher substrate concentration, observed in the present study, has previously been reported for both dark and photo-fermentation. However, none of these studies investigated the special case of microaerobic hydrogen production, only reported previously by one study (Abo-Hashesh and Hallenbeck, 2012).

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3.2.1.1. MADF Hydrogen Production

The effect of the three parameters can be evaluated by examining a perturbation plot of the hydrogen production response (Figure 1 A). It can be seen that variations in inoculum size (C) had the strongest effect, with hydrogen production more than tripling as it was varied from its lowest value to its highest. This was followed by variations in the oxygen concentration (B), effecting a two-fold increase in hydrogen production at its intermediate value, and then the lactate concentration (A) which gave relatively little change in hydrogen production as it was varied over its entire range. Optimum hydrogen production is achieved at the intermediate substrate concentration (A) and oxygen concentration (B); however, a bigger inoculum size (line C) could potentially produce more hydrogen (Figure 1A). One explanation for this effect is that in these experiments no fixed nitrogen was added, and therefore there was no cellular growth (data not shown). In addition, no nitrogenase synthesis in the dark is likely in these non-growing cells. Therefore, the more concentrated the inoculum, the higher the initial amount of enzyme (nitrogenase) that was added to the reactors and, consequently, the higher the potential hydrogen production. This line of reasoning is supported by additional experiments which showed that even though glutamate concentration was positively correlated with biomass growth, HY was negatively influenced by the addition of a fixed nitrogen source (data not shown). Using RSM (Response surface plots, Figure 2) it is possible to observe the interaction between two variables by keeping the third one at a fixed level (minimum, intermediate, or maximum). This methodology allows one to determine the optimal

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values for the studied variables by maximizing the responses, with the smallest closed ellipse indicating where the maximum predicted values lie and the greater interaction, the more elliptical the contours will be. Complete ellipses are not observed for hydrogen production, mostly likely due to the range of the inoculum size chosen. Nevertheless, interaction between the studied variables can be observed. Figure 2A-C depicts the interactive effects of lactate and oxygen concentrations at three fixed inoculum sizes (50, 100 and 150 %, respectively). The higher the inoculum size (150 %), the greater is the production of hydrogen. Furthermore, maximum hydrogen production was achieved at the highest inoculum size and oxygen concentration. This is mostly likely due to the fact that hydrogen production in the dark is dependent upon ATP produced through cellular respiration with O2. Therefore, the higher inoculum size, the greater is the demand for oxygen. The interaction between lactate concentration and inoculum size can be seen in Figure 2D-F. Increasing the oxygen concentration (from 3 to 15 %) at the same time that substrate and inoculum size are increased gives improved hydrogen production. The response surface suggests that maximum predicted hydrogen production could be achieved outside the range examined. A similar pattern is seen in Figure 2G-I, where the interaction of oxygen concentration and inoculum size are plotted. To summarise, the lowest HP were 5.1; 5.1; 8.1 and 8.8 µmoles H2 were found with runs 1, 2, 6 and 12, respectively (Table 2). For runs 1 and 2, lactate (2 mM) is likely to be the possible limiting factor. For run 6 it can be suggested that the O2 concentration (15 %) was the inhibiting factor since in that run there was the lower inoculum size (50 %) (low inoculum size/O2 concentration ratio = 50/15). For run 12, it is mostly likely that O2 supply was not enough (O2-3 %) since this was at the highest

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inoculum size 150 % (high inoculum size/O2 concentration ration = 150/3). Intermediate values of HP (53±0.9 µmoles H2) were observed at the central points (runs 7-10) with 6 mM lactate, 9 % O2 and 100 % inoculum size. By increasing both the O2 concentration (9 %) and the inoculum size (150 %), very similar HP was observed at run 11. Additionally, at run 16 an increase in the lactate concentration lead to the highest HP (Table 2).

3.2.1.2. MADF Hydrogen Yields

A perturbation plot showed that all the variables studied had a significant effect on the response HY (Figure 1B), as opposed to what was observed with the perturbation plot for HP. All the parameters examined maximized HY at the chosen central points. In addition, when RSM was used to examine the effects of variation in the process parameters, it can be seen that the examined variables and their range had strong and significant interactive effects on the response HY (mol H2/mol lactate) (Figure 3). Maximum HY (1.4±0.1 mol H2/mol lactate) was achieved within the experimental range, as seen in Figure 3 where the gray regions of the three-dimensional contour plots indicate optimization of HY (Figure 3B, E and H) which occurred at 6 mM lactate, 9 % O2 and 100 % inoculum size. Therefore, optimization of HY as the chosen variable was achieved within the range of variables tested. However, total HP was not maximized within this variable space and thus further studies would be needed for achieving maximum hydrogen production (HP).

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3.2.2. MADF Hydrogen Production and yields with Fed batch O2

Based on the regular batch studies discussed above, a set of experiments were performed using the following conditions: lactate concentration (6 mM), inoculum size (100 %) and an oxygen concentration varying from 3 to 15 % with agitation. Under these conditions, a significantly drop in the hydrogen yield was observed and hydrogen production was actually inhibited at 9 and 15 % oxygen (data not shown). The differences observed in these experiments, compared to the previous O2 batch experiments described above, is due to the agitation used. This is well known to affect gas-liquid mass transfer; with more rapid transfer, 9 and 15 % headspace oxygen effectively give higher dissolved oxygen concentrations, causing inhibition of hydrogen production. Therefore, a new range of oxygen concentrations was chosen (1-5 %) for the O2 fed batch experiments carried out with agitation. The parameters used, their range, and the responses obtained are given in Table 3. Under the O2 fed batch conditions used here, higher hydrogen production (173.5 µmoles H2) was observed than in the MADF experiments carried out with regular batch cultures with fixed initial O2; however, the hydrogen yields were lower. The perturbation plot for the response HP for the O2 fed batch (Figure 1C) is clearly unlike what was seen with the regular batch cultures (Figure 1A). It is evident that agitation plays a very important role in the influence of oxygen concentration (line B), with maximum effect being achieved this time in the middle of the range, only 3%, when the other two variables, lactate concentration and inoculum size are held constant at their central values. The lines that represent the lactate concentration (A), and inoculum size (C), both showed continuous upward curvature, meaning that at 3%

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oxygen intermittent feeding (central value) hydrogen production can be increased by increasing lactate concentration, or increasing inoculum size, to the maximum. The three-dimensional countor plots for hydrogen production using O2 fed batch MADF cultures are shown in Figure 4. Contrary to what was seen before, it was not possible to achieve optimized conditions for maximiztion of either HP nor HY (Figure 4 and 5). From the presented results it can be concluded that the greater the inoculum and the greater the substrate concentration, the higher HP. The effects of variation of the three parameters on hydrogen yields with at O2 fed batch with agitation are shown as a perturbation plot in Figure 1D. From this plot it can be seen that the lactate concentration showed the strongest effect in comparison to the other two variables. The main conclusion that can be drawn from the results observed with the present experimental set is that the use of agitation enables a better diffusion of the gas to the liquid reducing the amount of oxygen required. Additionally, the oxygen feeding strategy did not improve the HY in comparison to the regular batch even though the HP increased. Furthermore, is worth to mention that the experimental period for achieving the highest HP (173.5 µmoles H2) at the O2 fed batch was approximately 168 hours, while the highest HP at regular batch (71.3 µmoles H2) was achieved in 72 hours. From these results, we can conclude that the hydrogen production rate is nearly the same for both experimental sets. Therefore, from an economics point of view, it is possible to conclude that O2 fed batch MADF does not offer any advantage for microaerobic hydrogen production with R. palustris.

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Some previous results on biological hydrogen production from Since lactate is one of the main - of the dark fermentation of carbohydrates, a practical technology capable of converting lactate to additional hydrogen could greatly benefit a biohydrogen fermentation process. In theory, six additional moles of hydrogen could be extracted from each mole of lactate (equation (3)). Photosynthetic bacteria, such as Rps. palustris, are capable of achieving substrate conversion efficiencies of over 70 % of theoretical values since, in this growth mode, they can drive the reaction to completion using captured light energy. Indeed some studies have reported actual yields of 4 H2 / lactate or slightly greater (Fascetti and Todini, 1995; Hillmer and Gest, 1977; Lo et al., 2011; Tao et al., 2008), although other studies have reported lower hydrogen yields, close to two (Ren et al., 2009; Kim et al., 2012; Kim et al., 2013; Kim et al., 2014;) or even much lower (Barbosa et al., 2001; Wu et al., 2012). However, the present study is relatively unique as unlike these other studies it was conducted without the input of light, thus removing the need for problematic photobioreactors, but also removing a driving force for the conversion of lactate to hydrogen. Thus, the values obtained in the present study should be compared with those reported by Abo-Hashesh and Hallenbeck (2012), the only other study to have been carried out under microaerobic dark conditions. The hydrogen yields achieved in the present study are three and two-fold higher, for regular batch and O2 fed batch experiments, respectively, in comparison with those previously reported (Abo-Hashesh and Hallenbeck, 2012), demonstrating that DOE (design of experiments) can truly improve process responses.

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Thus, microaerobic dark fermentative hydrogen production has been demonstrated, opening the door for future studies which might enable optimization and higher yields. The present study has shown that oxygen levels are a critical factor and therefore the future optimization of the controlled supply of oxygen should bring about increased yields by decreasing the amount of substrate that, instead of being converted to hydrogen, is oxidized by excess oxygen. Similarly, optimization of the rate of oxygen supply could help maximize the rates of hydrogen production.

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Conclusions Hydrogen was produced by R. palustris under microaerobic dark fermentation in the presence of glutamate and yeast extract; however, ammonia completely inhibited HP. The yield varied with experimental conditions, and was higher using batch rather than at O2-fed batch experiments. The gradual, intermittent supply of oxygen allowed total substrate consumption. Indeed, this strategy increased HP, however, it did not enhance HYs. Finally, it has been shown here that microaerobic fermentation can be used to drive thermodynamically constrained reactions, such as the production of hydrogen from lactate. The yields obtained approach those observed under the much more widely studied condition of photofermentation.

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Acknowledgments This research was supported by a NSERC (Natural Sciences and Engineering Research Council (Canada) Discovery Grant to PCH. CZL was supported by a scholarship from CNPq/Brazil (Process 202426/2014-9) and ZYH thanks TUBITAK, 2214/A International Doctoral Research Fellowship Programme (Turkey). The views expressed in this article are those of the authors and do not reflect the official policy or position of the United States Air Force, the Department of Defense, or the US Government.

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29. Stams, A.J.M., Plugge, C.M., 2009. Electron transfer in syntrophic communities of anaerobic bacteria and archaea. Nat. Rev. Micro. 7, 568-577. 30. Tao, Y.Z., He, Y.L., Wu, Y.Q., Liu, F.H., Li, X.F., Zong, W.M., Zhou, Z.H., 2008. Characteristics of a new photosynthetic bacterial strain for hydrogen production and its application in wastewater treatment. Int. J. Hydrogen Energy 33, 963-973. 31. Thauer, R.K., Jungermann, K., Decker, K., 1977. Energy conservation in chemotrophic anaerobic bacteria. Bacteriol. Rev. 41, 100-180. 32. Weaver, P.F., Wall, J.D., Gest, H., 1975. Characterization of Rhodopseudomonas capsulata. Arch. Microbiol 105, 207-216. 33. Wu, S.C., Liou, S.Z., Lee, C.M., 2012. Correlation between bio-hydrogen production and polyhydroxybutyrate (PHB) synthesis by Rhodopseudomonas palustris WP3-5. Bioresour. Technol. 113, 44-50.

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FIGURE CAPTIONS: Figure 1: Perturbation plots depicting the effects of the three parameters: Line A (Lactate concentration), Line B (Oxygen concentration) and Line C (Inoculum size) on MADF. Fig.1A: Perturbation plot of the HP of regular batch. Fig.1B: Perturbation plot of the HY of regular batch. Fig.1C: Perturbation plot of HP at O2 fed batch with agitation. Fig.1D: Perturbation plot of HY at O2 fed batch with agitation. Figure 2: Three-dimensional contour plots for microaerobic hydrogen production (HP) (µmoles H2) at regular batch. A- Lactate versus O2 (inoculum size 50 %); B- Lactate versus O2 (inoculum size 100 %); C- Lactate versus O2 (inoculum size 150 %); D- Lactate versus inoculum size (3 % O2); E- Lactate versus inoculum size (9 % O2); F- Lactate versus inoulum size (15 % O2); G- O2 concentration versus inoculum size (2 mM lactate); H- O2 concentration versus inoculum size (6 mM lactate); I- O2 concentration versus inoculum size (10 mM lactate). ANOVA analysis showed that the model was significant for the HP response (p-value>0.0002). Additionally, the R2 value (0.9962) indicates the model fits well to the actual data. All model terms and their interaction were statistically significant, except model term B (p-value 0.2662) Figure 3: Three-dimensional contour plots for hydrogen yield (HY) (mol H2/mol lactate) at regular batch. A- Lactate versus O2 (50 % inoculum size); BLactate versus O2 (100 % inoculum size); C- Lactate versus O2 (150 % inoculum size); D- Lactate versus inoculum size (3 % O2); E- Lactate versus inoculum size (9 % O2); F- Lactate versus inoculum size (15 % O2); G- O2 concentration versus

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inoculum size (2 mM lactate); H- O2 concentration versus inoculum size (6 mM lactate); I - O2 concentration versus inoculum size (10 mM lactate). According to the ANOVA analysis the model is significant (p-value > 0.0337) for the response HY. Figure 4: Three-dimensional contour plots of the variation in hydrogen production (HP) (µmoles H2) at O2 fed batch. A- Lactate versus O2 (50 % inoculum); B- Lactate versus O2 (100 % inoculum); C- Lactate versus O2 (150 % inoculum); D- Lactate versus inoculum size (1 % O2); E- Lactate versus inoculum size (3 % O2); F- Lactate versus inoculum size (5 % O2); G- O2 concentration versus inoculum size (2 mM lactate); H- O2 concentration versus inoculum size (6 mM lactate); I- O2 concentration versus inoculum size (10 mM lactate). According to the ANOVA analysis the model is significant (p-value >0.0118) for the response HP. Additionally, the R2 value (0.9443) indicates the model fits well to the actual data. Figure 5. Three-dimensional contour plots for the hydrogen yield (HY) (mol H2/mol lactate) at O2 fed batch. A- Lactate versus O2 (50 % inoculum size); BLactate versus O2 (100 % inoculum size); C- Lactate versus O2 (150 % inoculum size); D- Lactate versus inoculum size (1 % O2); E- Lactate versus inoculum size (3 % O2); F- Lactate versus inoculum size (5 % O2); G- O2 concentration versus inoculum size (2 mM lactate); G- O2 concentration versus inoculum size (6 mM lactate); G- O2 concentration versus inoculum size (10 mM lactate). According to the ANOVA analysis the model is significant (p-value > 0.0004) for the response HY. Additionally, the R2 value (0.9859) indicates the model fits well to the actual data.

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Table 1: Plackett-Burman experimental design and total hydrogen production (HP)

Ru n

Lactat e (mM)

2 5 6 7 8 11 1 3 4 9 10 12

2 2 2 2 2 2 10 10 10 10 10 10

Glutamat e (mM)

NH4 (mM )

Yeast Extrac t (g/L)

O2 concentratio n (%)

0 5 5 0 5 0 5 5 0 5 0 0

0 5 5 5 0 0 0 5 0 0 5 5

0 0.5 0 0 0.5 0.5 0.5 0 0 0 0.5 0.5

3 3 10 10 10 3 10 3 10 3 3 10

Inoculu m size (%) 50 50 150 150 50 150 150 50 50 150 150 50

H2 productio n (µmoles H2 ) 12.8 0.2 0.85 0.85 2.2 8.2 11.9 0 24.3 13.6 1.2 0

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Table 2: Box-Behnken experimental design for microaerobic dark fermentation (MADF) with three independent variables at regular batch. Oxygen Inoculum a concentration Hydrogen size (%) Run (%) Production X1 Code X2 Code X3 Code 1 2 -1 9 0 50 -1 5.08 2 2 -1 15 1 100 0 5.08 3 2 -1 3 -1 100 0 13.21 4 2 -1 9 0 150 1 46.75 5 6 0 3 -1 50 -1 25.41 6 6 0 15 1 50 -1 8.1 c 7 6 0 9 0 100 0 54.55 c 8 6 0 9 0 100 0 52.51 c 9 6 0 9 0 100 0 53.19 c 10 6 0 9 0 100 0 49.80 11 6 0 15 1 150 1 51.8 12 6 0 3 -1 150 1 8.81 13 10 1 9 0 50 -1 14.91 14 10 1 15 1 100 0 28.80 15 10 1 3 -1 100 0 14.23 16 10 1 9 0 150 1 71.83 a total hydrogen production at the end of the batch, µmole H2 b mol H2/mol lactate c central points Lactate (mM)

b

Hydrogen Yield 0.07 0.08 0.24 0.74 0.71 0.06 1.48 1.25 1.47 1.3 0.36 0.26 0.11 0.13 0.42 0.7

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Table 3: Box-Behnken experimental design for O2 fed batch with three independent variables at O2 fed batch.

Run

Lactate (mM)

Oxygen concentration (%)

X1 Code X2 Code 1 2 -1 3 0 2 2 -1 5 1 3 2 -1 3 0 4 2 -1 1 -1 5 6 0 5 1 c 6 6 0 3 0 c 7 6 0 3 0 8 6 0 1 -1 9 6 0 5 1 10 6 0 1 -1 c 11 6 0 3 0 c 12 6 0 3 0 13 10 1 1 -1 14 10 1 3 0 15 10 1 5 1 16 10 1 3 0 a total hydrogen production at the end of the batch, µmole H2 b mol H2/mol lactate c central points

Inoculum size (%) X3 150 100 50 100 150 100 100 50 50 150 100 100 100 150 100 50

Code 1 0 -1 0 1 0 0 -1 -1 1 0 0 0 1 0 -1

a

Hydrogen Production

b

63.9 16.1 32.1 29.0 90.2 58.1 80.6 42.0 34.2 39.3 61.1 74.8 48.2 173.5 57.4 46.5

0.74 0.19 0.37 0.35 0.56 0.51 0.59 1.13 0.31 1.11 0.50 0.57 0.66 1.04 0.30 0.68

Hydrogen Yield

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HIGHLIGHTS

-

High hydrogen yield from lactate under dark microaerobic conditions demonstrated.

-

Addition of fixed nitrogen had a negative effect on hydrogen production.

-

HY was higher using regular batch culture, HP was higher using O2 fed batch.

-

Agitation plays an important role in MADF by augmenting oxygen diffusion.

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