In general, biological production of methane from algae or cyanobacteria is a two — step process. The first step is biomass production or capturing and conversion of sun light energy into new algal cells. The second step is a transformation of energy stored as biomass into a more applicable form, such as methane gas, through the ADP. Methane is easily stored, transported, and used for the production of heat or electricity. Methane can also be used as a motor fuel. The efficiency of methane production from sunlight energy relies on the performance of these coupled steps.

5.1 Algal Biomass Improvement

The performance of the biomass production step can be described by productivity per acre but the algal methane potential is controlled by algal biochemical composi­tion. In this section, we review factors that control and limit algal productivity and methane potential. We also describe methods used for the improvement of methane production from algae.

The ability of green algae to produce hydrogen was discovered over 70 years ago [438, 439]. Hydrogen is recognized in the US and EU as a promising future fuel [440]. While the technology of generating hydrogen from algae is far from being at the industrial scale, several laboratories are working on improving the hydrogen production efficiency by using transcriptomics, proteomics, and metabolomic data [441-443].

The ADP can be used to process surplus algal biomass into methane or hydro­gen. A combined biorefinery concept has recently been proposed [157]. During the first step, C. reinhardtii produces hydrogen via a sulfur deprivation method. During the second step, it is digested anaerobically for methane production. The authors reported approximately 123% more biogas production from algae after the hydro­gen production cycle compared to fresh algae due to the accumulation of lipids and carbohydrates during the sulfur deprivation step [443, 444].

Phototrophic Microbial Fuel Cell

Light energy can be converted into electrical energy via a PMFC [445-448]. The combination of an ADP and a PMFC in a closed loop system can produce methane and electricity [237]. In this system, algal biomass serves as a substrate for AD and provides oxygen (final electron acceptor) for the fuel cell. The liquid phase of digested biomass contains compounds and nutrients to supply electrons in the fuel cell and for subsequent use as media for algal growth. While the system failed to operate in continuous-flow mode, in batch experiments it successfully produced methane (0.32 L/gVS) and power (1.33 mW/m2 illuminated footprint area). Possible reasons for the failure of a continuous system are low biomass concentration, high nitrate concentration, or high water circulation flow rate [237] .

3.1 Classification of Gas Hydrate Deposits

Natural GH accumulations are divided into three main classes [124] based on sim­ple geologic features and the initial reservoir conditions. Class 1 deposits are com­posed of two layers: the HBL and an underlying two-phase fluid zone containing mobile gas and liquid water [129, 125] . Class 2 deposits comprise two zones: an HBL, overlying a zone of mobile water (hereafter referred to as WZ). Class 3 accu­mulations are composed of a single zone, the hydrate interval (HBL), and are char­acterized by the absence of an underlying zone of mobile fluids. In Classes 2 and 3, the entire HBL may be well within the hydrate stability zone and can exist under equilibrium or stable conditions. A fourth class (Class 4) pertains specifically to oceanic accumulations, and involves disperse, low-saturation hydrate (SH < 10%) deposits that lack confining geologic strata [140].

While the CH4 system that is used almost exclusively in gas production study is thought to be well understood and described, an important issue (with significant implications) that has yet to be investigated is the hysteresis between the P-T rela­tionships in a warming and cooling hydrate system. All predictions reported in the literature have relied exclusively on the warming P-T relationships, while the cool­ing P-T relationships have not even been quantified. The cooling P-T curve has a very different behavior (attributed to metastability) that is characterized by a long period of very slight pressure drop during continuous cooling, followed by a precipi­tous drop in P beyond a certain point. Because cooling and secondary hydrate forma­tion are quite common in the course of hydrate production [ 131, 132] , such P-T behavior can have a significant effect on production.

5.2.6 Fast P-T-X Parametric Relationships in Composite Hydrates

Even small amounts of a second hydrate-forming gas in addition to CH4 (a common occurrence in geologic GH deposits) can drastically alter the properties and behav­ior of hydrates. While statistical thermodynamics approaches [ 178 [ allow good descriptions of the composite system, these are cumbersome, slow, and unsuitable for use in numerical simulators. Thus, there is a significant need for fast parametric relationships describing the composite hydrate behavior over the P-T-X spectrum.

Steven W. Singer, Harry R. Beller, Swapnil Chhabra, Christopher J. Chang, and Jerry Adler

Abstract We are developing an integrated Microbial-ElectroCatalytic (MEC) system consisting of Ralstonia eutropha as a chemolithoautotrophic host for meta­bolic engineering coupled to a small-molecule electrocatalyst for the production of biofuels from CO2 and H2. R. eutropha is an aerobic bacterium that grows with CO2 as the carbon source and H2 as electron donor while producing copious amounts of poly- hydroxybutyrate. Metabolic flux from existing R. eutropha pathways is being diverted into engineered pathways that produce biofuels. Novel molybdenum electrocatalysts that can convert water to hydrogen in neutral aqueous media will act as chemical mediators to generate H2 from electrodes in the presence of engineered strains of R. eutropha. To increase the local concentration of H2, we are engineering R. eutropha’s outer-membrane proteins to tether the electrocatalysts to the bacterial surface. The integrated MEC system will provide a transformational new source of renewable liquid transportation fuels that extends beyond biomass-derived substrates.

S. W. Singer (H)

Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA e-mail: swsinger@lbl. gov

H. R. Beller

Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 , USA S. Chhabra

Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA C. J. Chang

Department of Chemistry, University of California-Berkeley, Berkeley, CA 94720, USA J. Adler

Logos Technologies, Arlington, VA 22203, USA

J. W. Lee (ed.), Advanced Biofuels and Bioproducts, DOI 10.1007/978-1-4614-3348-4_40, 1091 © Springer Science+Business Media New York 2013

1 Introduction

Liquid transportation fuels are a critical component of the energy infrastructure of the United States. New sources of liquid fuels are required to replace petroleum — derived fuels because current supplies of petroleum are unstable and CO2 produced by combustion of liquid transportation fuels is a significant contributor to green­house gas emissions. Currently, there is significant interest in transforming lignocel — lulosic biomass into liquid transportation fuels through hydrolysis of the biomass to monomeric sugars and fermentation to fuels, providing a carbon-neutral, renewable source of liquid fuel [20]. However, problems associated with generating engineered crops, efficient use of arable land, development of cost-effective pretreatment pro­cesses, and the cost of deconstructing enzymes still remain largely unsolved [24]. An elegant alternative to the production of cellulosic biofuels would be to transform CO2 directly into liquid fuels, mitigating CO2 emissions and creating a biofuel pro­duction process with the potential to be carbon-neutral [7]. Autotrophic microor­ganisms have evolved multiple pathways to utilize ubiquitous natural reductants to reduce CO2 [23]. Diverting these pathways to produce liquid fuels is an intriguing opportunity to develop fuels to replace petroleum-based fossil fuels. Algal and cyanobacterial species have significant potential for generating biofuels, however these organisms absorb light inefficiently and are expensive to culture at industrial scale [17]. Chemoautotrophic organisms that use inorganic reductants ([S2-, Fe(II), H2]) or electricity have the possibility to overcome these limitations, as they may be able to reduce CO2 more efficiently, are not dependent on available light, and may be adapted readily to industrial conditions [7] . However, these chemoautotrophic organisms, which are often isolated from extreme environments, tend to grow to low cell densities and are very difficult to manipulate genetically.

One class of chemoautotrophic bacteria, “Knallgas” bacteria that grow with H2/ CO2 under aerobic conditions, does not have these limitations. The model species of this class, Ralstonia eutropha, can grow to very high cell densities ( >200 g/L) and has been extensively manipulated genetically [16]. Under nutrient limitation, R. eutropha directs most of the reduced carbon flux generated by the Calvin cycle to synthesis of polyhydroxybutyrate (PHB), a biopolymeric compound stored in granules. Under growth with H2/CO2 , 61 g/L of PHB was formed in 40 h, which represents ~70% of total cell weight [10] . PHB and related polyhydroxyalkonate polymers have been produced at industrial scale and marketed as Biopol™ (Monsanto) and Mircel™ (Metabolix) [16].

The PHB synthesis pathway in R. eutropha involves three genes expressed as an operon (Fig. 1) [15]. The gene products of this operon are PhaA, a b-ketothiolase, PhaB, an acetoacetyl-CoA reductase, and PhaC, the PHB synthase (Fig. 1). Numerous mutants have been generated that are impaired in PHB synthesis and these mutagenesis studies have demonstrated that PHB synthesis can be blocked with minimal effects on cellular function [19, 22]. An R. eutropha strain generated by chemical mutagenesis that is impaired in PHB synthesis has been shown to secrete large amounts of pyruvate into the medium under autotrophic conditions,

PhaA = p-ketothiolase PhaB = NADPH-dependent reductase PhaC = PHB synthase

Fig. 1 Polyhydroxybutyrate (PHB) synthesis pathway in Ralstonia eutropha suggesting that the mutant maintains a similar magnitude of carbon flux in the absence of PHB synthesis [4]. Metabolic engineering strategies have been success­fully employed to increase carbon flux through the PHB pathway, suggesting that R. eutropha will be a suitable host for synthetic biology applications [5, 13].

Current efforts to produce biofuels using synthetic biology have focused on using model organisms (Escherichia coli and Saccharomyces cerevisiae) as hosts for met­abolic engineering [3, 6]. These efforts have concentrated on using biomass-derived carbohydrates as the sources for renewable biofuel generation [12]. These strategies require redirection of central metabolic pathways by introduction of new pathways that redirect metabolic flux to a desired end-product. This approach has been used to produce alcohols, alkenes, and isoprenoids that may be used as liquid fuel substi­tutes for petroleum products [8]. Rewiring the metabolism of these model organ­isms so that they can utilize CO2 as the carbon input for biofuel production would have substantial benefits in broadening the substrate scope for metabolic engineer­ing and reducing CO2 emissions. R. eutropha is an attractive host for biofuel pro­duction from CO2 as it already has the capability for autotrophic growth, is amenable to metabolic engineering, and expresses a metabolic pathway that supports significant carbon flux.

An inexpensive source of H2 will be essential for the effective development of R. eutropha as a biofuel-producing platform. For fuel production to be sensible, the method of H2 generation must utilize methods that do not themselves consume fossil-derived energy, or draw low-carbon energy away from carbon mitigating uses. Known small-molecule metal catalysts generally require organic acids, addi­tives, and/or solvents that are also incompatible for use with living organisms [9]. Molybdenum polypyridyl complexes (MoPy5) have been shown to be excellent catalysts for the electrochemical reduction H+ in neutral water at rates that approach to that of hydrogenase enzymes (Fig. 2) [11]. These electrocatalysts are stable and evolve H2 in seawater and are compatible with microbial growth media. R. eutropha is an ideal microbe to couple with electrocatalysis, as growth with H2 generated in situ by an electrode has already been demonstrated [18]. Electrocatalysis will be coupled in two ways: the MoPy5 catalyst will be tethered to the electrode surface and H2 generated at the surface will be used for chemoautotrophic growth and

biofuel production by engineered strains of R. eutropha. In the second configuration, MoPy5 catalysts will be tethered directly to the surface of engineered R. eutropha strains and the strains will interact directly with the electrode surface. In this configuration, the tethered catalyst will generate H2 at the electrode, which will be used by engineered R. eutropha strains for growth and biofuel production.

The integrated MEC (Microbial-ElectroCatalytic) system, the combination of a novel catalytic system to generate H2 directly from water coupled to a chemolithoau — totroph, R. eutropha, that is metabolically engineered to produce high titers of bio­fuels from H2 and CO2, will be a novel technology that will provide a new source of renewable liquid transportation fuels that extends beyond biomass-derived sub­strates (Fig. 3).

2 Experimental Approach

The thermochemical conversion of biomass through pyrolysis allows to convert dry biomass into a liquid, and to perform, at the same time, both the extraction of hydro­carbon-like material and the cracking of high molecular weight compounds (Fig. 11). The main advantages of this approach are: (1) avoiding the use of any solvent, thus increasing the “greenness” of the process and (2) producing useful co-products, like a combustible gas, usable as process energy source, and a biochar, suitable as soil — amending material as well as stable carbon sink. Thus, this strategy allows to exploit in different ways all the biomass constituents, the liquid bio-oil, and the residues (gas and biochar), increasing the net energy/CO2 balance of a hypothetical algal cultivation (Fig. 12).

In the present work, B. braunii pellets were treated through a fixed bed pyrolysis at 500°C following the same experimental procedure described in the literature [46]. The yields of the three major pyrolysis fractions (bio-oil, biochar, and vola­tiles) and their composition are shown in Table 5.

Pyrolysis gas yield is 37%, corresponding to 28% yield after feedstock water subtraction.

Gas composition is evaluated by pyrolysis coupled with dynamic solid-phase micro-extraction (Py-SPME) followed by GC-MS analysis [47]. This technique is capable of giving information on the composition of gaseous and semi-volatile pyrolysis products, useful to obtain an overall picture of the most volatile compounds. The analysis reveals the presence of light hydrocarbons, aromatic hydrocarbons

(mainly toluene), and some nitrogen-bearing aromatics (pyridine, indole) probably originated from pyrolysis of proteins.

Biochar yield is 38%, of which almost half of weight is composed by inorganic compounds; in fact, it retains almost all biomass ashes and for this reason biochar is not suitable as solid fuel, although usable as carbon stock. In order to evaluate the structural conformation of biochar, the material is visually characterized by scan­ning electronic microscopy (SEM) and the obtained pictures are shown in Fig. 13.

SEM image shows that biochar from 500°C pyrolysis is formed by a randomly aggregated sponge-like material. The dimension of particles is about 10-100 pm.

Bio-oil (25% on dry weight basis) is almost composed by a n-hexane soluble material; after hexane evaporation, this liquid fraction results in a black-reddish ash­free liquid (ashes less than 0.1%), relatively low viscous, of which only a very small part (1%) is insoluble in n-hexane. Moreover, the bio-oil is characterized by a negli­gible amount of water. This finding is quite surprising because the presence in the B. braunii feedstock of a significant amount of proteins (7%) and polysaccharides (20%), since the polar and oxygenated nature of these macromolecules, should give an expected larger amount of reaction water and of oil insoluble in n-hexane.

6

Time (min)

Fig. 14 GC-FID chromatogram of B. braunii pyrolysis oil

Reasonably, we can assume that the “vaporization” and hydrocarbon cracking processes are more effective than carbohydrate/proteins pyrolysis, and that the presence of non-lipid materials determines a low yield of pyrolysis products respect to hydrocarbon material. Nevertheless, if we consider the large carbohydrates content (20% of feedstock), usually able to produce a significant amount of organic tar (33% yield from cellulose using the same reactor [48]), such low amount of n-hexane — insoluble matter is noticeable. When a hydrocarbon-rich microalga as B. braunii is submitted to pyrolysis, proteins and polysaccharides form only a minor amount of bio-oil. From a practical point of view, this means that pyrolysis can be seen as a suitable “solvent-less extraction method” highly selective for hydrocarbons.

The chemical characterization by GC-FID and GC-MS of the n-hexane-soluble bio-oil reveals the presence of typical B. braunii linear dienes and trienes (C27H52, C29H56, C29H54 and C31H60) and, in addition, a series of random hydrocarbon fragments consisting in Cjj-C27 alkanes and alkenes (Fig. 14). From a quantitative point of view, almost the whole amount of the pyrolysis oil (around 90%) consists of GC detectable compounds, indicating that heavy cross-linked hydrocarbon polymers are depolymerised to smaller fragments during the pyrolytic treatment.

In order to have information on the volatilization properties of this potential new fuel, thermogravimetric analysis (TGA) was also done (Fig. 15).

TGA of pyrolytic oil shows four weight loss steps characterized by different extent. First, weight loss, probably generated by a gradual volatilization of small random length Cj 1-C27 hydrocarbons, starts gradually from 50°C and becomes important at 200-300°C. Around 300°C, a sharp weight loss derivatives peak, prob­ably deriving from the boiling of the olefin C2 9H56, is observed. In addition, two smaller weight losses are recorded at 380 and 450°C, probably related to C31H60 and residual high molecular weight matter in the oil. In general, TGA observation confirms the results obtained from GC analysis and gives an indication that

Fig. 15 TGA profile of B. braunii pyrolysis oil.

Dotted line: percent weight change; full line: temperature derivate of weight change.

HMW high molecular weight residue (e. g. ether lipids, or other long chain hydrocarbons)

20

10

0

n-hexane-soluble pyrolysis oil obtainable from B. braunii is lighter than B. braunii non-polar lipids and hydrocarbons from which it is produced (see Fig. 3). Moreover, whole n-hexane-soluble fraction boils out almost totally (>90% weight loss) before 400°C. This could be an indication that the liquid obtained could be directly used as fuel, without any further upgrading or modification.

The culture velocity within the solar receiver is very important, as the cells must be evenly distributed throughout the tubing to avoid extended periods within the dark zones located at the centre of the tubing. The maximum velocity obtained within the system is dependent on the size of micro-eddies in comparison to the algae cell dimension. Acien Fernandez et al. [1] found that the maximum velocity in an exter­nal loop reactor (ELR) for Phaeodactylum tricornutum strain was 1 m/s. This veloc­ity was obtained by a specific power input of 170 W/m3. However, the actual velocity used in the ELR was 0.5 m/s due to issues associated with the mechanical properties of the solar receiver. The velocity must be high enough to ensure turbulence, thus preventing bio-sedimentation. However, the liquid velocity cannot be applied at gratuitous speeds, as this could potentially cause damage to the algal cells. Generally the liquid velocity must comply with two constraints: a turbulent Reynolds number and a micro eddy length that is significantly larger than the cell dimensions [1].

1.3.3 pH

The pH of the cultivation system increases as the algal cells photosynthesise. The consumption of carbon dioxide and the production of dissolved oxygen from pho­tosynthesis can significantly alter the pH thereby impeding growth. The cultivation system requires a relatively neutral environment, usually maintained at pH ~8 [34]. To prevent variations in culture pH, appropriate control systems are incorporated to monitor the pH. Another technique to control variations in pH is to employ carbon dioxide injection points along the tube run. This prevents excessive culture pH and any carbon limitation that may occur [23]. However, this is not economically viable when considering large algal plants.

1.7.1 Cultivation Economics

The economic model, as shown in Fig. 9, identified the raceway pond as the cheap­est production system ($2.77/kg) followed by the HTR ($9.91/kg). The ELR was the most expensive option ($12.98). The greater complexity of the reactor-style systems was found to require a much greater level of FCI, $2.7 billion for the HTR system and $3.6 billion for the ELR system compared with only $0.73 billion for the raceway ponds. This FCI was represented in annual cost terms as the deprecia­tion of the cultivation system, expressed in Fig. 9, by the black equipment cost por­tion of the graph. Figure 9 shows these higher equipment costs are the major contributors to the greater overall production cost. Furthermore, the magnitude of FCI required to build the reactor-style systems makes investment in these alterna­tives unlikely, at least on such a vast scale.

The running cost of each cultivation system is represented by the grey segment in Fig. 9 and is examined in greater detail in Fig. 10, which divides the costs into specific components. Notably, the major contributors to the annual running costs shown in Fig. 10 were found to vary greatly between the raceway pond and the reactor-style systems. Major contributors to the annual running costs of the raceway

Raceway Pond HTR ELR

E Electricity H Culture Medium H Wastewater Treatment

□ Maintenance DD Other Expenses

Fig. 10 Breakdown of annual running costs for different cultivation systems pond were found to be the culture medium and the wastewater treatment, while in the reactor-style systems’ electricity consumption and maintenance costs were the great­est contributors to running costs. The larger volume of fluid processed in the raceway pond system, due to its lower volumetric productivity, led to greater culture medium and wastewater treatment costs. In contrast, the larger maintenance costs of the HTR and ELR systems resulted from the greater complexity of the system operation.

The considerably larger electricity consumption of the reactor-style systems could be attributed to the use of an airlift pump to mix the culture, which used significantly larger amounts of energy to operate than the simple paddle wheel used

H Capital Costs □ Electricity Costs □ Flocculant Cost

Fig. 11 Biomass dewatering costs for raceway pond (RP) in the raceway pond system. Despite the lower production costs of the raceway pond system, it is necessary to account for the risk of additional costs resulting from con­tamination of the algal culture. This risk is a significant drawback in the use of raceway ponds for cultivation compared to the use of reactor-style systems. Contamination results from a lack of control and exposure to the external environ­ment, and can lead to lower growth rates of biomass unsuitable for downstream.

Human health metrics are often overlooked in the analysis of alternative energy sources. Part of the reason for this is that, of the three classes of inputs surveyed here, these tend to have the highest embedded uncertainty. The exposure to hazard­ous substances varies significantly, and this can greatly impact the results of an analysis. Further, since limited toxicological data is available for many compounds, developing reliable causal relationships is a challenge. When impacts can be quantified in a life cycle context, they are often reported in terms of disability — adjusted life years.

Ignoring the contribution that human-health indicators may have on algae-to — energy life cycle studies could be an important oversight for several reasons. The most dangerous substances on the United States Environmental Protection Agency list of carcinogenic chemicals reveals that many are agricultural chemicals. If algae are deployed as an alternative to terrestrial agriculture, which is heavily reliant on harmful herbicides, fungicides, and pesticides, there could be a net advantage to adopting aquatic species for biomass generation. Of course, the algae cultivation sector is too young to know whether it will require significant flows of agricultural chemicals to cope with pests or other problems. Similarly, the water quality implica­tions of large-scale algae cultivation could have mixed impacts. On the one hand, algae could remove contaminants from water sources, serving effectively like a large ecosystem-level “liver” for toxin removal. On the other hand, algae could excrete low levels of toxic chemicals as exemplified by coastal red tides. In short, any large-scale production of algae is likely to have some human health conse­quence and even though it is difficult to predict how those will manifest at this early stage, it is not difficult to anticipate that better tools will be needed to understand these relationships as the technology matures and becomes deployed.

Chlorophyll content (a and b) was determined spectrophotometrically at the maxi­mum absorption wavelengths for each chlorophyll. DR 5000 UV-vis spectropho­tometer from Hach, USA, was used for all analyses. The wavelength maxima, A, for chlorophyll a and chlorophyll b are, respectively, 664 and 647 nm in 90% ace­tone and 665 and 652 nm in 90% methanol. One milligram of either chlorophyll a or chlorophyll b standard was dissolved in either a mixture of acetone (45 mL) and water (5 mL) or a mixture of methanol (45 mL) and water (5 mL) to form stock solutions. All of the stock solutions were then diluted with their respective solvent mixtures (either 90% acetone or 90% methanol) to form standard solutions with five different concentrations (0.004, 0.008,0.0012, 0.0016, and 0.02 mg/mL). Calibration curves of each chlorophyll type in a particular solvent mixture were created by plot­ting the absorbance of its standard solutions (at the maxima of both chlorophyll a and chlorophyll b) against concentration. In accordance to Lambert-Beer law, specific absorbance coefficient, a (in mL/mg/cm), is the gradient of the linear por­tion of the calibration curve [26]. Table 5 summarizes the wavelength maximas,

Amax, and the specific absorbance coefficents, a, of chlorophyll a and chlorophyll b in 90% acetone and 90% methanol. Concentrations of chlorophyll a and chlorophyll b in a pigment extract containing both chlorophylls can be simultaneously deter­mined using an extension of Lambert-Beer law which takes into account the contri­bution of chlorophyll b absorbance to the absorbance of chlorophyll a at chlorophyll a maxima and vice versa. Principles of this extended theorem and the derivation of its formulae can viewed elsewhere [26]. Based on the a values obtained in Table 5, concentration of chlorophyll a, Ca (mg/mL), and concentration of chlrorophyll b, Cb (mg/mL), in the diluted extract sample are calculated as follows:

For extracts in 90% acetone and 10% water,

Ca = 0.00964A664 — 0.00175Аб47 (1)

Cb = 0.01487A647 — 0.00410A664 (2)

where A664 and A647 are the absorbances of the diluted extract sample at 664 and 647 nm respectively.

For extracts in 90% methanol and 10% water,

Ca = 0.02215A665 — 0.01252A652 (3)

Cb = 0.01978A652 -0.00894A665 (4)

Where A665 and A652 are the absorbances of the diluted extract sample at 665 and 652 nm respectively.

The concentrations of chlorophyll in the original extract sample were obtained by multiplying the concentrations of chlorophyll in the diluted extract sample with a dilution factor of 24. Chlorophyll a yield (g chlorophyll a/g dried microalgae) was the product of concentration of chlorophyll a in the original extract sample and volume of acetone or methanol used in extraction (100 mL). Chlorophyll b yield was calculated in a similar manner, while chlorophyll yield was the sum of chloro­phyll a and chlorophyll b yields.