This study is supported by an NSF program (Research Experiences for Undergraduates) at Washington University in St. Louis.

1. Algal Cultivation and Scale-up
<ol>
	<li>Prepare the culture medium using deionized water containing 0.55 g/L-1 urea, 0.1185 g/L-1 KH2PO4, 0.102 g/L-1 MgSO4&#183;7H2O, 0.015 g/L-1 FeSO4&#183;7H2O and 22.5 &#181;l microelements (18.5 g/L-1 H3BO3, 21.0 g/L-1 CuSO4&#183;5H2O, 73.2 g/L-1 MnCl2&#183;4H2O, 13.7 g/L-1 CoSO4&#183;7H2O, 59.5 g/L-1 ZnSO4&#183;5H2O, 3.8 g/L-1 (NH4)6Mo7O24&#183;4H2O, 0.31 g/L-1 NH4VO3). Adjust medium pH to 7-8. Sterilize culture medium via 0.22 &#181;m syringe filter.</li>
	<li>Inoculate Chlorella sp. from a single colony on a fresh agar plate into a shake flask containing 50 mL medium with a sterile inoculating loop. Culture algae under 150 rpm and 30 &#176;C for six days (continuous light condition, photon flux = 40-50 &#181;mol m-2 sec-1). Monitor cell density by a spectrophotometer (OD730).</li>
	<li>Transfer 50 mL algal culture (middle-log growth phase, OD730 &#62;1) into a 2-L glass flask (with ~1 L sterilized culture medium). Pump filtered air (or CO2) into the culture during the incubation (for 5 days).</li>
	<li>Transfer 1 L algal culture into a 20-L glass carboy containing 15 L non-sterilized culture medium (at this stage, risk of microbial contamination is small), then culture algae under same condition as stated in step 1.3.</li>
	<li>Place 15 L fresh algal culture (OD730 = 2) and 85 L non-sterilized medium into a flat plate photobioreactor (equipped with light-emitting diodes, computer controller, gas mixture, analyzers for cell optical density, pH, dissolved oxygen, temperature and dissolved CO2). Pump the flue gas/air mixture into the bioreactor.</li>
	<li>Thoroughly dry-clean the photobioreactor using 70% ethanol after biomass harvest (OD730 &#62;20).</li>
</ol>
2. Laboratory Demonstration of Flue Gas Treatment Using Small Photobioreactors
<ol>
	<li>Inoculate algal cultures in glass bottles (200 ml/min medium/bottle, initial OD730 ~0.3).</li>
	<li>Burn natural gas and pump the flue gas (~250 cm3 min-1) through a funnel, a condenser tube, and a 0.5 L washing bottle (containing water/limestone slurry).</li>
	<li>The mass flow controllers control the flue gas flow into algal culture (Figure 1). Flue gas pulses include two modes: flue gas-on and flue gas-off (pump air instead).</li>
</ol>
3. Kinetic Model Development
The kinetic model assumes: (1) the cultures are homogeneous systems. (2) CO2 concentration and light intensity in the cultures are the limiting factors for algal growth. (3) CO2 partial pressure and its liquid phase equilibrium with H2CO3, HCO3-, and CO32- is simplified with Henry&#39;s Law). The model equations are:
<img alt="Equations 1 and 2" fo:content-width="4in" src="/files/ftp_upload/50718/50718eq1and2.jpg" width="500px" />
X is the biomass (kg&#183;m-3). S is the dissolved CO2 (mol&#183;m-3). P is the CO2 partial pressure in the gas phase (Pa). pi is the partial pressure of ith toxic compound in the gas (such as NOx and SOx). Pmax.i is the partial pressure of toxic gas to have full inhibition on biomass growth. &#951;i is the empirical coefficient. Ks is the Michaelis-Menten constant of CO2 (mol&#183;m-3). KI is the inhibition constant of CO2 (mol&#183;m-3). K is the Michaelis-Menten constant of light intensity (&#181;mol&#183;m-2&#183;sec-1). H is the Henry&#39;s constant for CO2 (Pa&#183;m3&#183;mol-1). KLa is the mass transfer rate of CO2 (hr-1). I is the average light intensity, &#181;mol&#183;m-2&#183;sec-1, which can be calculated as follows (Eq. (3)) 9.
<img alt="Equation 3" fo:content-width="4in" src="/files/ftp_upload/50718/50718eq3.jpg" width="500px" />
The definition of model parameters is in Table 1. The initial conditions assume that biomass and dissolved CO2 concentrations are 100 mg/L and 13 &#181;mol/L, respectively. The volumetric mass transfer coefficient can be estimated by empirical correlation to bioreactor parameters10:
<img alt="Equation 4" fo:content-width="4in" src="/files/ftp_upload/50718/50718eq4.jpg" width="500px" />
Pg/V is the power consumption of the aerated system in the bioreactor (W/m3). ugs is the superficial velocity of the gas flow through the bioreactor (m/sec). &#945;, &#946;, and &#947; are constants related to mixing conditions.
<ol>
	<li>Construct a Simulink file for the model simulation (Screen shots are given in the Supporting Material I).
	<ol>
		<li>Choose File/New/Model on the MATLAB interface to create a Simulink model, and open &#34;Library Browser&#34; (screen shot 1).</li>
		<li>Choose &#39;Subsystem&#39; block in the library browser to create the Subsystems for Equation 1 and 2. Drag one subsystem block to the Simulink model file, change its name to &#39;Equation 1&#39;, and then repeat the same steps for Equation 2.</li>
		<li>Create appropriate blocks and parameters in each subsystem (screen shot 3). Double click the &#39;Equation 1&#39; block, choose appropriate blocks from the library browser and connect them with arrows that denote the calculation sequence, double click the blocks to set up the parameters, and repeat these steps for the other subsystem. 
		Note: 1) The sequence should start with input blocks and conclude with output blocks; 2) The operator blocks for addition, subtraction, multiplication, division and integration can be all found in the library browser, and we suggest users explore the help files of the Simulink to understand how to use them; 3) The optimization solver can be set through the pathway Simulation/Configuration parameters on the toolbar.</li>
		<li>Link the two subsystems to represent model equations (1 and 2). Connect the output of one subsystem to the input of the other subsystem by arrow if necessary. For example, the dissolved CO2 concentration is the output in the Equation 2 subsystem, and also the input of the Equation 1 subsystem.</li>
		<li>Use &#39;Pulse Generator&#39; block as the inputs for &#39;Equation 2&#39; to simulate the on-off CO2 pulses; use &#39;Constant&#39; block as the surface light input value. Double click the blocks to change the parameters such as the period time and amplitude.</li>
		<li>Choose &#39;Mux&#39; block in the library browser. Connect all the outputs to &#39;Mux&#39; and then connect it to &#39;To Workspace&#39; block that stores the simulated results.</li>
		<li>Define the &#39;Simulation stop time&#39; on the top toolbar, click the button "<img src="/files/ftp_upload/50718/playarrow.jpg" alt="Arrow" width="10px" fo:content-width="0.1in" fo:src="/files/ftp_upload/50718/playarrow.jpg" />" to start the simulation, and the results will be shown in the MATLAB workspace (screen shot 4).</li>
	</ol>
	</li>
	<li>Apply dynamic optimization approach to profile optimal CO2 conditions.</li>
</ol>
To find the changes of inlet inflow CO2 profile (Popt) that maximize biomass production11, MATLAB &#39;fmincon&#39; function and CVP (control vector parameterization)12 are used. Figure 2 illustrates the optimization algorithm (see MATLAB programming codes in the Supporting Material II).

<ol>
	<li>Granite, E. J., O'Brien, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review+of+novel+methods+for+carbon+dioxide+separation+from+flue+and+fuel+gases.">Review of novel methods for carbon dioxide separation from flue and fuel gases.</a> Fuel Process. Technol. 86, 1423-1434 (2005).</li><li>Chisti, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biodiesel+from+microalgae.">Biodiesel from microalgae.</a> Biotechnol. Adv. 25, 294-306 (2007).</li><li>Li, Y., Horsman, M., Wu, N., Lan, C. Q., Dubois-Calero, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biofuels+from+microalgae.">Biofuels from microalgae.</a> Biotechnol. Prog. 24, 815-820 (2008).</li><li>Schenk, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Second+generation+biofuels:+high-efficiency+microalgae+for+biodiesel+production.">Second generation biofuels: high-efficiency microalgae for biodiesel production.</a> BioEnergy Research. 1, 20-43 (2008).</li><li>Kumar, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhanced+CO2+fixation+and+biofuel+production+via+microalgae:+recent+developments+and+future+directions.">Enhanced CO2 fixation and biofuel production via microalgae: recent developments and future directions.</a> Trends Biotechnol. 28, 371-380 (2010).</li><li>Kumar, K., Dasgupta, C. N., Nayak, B., Lindblad, P., Das, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+suitable+photobioreactors+for+CO2+sequestration+addressing+global+warming+using+green+algae+and+cyanobacteria.">Development of suitable photobioreactors for CO2 sequestration addressing global warming using green algae and cyanobacteria.</a> Bioresour. Technol. 102, 4945-4953 (2011).</li><li>Lee, J. -. N., Lee, J. -. S., Shin, C. -. S., Park, S. -. C., Kim, S. -. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+to+enhance+tolerances+of+Chlorella+KR-1+to+toxic+compounds+in+flue+gas.">Methods to enhance tolerances of Chlorella KR-1 to toxic compounds in flue gas.</a> Appl. Biochem. Biotechnol. 84-86, 329-342 (2000).</li><li>Zeiler, K. G., Heacox, D. A., Toon, S. T., Kadam, K. L., Brown, L. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+microalgae+for+assimilation+and+utilization+of+carbon+dioxide+from+fossil+fuel-fired+power+plant+flue+gas.">The use of microalgae for assimilation and utilization of carbon dioxide from fossil fuel-fired power plant flue gas.</a> Energy Conversion and Management. 36 (95), 707-712 (1995).</li><li>Mart&#237;nez, M. E., Camacho, F., Jim&#233;nez, J. M., Esp&#237;nola, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+light+intensity+on+the+kinetic+and+yield+parameters+of+Chlorella+pyrenoidosa+mixotrophic+growth.">Influence of light intensity on the kinetic and yield parameters of Chlorella pyrenoidosa mixotrophic growth.</a> Process Biochem. 32 (96), 93-98 (1997).</li><li>Van't Riet, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review+of+measuring+methods+and+results+in+nonviscous+gas-liquid+mass+transfer+in+stirred+vessels.">Review of measuring methods and results in nonviscous gas-liquid mass transfer in stirred vessels.</a> Ind. Eng. Chem. Process. 18, 357-364 (1979).</li><li>Methekar, R., Ramadesigan, V., Braatz, R. D., Subramanian, V. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimum+charging+profile+for+lithium-ion+batteries+to+maximize+energy+storage+and+utilization.">Optimum charging profile for lithium-ion batteries to maximize energy storage and utilization.</a> ECS Trans. 25, 139-146 (2010).</li><li>Kameswaran, S., Biegler, L. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simultaneous+dynamic+optimization+strategies:+Recent+advances+and+challenges.">Simultaneous dynamic optimization strategies: Recent advances and challenges.</a> Comput. Chem. Eng. 30, 1560-1575 .</li><li>He, L., Subramanian, V. R., Tang, Y. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+analysis+and+model-based+optimization+of+microalgae+growth+in+photo-bioreactors+using+flue+gas.">Experimental analysis and model-based optimization of microalgae growth in photo-bioreactors using flue gas.</a> Biomass Bioenergy. 41, 131-138 (2012).</li><li>Novak, J. T., Brune, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inorganic+carbon+limited+growth+kinetics+of+some+freshwater+algae.">Inorganic carbon limited growth kinetics of some freshwater algae.</a> Water Res. 19 (85), 215-225 (1985).</li><li>Landry, M. R., Haas, L. W., Fagerness, V. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamics+of+microbial+plankton+communities+experiments+in+Kaneohe+Bay,+Hawaii.">Dynamics of microbial plankton communities experiments in Kaneohe Bay, Hawaii.</a> Mar. Ecol. 16, 127-133 (1984).</li><li>Silva, H. J., Pirt, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Carbon+dioxide+inhibition+of+photosynthetic+growth+of+Chlorella.">Carbon dioxide inhibition of photosynthetic growth of Chlorella.</a> J. Gen. Microbiol. 130, 2833-2838 (1984).</li><li>Powell, E. E., Mapiour, M. L., Evitts, R. W., Hill, G. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growth+kinetics+of+Chlorella+vulgaris+and+its+use+as+a+cathodic+half+cell.">Growth kinetics of Chlorella vulgaris and its use as a cathodic half cell.</a> Bioresour. Technol. 100, 269-274 (2009).</li><li>Sawyer, C. N., McCarty, P. L., Parkin, G. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Chemistry for environmental engineering and science. , 144 (2003).</li><li>Doucha, J., Straka, F., L&#237;vansk&#253;, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Utilization+of+flue+gas+for+cultivation+of+microalgae+Chlorella+sp.+in+an+outdoor+open+thin-layer+photobioreactor.">Utilization of flue gas for cultivation of microalgae Chlorella sp. in an outdoor open thin-layer photobioreactor.</a> J. Appl. Phycol. 17, 403-412 (2005).</li><li>Thimijan, R. W., Heins, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Photometric,+radiometric,+and+quantum+light+units+of+measure:+a+review+of+procedures+for+interconversion.">Photometric, radiometric, and quantum light units of measure: a review of procedures for interconversion.</a> Hortscience. 18, 818-822 (1983).</li></ol>

These authors have nothing to disclose.

In this study, we demonstrate the experimental protocol for scaling up algal cultivations in photobioreactors. We also examine several methods for flue gas inputs to promote algal growth. Using a mass transfer and bio-reaction model, we demonstrate that the CO2 mass transfer coefficient KLa (determined by bioreactor mixing condition and CO2 superficial velocity) strongly influences algal growth. The model simulation indicates continuous on-off flue gas pulses with short pulse width and hig...

<table border="0" cellpadding="0" cellspacing="0" width="636">
	<tbody>
		<tr height="21"> 
			<td height="21" style="height:21px;width:212px;">Spectrophotometer</td>
			<td style="width:212px;">Thermal Scientific, Texas USA</td>
			<td style="width:212px;"> </td>
		</tr>
		<tr height="39">
			<td height="39" style="height:39px;width:212px;">CO2 gas analyzer</td>
			<td style="width:212px;">LI-COR, Biosciences, Nebraska USA</td>
			<td style="width:212px;"> </td>
		</tr>
		<tr height="39">
			<td height="39" style="height:39px;width:212px;">Mass flow controllers</td>
			<td style="width:212px;">OMEGA Engineering INC, Connecticut USA</td>
			<td style="width:212px;">FMA5416</td>
		</tr>
		<tr height="58">
			<td height="58" style="height:58px;width:212px;">Data acquisition card</td>
			<td style="width:212px;">Measurement Computing Corporation, Massachusetts USA</td>
			<td style="width:212px;">USB-1208FS</td>
		</tr>
		<tr height="39">
			<td height="39" style="height:39px;width:212px;">Filters</td>
			<td style="width:212px;">Aerocolloid LLC, Minnesota USA</td>
			<td style="width:212px;"> </td>
		</tr>
		<tr height="39">
			<td height="39" style="height:39px;width:212px;">MATLAB/Simulink</td>
			<td style="width:212px;">Mathworks, Massachusetts USA</td>
			<td style="width:212px;">R2010a</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:212px;">Glass bottles</td>
			<td style="width:212px;">Fisher USA</td>
			<td style="width:212px;"> </td>
		</tr>
	</tbody>
</table>

optimize flue gas settings to promote microalgae growth in photobioreactors via computer simulations

Flue gas from power plants can promote algal cultivation and reduce greenhouse gas emissions1. Microalgae not only capture solar energy more efficiently than plants3, but also synthesize advanced biofuels2-4. Generally, atmospheric CO2 is not a sufficient source for supporting maximal algal growth5. On the other hand, the high concentrations of CO2 in industrial exhaust gases have adverse effects on algal physiology. Consequently, both cultivation conditions (such as nutrients and light) and the control of the flue gas flow into the photo-bioreactors are important to develop an efficient &ldquo;flue gas to algae&rdquo; system. Researchers have proposed different photobioreactor configurations4,6 and cultivation strategies7,8 with flue gas. Here, we present a protocol that demonstrates how to use models to predict the microalgal growth in response to flue gas settings. We perform both experimental illustration and model simulations to determine the favorable conditions for algal growth with flue gas. We develop a Monod-based model coupled with mass transfer and light intensity equations to simulate the microalgal growth in a homogenous photo-bioreactor. The model simulation compares algal growth and flue gas consumptions under different flue-gas settings. The model illustrates: 1) how algal growth is influenced by different volumetric mass transfer coefficients of CO2; 2) how we can find optimal CO2 concentration for algal growth via the dynamic optimization approach (DOA); 3) how we can design a rectangular on-off flue gas pulse to promote algal biomass growth and to reduce the usage of flue gas. On the experimental side, we present a protocol for growing Chlorella under the flue gas (generated by natural gas combustion). The experimental results qualitatively validate the model predictions that the high frequency flue gas pulses can significantly improve algal cultivation.

Our previous experimental analysis indicates that continuous flue gas exposure adversely affects the Chlorella growth, while decreasing CO2 exposure time is able to alleviate this inhibition13. To better understand the flue gas inflow and algal growth relationship, we develop an empirical model to simulate the biomass growth in the presence of flue gas. We assume that the flue gas contains 15% CO2 (note: The typical CO2 concentration from coal combustion is 10-15%, whi...

Watch this Scientific Journal Video about Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations at JoVE.com

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Flue gas from power plants is a cheap CO2 source for algal growth. We have built prototype &#34;flue gas to algal cultivation&#34; systems and described how to scale up the algal cultivation process. We have demonstrated the use of a mass-transfer bio-reaction model to simulate and to design the optimal operation of flue gas for the growth of Chlorella sp. in algal photo-bioreactors.

Flue gas from power plants can promote algal cultivation and reduce greenhouse gas emissions1. Microalgae not only capture solar energy more efficiently ...

optimize-flue-gas-settings-to-promote-microalgae-growth

Research

JoVE Journal

Environment

14.2K Views.  Washington University in St. Louis, St. Louis. Flue gas from power plants is a cheap CO2 source for algal growth. We have built prototype "flue gas to algal cultivation" systems and described how to scale up the algal cultivation process. We have demonstrated the use of a mass-transfer bio-reaction model to simulate and to design the optimal operation of flue gas for the growth of Chlorella sp. in algal photo-bioreactors.

Video: Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Analysis of Fatty Acid Content and Composition in Microalgae

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of microalgal biomass. These fatty acids can be present in different acyl-lipid classes. Especially the fatty acids present in triacylglycerol (TAG) are of commercial interest, because they can be used for production of transportation fuels, bulk chemicals, nutraceuticals (&omega;-3 fatty acids), and food commodities. To develop commercial applications, reliable analytical methods for quantification of fatty acid content and composition are needed. Microalgae are single cells surrounded by a rigid cell wall. A fatty acid analysis method should provide sufficient cell disruption to liberate all acyl lipids and the extraction procedure used should be able to extract all acyl lipid classes.
With the method presented here all fatty acids present in microalgae can be accurately and reproducibly identified and quantified using small amounts of sample (5 mg) independent of their chain length, degree of unsaturation, or the lipid class they are part of.
This method does not provide information about the relative abundance of different lipid classes, but can be extended to separate lipid classes from each other.
The method is based on a sequence of mechanical cell disruption, solvent based lipid extraction, transesterification of fatty acids to fatty acid methyl esters (FAMEs), and quantification and identification of FAMEs using gas chromatography (GC-FID). A TAG internal standard (tripentadecanoin) is added prior to the analytical procedure to correct for losses during extraction and incomplete transesterification.

A method for the determination of fatty acid content and composition in microalgae based on mechanical cell disruption, solvent based lipid extraction, transesterification, and quantification and identification of fatty acids using gas chromatography is described. A tripentadecanoin internal standard is used to compensate for the possible losses during extraction and incomplete transesterification.

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of ...

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers

Measurement of greenhouse gas (GHG) fluxes between the soil and the atmosphere, in both managed and unmanaged ecosystems, is critical to understanding the biogeochemical drivers of climate change and to the development and evaluation of GHG mitigation strategies based on modulation of landscape management practices. The static chamber-based method described here is based on trapping gases emitted from the soil surface within a chamber and collecting samples from the chamber headspace at regular intervals for analysis by gas chromatography. Change in gas concentration over time is used to calculate flux. This method can be utilized to measure landscape-based flux of carbon dioxide, nitrous oxide, and methane, and to estimate differences between treatments or explore system dynamics over seasons or years. Infrastructure requirements are modest, but a comprehensive experimental design is essential. This method is easily deployed in the field, conforms to established guidelines, and produces data suitable to large-scale GHG emissions studies.

This article showcases the static chamber-based method for measurement of greenhouse gas flux from soil systems. With relatively modest infrastructure investments, measurements may be obtained from multiple treatments/locations and over timeframes ranging from hours to years.

Measurement of greenhouse gas (GHG) fluxes between the soil and the atmosphere, in both managed and unmanaged ecosystems, is critical to understanding the ...

Quantification of Heavy Metals and Other Inorganic Contaminants on the Productivity of Microalgae

Increasing demand for renewable fuels has researchers investigating the feasibility of alternative feedstocks, such as microalgae. Inherent advantages include high potential yield, use of non-arable land and integration with waste streams. The nutrient requirements of a large-scale microalgae production system will require the coupling of cultivation systems with industrial waste resources, such as carbon dioxide from flue gas and nutrients from wastewater. Inorganic contaminants present in these wastes can potentially lead to bioaccumulation in microalgal biomass negatively impact productivity and limiting end use. This study focuses on the experimental evaluation of the impact and the fate of 14 inorganic contaminants (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sb, Se, Sn, V and Zn) on Nannochloropsis salina growth. Microalgae were cultivated in photobioreactors illuminated at 984 &#181;mol m-2 sec-1 and maintained at pH 7 in a growth media polluted with inorganic contaminants at levels expected based on the composition found in commercial coal flue gas systems. Contaminants present in the biomass and the medium at the end of a 7 day growth period were analytically quantified through cold vapor atomic absorption spectrometry for Hg and through inductively coupled plasma mass spectrometry for As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Sb, Se, Sn, V and Zn. Results show N. salina is a sensitive strain to the multi-metal environment with a statistical decrease in biomass yieldwith the introduction of these contaminants. The techniques presented here are adequate for quantifying algal growth and determining the fate of inorganic contaminants.

Integration of microalgal cultivation with industrial flue gas will ultimately introduce heavy metals and other inorganic compounds into the growth media. This study presents a procedure used to determine the end fate and impact of heavy metals and inorganic contaminants on the growth of Nannochloropsis salina grown in photobioreactors.

Increasing demand for renewable fuels has researchers investigating the feasibility of alternative feedstocks, such as microalgae. Inherent advantages ...

Laboratory Simulation of an Iron(II)-rich Precambrian Marine Upwelling System to Explore the Growth of Photosynthetic Bacteria

A conventional concept for the deposition of some Precambrian Banded Iron Formations (BIF) proceeds on the assumption that ferrous iron [Fe(II)] upwelling from hydrothermal sources in the Precambrian ocean was oxidized by molecular oxygen [O2] produced by cyanobacteria. The oldest BIFs, deposited prior to the Great Oxidation Event (GOE) at about 2.4 billion years (Gy) ago, could have formed by direct oxidation of Fe(II) by anoxygenic photoferrotrophs under anoxic conditions. As a method for testing the geochemical and mineralogical patterns that develop under different biological scenarios, we designed a 40 cm long vertical flow-through column to simulate an anoxic Fe(II)-rich marine upwelling system representative of an ancient ocean on a lab scale. The cylinder was packed with a porous glass bead matrix to stabilize the geochemical gradients, and liquid samples for iron quantification could be taken throughout the water column. Dissolved oxygen was detected non-invasively via optodes from the outside. Results from biotic experiments that involved upwelling fluxes of Fe(II) from the bottom, a distinct light gradient from top, and cyanobacteria present in the water column, show clear evidence for the formation of Fe(III) mineral precipitates and development of a chemocline between Fe(II) and O2. This column allows us to test hypotheses for the formation of the BIFs by culturing cyanobacteria (and in the future photoferrotrophs) under simulated marine Precambrian conditions. Furthermore we hypothesize that our column concept allows for the simulation of various chemical and physical environments &#8212; including shallow marine or lacustrine sediments.

Laboratory Simulation of an IronII-rich Precambrian Marine Upwelling System to Explore the Growth of Photosynthetic Bacteria

We simulated a Precambrian ferruginous marine upwelling system in a lab-scale vertical flow-through column. The goal was to understand how geochemical profiles of O2 and Fe(II) evolve as cyanobacteria produce O2. The results show the establishment of a chemocline due to Fe(II) oxidation by photosynthetically produced O2.

A conventional concept for the deposition of some Precambrian Banded Iron Formations (BIF) proceeds on the assumption that ferrous iron [Fe(II)] upwelling ...

A Gusseted Thermogradient Table to Control Soil Temperatures for Evaluating Plant Growth and Monitoring Soil Processes

Thermogradient tables were first developed in the 1950s primarily to test seed germination over a range of temperatures simultaneously without using a series of incubators. A temperature gradient is passively established across the surface of the table between the heated and cooled ends and is lost quickly at distances above the surface. Since temperature is only controlled on the table surface, experiments are restricted to shallow containers, such as Petri dishes, placed on the table. Welding continuous aluminum vertical strips or gussets perpendicular to the surface of a table enables temperature control in depth via convective heat flow. Soil in the channels between gussets was maintained across a gradient of temperatures allowing a greater diversity of experimentation. The gusseted design was evaluated by germinating oat, lettuce, tomato, and melon seeds. Soil temperatures were monitored using individual, battery-powered dataloggers positioned across the table. LED lights installed in the lids or along the sides of the gradient table create a controlled temperature chamber where seedlings can be grown over a range of temperatures. The gusseted design enabled accurate determination of optimum temperatures for fastest germination rate and the highest percentage germination for each species. Germination information from gradient table experiments can help predict seed germination and seedling growth under the adverse soil conditions often encountered during field crop production. Temperature effects on seed germination, seedling growth, and soil ecology can be tested under controlled conditions in a laboratory using a gusseted thermogradient table.

Traditional thermogradient tables create a range of temperatures across the surface. Welding gussets perpendicular to the surface of a thermogradient table will control temperature in depth increasing possible research applications.

Thermogradient tables were first developed in the 1950s primarily to test seed germination over a range of temperatures simultaneously without using a ...

Procedures of Laboratory Fumigation for Pest Control with Nitric Oxide Gas

Nitric oxide (NO) is a newly discovered fumigant for postharvest pest control. This paper provides detailed protocols for conducting NO fumigation on fresh products and procedures for residue analysis and product quality evaluation. An airtight fumigation chamber containing fresh fruit and vegetables is first flushed with nitrogen (N2) to establish an ultralow oxygen (ULO) environment followed by injection of NO. The fumigation chamber is then kept at a low temperature of 2 - 5 &#176;C for a specified time period necessary to kill a target pest to complete a fumigation treatment. At the end of a fumigation treatment, the fumigation chamber is flushed with N2 to dilute NO prior to opening the chamber to ambient air to prevent the reaction between NO and O2, which produces NO2 and may damage delicate fresh products. At different times after NO fumigation, NO2 in headspace and nitrate and nitrite in liquid samples were measured as residues. Product quality was evaluated after 2 weeks of post-treatment cold storage to determine effects of NO fumigation on product quality. Keeping&#160;O2 from reacting with NO is critical to NO fumigation and is an important part of the protocols. Measuring NO levels is challenging and a practical solution is provided. Possible protocol modifications are also suggested for measuring NO levels in the fumigation chambers as well as residues. NO fumigation has the potential to be a practical alternative to methyl bromide fumigation for postharvest pest control on fresh and stored products. This publication is intended to assist other researchers in conducting NO fumigation research for postharvest pest control and accelerating the development of NO fumigation for practical applications.

This paper describes nitric oxide (NO) fumigation protocols for postharvest pest control. Fumigation chambers are flushed with nitrogen (N2) to establish ultralow oxygen conditions before NO is injected. At the end, chambers are flushed with N2 to dilute NO before exposing products to ambient air to prevent exposure to NO2.

Nitric oxide (NO) is a newly discovered fumigant for postharvest pest control. This paper provides detailed protocols for conducting NO fumigation on ...

Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure

A lab-scaled simulated bedded pack model was developed to study air quality and nutrient composition of deep-bedded packs used in cattle mono-slope facilities. This protocol has been used to effectively evaluate many different bedding materials, environmental variables (temperature, humidity), and potential mitigation treatments that can improve air quality in commercial deep-bedded mono-slope facilities. The model is dynamic and allows researchers to easily collect many chemical and physical measurements from the bedded pack. Weekly measurements, collected over the course of six to seven weeks, allows sufficient time to see changes in air quality measurements over time as the bedded pack matures. The data collected from the simulated bedded packs is within the range of concentrations previously measured in commercial deep-bedded mono-slope facilities. Past studies have demonstrated that 8 - 10 experimental units per treatment are sufficient to detect statistical differences among the simulated bedded packs. The bedded packs are easy to maintain, requiring less than 10 minutes of labor per bedded packs per week to add urine, feces, and bedding. Sample collection using the gas sampling system requires 20 - 30 minutes per bedded pack, depending on the measurements that are being collected. The use of lab-scaled bedded packs allows the researcher to control variables such as temperature, humidity, and bedding source that are difficult or impossible to control in a research or commercial facility. While not a perfect simulation of &#34;real-world&#34; conditions, the simulated bedded packs serve as a good model for researchers to use to examine treatment differences among bedded packs. Several lab-scale studies can be conducted to eliminate possible treatments before trying them in a research or commercial-sized facility.

A protocol has been developed to measure gases, odors, and nutrient composition in lab-scaled bedded manure packs, which can be used to study ways to improve air quality in commercial cattle facilities using deep-bedded manure packs.

A lab-scaled simulated bedded pack model was developed to study air quality and nutrient composition of deep-bedded packs used in cattle mono-slope ...

Experimental Study of the Relationship Between Particle Size and Methane Sorption Capacity in Shale

The amount of adsorbed shale gas is a key parameter used in shale gas resource evaluation and target area selection, and it is also an important standard for evaluating the mining value of shale gas. Currently, studies on the correlation between particle size and methane adsorption are controversial. In this study, an isothermal adsorption apparatus, the gravimetric sorption analyzer, is used to test the adsorption capacity of different particle sizes in shale to determine the relationship between the particle size and the adsorption capacity of shale. Thegravimetric method requires fewer parameters and produces better results in terms of accuracy and consistency than methods like the volumetric method. Gravimetric measurements are performed in four steps: a blank measurement, preprocessing, a buoyancy measurement, and adsorption and desorption measurements. Gravimetric measurement is presently considered to be a more scientific and accurate method of measuring the amount of adsorption; however, it is time-consuming and requires a strict measuring technique. A Magnetic Suspension Balance (MSB) is the key to verify the accuracy and consistency of this method. Our results show that adsorption capacity and particle size are correlated, but not a linear correlation, and the adsorptions in particles sieved into 40 - 60 and 60 - 80 meshes tend to be larger. We propose that the maximum adsorption corresponding to the particle size is approximately 250 &#181;m (60 mesh) in the shale gas fracturing.

We use an isothermal adsorption apparatus, the gravimetric sorption analyzer, to test the adsorption capacity of different particle sizes of shale, in order to find out the relationship between particle size and the adsorption capacity of shale.

The amount of adsorbed shale gas is a key parameter used in shale gas resource evaluation and target area selection, and it is also an important standard ...

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive harvest or expensive equipment. We present a customizable and affordable rhizobox method that allows the non-destructive visualization of root growth over time and is particularly well-suited to studying root plasticity in response to localized resource patches. The method was validated by assessing maize genotypic variation in plasticity responses to patches containing 15N-labeled legume residue. Methods are described to obtain representative developmental measurements over time, measure root length density in resource-containing and control patches, calculate root growth rates, and determine 15N recovery by plant roots and shoots. Advantages, caveats, and potential future applications of the method are also discussed. Although care must be taken to ensure that experimental conditions do not bias root growth data, the rhizobox protocol presented here yields reliable results if carried out with sufficient attention to detail.

Visualizing and measuring root growth in situ is extremely challenging. We present a customizable rhizobox method to track root development and proliferation over time in response to nutrient enrichment. This method is used to analyze maize genotypic differences in root plasticity in response to an organic nitrogen source.

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive ...

Cultivation of Green Microalgae in Bubble Column Photobioreactors and an Assay for Neutral Lipids

There is significant interest in the study of microalgae for engineering applications such as the production of biofuels, high value products, and for the treatment of wastes. As most new research efforts begin at laboratory scale, there is a need for cost-effective methods for culturing microalgae in a reproducible manner. Here, we communicate an effective approach to culture microalgae in laboratory-scale photobioreactors, and to measure the growth and neutral lipid content of that algae. Instructions are also included on how to set up the photobioreactor system. Although the example organisms are species of Chlorella and Auxenochlorella, this system can be adapted to cultivate a wide range of microalgae, including co-cultures of algae with non-algae species. Stock cultures are first grown in bottles to produce inoculum for the photobioreactor system. Algae inoculum is concentrated and transferred to photobioreactors for cultivation in batch mode. Samples are collected daily for the optical density readings. At the end of the batch culture, cells are harvested by centrifuge, washed, and freeze dried to obtain a final dry weight concentration. The final dry weight concentration is used to create a correlation between the optical density and the dry weight concentration. A modified Folch method is subsequently used to extract total lipids from the freeze-dried biomass and the extract is assayed for its neutral lipid content using a microplate assay. This assay has been published previously but protocol steps were included here to highlight critical steps in the procedure where errors frequently occur. The bioreactor system described here fills a niche between simple flask cultivation and fully-controlled commercial bioreactors. Even with only 3-4 biological replicates per treatment, our approach to culturing algae leads to tight standard deviations in the growth and lipid assays.

Here, we present a protocol to construct lab-scale bubble column photobioreactors and use them to culture microalgae. It also provides a method for the determination of the culture growth rate and neutral lipid content.

There is significant interest in the study of microalgae for engineering applications such as the production of biofuels, high value products, and for the ...