Este estudio se apoya en un programa de la NSF (Investigue Experiencias para estudiantes universitarios) en la Universidad de Washington en St. Louis.

1. El cultivo de algas y scale-up <ol><li> Preparar el medio de cultivo utilizando agua desionizada que contiene 0,55 g / L -1 de urea, 0,1185 g / L -1 KH 2 PO 4, 0,102 g / L -1 MgSO4 · 7 H2O, 0,015 g / L -1 FeSO4 · 7H 2 O y 22,5 mu l microelementos (18,5 g / L -1 H 3 BO 3, 21,0 g / L -1 CuSO 4 · 5H 2 O, 73,2 g / L -1 MnCl2 · 4H 2 O, 13,7 g / L -1 Coso 4 · 7H 2 O, 59,5 g / L -1 ZnSO4 · 5H 2 O, 3,8 g / L -1 (NH 4) 6 Mo 7 O 24 · 4H 2 O, 0,31 g / L -1 de NH 4 VO 3 ). Ajustar el pH del medio a 7-8. Esterilizar medio de cultivo a través de 0,22 micras filtro de jeringa. </li><li> Inocular Chlorella sp. a partir de una sola colonia en una placa de agar fresco en un fla shakeSK que contiene 50 ml de medio con un asa de inoculación estéril. Algas Cultura por debajo de 150 rpm y 30 º C durante seis días (condiciones de luz continua, flujo de fotones = 40-50 mol m -2 s -1). Monitor de densidad celular por un espectrofotómetro (OD 730). </li><li> Transferir 50 ml de cultivo de algas (fase de crecimiento medio de registro, OD 730&gt; 1) en un matraz de vidrio de 2-L (con ~ 1 L de medio de cultivo esterilizado). Bomba de aire filtrado (o CO 2) en el cultivo durante la incubación (durante 5 días). </li><li> Transferencia de 1 L de cultivo de algas en una bombona de vidrio de 20 l que contiene 15 L de medio no esterilizada de cultivo (en esta etapa, el riesgo de contaminación microbiana es pequeño), a continuación, el cultivo de algas bajo la misma condición como se indica en el paso 1.3. </li><li> Coloque 15 L de cultivo fresco de algas (OD 730 = 2) y 85 L medio no esterilizados en un fotobiorreactor placa plana (equipada con diodos emisores de luz, control de computadora, mezcla de gases, analizadores de densidad óptica de células, pH, oxígeno disueltogen, la temperatura y el CO2 disuelto). Bombear la mezcla de gas de combustión / aire en el biorreactor. </li><li> Completamente seco-limpio del fotobiorreactor utilizando etanol al 70% después de la cosecha de la biomasa (OD 730&gt; 20). </li></ol> 2. Demostración del Laboratorio de Tratamiento de gases Usando fotobioreactores Pequeños <ol><li> Inocular cultivos de algas en botellas de vidrio (200 ml / min medio / botella, OD inicial 730 ~ 0,3). </li><li> Queman gas natural y bombear el gas de combustión (~ 250 cm 3 min -1) a través de un embudo, un tubo condensador, y una botella de lavado 0.5 L (que contiene suspensión de agua / piedra caliza). </li><li> Los controladores de flujo másico controlan el flujo de gas de combustión en el cultivo de algas (Figura 1). Pulsos de gas de salida incluyen dos modos: con gas de combustión y los gases de combustión off (bomba de aire en su lugar). </li></ol> 3. Desarrollo del Modelo Cinético El modelo cinético asume: (1) las culturas son sistema homogéneos. (2) la concentración de CO 2 y la intensidad de la luz en los cultivos son los factores limitantes para el crecimiento de algas. (3) CO 2 presión parcial y su equilibrio de fases líquido con H 2 CO 3, HCO 3 - y CO 3 2 - se simplifica con la Ley de Henry). Las ecuaciones del modelo son: <img alt="Las ecuaciones 1 y 2" fo:content-width="4in" src="/files/ftp_upload/50718/50718eq1and2.jpg" width="500px" /> X es la biomasa (kg · m-3). S es el CO2 disuelto (mol · m-3). P es la presión parcial de CO 2 en la fase de gas (Pa). p i es la presión parcial del i th compuesto tóxico en el gas (como NO x y SO x). P max.i es la presión parcial del gas tóxico para tener la inhibición total en el crecimiento de la biomasa. η i es el coeficiente empírico. K s es la constante de Michaelis-Mentende CO 2 (mol · m-3). K I es la constante de inhibición de CO 2 (mol · m-3). K es la constante de Michaelis-Menten de la intensidad de la luz (mol · m -2 · s -1). H es la constante de Henry para el CO 2 (Pa · m 3 mol -1). La K es la tasa de transferencia de masa de CO 2 (-1 hora). I es el promedio de intensidad de luz, mol m -2 s -1, que se puede calcular de la siguiente manera (Ec. (3)) 9. <img alt="Ecuación 3" fo:content-width="4in" src="/files/ftp_upload/50718/50718eq3.jpg" width="500px" /> La definición de los parámetros del modelo se encuentra en la Tabla 1. Las condiciones iniciales suponen que la biomasa y CO 2 disuelto concentraciones son 100 mg / L y 13 mmol / L, respectivamente. El coeficiente de transferencia de masa volumétrica puede ser estimado por correlación empíricación de biorreactor parámetros 10: <img alt="Ecuación 4" fo:content-width="4in" src="/files/ftp_upload/50718/50718eq4.jpg" width="500px" /> P g / V es el consumo de energía del sistema de gasificado, en el biorreactor (W / m 3). U GS es la velocidad superficial del flujo de gas a través del biorreactor (m / seg). α, β, y γ son constantes relacionadas con las condiciones de mezcla. <ol><li> Construir un archivo de Simulink para la simulación del modelo (Las capturas de pantalla se dan en el material de apoyo I). <ol><li> Seleccione Archivo / Nuevo / modelo en la interfaz de MATLAB para crear un modelo de Simulink, y abrir &quot;Browser Library&quot; (captura de pantalla 1). </li><li> Elija bloque &#39;subsistema&#39; en el navegador de la biblioteca para crear los subsistemas de la ecuación 1 y 2. Arrastre una cuadra del subsistema en el fichero de modelo de Simulink, cambiará su nombre a &#39;la ecuación 1&#39;, y repita los mismos pasos para la Ecuación 2. </li><li&gt; Crear bloques y parámetros adecuados en cada subsistema (captura de pantalla 3). Haga doble clic en el bloque de la &#39;ecuación 1&#39;, seleccione los bloques apropiados desde el navegador de la biblioteca y su conexión con las flechas que indican la secuencia de cálculo, haga doble clic en los bloques para configurar los parámetros, y repita estos pasos para el otro subsistema. Nota: 1) La secuencia debe empezar con los bloques de entrada y concluir con los bloques de salida, 2) Los bloques de operador para la suma, resta, multiplicación, división y la integración puede ser todo lo que se encuentra en el navegador de la biblioteca, y sugerimos a los usuarios explorar los archivos de ayuda de el Simulink para entender cómo usarlos; 3) El solucionador de optimización se puede establecer a través de los parámetros vía Simulación / Configuración en la barra de herramientas. </li><li> Vincular los dos subsistemas de representar ecuaciones del modelo (1 y 2). Conectar la salida de un subsistema a la entrada del otro subsistema por la flecha si es necesario. Por ejemplo, la concentración disuelta de CO 2 es la Output en la Ecuación 2 subsistema, y ​​también la entrada del subsistema de la Ecuación 1. </li><li> Use &#39;generador de pulso&#39; de bloques como los insumos para la &#39;ecuación 2&#39; para simular las CO 2 pulsos de encendido y apagado, el uso de bloque &#39;constante&#39; como valor de entrada de luz superficial. Haga doble clic en los bloques para cambiar los parámetros, tales como el período de tiempo y amplitud. </li><li> Elija bloque &#39;Mux&#39; en el navegador de la biblioteca. Conecte todas las salidas a &quot;Mux&quot; y luego conectarlo a &#39;Para Workspace&#39; bloque que almacena los resultados simulados. </li><li> Definir el &quot;tiempo de parada Simulación&quot; en la barra de herramientas superior, haga clic en el botón &quot; <img src="/files/ftp_upload/50718/playarrow.jpg" alt="Flecha" width="10px" fo:content-width="0.1in" fo:src="/files/ftp_upload/50718/playarrow.jpg" /> &quot;Para iniciar la simulación, y los resultados se muestran en el espacio de trabajo de MATLAB (captura de pantalla 4). </li></ol></li><li> Aplicar el enfoque de optimización dinámica al perfil óptimo CO 2 condiciones. </li&gt; </ol> Para encontrar los cambios de flujo de entrada 2 perfil CO (P OPT) que maximizan la producción de biomasa 11, la función de MATLAB &#39;fmincon&#39; y CVP (control vectorial parametrización) 12 se utilizan. Figura 2 ilustra el algoritmo de optimización (ver los códigos de programación de MATLAB en el apoyo Material II).

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En este estudio, se demuestra el protocolo experimental para la ampliación de cultivos de algas en fotobiorreactores. También examinamos varios métodos para las entradas de gases de combustión para promover el crecimiento de algas. El uso de un modelo de transferencia de masa y bio-reacción, se demuestra que el coeficiente de transferencia de masa de CO 2 K La (determinado por la condición biorreactor mezcla y CO 2 velocidad superficial) influye fuertemente en el crecimiento de alga...

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

Los gases de combustión de las centrales puede promover el cultivo de algas y reducir las emisiones de gases de efecto invernadero 1. Las microalgas no sólo capta la energía solar más eficiente que las plantas 3, sino también sintetizar biocombustibles avanzados 2-4. En general, el CO2 atmosférico no es una fuente suficiente para apoyar el crecimiento de algas máxima 5. Por otra parte, las altas concentraciones de CO 2 en los gases de escape industriales tienen efectos adversos sobre la fisiología de algas. Por consiguiente, ambas condiciones de cultivo (tales como nutrientes y luz) y el control del flujo de gas de combustión en los fotobiorreactores son importantes para desarrollar un &quot;gas de combustión para las algas&quot; eficiente sistema. Los investigadores han propuesto diferentes configuraciones de fotobiorreactores 4,6 y estrategias de cultivo 7,8 con salida de humos. A continuación, presentamos un protocolo que se muestra cómo utilizar los modelos para predecir el crecimiento de microalgas en respuesta a los ajustes de combustión de gas. Nos PERForm ambos ilustrativos y modelos experimentales simulaciones para determinar las condiciones favorables para el crecimiento de las algas con los gases de combustión. Desarrollamos un modelo basado en Monod junto con la transferencia de masa y las ecuaciones de intensidad de luz para simular el crecimiento de microalgas en un homogénea foto-biorreactor. La simulación del modelo se comparan los consumos de gas de algas de crecimiento y de combustión bajo diferentes configuraciones de los gases de combustión. El modelo muestra: 1) cómo el crecimiento de las algas está influenciada por diferentes coeficientes de transferencia de masa volumétrica de CO 2, 2) cómo podemos encontrar una óptima concentración de CO 2 para el crecimiento de algas a través del enfoque de optimización dinámica (DOA), 3) cómo podemos diseñar un rectangular on-off de impulsos de gas de combustión para promover el crecimiento de la biomasa de algas y para reducir el uso de gas de combustión. En el lado experimental, se presenta un protocolo para el cultivo de Chlorella bajo el gas de combustión (generado por la combustión de gas natural). Los resultados experimentales cualitativamente validan las predicciones del modelo de que los gases de combustión de alta frecuencia de la PULSES pueden mejorar significativamente el cultivo de algas.

Nuestro análisis experimental previa indica que la exposición a los gases de combustión continua afecta negativamente al crecimiento de Chlorella, mientras que la disminución de CO 2 Tiempo de exposición es capaz de aliviar esta inhibición 13. Para comprender mejor el flujo de entrada de gas de combustión y la relación crecimiento de las algas, desarrollamos un modelo empírico para simular el crecimiento de la biomasa en la presencia de gases de combustión. Suponemos que el gas d...

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

Los gases de combustión de las centrales eléctricas es una fuente de CO2 baratas para el crecimiento de algas. Hemos construido &quot;de gases de combustión a la de algas de cultivo&quot; prototipo de los sistemas y se describe la manera de ampliar el proceso de cultivo de algas. Hemos demostrado el uso de un modelo de bio-reacción de transferencia de masa para simular y diseñar la operación óptima de los gases de combustión para el crecimiento de Chlorella sp. en algas fotobiorreactores.

Optimizar la combustión Gas Ciudad para promover el crecimiento de microalgas en fotobiorreactores a través de Simulaciones computacionales

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

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14.2K vistas.  Washington University in St. Louis, St. Louis.  Los gases de combustión de las centrales eléctricas es una fuente de CO2 baratas para el crecimiento de algas. Hemos construido "de gases de combustión a la de algas de cultivo" prototipo de los sistemas y se describe la manera de ampliar el proceso de cultivo de algas. Hemos demostrado el uso de un modelo de bio-reacción de transferencia de masa para simular y diseñar la operación óptima de los gases de combustión para el crecimiento de ...

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.

Análisis de contenido de ácido graso y Composición en microalgas

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

Investigación

Medio ambiente

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.

Medición de Flujo de Gas de efecto invernadero procedentes de los suelos agrícolas Usando cámaras estáticas

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.

La cuantificación de los metales pesados ​​y otros contaminantes inorgánicos en la productividad de microalgas

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.

Simulación de un laboratorio de hierro (II) ricos sistema de afloramiento Precámbrico marina para explorar el crecimiento de bacterias fotosintéticas

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.

Una tabla de fuelle Thermogradient para controlar las temperaturas del suelo para evaluar el crecimiento de la planta y Seguimiento de los procesos del suelo

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.

Procedimientos de laboratorio fumigación Control de plagas con Gas de óxido nítrico

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.

Modelo de escala de laboratorio para evaluar olor y concentraciones de Gas emitidas por profundo camas Pack abono

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.

Estudio experimental de la relación entre tamaño de partícula y la capacidad de absorción del metano en la pizarra

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.

Un protocolo de Rhizobox optimizada para visualizar el crecimiento de las raíces y la capacidad de respuesta a nutrientes localizadas

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.

Cultivo de microalgas verdes en fotobiorreactores de columna de burbuja y un análisis de lípidos neutros

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