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14 results for “CO2 diffusion”
Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach"
<p>Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach", published in Journal of Catalysis 2022 408:1–8, doi: <a href="https://doi.org/10.1016/j.jcat.2022.02.014">10.1016/j.jcat.2022.02.014</a></p> <p>Folder names describe the type of data content.</p>
Dataset for publication "Enhancing C≥2 product selectivity in electrochemical CO2 reduction by controlling the microstructure of gas diffusion electrodes"
<p>Data used for publication:</p> <p>Broad topic: electrochemical reduction of CO2 using gas diffusion electrodes and neutral electrolyte</p> <p>Data is devided in subfolders named after the figure of the paper.</p> <p>Raw data, processed data, and Origin/Power Point files are all contained in the subfolders </p> <p>A subfolder corresponding to a sample contains: data from a potentiostat, gas and liquid chromatograms, recording of flow, pressure and temperature, tables of calculated Faradaic efficiency (FE), png image of the FE vs t, zipped raw files.</p> <p>.json file was created using a yadg scheme (https://dgbowl.github.io/yadg/master/index.html), and data was processed by a dgpost scheme (<a href="https://pypi.org/project/dgpost/">https://dgbowl.github.io/dgpost/master/index.html</a>)</p>
Dataset for publication "Importance of Substrate Pore Size and Wetting Behavior in Gas Diffusion Electrodes for CO2 Reduction"
<p>Dataset for the publication "Importance of Substrate Pore Size and Wetting Behavior in Gas Diffusion Electrodes for CO2 Reduction" containing war and processed data used to compose the various figures. </p> <p>DOI Publication: <a href="https://doi.org/10.1021/acsaem.2c03054">https://doi.org/10.1021/acsaem.2c03054</a> </p>
Dataset for "High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO2 diffusion and efficient light use"
<p>Dataset used in the paper</p> <p>Retta MA, Van Doorselaer L, Driever SM, Yin X, de Ruijter NCA, Verboven P, Nicolaï BM, Struik PC. High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO<sub>2</sub> diffusion and efficient light use. New Phytol. 2024 Sep 18. doi: 10.1111/nph.20136. PMID: 39294895.</p> <p>Please cite the paper presenting this datase.</p> <h1><strong>Plant Species and Inbred Lines:</strong></h1> <ul> <li><em>Hirschfeldia incana L. (7th generation inbred line 190003 HIN-NIJ-07-B) </em></li> <li><em>Brassica nigra L. (3rd generation inbred line 210093 BNI-DG1-03-B)</em></li> <li><em>Brassica rapa L. (inbred line ‘R-o-18’)</em></li> <li><em>Arabidopsis thaliana (accession Columbia)</em></li> </ul> <h1><strong>Growth Conditions:</strong></h1> <ul> <li><em>Media:</em> Rock-wool blocks (Grodan Plantop, Roermond, Netherlands, 10×10×7.5 cm)</li> <li><em>Fertigation:</em> Nitrogen-rich nutrient solution via automated dripping system.</li> <li><em>Light Conditions:</em> 12 h day/12 h night, light intensity of 200 µmol m-2 s-1 and 1800 µmol m-2 s-1</li> <li><em>Temperature:</em> Day/Night temperatures of 23 °C and 20 °C, respectively.</li> <li><em>Relative Humidity:</em> 70%</li> </ul> <h1><strong>Codes</strong></h1> <p><strong>Species:</strong></p> <ul> <li><em>Hirschfeldia incana L. - H. incana</em></li> <li><em>Brassica nigra L. - B. nigra</em></li> <li><em>Brassica rapa L. - B. rapa</em></li> <li><em>Arabidopsis thaliana - A. thaliana</em></li> </ul> <p><strong>Light conditions:</strong></p> <ul> <li><em>High light - HL</em></li> <li><em>Low light - LL</em></li> </ul> <p><strong>Replicates:</strong></p> <ul> <li><em>Biological replicates were labeled with numbers, e.g. replicate one from high light grown Hirschfeldia incana is referred to as HiHL1</em></li> </ul> <h1><strong>Measurements</strong></h1> <h2><strong>Leaf Gas Exchange and Chlorophyll Fluorescence Measurements (GasExchangeData.zip):</strong></h2> <ul> <li>Four leaves per species per treatment.</li> <li>Conducted using a LI-6800 (LI-COR, Lincoln, NE, USA) on the mid-position of the youngest fully expanded leaf.</li> <li>The resoponse of photosynthesis to irradiance and external CO2 concentrations augumneted with multi-phase flash fluorescence were made</li> </ul> <h2><strong>Optical Properties Measurement </strong>(<strong>Absorbance & chlorophyll.zip):</strong></h2> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li>Leaf transmittance and reflectance measured using a dual channel spectrophotometer (absorptance_reflac_data_355_750.xlsx)</li> <li>Chlorophyll content measured using a spectrophotometer (Chlorophyll.xlsx).</li> </ul> <h2><strong>Stomatal Density and Size Analysis </strong>(<strong>Stomata.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from three different plants.</li> <li><em>Leaf-side:</em> abaxial and adxial leaf side.</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li>Stomatal imprints made using clear nail polish, imaged using a light microscope at 20x.</li> </ul> <p><strong>Data Output:</strong></p> <ul> <li><em>Imaging Results:</em> .jpg files organised under folders for species e.g. AtHL\R1 T+B.zip contains images ofimprints of top (T) and bottom (B) leaf sides from replicate plant 1 (R1) of A. thaliana grown under high light (AtHL). The images are named as for example, AT_HL_BOTTOM_R1_A_stacked_minimum.jpg, The leters A to E label various imges made from one imprint.</li> </ul> <h2><strong>Light and Electron Microscopy of Leaf Sections (</strong><strong>CellwallChloroplast.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from four different plants.</li> </ul> <p><strong>Sample preparation</strong></p> <ul> <li>Leaf samples fixed, dehydrated, embedded in Araldite, and sectioned for imaging.</li> <li>1 µm think sections were made for light microscopy</li> <li>Sections of 70 nm were double stained for TEM</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li>Mesophyll cells imaged at 400x and 700x to measure chloroplast coverage.</li> <li>Electron microscopy performed with Zeiss EM900 electron microscope.</li> </ul> <h2><strong>Mesophyll Chlorophyll (ConfocalData.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Three leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from three different plants.</li> <li><em>Thickness:</em> 200 ± 10 µm sections prepared using a sliding microtome</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li><em>Microscope:</em> Leica DM8 inverted scope equipped with a Stellaris 5 confocal microscope (Leica Microsystems, Wetzlar, Germany).</li> <li><em>Excitation:</em> 490 nm excitation laser line</li> <li><em>Fluorescence Recording:</em> Chlorophyll autofluorescence recorded in a spectral range of 660−700 nm.</li> <li><em>Objective:</em> Leica objective ×10/0.4 NA.</li> <li><em>Z-Stacks:</em> 85–112 µm depth, two random positions per sample</li> </ul> <p><strong>Data Output:</strong></p> <ul> <li><em>Imaging Results:</em> Z-stacks of chlorophyll autofluorescence in mesophyll cells.</li> <li><em>Spectral Information:</em> Chlorophyll autofluorescence recorded in the 660−700 nm range.</li> </ul> <p><strong>Analysis:</strong></p> <ul> <li><em>Software:</em> The confocal files are in .lif format and can be viewed using Leica application suite (LASx), ImageJ</li> </ul>
Axial diffusion of respired CO2 confounds stem respiration estimates during the dormant season
<p>Efflux-based estimates of stem respiration in oak trees during the dormant season were biased by axial diffusion of locally respired CO<sub>2</sub>. Light-induced axial CO<sub>2 </sub>diffusion along the stem due to woody tissue photosynthesis may lead to equivocal estimates of stem respiratory coefficients during the dormant season, which are generally used to estimate maintenance respiration throughout the year.</p>
Impact of Clean Energy on CO2 Emissions and Economic Growth within the Phases of Renewables Diffusion in Selected European Countries
<p>This study explores the impact of clean energy and non-renewable energy consumption on CO<sub>2</sub> emissions and economic growth within two phases (formative and expansion) of renewable energy diffusion for three selected countries (France, Spain, and Sweden). The vector autoregression (VAR) model is estimated on the basis of annual data disaggregated into quarterly data. The Granger causality results reveal distinctive differences in the causality patterns across countries and two phases of renewables diffusion. Clean energy consumption contributes to a decline of emissions more clearly in the expansion phase in France and Spain. However, this effect seems to be counteracted by the increases in emissions due to economic growth and non-renewable energy consumption. Therefore, clean energy consumption has not yet led to a decoupling of economic growth from emissions in France and Spain; in contrast, the findings for Sweden evidence such a decoupling due to the neutrality between economic growth and emissions. Generally, the findings show that despite the enormous growth of renewables and active mitigation policies, CO<sub>2</sub> emissions have not substantially decreased in selected countries or globally. Focused and coordinated policy action, not only at the EU level but also globally, is urgently needed to overhaul existing fossil-fuel economies into low-carbon economies and ultimately meet the relevant climate targets.</p>
Data from: Does stomatal patterning in amphistomatous leaves minimize the CO2 diffusion path length within leaves?
<div> <div> <div> <p>Photosynthesis is co-limited by multiple factors depending on the plant and its environment. These include biochemical rate limitations, internal and external water potentials, temperature, irradiance, and carbon dioxide (CO<sub>2</sub>). Amphistomatous leaves have stomata on both abaxial and adaxial leaf surfaces. This feature is considered an adaptation to alleviate CO<sub>2</sub> diffusion limitations in productive environments as the diffusion path length from stomate to chloroplast is effectively halved in amphistomatous leaves. Plants may also reduce CO<sub>2</sub> limitations through other aspects of optimal stomatal anatomy: stomatal density, distribution, patterning, and size. A number of studies have demonstrated that stomata are overdispersed compared to a random distribution on a single leaf surface; however, despite their prevelance in nature and near ubiquity among crop species, much less is known about stomatal anatomy in amphistomatous leaves, especially the coordination between leaf surfaces. Here we use novel spatial statistics based on simulations and photosynthesis modeling to test hypotheses about how amphistomatous plants may optimize CO<sub>2</sub> diffusion in the model angiosperm <em>Arabidopsis thaliana</em> grown in different light environments. We find that 1) stomata are overdispersed, but not ideally dispersed, on both leaf surfaces across all light treatments; 2) the patterning of stomata on abaxial and adaxial leaf surfaces is independent; and 3) the theoretical improvements to photosynthesis from abaxial-adaxial stomatal coordination are miniscule (≪ 1%) across the range of feasible parameter space. However, we also find that 4) stomatal size is correlated with the mesophyll volume that it supplies with CO<sub>2</sub>, suggesting that plants may optimize CO<sub>2</sub> diffusion limitations through alternative pathways other than ideal, uniform stomatal spacing. We discuss the developmental, physical, and evolutionary constraits which may prohibit plants from reaching this theoretical adaptive peak of uniform stomatal spacing and inter-surface stomatal coordination. These findings contribute to our understanding of variation in the anatomy of amphistomatous leaves.</p> </div> </div> </div>
Data from: Does stomatal patterning in amphistomatous leaves minimize the CO2 diffusion path length within leaves?
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MicroCT data to 'Maximum CO2 diffusion inside leaves is limited by the scaling of cell size and genome size'
<p>This dataset is presented in the following publication. Please cite this publication if you use the dataset.</p> <p><em>Théroux-Rancourt Guillaume, Roddy Adam B., Earles J. Mason, Gilbert Matthew E., Zwieniecki Maciej A., Boyce C. Kevin, Tholen Danny, McElrone Andrew J., Simonin Kevin A. and Brodersen Craig R. 2021. <strong>Maximum CO<sub>2</sub> diffusion inside leaves is limited by the scaling of cell size and genome size.</strong> Proceedings of the Royal Society B. 288: 20203145. doi:<a href="https://doi.org/10.1098/rspb.2020.3145">10.1098/rspb.2020.3145</a></em></p> <p>We collected leaf samples from botanical gardens, greenhouses, and field sites, which were then transported to one of three synchrotron-based microCT beamlines for imaging. To- and three-dimensional data were then extracted from the microCT images to characterize the structural anatomy of the different study species. Those data were then analyzed and correlated with genome size data available from the Kew Plant DNA C-values database (https://cvalues.science.kew.org) or newly collected by us.</p> <p>From the botanical garden collections we selected representative species from across the vascular plant phylogeny, aiming to capture as much variation in anatomical/physiological/ecological traits as possible while also sampling species that represented important divergences in the phylogeny. Within clades, we also sampled species that spanned ecological breadth (e.g. xerophytic ferns). We focused solely on C3 terrestrial vascular plants, meaning that we did not sample C4 or CAM species, which have different photosynthetic biochemistry and associated anatomy.</p> <p>In most cases, single microCT scans were used per species. This constraint was primarily due to the extremely limited amount of time available at the microCT facility, as well as the labor intensive process of producing the final volume renderings and data analysis.</p> <p>MicroCT scans were collected by GTR, JME, ABR, CRB, AJM, CKB, MJZ, and DT at one of the three microCT beamlines from fresh leaf material. Leaves were cut at the base of the petiole or short stem segment, the cut end was wrapped in wet paper towels, and the entire shoot immediately put in a plastic bag before being transported to the synchrotron and scanned within 36 h of excision. Samples were prepared before each scan (less than 30 min) by excising a small sample that was then enclosed between two pieces of Kapton (polyimide) tape to prevent desiccation while allowing high X-ray transmittance.</p> <p>All microCT data were collected at the Lawrence Berkeley National Laboratory (LBNL) tomography beamline 8.3.2, the Swiss Light Source (SLS) TOMCAT Tomography beamline of the Paul Scherrer Institute, or the Advanced Photon Source tomography beamline 2-BM-A,B of Argonne National Laboratory (ANL). MicroCT datasets were reconstructed from the raw projection images obtained using TomoPy, an open-source Python-based framework for reconstructing tomographic data (LBNL), or using the in-house reconstruction platform of the beamline (SLS, ANL). LBNL and SLS data can be reconstruction using TomoPy, and this software is available at the following link: <a href="http://microct.lbl.gov/software">http://microct.lbl.gov/software</a></p> <p>Image stacks were segmented using the open-source software ImageJ either manually or using an automated machine learning algorithm (Théroux-Rancourt et al., 2020: doi:<a href="https://doi.org/10.1002/aps3.11380">10.1002/aps3.11380</a>). Traits were extracted from the segmented image stacks using ImageJ, the BoneJ plugin of ImageJ, or with an open-source Python-program available at: <a href="https://github.com/plant-microct-tools/leaf-traits-microct">https://github.com/plant-microct-tools/leaf-traits-microct</a>. Because of the multiple synchrotron and multiple sessions involved in acquiring this dataset, and because the analysis process lasted several years and was carried out by several persons, the size of each image stack and the details in each stack vary. What is present in each stack is the airspace and the whole mesophyll (i.e. the leaf without the epidermis), and in the majority of the cases, the vasculature is also segmented.</p>
Data from: Experimental study of the supercritical CO2 diffusion coefficient in porous media under reservoir conditions
Reliable measurement of CO2 diffusion coefficient in consolidated oil-saturated porous media is critical for design, performance of carbon capture and storage (CCS) and CO2 enhanced oil recovery (EOR) project. In this study, a thorough experimental investigation on supercritical CO2 diffusion in n-decane saturated Berea core (50 and 100 mD) was conducted at elevated pressure (10 to 25 MPa) and temperature (333.15 to 373.15 K), which simulated the real reservoir condition. Supercritical CO2 diffusion coefficients in Berea core were calculated by a model appropriate for diffusion in porous media based on Fick's law. The results show that with an increase in pressure, temperature and permeability, the supercritical CO2 diffusion coefficient increases correspondingly. The supercritical CO2 diffusion coefficient first increases slowly at 10 MPa and then grow significantly with the increase of pressure. The effect of temperature becomes weaker at elevated pressure condition. And the effect of permeability keeps steady despite of temperature change in experiments. The effect of gas state and porous media on supercritical CO2 diffusion coefficient is further discussed by comparing the results in this study with previous research. Based on the experimental results, an empirical correlation for supercritical CO2 diffusion coefficient in n-decane saturated porous media is developed. The experimental results contribute to the study of supercritical CO2 diffusion in compact porous media.
Data from: Experimental study of the supercritical CO2 diffusion coefficient in porous media under reservoir conditions
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Data_Influence of flow and pressure distribution inside a gas diffusion electrode on the performance of a flow-by CO2 electrolyzer
<p>The folder contains the processed data of figures 3, 4, 6, 7, 8, 10, S1 and S2 that appear in:</p> <p>Bert De Mot, Jonas Hereijgers, Miguel Duarte, Tom Breugelmans, Influence of flow and pressure distribution inside a gas diffusion electrode on the performance of a flow-by CO2 electrolyzer, Chemical Engineering Journal, Volume 378, 2019, 122224, ISSN 1385-8947, https://doi.org/10.1016/j.cej.2019.122224.</p>
Data_Electrochemical Reduction of CO2: Effect of Convective CO2 Supply in Gas Diffusion Electrodes
<p>The folder contains the processed data of figures 3, 4, 5 and 6 that appear in:</p> <p>M. Duarte, B. De Mot, J. Hereijgers, T. Breugelmans, Electrochemical Reduction of CO2: Effect of Convective CO2 Supply in Gas Diffusion Electrodes, ChemElectroChem. 6 (2019)</p> <p><a href="https://doi.org/10.1002/celc.201901454">https://doi.org/10.1002/celc.201901454</a></p>
Steering Hydrocarbon Selectivity in CO2 Electroreduction over Soft-landed CuOx Nanoparticle-Functionalized Gas Diffusion Electrodes
<p>The folder contains the tabulated data of figures 3,4,5,6, S3, S12 and S13 in excel, as well as the edited figures in ppt and tiff format, as appeared in:</p> <p>Steering Hydrocarbon Selectivity in CO2 Electroreduction over Soft-landed CuOx Nanoparticle-Functionalized Gas Diffusion Electrodes. <em>ACS Applied Materials & Interfaces</em> 14 (2), 2691-2702.</p>
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