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3,339 results for “heterogeneities”
Plot-Rice v1.0: A global plot-based rice benchmark dataset with spatiotemporal heterogeneity for scientific deep learning
<p>This dataset (Plot-Rice v1.0) offers a global rice benchmark dataset for scientific deep learning at a 10-meter resolution for the year 2023. Plot-Rice v1.0 is constructed based on Sentinel-1 and Sentinel-2 images, encompassing plot-level rice labels and corresponding multi-source feature time series from 20 countries worldwide. It fully considers the spatiotemporal heterogeneity of rice and supports continuous updates, providing a data benchmark for performance comparisons of deep learning models.</p>
Prevalence of endoepicardial asynchrony and breakthrough patterns in a bilayer computational model of heterogeneous endoepicardial dissociation in the left atrium
Open the record for dataset details and reuse information.
Data from: Environmental heterogeneity and not vicariant biogeographic barriers generate community wide population structure in desert adapted snakes
Genetic structure can be influenced by local adaptation to environmental heterogeneity and biogeographic barriers, resulting in discrete population clusters. Geographic distance among populations, however, can result in continuous clines of genetic divergence that appear as structured populations. Here we evaluate the relevant importance of these three factors over a landscape characterized by environmental heterogeneity and the presence of a hypothesized biogeographic barrier in producing population genetic structure within 13 codistributed snake species using a genomic dataset. We demonstrate that geographic distance and environmental heterogeneity across western North America contribute to population genomic divergence. Surprisingly, landscape features long thought to contribute to biogeographic barriers play little role in divergence community wide. Our results suggest that isolation by environment is the most important contributor to genomic divergence. Furthermore, we show that models of population clustering that incorporate spatial information consistently outperform nonspatial models, demonstrating the importance of considering geographic distances in population clustering. We argue that environmental and geographic distances as drivers of community-wide divergence should be explored before assuming the role of biogeographic barriers.
Data from: Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilization
Environmental change can result in substantial shifts in community composition. The associated immigration and extinction events are likely constrained by the spatial distribution of species. Still, studies on environmental change typically quantify biotic responses at single spatial (time series within a single plot) or temporal (spatial beta-diversity at single time points) scales, ignoring their potential interdependence. Here, we use data from a global network of grassland experiments to determine how turnover responses to two major forms of environmental change – fertilization and herbivore loss – are affected by species pool size and spatial compositional heterogeneity. Fertilization led to higher rates of local extinction whereas turnover in herbivore exclusion plots was driven by species replacement. Overall, sites with more spatially heterogeneous composition showed significantly higher rates of annual turnover, independent of species pool size and treatment. Taking into account spatial biodiversity aspects will therefore improve our understanding of consequences of global and anthropogenic change on community dynamics.
Environmental range per unit space determines a unimodal pattern of species richness along a heterogeneity gradient
<p>Although many studies have focused on the effects of the environment and area on local patterns of species richness, few studies have demonstrated how to reconcile the availability of more niches with smaller habitat areas in heterogeneous localities. Here, the environmental range that a species prefers was defined as a niche; a space was defined as an available space if the environmental range of the space matches a niche; and the metric "environmental range per unit space (ERUS)" was presented to describe the heterogeneity of localities. Because the spaces with stressful environmental ranges, outside the niche, increased with increasing heterogeneity, available spaces did not continue to indefinitely increase, but the proportion of available spaces in spaces was low at high heterogeneous localities. Consequently, the probability of species occurring in their respective available spaces was unimodal. Due to the presence of large spaces in homogenous localities and more spaces in heterogeneous localities, the interval distances between nonadjacent spaces were large in these localities. Together, the changes in the number and proportion of available spaces and distance between spaces determined the unimodal probability of species dispersing into their respective available spaces. Thus, the probability of species coexisting was unimodal because it is important for coexisting species to grow in their respective available spaces. The probability of population extinction increased with increasing heterogeneity because the available spaces became narrower. In this way, the ERUS strongly influence species richness: a unimodal richness along the heterogeneity gradient occurred in suitable, suboptimal<i> </i>and stressful environments, and a unimodal algae richness occurred in a lake and river. These results challenge the viewpoint that richness increases with heterogeneity, providing information about the conservation of richness in very homogeneous and heterogeneous regions, and highlighting the importance of balancing the roles of environment and space in understanding and predicting richness.</p>
Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries: Datasets
<p>Sinogram XRD-CT data used in the manuscript entitled "Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries".</p>
Gravity and Heterogeneous Trade Cost Elasticities
<p>Replication package for "Gravity and Heterogeneous Trade Cost Elasticities," by Natalie Chen and Dennis Novy, Economic Journal.</p>
Leaf size variations in a dominant desert shrub, Reaumuria soongarica, adapted to heterogeneous environments
<p>The climate in arid Central Asia (ACA) has changed rapidly in recent decades, but the ecological consequences of this are far from clear. To predict the impacts of climate change on ecosystem functioning, greater attention should be given to the relationships between leaf functional traits and environmental heterogeneity. As a dominant constructive shrub widely distributed in ACA, <em>Reaumuria soongarica</em> provided us with an ideal model to understand how leaf functional traits of desert ecosystems responded to the heterogeneous environments of ACA. Here, to determine the influences of genetic and ecological factors, we characterized species-wide variations in leaf traits among 30 wild populations of <em>R. soongarica</em> and 16 populations grown in a common garden. We found that the leaf length, width, and leaf length to width ratio (L/W) of the northern lineage were significantly larger than those of other genetic lineages, and principal component analysis based on the <em>in situ</em> environmental factors distinguished the northern lineage from the other lineages studied. With increasing latitude, leaf length, width, and L/W in the wild populations increased significantly. Leaf length and L/W were negatively correlated with altitude, and first increased and then decreased with increasing mean annual temperature (MAT) and mean annual precipitation (MAP). Stepwise regression analyses further indicated that leaf length variation was mainly affected by latitude. However, leaf width was uncorrelated with altitude, MAT or MAP. The common garden trial showed that leaf width variation among the eastern populations was caused by both local adaptation and phenotypic plasticity. Our findings suggest that <em>R. soongarica</em> preferentially changes leaf length to adjust leaf size to cope with environmental change. We also reveal phenotypic evidence for ecological speciation of <em>R. soongarica</em>. These results will help us better understand and predict the consequences of climate change for desert ecosystem functioning.</p>
Modern phenotypic typing technologies for monitoring and evaluating heterogeneity in bacterial populations
<p>Modern phenotypic typing technologies for monitoring and evaluating heterogeneity in bacterial populations</p> <p>Современные технологии фенотипического типирования для мониторинга и оценки гетерогенности бактериальных популяций</p>
Figure 1 in Butterfly fauna (Lepidoptera, Papilionoidea) in a heterogeneous area between two biodiversity hotspots in Minas Gerais, Brazil
Figure 1. Location of the study site, the 'Serra do Rola-Moça' State Park (PESRM), in Minas Gerais state, Brazil.
Figure 5 in Butterfly fauna (Lepidoptera, Papilionoidea) in a heterogeneous area between two biodiversity hotspots in Minas Gerais, Brazil
Figure 5. Non-metric multidimensional scaling (NMDS) of the butterfly species composition in sampling sites of Cerrado domain, Atlantic Rainforest and Forest-Cerrado transition areas, in the 'Serra do Rola-Moça' State Park, Minas Gerais state, Brazil.
Figure 4 in Butterfly fauna (Lepidoptera, Papilionoidea) in a heterogeneous area between two biodiversity hotspots in Minas Gerais, Brazil
Figure 4. Sample coverage of species richness in rainy and dry seasons, in the 'Serra do Rola-Moça' State Park, Minas Gerais state, Brazil.
Figure 2 in Butterfly fauna (Lepidoptera, Papilionoidea) in a heterogeneous area between two biodiversity hotspots in Minas Gerais, Brazil
Figure 2. Sample coverage of species richness in rainy season, in the'Serra do Rola-Moça' State Park, Minas Gerais state, Brazil.
Figure 3 in Butterfly fauna (Lepidoptera, Papilionoidea) in a heterogeneous area between two biodiversity hotspots in Minas Gerais, Brazil
Figure 3. Sample coverage of species richness in dry season, in the 'Serra do Rola-Moça' State Park, Minas Gerais state, Brazil.
Aerosol–cloud interactions: the representation of heterogeneous ice activation in cloud models
<p>The files contain the data sets shown in the publication "Aerosol–cloud interactions: the representation of heterogeneous ice activation in cloud models" to appear in final form in the journal Atmos. Chem. Phys. in 2021. A preliminary discussion version of the manuscript is available under https://doi.org/10.5194/acp-21-1-2021.</p> <p>The files include a power point slide containing the schematic Fig.1 and xmgrace plot files containing the research data (ASCII) shown in Figs.2,3,4, and A1. Specifically (Gn means graph #n, Sm means set # m):</p> <p>in Fig.2, the black solid curve is listed under G0.S0; the blue solid curve G0.S1; dashed = G0.S2; dot-dashed = G0.S3</p> <p>in Fig.3, curve (a) = G0.S0; black curve (b) = G1.S0, red curve (b) = G1.S1; black solid (c) = G2.S0 black dashed (c) = G2.S3; red solid (c) = G2.S1, red dashed (c) = G2.S2; curve (d) = G3.S0, black curve (e) = G4.S0; red curve (e) = G4.S1, black solid (f) = G5.S0, black dashed (f) = G5.S4, red solid (f) = G5.S1, red dashed (f) = G5.S2</p> <p>in Fig.4, solid orange curve (a) = G0.S0, dashed orange (a) = G0.S1, solid red (a) G0.S2, dashed red (a) = G0.S3, solid blue (a) = G0.S4, dashed blue (a) = G0.S5; solid curve (b) = G1.S0, dashed (b) = G1.S1</p> <p>in Fig.A1, solid black curve = G0.S0, solid blue = G0.S1</p>
Data and code - Disentangling dispersion from mean reveals true heterogeneity-diversity relationships
<p>Data and code for reproducing figures and results for the manuscript entitled "Disentangling dispersion from mean reveals true heterogeneity-diversity relationships". Published in <a href="https://doi.org/10.1038/s41467-025-64287-0">Nature Communications</a>. See references for data sources.</p> <p>Code tested with Julia version 1.11.1.</p> <p><strong>How to cite this repository</strong></p> <p>If using code or data from this repository, please cite the original publication (<a href="https://doi.org/10.1038/s41467-025-64287-0">Pellett and Valbuena, 2025</a>) and respective data source (see references and README.txt in respective data folder).</p> <p><strong>Update 2024-07-09</strong></p> <p>Minor changes to figure sizes and use of paired-sample t-tests when assessing empirical observations of heterogeneity measures.</p> <p><strong>Update 2024-08-04</strong></p> <p>Step by step instructions included in README</p> <p>Manifest.toml file included with julia and package version requirements.</p> <p><strong>Update 2024-11-18</strong></p> <p>Update following peer review feedback:</p> <p>Analysis of an additional dataset from MacArthurs' seminal paper on foliage height diversity.</p> <p>Hypothesis test of negligible trend for delta</p> <p>Modified extended data figures</p> <p><strong>Update 2025-05-16</strong></p> <p>Update following second round of peer review feedback:</p> <p>Change to equation notation for figure 2</p> <p>Additional script for simulation and review report figures </p> <p><strong>Update 2025-09-29</strong></p> <p>Figure text size adjustments after final round of peer review and editor requests.</p>
Dataset for "Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness"
<p>The micrometeorological dataset used in</p> <p>Tuovinen, J.-P., Aurela, M., Hatakka, J., Räsänen, A., Virtanen, T., Mikola, J., Ivakhov, V., Kondratyev, V. and Laurila, T.: Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness. <em>Biogeosciences Discussions</em>, https://doi.org/10.5194/bg-2018-155, 2018 (accepted for publication in <em>Biogeosciences</em>).</p> <p> </p>
Heterogeneously catalyzed steam reforming processes in chemical reactors employing microchannel process technology
<p><strong>Heterogeneously catalyzed steam reforming processes in chemical reactors employing microchannel process technology</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>The oxidation of sodium sulfite solutions by dissolved oxygen is greatly accelerated by minute traces of copper ions in the homogeneous liquid system. This system is of special interest since it has been shown that the process is a chain reaction. In this case many thousands of molecules of sodium sulfite can be oxidized to sulfate if the initial activation process is produced by absorption of a limited number of quanta (discrete energy measures) of light. The best example of a light-initiated chain reaction is the photocombination of hydrogen and chlorine; as many as one million molecules of hydrogen chloride can be formed by absorption of a single light quantum. It is of interest to note that such chain reactions can be retarded by the presence of negative catalysts, more commonly termed inhibitors. These are materials that slow down the overall reaction by shortening the reaction chains, generally by entering into a non-chain reaction with one of the chemical components that maintain the chain. A wide variety of substances, including alcohols, sugars, and phenols, have been found to act as inhibitors of the oxidation of sulfite solutions. In certain instances, two or more catalysts present at the same time produce effects greater than either would produce alone. It is then customary to speak of promoter action. Thus, iron ions in solution fortify the action of copper ions in catalyzing a reaction between hydrogen peroxide and iodine. It is assumed that each catalyst activates only one of the reactants.</p> <p>Streamwise distance (meter), Heterogeneous reaction rate along the length of the reactor (mole per square meter per second)</p> <p>0 4.44959</p> <p>0.00025 4.45878</p> <p>0.0005 4.48183</p> <p>0.00075 4.51166</p> <p>0.001 4.54417</p> <p>0.00125 4.58104</p> <p>0.0015 4.62817</p> <p>0.00175 4.68814</p> <p>0.002 4.75712</p> <p>0.00225 4.82549</p> <p>0.0025 4.88385</p> <p>0.00275 4.92753</p> <p>0.003 4.95729</p> <p>0.00325 4.98021</p> <p>0.0035 5.00483</p> <p>0.00375 5.03541</p> <p>0.004 5.07069</p> <p>0.00425 5.10466</p> <p>0.0045 5.13141</p> <p>0.00475 5.14874</p> <p>0.005 5.15804</p> <p>0.00525 5.16465</p> <p>0.0055 5.17504</p> <p>0.00575 5.19208</p> <p>0.006 5.21419</p> <p>0.00625 5.23637</p> <p>0.0065 5.25342</p> <p>0.00675 5.26315</p> <p>0.007 5.26706</p> <p>0.00725 5.26999</p> <p>0.0075 5.27698</p> <p>0.00775 5.2902</p> <p>0.008 5.30797</p> <p>0.00825 5.32557</p> <p>0.0085 5.33865</p> <p>0.00875 5.34553</p> <p>0.009 5.34758</p> <p>0.00925 5.34898</p> <p>0.0095 5.35409</p> <p>0.00975 5.36492</p> <p>0.01 5.38001</p> <p>0.01025 5.39497</p> <p>0.0105 5.40558</p> <p>0.01075 5.41024</p> <p>0.011 5.41031</p> <p>0.01125 5.41008</p> <p>0.0115 5.41383</p> <p>0.01175 5.42315</p> <p>0.012 5.43632</p> <p>0.01225 5.44943</p> <p>0.0125 5.45885</p> <p>0.01275 5.46302</p> <p>0.013 5.46291</p> <p>0.01325 5.46232</p> <p>0.0135 5.46534</p> <p>0.01375 5.47361</p> <p>0.014 5.48559</p> <p>0.01425 5.49703</p> <p>0.0145 5.50449</p> <p>0.01475 5.50685</p> <p>0.015 5.5052</p> <p>0.01525 5.50292</p> <p>0.0155 5.5039</p> <p>0.01575 5.5099</p> <p>0.016 5.51963</p> <p>0.01625 5.52926</p> <p>0.0165 5.53479</p> <p>0.01675 5.53509</p> <p>0.017 5.53187</p> <p>0.01725 5.52862</p> <p>0.0175 5.52867</p> <p>0.01775 5.53341</p> <p>0.018 5.54174</p> <p>0.01825 5.54974</p> <p>0.0185 5.5541</p> <p>0.01875 5.5541</p> <p>0.019 5.55067</p> <p>0.01925 5.54664</p> <p>0.0195 5.54565</p> <p>0.01975 5.54956</p> <p>0.02 5.55697</p> <p>0.02025 5.56395</p> <p>0.0205 5.56722</p> <p>0.02075 5.56603</p> <p>0.021 5.56165</p> <p>0.02125 5.55689</p> <p>0.0215 5.55505</p> <p>0.02175 5.55776</p> <p>0.022 5.56342</p> <p>0.02225 5.56878</p> <p>0.0225 5.57095</p> <p>0.02275 5.56887</p> <p>0.023 5.56341</p> <p>0.02325 5.55734</p> <p>0.0235 5.55408</p> <p>0.02375 5.55524</p> <p>0.024 5.55933</p> <p>0.02425 5.56278</p> <p>0.0245 5.56283</p> <p>0.02475 5.55851</p> <p>0.025 5.55036</p> <p>0.02525 5.54139</p> <p>0.0255 5.53503</p> <p>0.02575 5.53218</p> <p>0.026 5.53104</p> <p>0.02625 5.52834</p> <p>0.0265 5.52161</p> <p>0.02675 5.51435</p> <p>0.027 5.50735</p> <p>0.02725 5.50059</p> <p>0.0275 5.49358</p> <p>0.02775 5.48678</p> <p>0.028 5.47965</p> <p>0.02825 5.47246</p> <p>0.0285 5.46597</p> <p>0.02875 5.45839</p> <p>0.029 5.45181</p> <p>0.02925 5.4442</p> <p>0.0295 5.43768</p> <p>0.02975 5.43001</p> <p>0.03 5.42345</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Heterogeneously catalyzed continuous flow reactors with highly insulating wall materials for the production of hydrogen by steam reforming
<p><strong>Heterogeneously catalyzed continuous flow reactors with highly insulating wall materials for the production of hydrogen by steam reforming</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>Catalysts may be classified generally according to their physical state, their chemical nature, or the nature of the reactions that they catalyze. Catalysts may be gases, liquids, or solids. In homogeneous catalysis, the catalyst is molecularly dispersed in the same phase (usually gaseous or liquid) as the reactants. In heterogeneous catalysis the reactants and the catalyst are in different phases, separated by a phase boundary. Most commonly, heterogeneous catalysts are solids, and the reactants are gases or liquids. When the catalyst and the reacting substances are present together in a single state of matter, usually as a gas or a liquid, it is customary to classify the reactions as cases of homogeneous catalysis. Oxides of nitrogen serve as catalysts for the oxidation of sulfur dioxide in the lead chamber process for producing sulfuric acid, an instance of homogeneous catalysis in which the catalyst and reactants are gases. Traces of water vapour catalyze some gas reactions, for example, the interaction of carbon monoxide and oxygen, which proceeds only slowly in dry conditions. Sulfuric acid used as a catalyst for the formation of diethyl ether from ethyl alcohol is an example of homogeneous catalysis in the liquid phase (when the products, water and ether, are continuously removed by distillation); by this method, considerable quantities of alcohol can be converted to ether with a single charge of sulfuric acid. The inversion of cane sugar and the hydrolysis of esters by acid solutions also are examples of homogeneous catalysis in the liquid phase.</p> <p>Streamwise distance (meter), Heterogeneous reaction rate along the length of the reactor (mole per square meter per second)</p> <p>0 2.51969</p> <p>0.00025 2.52898</p> <p>0.0005 2.55637</p> <p>0.00075 2.59905</p> <p>0.001 2.65199</p> <p>0.00125 2.71215</p> <p>0.0015 2.779</p> <p>0.00175 2.85188</p> <p>0.002 2.929</p> <p>0.00225 3.007</p> <p>0.0025 3.08264</p> <p>0.00275 3.15412</p> <p>0.003 3.22107</p> <p>0.00325 3.28478</p> <p>0.0035 3.34725</p> <p>0.00375 3.40961</p> <p>0.004 3.47145</p> <p>0.00425 3.53115</p> <p>0.0045 3.58689</p> <p>0.00475 3.63782</p> <p>0.005 3.68446</p> <p>0.00525 3.72848</p> <p>0.0055 3.77185</p> <p>0.00575 3.81555</p> <p>0.006 3.85919</p> <p>0.00625 3.90117</p> <p>0.0065 3.93975</p> <p>0.00675 3.9745</p> <p>0.007 4.00595</p> <p>0.00725 4.03554</p> <p>0.0075 4.06508</p> <p>0.00775 4.09527</p> <p>0.008 4.1257</p> <p>0.00825 4.15508</p> <p>0.0085 4.18203</p> <p>0.00875 4.20586</p> <p>0.009 4.22692</p> <p>0.00925 4.24666</p> <p>0.0095 4.26675</p> <p>0.00975 4.28804</p> <p>0.01 4.31009</p> <p>0.01025 4.33162</p> <p>0.0105 4.35124</p> <p>0.01075 4.36833</p> <p>0.011 4.38322</p> <p>0.01125 4.39748</p> <p>0.0115 4.41272</p> <p>0.01175 4.42943</p> <p>0.012 4.44708</p> <p>0.01225 4.46422</p> <p>0.0125 4.47947</p> <p>0.01275 4.49245</p> <p>0.013 4.50367</p> <p>0.01325 4.51438</p> <p>0.0135 4.52587</p> <p>0.01375 4.53892</p> <p>0.014 4.55319</p> <p>0.01425 4.56709</p> <p>0.0145 4.57965</p> <p>0.01475 4.59029</p> <p>0.015 4.59928</p> <p>0.01525 4.60791</p> <p>0.0155 4.61738</p> <p>0.01575 4.62855</p> <p>0.016 4.64119</p> <p>0.01625 4.6539</p> <p>0.0165 4.665</p> <p>0.01675 4.67399</p> <p>0.017 4.68163</p> <p>0.01725 4.68922</p> <p>0.0175 4.69783</p> <p>0.01775 4.70789</p> <p>0.018 4.71927</p> <p>0.01825 4.73043</p> <p>0.0185 4.74005</p> <p>0.01875 4.74791</p> <p>0.019 4.75432</p> <p>0.01925 4.76037</p> <p>0.0195 4.7674</p> <p>0.01975 4.77614</p> <p>0.02 4.78619</p> <p>0.02025 4.79589</p> <p>0.0205 4.80412</p> <p>0.02075 4.81091</p> <p>0.021 4.81662</p> <p>0.02125 4.82204</p> <p>0.0215 4.8285</p> <p>0.02175 4.83678</p> <p>0.022 4.84603</p> <p>0.02225 4.85522</p> <p>0.0225 4.8636</p> <p>0.02275 4.87063</p> <p>0.023 4.87617</p> <p>0.02325 4.88134</p> <p>0.0235 4.88764</p> <p>0.02375 4.89563</p> <p>0.024 4.90453</p> <p>0.02425 4.91307</p> <p>0.0245 4.92044</p> <p>0.02475 4.92599</p> <p>0.025 4.92992</p> <p>0.02525 4.93332</p> <p>0.0255 4.93759</p> <p>0.02575 4.94309</p> <p>0.026 4.94878</p> <p>0.02625 4.95359</p> <p>0.0265 4.95679</p> <p>0.02675 4.95736</p> <p>0.027 4.95533</p> <p>0.02725 4.95189</p> <p>0.0275 4.94807</p> <p>0.02775 4.94582</p> <p>0.028 4.94248</p> <p>0.02825 4.9391</p> <p>0.0285 4.93625</p> <p>0.02875 4.93336</p> <p>0.029 4.93052</p> <p>0.02925 4.92708</p> <p>0.0295 4.92401</p> <p>0.02975 4.92139</p> <p>0.03 4.91781</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
Heterogeneously catalyzed steam reforming reactors with highly insulating wall materials for hydrogen production
<p><strong>Heterogeneously catalyzed steam reforming reactors with highly insulating wall materials for hydrogen production</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com</p> <p> </p> <p>The design relates to a thermally coupled monolith reactor, used to thermally contact endothermic and exothermic reaction streams in adjacent channels. The geometry allows intimate thermal contact whilst keeping the streams from becoming mixed. The reactor body is constructed by modification of a substantially rigid and essentially nonporous monolith honeycomb. Prior to modification the monolith consists of a honeycombed body having a matrix of thin walls defining a multiplicity of discrete channels which pass through the body of the structure from one face to the opposing face. The monolith is modified in such a way as to produce a rigid body containing at least two discreet process flow paths which have a number of dividing walls in common. For the purpose of this design, a channel is defined as any individual passageway through the monolith body and a flow path is the group of channels used for a single reaction. The monolith body may be constructed from a number of materials using a range of techniques. Suitable materials include ceramics with a low coefficient of thermal expansion which are readily extrudable. These include, but are not limited to, mullite, corderite, alumina, and silica. Other materials include metals which may be extruded, welded, brazed, or diffusion bonded to make such structures. Using metals, it is sometimes useful to start with metal oxide powders, which are then bonded and reduced to the metallic state. Suitable metals include copper, aluminium, stainless steel, iron, titanium, and mixtures or alloys thereof.</p> <p>Streamwise distance (meter), Heterogeneous reaction rate along the length of the reactor (mole per square meter per second)</p> <p>0 5.24167</p> <p>0.00025 5.35038</p> <p>0.0005 5.59568</p> <p>0.00075 5.86537</p> <p>0.001 6.11865</p> <p>0.00125 6.34243</p> <p>0.0015 6.53109</p> <p>0.00175 6.68219</p> <p>0.002 6.79554</p> <p>0.00225 6.87267</p> <p>0.0025 6.91642</p> <p>0.00275 6.93001</p> <p>0.003 6.91683</p> <p>0.00325 6.88071</p> <p>0.0035 6.82557</p> <p>0.00375 6.75504</p> <p>0.004 6.67218</p> <p>0.00425 6.58003</p> <p>0.0045 6.48092</p> <p>0.00475 6.37655</p> <p>0.005 6.26889</p> <p>0.00525 6.15969</p> <p>0.0055 6.05031</p> <p>0.00575 5.9419</p> <p>0.006 5.83551</p> <p>0.00625 5.73198</p> <p>0.0065 5.63166</p> <p>0.00675 5.53473</p> <p>0.007 5.44181</p> <p>0.00725 5.35303</p> <p>0.0075 5.26832</p> <p>0.00775 5.18794</p> <p>0.008 5.11205</p> <p>0.00825 5.0406</p> <p>0.0085 4.9734</p> <p>0.00875 4.91027</p> <p>0.009 4.85116</p> <p>0.00925 4.79592</p> <p>0.0095 4.74421</p> <p>0.00975 4.69592</p> <p>0.01 4.6509</p> <p>0.01025 4.60897</p> <p>0.0105 4.57006</p> <p>0.01075 4.53403</p> <p>0.011 4.50066</p> <p>0.01125 4.46966</p> <p>0.0115 4.44077</p> <p>0.01175 4.41409</p> <p>0.012 4.38962</p> <p>0.01225 4.36729</p> <p>0.0125 4.34699</p> <p>0.01275 4.32857</p> <p>0.013 4.31176</p> <p>0.01325 4.29635</p> <p>0.0135 4.28232</p> <p>0.01375 4.26966</p> <p>0.014 4.2583</p> <p>0.01425 4.24794</p> <p>0.0145 4.23865</p> <p>0.01475 4.23021</p> <p>0.015 4.22255</p> <p>0.01525 4.21587</p> <p>0.0155 4.21007</p> <p>0.01575 4.20509</p> <p>0.016 4.20091</p> <p>0.01625 4.1973</p> <p>0.0165 4.19387</p> <p>0.01675 4.19079</p> <p>0.017 4.1885</p> <p>0.01725 4.18682</p> <p>0.0175 4.18524</p> <p>0.01775 4.18388</p> <p>0.018 4.18298</p> <p>0.01825 4.18213</p> <p>0.0185 4.18136</p> <p>0.01875 4.18095</p> <p>0.019 4.1809</p> <p>0.01925 4.1808</p> <p>0.0195 4.18068</p> <p>0.01975 4.18095</p> <p>0.02 4.18151</p> <p>0.02025 4.18213</p> <p>0.0205 4.18266</p> <p>0.02075 4.18322</p> <p>0.021 4.18389</p> <p>0.02125 4.18446</p> <p>0.0215 4.18499</p> <p>0.02175 4.18578</p> <p>0.022 4.18649</p> <p>0.02225 4.18705</p> <p>0.0225 4.18769</p> <p>0.02275 4.18848</p> <p>0.023 4.18913</p> <p>0.02325 4.18948</p> <p>0.0235 4.18969</p> <p>0.02375 4.18992</p> <p>0.024 4.18989</p> <p>0.02425 4.18952</p> <p>0.0245 4.18926</p> <p>0.02475 4.18907</p> <p>0.025 4.18838</p> <p>0.02525 4.18717</p> <p>0.0255 4.18578</p> <p>0.02575 4.18424</p> <p>0.026 4.18227</p> <p>0.02625 4.17979</p> <p>0.0265 4.17704</p> <p>0.02675 4.17371</p> <p>0.027 4.16989</p> <p>0.02725 4.16595</p> <p>0.0275 4.16173</p> <p>0.02775 4.15674</p> <p>0.028 4.15105</p> <p>0.02825 4.14506</p> <p>0.0285 4.13866</p> <p>0.02875 4.13164</p> <p>0.029 4.12371</p> <p>0.02925 4.11536</p> <p>0.0295 4.1047</p> <p>0.02975 4.07045</p> <p>0.03 4.04272</p> <p>Contributor: Junjie Chen, ORCID: 0000-0001-5055-4309, E-mail address: komcjj@gmail.com, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p>
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