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71 results for “Porous media”
Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media
<p>Dataset associated to the publication</p><p>"Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media"</p><p>By</p><p>David Scheidweiler, Ankur Deep Bordoloi, Wenqiao Jiao, Vladimir Sentchilo, Monica Bollani, Audam Chhun, Philipp Engel and Pietro de Anna</p><p>Folder named "Figure_X" contains the original raw data, analysed data and source data for each plot within figure "X" on the manuscript and supplementary information.</p><p>We do not provide raw data for each replica as one flow&growth experiment consists in 50 large images for a total of about 12 GB per dataset. Thus, we provide here the original data for the Wild Type experiment and the control D-luxS mutant. The data for the replicas and other control experiment can be available upon request.</p><p>We provide Matlab scripts to read and analyze the original images.</p>
Channeling: a new class of dissolution in complex porous media
<p>ModelAandBGeometries.7z contains the original 12,000 x 12,000 pixel geometries created for Models A and B in Menke et al. 2022 PNAS. They were subsequently binned by 12 in each direction and padded by 2 on all sides to get the 1,004 x 1,004 pixel geometries input into GeoChemFoam. The original location and radius of each bead is supplied in the .hdf5 file as 'rad', 'x_coor', and y_coor'. </p> <p>ModelA_Pe##_K##.hdf5 and ModelB_Pe##_K##.hdf5 contain all of the simulation results for each flow and reaction scenario. This includes porosity, permeability, time_s, concentration, velocity, pores, grains, throats, and moments for all output timesteps. Pore2 & throat2 denote analyses with the fully dissolved section of the model excluded. </p> <p>The model (GeoChemFoam) used to run these dissolution scenarios can be downloaded with tutorials at https://github.com/GeoChemFoam/. The script used to make the micromodel geometries can be found at https://github.com/hannahmenke/PNAS2022.</p>
Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile
<p>The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore-network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.</p>
Dataset for "A capillary bundle model for the electrical conductivity of saturated frozen porous media"
<p>This dataset supports the research study 'A capillary bundle model for the electrical conductivity of saturated frozen porous media' by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng and L. D Thanh.</p> <p>We provide the experimental data from this study and the published data from Coperey et al. (2019a,b) and Duvillard et al. (2018, 2021) for verifing the proposed model with different PSDs (lognormal and fractal distribution).</p> <p>Matlab code Description:</p> <p>Untitled 1- the code for determining the electrical conductivity as a function of temperature and the sensitive analysis;</p> <p>Untitled 2- the code for comparison between the experimental data and the proposed model;</p> <p>Untitled 3- the code for comparison of the contribution between the bulk conduction and surface conduction to the total electrical conductivity;</p> <p>Untiled 4- the code for evolution of the effective formation factor as a function of the temperature.</p>
Dataset for "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities"
<p>This dataset supports the research study "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities" by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng, A. Mendieta, G. Lin, and L. D. Thanh.<br> We provide the experimental data of electrical condutivity and unfrozen water saturation with different initial water saturations and salinities. Meanwhile, we also offer the matlab code for calculating the predicted values of electrical conductivity and apparent formation factor.</p> <p>Each file has its header, describing each column.</p> <p> </p>
Scripts, models, and data for manuscript "On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media"
<p>This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript</p> <p>Bücker, M., Flores Orozco, A., Undorf, S., and Kemna, A., 2019, <em>On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media</em>, submitted to JGR: Solid Earth.</p> <p>If you find this data useful in your own research, please cite this manuscript.</p>
Data: "Solute transport in bounded porous media (...)" by Sole-Mari et al. (2020)
<p>Data corresponding to the results of the Monte Carlo set of simulations described in: "Solute transport in bounded porous media characterized by Generalized Sub-Gaussian log-conductivity distributions". Please send questions to guillem.sole.mari@outlook.com.</p>
Dataset for Pore-scale investigation of forced imbibition in porous media
<p>Dataset for the manuscript titled" Pore-scale investigation of forced imbibition in porous media"</p>
Dataset for "Experimental and Modeling Insights into Mixing-Limited Reactive Transport in Heterogeneous Porous Media: Role of Stagnant Zones"
<p>This dataset contains the observed and simulated BTC of bimolecular transport experiment that was involved in "Yin et al., Experimental and Modeling Insights into Mixing-Limited Reactive Transport in Heterogeneous Porous Media: Role of Stagnant Zones".</p>
A closer look: High-resolution pore-scale simulations of solute transport and mixing through porous media columns
<p>This dataset contains the results of fluid flow (Navier-Stokes) and solute transport (Advection-Diffusion) simulations within columns of granular media generated by virtual gravitational settling of spherical grains. The experiments comprise three media with different degrees of grain-size variability; a range of grain-Peclet numbers is explored. See the homonymous research paper by Sole-Mari et al. (2022, Water Resources Research) for more information.</p> <p>Grains.zip: Positions and radii of the spherical grains for each value of grain-size variability sigma (Matlab's .mat format).</p> <p>ResultsCoarse.zip: Coarse-scale data presented in the aforementioned WRR paper (Matlab's .mat format).</p> <p>Link to the full micro-scale dataset: (soon available)</p> <p>We thankfully acknowledge the computer resources at MareNostrum and the technical support provided by the Barcelona Supercomputing Center (AECT-2019-3-0014).</p> <p> </p>
Data set: On the porosity-dependent permeability and conductivity of triply periodic minimal surface based porous media
<p>This file contains all processed data from the simulations and calculations.</p>
Temperature effect on non-Darcian flow in low-permeability porous media
<p>This dataset includes the measured threshold gradients and permeabilities of low permeability porous at 3 temperatures, and the measured hydraulic gradients and flow velocities of different permeabilities at 3 temperatures. The experiment is designed to reveal the temperature effects on the non-Darcian flow in low permeability porous media. </p>
Suppressing viscous fingering in structured porous media
<p>Finger-like protrusions that form along fluid−fluid displacement fronts in porous media are often excited by hydrodynamic instability<br> when low-viscosity fluids displace high-viscosity resident fluids. Such interfacial instabilities are undesirable in many natural and engineered displacement processes.We report a phenomenon whereby gradual and monotonic variation of pore sizes along the<br> front path suppresses viscous fingering during immiscible displacement, that seemingly contradicts conventional expectation of enhanced instability with pore size variability. Experiments and pore-scale numerical simulations were combined with an analytical<br> model for the characteristics of displacement front morphology as a function of the pore size gradient. Our results suggest that the<br> gradual reduction of pore sizes act to restrain viscous fingering for a predictable range of flow conditions (as anticipated by gradient<br> percolation theory). The study provide insights into ways for suppressing unwanted interfacial instabilities in porous media, and provides design principles for new engineered porous media such as exchange columns, fabric, paper, and membranes with respect to their desired immiscible displacement behavior.</p>
Hybrid nanoplasmonic porous biomaterial scaffold for liquid biopsy diagnostics using extracellular vesicles: data and media
<p>Raw datasets and media accompanying the manuscript: <strong>Hybrid nanoplasmonic porous biomaterial scaffold for liquid biopsy diagnostics using extracellular vesicles</strong>, published in ACS Sensors</p>
Mesh and velocity fields for "Resolving Pore-scale Concentration Gradients for Transverse Mixing and Reaction in Porous Media"
<p>This dataset contains the data needed to reproduce the results in the manuscript "Resolving Pore-scale Concentration Gradients for Transverse Mixing and Reaction in Porous Media". The three folders refer to (1) uniform flow, (2) flow through a bead pack, and (3) flow through a Berea sample (from the 11 sandstones repository, Digital Rocks Portal). The meshes and the velocity fields (in the subfolder "velocity") are in a HDF5 format. The meshes and velocity fields can be read using FEniCS version 2019.2.0. The velocity fields need 2nd order Lagrange tetrahedral elements.</p>
Data, Figures and Codes for "Experimental analyses of pore-size dependent biomineralization in porous media under various flow rate and bacterial density scenarios"
<pre>This repository contains the data, codes and figures for the manuscript <br>"Experimental analyses of pore-size-dependent biomineralization in porous media under various flow rate and bacterial density scenarios". <br><br>Comments welcome. </pre>
Supplementary data for 'Ferrofluid impregnation efficiency and its spatial variability in natural and synthetic porous media: Implications for magnetic pore fabric studies'
<p>Supplementary data for the manuscript 'Ferrofluid impregnation efficiency and its spatial variability in natural and synthetic porous media: Implications for magnetic pore fabric studies'</p>
Density driven flow in porous media by 3D print_measured data
<p>Density-driven convection in porous media constructed with 3D printed porous blocks were performed. This dataset is the measured total dissolved MEG and flux across the top interface.</p>
Dataset to "Legendre-Fenchel transforms capture layering transitions in porous media"
<p>This is the dataset presented in the article "Legendre-Fenchel transforms capture layering transitions in porous media" [1]. This dataset contains three files, "isobaric_compression.csv", "isobaric_expansion.csv", and "isometric.csv". The columns are the same in each file. They are flow left to right</p> <ol> <li>Temperature</li> <li>Standard deviation of the temperature</li> <li>Number of fluid particles</li> <li>Normal pressure</li> <li>Standard deviation of the normal pressure</li> <li>Height</li> <li>Standard deviation of the height</li> <li>Potential energy</li> <li>Standard deviation of the potential energy</li> <li>Kinetic energy</li> <li>Standard deviation of the kinetic energy</li> </ol> <p>[1] https://doi.org/10.1039/D1NA00846C</p>
Dataset of "Modelling the Frequency-Dependent Effective Excess Charge Density in Partially Saturated Porous Media"
<p>This dataset supports the research study 'Modelling the Frequency-Dependent Effective Excess Charge Density in Partially Saturated Porous Media' by S. G. Solazzi, L. D Thanh, K. Hu, and D. Jougnot.</p> <p>We provide with a commented MATLAB code (Freq_Sat_SP.m) for estimating the frequency-dependent (i) effective excess charge density, (ii) the electrokinetic coupling coefficient, and (iii) the effective dynamic permeability for partially saturated porous media. The code allows to consider fractal, lognormal, and double lognormal pore size distributions.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.