Optical Microscopy and log data of Enzymatically Induced Calcite Precipitation (EICP) in microfluidic cells (Quasi-2D-structure) (doi:10.18419/darus-1799)

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Document Description

Citation

Title:

Optical Microscopy and log data of Enzymatically Induced Calcite Precipitation (EICP) in microfluidic cells (Quasi-2D-structure)

Identification Number:

doi:10.18419/darus-1799

Distributor:

DaRUS

Date of Distribution:

2022-03-30

Version:

1

Bibliographic Citation:

Weinhardt, Felix; Deng, Jingxuan; Steeb, Holger; Class, Holger, 2022, "Optical Microscopy and log data of Enzymatically Induced Calcite Precipitation (EICP) in microfluidic cells (Quasi-2D-structure)", https://doi.org/10.18419/darus-1799, DaRUS, V1

Study Description

Citation

Title:

Optical Microscopy and log data of Enzymatically Induced Calcite Precipitation (EICP) in microfluidic cells (Quasi-2D-structure)

Identification Number:

doi:10.18419/darus-1799

Authoring Entity:

Weinhardt, Felix (University of Stuttgart)

Deng, Jingxuan (University of Minnesota)

Steeb, Holger (University of Stuttgart)

Class, Holger (University of Stuttgart)

Other identifications and acknowledgements:

Weinhardt, Felix

Other identifications and acknowledgements:

Deng, Jingxuan

Other identifications and acknowledgements:

Class, Holger

Other identifications and acknowledgements:

Steeb, Holger

Producer:

Chair for Continuum-Mechanics

Porous Media Lab

Grant Number:

SFB 1313 - 327154368

Distributor:

DaRUS

Access Authority:

Weinhardt, Felix

Access Authority:

Steeb, Holger

Depositor:

Weinhardt, Felix

Date of Deposit:

2021-04-14

Holdings Information:

https://doi.org/10.18419/darus-1799

Study Scope

Keywords:

Earth and Environmental Sciences, Engineering, Enzymatically Induced Calcite Precipitation, Biomineralization, Porous Media, Porosity-permeability Relationship, Microfluidics

Abstract:

<b>Content:</b> <br /> This dataset includes raw as well as processed data from three experiments (Quasi-2D-1, Quasi-2D-2 and Quasi-2D-3). Each dataset consists of the readouts from the pressure sensor(s), as logged with the use of QmixElements (<i>[Name of Experiment]</i>_logFiles_QMIX), raw images ( <i>[Name of Experiment]</i>_rawImages ), and segmented images ( <i>[Name of Experiment]</i>_segmentedImages ). <br /> <br /> <b>*.CSV files:</b> <br /> For each experiment three *.csv files are given in the corresponding folders ( <i>[Name of Experiment]</i>_logFiles_QMIX ): <ol type=a> <li> initial permeability measurement ( <i>[Name of Experiment]</i>_PermInitial )</li> <li> continuous injection of reactive solution ( <i>[Name of Experiment]</i>_ContinuousInjection )</li> <li> result file combined porosity and permeability data during the continuous injection ( <i>[Name of Experiment]</i>_Result )</li> </ol> The initial permeability files (1) and the continuous injection files (2) are essentially the output of the software QMixElements which is used to operate the syringe pumps and log the pressure sensors. It consists of the timestamps, flow rates and pressure drops of the domain. In order to determine the initial permeability in a reliable way (1), a series of different flow rates was applied and the corresponding pressure drops were monitored. From that, the permeability can be calculated using Darcy's Law. <br> From the monitored flow and pressure data in the continuous injection files (2), the permeability evolution can be calculated. The result file (3) is a combination of the processed optical microscopy images (see below for more information) and the monitored flow and pressure data. It consinsts of timestamps, flow rates, pressure drops, normalized permeabilities and porosities. <br /> <br /> <i><b>Note:</b> When estimating the permeability reduction over time during stage 2), e.g K/K<sub>0</sub> = Δp<sub>0</sub>/Δp(t) , the initial pressure drop (Δp<sub>0</sub>) is very crucial. Small variations and uncertainties effect the estimated permeability reduction tremendously. Due to the small flow rates in the experiments, the measured initial pressure drop is very small compared to the pressure range of the sensors and falls within the error limit of the sensor itself, casting the measurements quite uncertain. Therefore, it is recommended to back calculate the initial pressure drop based on the initial permeability measurement of stage 1) and the flow rate of stage 2) </i> <br /> <br /> <b>Raw images:</b> <br /> Images taken by optical microscopy are given in the folders <i>[Name of Experiment]</i>_rawImages. These are synchronized with the pressure measurements found in the log files from QMixElements, which is realized by naming each image with its corresponding timestamp: <i>[YYYY_MM_DD_hh-mm-ss]</i>. The timestamps correspond to the timestamps in the log files. Images were captured at one frame per minute. The physical resolution of the images are: 8.93 &mu;m/pixel (Quasi-2D-1), 8.96 &mu;m/pixel (Quasi-2D-2) and 9.31 &mu;m/pixel (Quasi-2D-3). These were calculated based on the known length of the domain divided by the number of pixels. <br /> <br /> <b>Segmented images:</b> <br /> The segmented images are saved in the folders <i>[Name of Experiment]</i>_segmentedImages. Not all of the raw images were processed: The first image was used for creating a mask, then one image every 10 minutes was processed. The resulting images are segemented into three phases: <ul> <li>void space: (black: grayscale value of 0) </li> <li>solid inclusions, based on the mask: (gray: grayscale value of 100)</li> <li>precipitates (white: grayscale value of 255)</li> <ul>

Kind of Data:

Image data and log files (csv)

Methodology and Processing

Sources Statement

Data Access

Other Study Description Materials

Related Publications

Citation

Title:

Weinhardt, F.; Deng, J.; Hommel, J.; Vahid Dastjerdi, S.; Gerlach, R.; Steeb, H.; Class, H. (2022). Spatiotemporal Distribution of Precipitates and Mineral Phase Transition During Biomineralization Affect Porosity–Permeability Relationships. <i>Transport in Porous Media</i> <b>143</b>, 527–549 (2022).

Identification Number:

10.1007/s11242-022-01782-8

Bibliographic Citation:

Weinhardt, F.; Deng, J.; Hommel, J.; Vahid Dastjerdi, S.; Gerlach, R.; Steeb, H.; Class, H. (2022). Spatiotemporal Distribution of Precipitates and Mineral Phase Transition During Biomineralization Affect Porosity–Permeability Relationships. <i>Transport in Porous Media</i> <b>143</b>, 527–549 (2022).

Other Study-Related Materials

Label:

Quasi-2D-1_logFiles_QMIX.tar.gz

Notes:

application/gzip

Other Study-Related Materials

Label:

Quasi-2D-1_rawImages.tar.gz

Notes:

application/gzip

Other Study-Related Materials

Label:

Quasi-2D-1_segmentedImages.tar.gz

Notes:

application/gzip

Other Study-Related Materials

Label:

Quasi-2D-2_logFiles_QMIX.tar.gz

Notes:

application/gzip

Other Study-Related Materials

Label:

Quasi-2D-2_rawImages.tar.gz

Notes:

application/gzip

Other Study-Related Materials

Label:

Quasi-2D-2_segmentedImages.tar.gz

Notes:

application/gzip

Other Study-Related Materials

Label:

Quasi-2D-3_logFiles_QMIX.tar.gz

Notes:

application/gzip

Other Study-Related Materials

Label:

Quasi-2D-3_rawImages.tar.gz

Notes:

application/gzip

Other Study-Related Materials

Label:

Quasi-2D-3_segmentedImages.tar.gz

Notes:

application/gzip