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Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach

The reactive transport code CrunchClay was used to derive effective diffusion coefficients (D(e)), clay porosities (ε), and adsorption distribution coefficients (K(D)) from through-diffusion data while considering accurately the influence of unavoidable experimental biases on the estimation of these...

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Autores principales: Tournassat, Christophe, Steefel, Carl I., Fox, Patricia M., Tinnacher, Ruth M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10497684/
https://www.ncbi.nlm.nih.gov/pubmed/37700033
http://dx.doi.org/10.1038/s41598-023-42260-5
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author Tournassat, Christophe
Steefel, Carl I.
Fox, Patricia M.
Tinnacher, Ruth M.
author_facet Tournassat, Christophe
Steefel, Carl I.
Fox, Patricia M.
Tinnacher, Ruth M.
author_sort Tournassat, Christophe
collection PubMed
description The reactive transport code CrunchClay was used to derive effective diffusion coefficients (D(e)), clay porosities (ε), and adsorption distribution coefficients (K(D)) from through-diffusion data while considering accurately the influence of unavoidable experimental biases on the estimation of these diffusion parameters. These effects include the presence of filters holding the solid sample in place, the variations in concentration gradients across the diffusion cell due to sampling events, the impact of tubing/dead volumes on the estimation of diffusive fluxes and sample porosity, and the effects of O-ring-filter setups on the delivery of solutions to the clay packing. Doing so, the direct modeling of the measurements of (radio)tracer concentrations in reservoirs is more accurate than that of data converted directly into diffusive fluxes. While the above-mentioned effects have already been described individually in the literature, a consistent modeling approach addressing all these issues at the same time has never been described nor made easily available to the community. A graphical user interface, CrunchEase, was created, which supports the user by automating the creation of input files, the running of simulations, and the extraction and comparison of data and simulation results. While a classical model considering an effective diffusion coefficient, a porosity and a solid/solution distribution coefficient (D(e)–ε–K(D)) may be implemented in any reactive transport code, the development of CrunchEase makes it easy to apply by experimentalists without a background in reactive transport modeling. CrunchEase makes it also possible to transition more easily from a D(e)–ε–K(D) modeling approach to a state-of-the-art process-based understanding modeling approach using the full capabilities of CrunchClay, which include surface complexation modeling and a multi-porosity description of the clay packing with charged diffuse layers.
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spelling pubmed-104976842023-09-14 Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach Tournassat, Christophe Steefel, Carl I. Fox, Patricia M. Tinnacher, Ruth M. Sci Rep Article The reactive transport code CrunchClay was used to derive effective diffusion coefficients (D(e)), clay porosities (ε), and adsorption distribution coefficients (K(D)) from through-diffusion data while considering accurately the influence of unavoidable experimental biases on the estimation of these diffusion parameters. These effects include the presence of filters holding the solid sample in place, the variations in concentration gradients across the diffusion cell due to sampling events, the impact of tubing/dead volumes on the estimation of diffusive fluxes and sample porosity, and the effects of O-ring-filter setups on the delivery of solutions to the clay packing. Doing so, the direct modeling of the measurements of (radio)tracer concentrations in reservoirs is more accurate than that of data converted directly into diffusive fluxes. While the above-mentioned effects have already been described individually in the literature, a consistent modeling approach addressing all these issues at the same time has never been described nor made easily available to the community. A graphical user interface, CrunchEase, was created, which supports the user by automating the creation of input files, the running of simulations, and the extraction and comparison of data and simulation results. While a classical model considering an effective diffusion coefficient, a porosity and a solid/solution distribution coefficient (D(e)–ε–K(D)) may be implemented in any reactive transport code, the development of CrunchEase makes it easy to apply by experimentalists without a background in reactive transport modeling. CrunchEase makes it also possible to transition more easily from a D(e)–ε–K(D) modeling approach to a state-of-the-art process-based understanding modeling approach using the full capabilities of CrunchClay, which include surface complexation modeling and a multi-porosity description of the clay packing with charged diffuse layers. Nature Publishing Group UK 2023-09-12 /pmc/articles/PMC10497684/ /pubmed/37700033 http://dx.doi.org/10.1038/s41598-023-42260-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Tournassat, Christophe
Steefel, Carl I.
Fox, Patricia M.
Tinnacher, Ruth M.
Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach
title Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach
title_full Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach
title_fullStr Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach
title_full_unstemmed Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach
title_short Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach
title_sort resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10497684/
https://www.ncbi.nlm.nih.gov/pubmed/37700033
http://dx.doi.org/10.1038/s41598-023-42260-5
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