Data-driven optimisation of variation of residual concentration to estimate the hydrogen diffusion coefficient and uptake via MATLABMendeley DataMendeley Data
This article presents the data collected during experimental solubility tests in University of Bergamo lab and the developed code to estimate the principal parameters for hydrogen uptake. The experimental approach proposed involves in two-step process: the first phase consists of electrochemical cha...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925006158 |
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| author | Luca Gritti Denny Coffetti Marina Cabrini Tommaso Pastore |
| author_facet | Luca Gritti Denny Coffetti Marina Cabrini Tommaso Pastore |
| author_sort | Luca Gritti |
| collection | DOAJ |
| description | This article presents the data collected during experimental solubility tests in University of Bergamo lab and the developed code to estimate the principal parameters for hydrogen uptake. The experimental approach proposed involves in two-step process: the first phase consists of electrochemical charging to saturate the metallic sample via cathodic polarization at potentials lower than the equilibrium potential for hydrogen evolution, followed by a second phase of discharging under anodic polarization at potentials higher than the equilibrium potential. During the discharging phase, the time-dependent anodic current is influenced by the flux of diffusible hydrogen exiting the sample. This flux is governed by the initial concentration of diffusible hydrogen in the material, the diffusion coefficient, and the time elapsed between the end of the charging step and the beginning of the discharging phase. Via a data-driven optimisation it is possible to obtain the characteristic parameter of hydrogen diffusion in the material (apparent diffusion coefficient, hydrogen uptake and waiting time) using the MATLAB code. The data permit to elaborate the cylindrical geometry, however it is possible to modify the target curve via a simulation on a specific geometry (not included in the data) and use the same method to elaborate the experimental data of a specific geometry. |
| format | Article |
| id | doaj-art-a07d48756cc84b07bfbfc3b1a93b8a7e |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-a07d48756cc84b07bfbfc3b1a93b8a7e2025-08-20T03:58:41ZengElsevierData in Brief2352-34092025-10-016211189110.1016/j.dib.2025.111891Data-driven optimisation of variation of residual concentration to estimate the hydrogen diffusion coefficient and uptake via MATLABMendeley DataMendeley DataLuca Gritti0Denny Coffetti1Marina Cabrini2Tommaso Pastore3Corresponding author.; University of Bergamo, Bergamo, ItalyUniversity of Bergamo, Bergamo, ItalyUniversity of Bergamo, Bergamo, ItalyUniversity of Bergamo, Bergamo, ItalyThis article presents the data collected during experimental solubility tests in University of Bergamo lab and the developed code to estimate the principal parameters for hydrogen uptake. The experimental approach proposed involves in two-step process: the first phase consists of electrochemical charging to saturate the metallic sample via cathodic polarization at potentials lower than the equilibrium potential for hydrogen evolution, followed by a second phase of discharging under anodic polarization at potentials higher than the equilibrium potential. During the discharging phase, the time-dependent anodic current is influenced by the flux of diffusible hydrogen exiting the sample. This flux is governed by the initial concentration of diffusible hydrogen in the material, the diffusion coefficient, and the time elapsed between the end of the charging step and the beginning of the discharging phase. Via a data-driven optimisation it is possible to obtain the characteristic parameter of hydrogen diffusion in the material (apparent diffusion coefficient, hydrogen uptake and waiting time) using the MATLAB code. The data permit to elaborate the cylindrical geometry, however it is possible to modify the target curve via a simulation on a specific geometry (not included in the data) and use the same method to elaborate the experimental data of a specific geometry.http://www.sciencedirect.com/science/article/pii/S2352340925006158Data-driven optimisationHydrogen modellingHydrogen uptakeApparent diffusion coefficientDischarging target curve |
| spellingShingle | Luca Gritti Denny Coffetti Marina Cabrini Tommaso Pastore Data-driven optimisation of variation of residual concentration to estimate the hydrogen diffusion coefficient and uptake via MATLABMendeley DataMendeley Data Data in Brief Data-driven optimisation Hydrogen modelling Hydrogen uptake Apparent diffusion coefficient Discharging target curve |
| title | Data-driven optimisation of variation of residual concentration to estimate the hydrogen diffusion coefficient and uptake via MATLABMendeley DataMendeley Data |
| title_full | Data-driven optimisation of variation of residual concentration to estimate the hydrogen diffusion coefficient and uptake via MATLABMendeley DataMendeley Data |
| title_fullStr | Data-driven optimisation of variation of residual concentration to estimate the hydrogen diffusion coefficient and uptake via MATLABMendeley DataMendeley Data |
| title_full_unstemmed | Data-driven optimisation of variation of residual concentration to estimate the hydrogen diffusion coefficient and uptake via MATLABMendeley DataMendeley Data |
| title_short | Data-driven optimisation of variation of residual concentration to estimate the hydrogen diffusion coefficient and uptake via MATLABMendeley DataMendeley Data |
| title_sort | data driven optimisation of variation of residual concentration to estimate the hydrogen diffusion coefficient and uptake via matlabmendeley datamendeley data |
| topic | Data-driven optimisation Hydrogen modelling Hydrogen uptake Apparent diffusion coefficient Discharging target curve |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925006158 |
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