Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26261
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dc.contributor.authorMartinez, Q-
dc.contributor.authorChen, C-
dc.contributor.authorXia, J-
dc.contributor.authorBahai, H-
dc.date.accessioned2023-04-10T11:31:20Z-
dc.date.available2023-04-10T11:31:20Z-
dc.date.issued2023-03-15-
dc.identifierORCID iDs: Jun Xia https://orcid.org/0000-0002-2547-3483; Hamid Bahai https://orcid.org/0000-0002-3476-9104.-
dc.identifier.citationMartinez, Q. et al. (2023) 'Sequence-to-Sequence Change-Point Detection in Single-Particle Trajectories via Recurrent Neural Network for Measuring Self-Diffusion', Transport in Porous Media, 147. pp. 679 - 701. doi: 10.1007/s11242-023-01923-7en_US
dc.identifier.issn0169-3913-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/26261-
dc.descriptionData Availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.en_US
dc.description.abstractCopyright © The Author(s) 2023. A recurrent neural network is developed for segmenting between anomalous and normal diffusion in single-particle trajectories. Accurate segmentation infers a distinct change point that is used to approximate an Einstein linear regime in the mean-squared displacement curve via the transition density function, a unique physical descriptor for short-lived and delayed transiency. Through several artificial and simulated scenarios, we demonstrate the compelling accuracy of our model for dissecting linear and nonlinear behaviour. The inherent practicality of our model lies in its ability to substantiate the self-diffusion coefficient through offline trajectory segmentation, which is opposed to the common ‘best-guess’ linear fitting standard. Additionally, we show that the transition density function has fundamental implications and correspondence to underlying mechanisms that influence transition. In particular, we show that the known proportionality between salt concentration and diffusion of water also influences delayed anomalous behaviour.en_US
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC; Grant number EP/T033940/1); Royal Society (Grant number IES\R3\193152).en_US
dc.format.extent679 - 701-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.rightsCopyright © The Author(s) 2023. Rights and permissions: 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 https://creativecommons.org/licenses/by/4.0/.-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectdiffusionen_US
dc.subjectanomalous diffusionen_US
dc.subjectchange-point detectionen_US
dc.subjectrecurrent neural networken_US
dc.subjectcarbon storageen_US
dc.titleSequence to Sequence Change-Point Detection in Single Particle Trajectories via Recurrent Neural Network for Measuring Self-Diffusionen_US
dc.typeArticleen_US
dc.relation.isPartOfTransport in Porous Media-
pubs.publication-statusPublished-
pubs.volume147-
dc.identifier.eissn1573-1634-
dc.rights.holderThe Author(s)-
Appears in Collections:Dept of Mechanical and Aerospace Engineering Research Papers

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