Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/29186
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dc.contributor.authorHassanloo, H-
dc.contributor.authorWang, X-
dc.date.accessioned2024-06-15T16:39:40Z-
dc.date.available2024-06-15T16:39:40Z-
dc.date.issued2024-06-13-
dc.identifierORCiD: Xinyan Wang https://orcid.org/0000-0002-1988-3742-
dc.identifier100734-
dc.identifier.citationHassanloo, H. and Wang, X. (2024) 'Unveiling the inherent properties and impact of ultrafine nanobubbles in polar and alcoholic media through unsupervised machine learning and atomic insight', International Journal of Thermofluids, 23,100734, pp. 1 - 12. doi: 10.1016/j.ijft.2024.100734.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/29186-
dc.descriptionData availability: the data of this paper can be accessed from the Brunel University London data archive, figshare at [https://doi.org/10.17633/rd.brunel.26139844.v1] https://brunel.figshare.com .en_US
dc.description.abstractThe presence of dissolved gas in the host medium in various industrial processes, such as the presence of oxygen and hydrogen during water splitting or carbon dioxide in fuel cells, or the dissolution of nitrogen for industrial applications, increases the probability of the formation of bubbles at nano-scale. Nanobubbles (NBs), spanning tens to hundreds of nanometres, display exceptional attributes, including remarkable stability, enduring longevity, and an impressive surface-to-volume ratio. These qualities firmly position NBs as pivotal entities with applications that extend across diverse industries, encompassing fields such as energy and chemistry. Therefore, it is crucial to have a comprehensive understanding of their inherent properties and behaviour right from the nucleation stage to grasp their distinctive traits fully. This paper focuses on the combination of data-driven and molecular dynamics simulation to provide a clear understanding of the mechanisms behind NBs nucleation and behaviour in liquids and identify their characteristics. The nucleation process of four gases in two potential liquids for hosting NBs, namely water and methanol, was investigated through scattering nitrogen, oxygen, carbon dioxide, and hydrogen in host liquids using molecular dynamics simulations. Then, high-throughput screening based on the DBSCA (density-based spatial clustering of applications with noise) algorithm was used to screen the formed NB clusters through dissolved gases in the host liquids to determine the size of formed NBs, their density, and motion over time. The findings offer unique insights, indicating that the density of the surrounding liquids is altered and decreased by formed nanobubbles, influenced by the establishment of a nanolayer. The lowest density was recorded for hydrogen and nitrogen nanobubbled methanol samples at 0.61673 and 0.68918 g/ml, respectively, and for hydrogen and nitrogen nanobubbled water samples at 0.93797 and 0.93722 g/ml, respectively. Additionally, the highest density among the scattered gases was observed in non-nanobubbled carbon dioxide methanol and water samples at 0.77724 and 0.99588 g/ml, respectively. Furthermore, the viscosity of the host liquids is influenced, particularly in the presence of high-density nanobubbles, as the highest viscosity was observed for nitrogen nanobubbled water samples at 0.00103 Pa.s.en_US
dc.description.sponsorshipThis work was supported by a UKRI Future Leaders Fellowship (MR/T042915/1) and EPSRC DTP (EP/T518116/1-2688449). MD simulations were run on ARCHER2 and MMM Hub Young, the UK's National Supercomputing Service.en_US
dc.format.extent1 - 12-
dc.format.mediumElectronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.urihttps://doi.org/10.17633/rd.brunel.26139844.v1-
dc.rightsCopyright © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/).-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectbulk nanobubblesen_US
dc.subjectnucleation and stabilityen_US
dc.subjectthermophysical propertiesen_US
dc.subjectunsupervised machine learningen_US
dc.subjectmolecular dynamics simulationen_US
dc.titleUnveiling the inherent properties and impact of ultrafine nanobubbles in polar and alcoholic media through unsupervised machine learning and atomic insighten_US
dc.typeArticleen_US
dc.date.dateAccepted2024-05-13-
dc.identifier.doihttps://doi.org/10.1016/j.ijft.2024.100734-
dc.relation.isPartOfInternational Journal of Thermofluids-
pubs.publication-statusPublished-
pubs.volume23-
dc.identifier.eissn2666-2027-
dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/legalcode.en-
dc.rights.holderThe Authors-
Appears in Collections:Dept of Mechanical and Aerospace Engineering Research Papers

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