Modelling and impact of tensiometer plate geometry and sample volume on biosurfactant surface activity assessment

Biosurfactants are molecules with hydrophilic and hydrophobic moieties with the capacity to reduce the surface tension of water. Given the limited quantity of biosurfactant extracts in laboratories, it is recommended to use equipment that requires minimal sample quantities for detecting the presence...

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Veröffentlicht in:Heliyon 2024-10, Vol.10 (19), p.e38325, Article e38325
Hauptverfasser: Russo-Martínez, N., Vecino, X., Moldes, A.B., Cruz, J.M.
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Sprache:eng
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Zusammenfassung:Biosurfactants are molecules with hydrophilic and hydrophobic moieties with the capacity to reduce the surface tension of water. Given the limited quantity of biosurfactant extracts in laboratories, it is recommended to use equipment that requires minimal sample quantities for detecting the presence of biosurfactants. In this work, commercial glycolipids biosurfactants (rhamnolipids or sophorolipids) were diluted in water and subjected to different analyses to obtain their minimum surface tension (ST) reduction and their critical micellar concentration (CMC). The independent variables of the study were: the geometry of platinum plate (rectangular or cylindrical), the sample volume (2, 4 and 20 mL) and the container material consisting of either glass or polytetrafluoroethylene (PTFE). The variation of ST with biosurfactant concentration was studied based on the isotherm model proposed by Li & Lu. It was observed that the profile of ST values did not vary so much using the different independent variables described, observing that platinum rectangular plate can be used for volumes of 4 mL biosurfactants instead of cylindrical plate usually recommended for volumes lower than 20 mL, the container material was also not significant based on the Pearson and Spearman statistical treatment. Moreover, well-fitting regression model results were obtained for a non-commercial biosurfactant extract obtained from a residual stream of the dairy industry, predicting values close to the observed data. [Display omitted]
ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e38325