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• W3197803519 endingPage "108404" @default.
• W3197803519 startingPage "108404" @default.
• W3197803519 abstract "Root-secreted mucilage and microbially produced extracellular polymeric substances (EPS) modify soil physical and biogeochemical processes. Most studies infer the effects of these polymeric substances from soil bulk behaviour rather than investigating the pore scale. This investigation quantified the isolated physical behaviour of mucilage in a simplified pore-scale setup. We placed drops of mucilage of different concentrations between two flat surfaces to form liquid bridges and monitored their drying using optical imaging and magnetic resonance imaging (MRI). We used our observations to validate a polymer-based multi-phase model that characterises the gel-water-air interactions. In the experiments, while pure water liquid bridges rupture, the mucilage buckled under drying, but maintained connection between the surfaces. MRI showed more water was lost from the central region in the middle of the two plates. In the model, mucilage gel accumulated near the boundaries where surface adhesion occurs. The modelled accumulation times overlapped with monitored bridge buckling for the different concentrations, showing the model can predict the observed transition at which the mixture no longer behaves like a pure liquid. Results suggest that the earlier phase transitions observed for higher mucilage concentrations show a potential mechanism for the greater drought tolerance for plant roots and increase the soil water holding capacity. Furthermore, we discuss potential applications of our model for describing the impacts that microbial biofilms may have on soil structure along with impacts of soil fauna on soil physical functions." @default.
• W3197803519 created "2021-09-13" @default.
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• W3197803519 date "2021-11-01" @default.
• W3197803519 modified "2023-10-10" @default.
• W3197803519 title "Physical characterisation of chia mucilage polymeric gel and its implications on rhizosphere science - Integrating imaging, MRI, and modelling to gain insights into plant and microbial amended soils" @default.
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• W3197803519 doi "https://doi.org/10.1016/j.soilbio.2021.108404" @default.
• W3197803519 hasPublicationYear "2021" @default.
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