RESUMEN
In this study, we present a rapid, cost-effective Python-driven computer vision approach to quantify the prevalent "gloppiness" phenomenon observed in complex fluids and gels. We discovered that rheology measurements obtained from commercial shear rheometers do show some hints, but do not exhibit a strong correlation with the extent of "gloppiness". To measure the "gloppiness" level of laboratory-produced shower gel samples, we employed the rupture time of jetting flow and found a significant correlation with data gathered from the technical insight panelist team. While fully comprehending the "gloppiness" phenomenon remains a complex challenge, the Python-based computer vision technique utilizing jetting flow offers a promising, efficient, and affordable solution for assessing the degree of "gloppiness" for commercial liquid and gel products in the industry.
RESUMEN
A device to measure the height loss of a sample extruded from a syringe on a surface is described thus emulating toothpaste extrusion from a tube with the goal to predict shape retention of the extruded ribbon. Correlations with rheological tests are considered with a special focus on experiments more likely to be implementable in an industrial environment. In agreement with previous studies, the instantaneous viscosity maximum measured in a stress ramp test is a good predictor of ribbon height loss. Up- and down-shear flow curves of the thixotropic loop were fitted with a generalized Casson equation and the correlations of the fitting parameters with the height loss were also considered. Yield stress extracted from the up-shear flow curve as well as the shape of the curve is found to define the ribbon height loss as well as the degree of thixotropy which may be quantified by the width of the loop or simply as the ratio of viscosities at low shear rate.
