Modeling the Batch Sedimentation of Calcium Carbonate Particles in Laboratory Experiments-A Systematic Approach.

The design of continuous thickeners and clarifiers is commonly based on the solid flux theory. Batch sedimentation experiments conducted with solid concentrations still provide useful information for their application. The construction of models for the velocity of settling allows the estimation of the flux of solids throughout time, which can, in turn, be used to find the area of the units required to achieve a given solid concentration in the clarified stream. This paper addresses the numerical treatment of data obtained from batch sedimentation experiments of calcium carbonate particles. We propose a systematic framework to fit a model that is capable of representing the process features that involve (i) the numerical differentiation of data to generate initial estimates for the instantaneous velocity of settling; (ii) the integration of a differential equation to fit the model for the velocity of settling; and (iii) the assessment of the quality of the fit using common statistical indicators. The model used for demonstration has a theoretical basis combined with an empirical component to account for the effect of the particle concentrations and their state of aggregation. The values of the numerical parameters obtained are related to the characteristic dimensions of the aggregates and their mass-length fractal dimensions.

Using rheological monitoring to determine the gelation kinetics of chitosan-based systems.

The modeling of polymeric reactions is a topic of large interest. The gelation reactions that may result from self-crosslinking or hybrid (agent based-) crosslinking are examples with interest specially in biomaterials applications. The composition of polymer entities during the reaction is hard to follow, and their concentration is not a good measure of the system dynamics. One alternative is monitoring the rheological behavior of the reacting mass, and relate the elastic modulus of the mixture with the rheological degree of conversion. In this paper we use rheological data to fit Malkin and Kulichikin (1996) [1] based models to describe the crosslinking of chitosan. First, the self-crosslinking of chitosan is considered. Then, the agent-based crosslinking reaction promoted by genipin is addressed. We use dynamical rheological data to fit the reaction models. The model fitting problem generated using Maximum Likelihood principle with heteroscedastic prediction error variance is formulated as a Dynamic Optimization problem and subsequently solved with a sequential approach. Parametric confidence regions are computed using the linear approximation of the covariance matrix at the optimum. Further, the parameters correlation matrix is also determined and used to qualitatively infer about the practical identifiability. The reaction order obtained for self-crosslinking kinetics is 1.3375 ± (0.0151) - approximately of first order -, and is 2.2402 ± (0.0373) for hybrid crosslinking (approximately of second order). In both cases we prove the error variance model is heteroskedastic and the model is identifiable. The approach proposed herein can be extended to other polymer systems.

Flow of Tunable Elastic Microcapsules through Constrictions.

We design and fabricate elastically tunable monodisperse microcapsules using microfluidics and cross-linkable polydimethylsiloxane (PDMS). The overall stiffness of the microcapsules is governed by both the thickness and cross-link ratio of the polymer shell. Flowing suspensions of microcapsules through constricted spaces leads to transient blockage of fluid flow, thus altering the flow behavior. The ability to tune microcapsule mechanical properties enables the design of elastic microcapsules that can be tailored for desired flow behavior in a broad range of applications such as oil recovery, reactor feeding, red blood cell flow and chemical targeted delivery.

A puzzle used to teach the cardiac cycle.

The aim of the present article is to describe a puzzle developed for use in teaching cardiac physiology classes. The puzzle presents figures of phases of the cardiac cycle and a table with five columns: phases of cardiac cycle, atrial state, ventricular state, state of atrioventricular valves, and pulmonary and aortic valves. Chips are provided for use to complete the table. Students are requested to discuss which is the correct sequence of figures indicating the phases of cardiac cycle. Afterward, they should complete the table with the chips. Students of biology, dentistry, medicine, pharmacy, and nursing graduation courses from seven institutions performed the puzzle evaluation. They were invited to indicate whether the puzzle had been useful for learning about the subject by filling one of four alternatives. Of the students, 4.6% answered that it was not necessary but helped them to confirm what they had learned, 64.5% reported that although they had previously understood the cardiac cycle, the puzzle helped them to solve doubts and promoted a better understanding of it, and 30.9% said that they needed the puzzle to understand the cardiac cycle, without differences among courses, institutions, and course semesters. The results of the present study suggest that a simple and inexpensive puzzle may be useful as an active learning methodology applied after the theoretical lecture, as a complementary tool for studying cardiac cycle physiology.