Development of novel chitosan / guar gum inks for extrusion-based 3D bioprinting: process, printability and properties

The major limitation of 3D bioprinting is the availability of inks. In order to develop new ink formulations, both their rheological behavior to obtain the best printability and the target bio-printed objects conformities must be studied. In this paper, for the first time in our knowledge, the preparation and the characterization of novel ink formulations based on two natural biocompatible polysaccharides, chitosan (CH) and guar gum (GG), are presented. Five ink formulations containing different proportions of CH and GG were prepared and characterized in terms of rheological properties and solvent evaporation. Their printability was assessed (by varying the nozzle diameter, pressure and speed) using an extrusion-based 3D bioprinting process performed directly in air at 37 °C. Results showed that the incorporation of GG improved both the printability of the pure chitosan ink by increasing the viscosity of the solution and the shape fidelity by accelerating the solvent evaporation. We showed that the ink containing 15% (w/w) of GG and 85% (w/w) of CH had the best printability. This formulation was therefore used for the preparation of membranes that were characterized by infrared spectroscopy (FTIR) and X-Ray Diffraction (XRD) before and after gelation as well as for their mechanical properties (Young modulus, strength and strain at break). The optimal process printing parameters were determined to be: 27 G micronozzle, extrusion pressure below 2 bars and robot head speed between 20 and 25 mm/s. This novel ink formulation is a guideline for developing 2D scaffolds (such as auto-supported membranes) or 3D scaffolds for biomedical applications.publishe

Error covariance tuning in variational data assimilation: application to an operating hydrological model

Because the true state of complex physical systems is out of reach for real-world data assimilation problems, error covariances are uncertain and their specification remains very challenging. These error covariances are crucial ingredients for the proper use of data assimilation methods and for an effective quantification of the a posteriori errors of the state estimation. Therefore, the estimation of these covariances often involves at first a chosen specification of the matrices, followed by an adaptive tuning to correct their initial structure. In this paper, we propose a flexible combination of existing covariance tuning algorithms, including both online and offline procedures. These algorithms are applied in a specific order such that the required assumption of current tuning algorithms are fulfilled, at least partially, by the application of the ones at the previous steps. We use our procedure to tackle the problem of a multivariate and spatially-distributed hydrological model based on a precipitation-flow simulator with real industrial data. The efficiency of different algorithmic schemes is compared using real data with both quantitative and qualitative analysis. Numerical results show that these proposed algorithmic schemes improve significantly short-range flow forecast. Among the several tuning methods tested, recently developed CUTE and PUB algorithms are in the lead both in terms of history matching and forecast

Real-time Raman spectroscopy measurements to study the uniaxial tension of isotactic polypropylene: a global overview of microstructural deformation mechanisms

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