Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/111151
Title: Experimental analysis of the enzymatic degradation of polycaprolactone: Microcrystalline cellulose composites and numerical method for the prediction of the degraded geometry
Authors: Abdelfatah, Jacob 
Paz Hernández, Rubén 
Alemán Domínguez, María Elena 
Monzón Verona, Mario Domingo 
Donate González, Ricardo 
Winter Althaus, Gabriel 
UNESCO Clasification: 3303 ingeniería y tecnología químicas
Keywords: Enzymatic Degradation
Microcrystalline Cellulose
Monte Carlo Method
Numerical Method
Polycaprolactone, et al
Issue Date: 2021
Project: Mejora de la Biofuncionalidad de Scaffolds Polimericos Obtenidos Por Fabricacion Aditiva 
Journal: Materials 
Abstract: The degradation rate of polycaprolactone (PCL) is a key issue when using this material in Tissue Engineering or eco-friendly packaging sectors. Although different PCL-based composite materials have been suggested in the literature and extensively tested in terms of processability by material extrusion additive manufacturing, little attention has been paid to the influence of the fillers on the mechanical properties of the material during degradation. This work analyses the possibility of tuning the degradation rate of PCL-based filaments by the introduction of microcrystalline cellulose into the polymer matrix. The enzymatic degradation of the composite and pure PCL materials were compared in terms of mass loss, mechanical properties, morphology and infrared spectra. The results showed an increased degradation rate of the composite material due to the presence of the filler (enhanced interaction with the enzymes). Additionally, a new numerical method for the prediction of the degraded geometry was developed. The method, based on the Monte Carlo Method in an iterative process, adjusts the degradation probability according to the exposure of each discretized element to the degradation media. This probability is also amplified depending on the corresponding experimental mass loss, thus allowing a good fit to the experimental data in relatively few iterations.
URI: http://hdl.handle.net/10553/111151
ISSN: 1996-1944
DOI: 10.3390/ma14092460
Source: Materials [EISSN 1996-1944], v. 14 (9), 2460, (Mayo 2021)
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