Issue 20, 2023

Mechanism and kinetics of enzymatic degradation of polyester microparticles using a shrinking particle–shrinking core model

Abstract

Generalized shrinking particle (SPM) and shrinking core (SCM) models were developed to the kinetics of heterogenous enzymatic degradation of polymer microparticles in a continuous microflow system. This enzymatic degradation was performed in a microfluidic device designed to both physically separate and immobilize the microparticles. Then time-resolved measurements were made using image processing of the physical changes of the particles during degradation. The kinetics of enzyme-polymer intermediate formation, enzymatic bond cleavage, and enzyme diffusion through the layer of degraded substrate (SCM only) were mathematically derived to predict the time-resolved degradation of the substrate. The proposed models were tested against the degradation of 15–25 μm particles of polycaprolactone (PCL) and poly (butylene adipate-co-terephthalate) (PBAT) by cutinase enzyme from Humicola insolens. Degradation of PCL microparticles followed the SPM model and its kinetics were found to be zero-order, while the SCM model applied to PBAT microparticles showed first-order kinetics. Further, the degradation of polybutylene succinate (PBS), and poly butylene-sebacate-co-terephthalate (PBSeT) microparticles demonstrated wide applicability of the method. The use of image processing simplifies the required analysis by eliminating the need to remove aliquots or concentrate effluent for additional analytical characterization.

Graphical abstract: Mechanism and kinetics of enzymatic degradation of polyester microparticles using a shrinking particle–shrinking core model

Supplementary files

Article information

Article type
Paper
Submitted
03 Jul 2023
Accepted
17 Sep 2023
First published
18 Sep 2023

Lab Chip, 2023,23, 4456-4465

Author version available

Mechanism and kinetics of enzymatic degradation of polyester microparticles using a shrinking particle–shrinking core model

H. Torabi, F. Javi, T. W. Deisenroth, T. V. Pho, V. Barbright and A. Abbaspourrad, Lab Chip, 2023, 23, 4456 DOI: 10.1039/D3LC00581J

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