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Cellular Automaton Simulation for Degradation of Poly Lactic Acid with Acceleratable Reaction-Diffusion Model.

Chao GuoYi Niu
Published in: ACS biomaterials science & engineering (2019)
Biodegradability is a fundamental property of poly lactic acid (PLA). Numerous simulation algorithms based on reaction-diffusion model have been utilized to analyze the degradation behaviors of PLA. In the present work, a cellular automaton (CA) algorithm combined with an acceleratable reaction-diffusion model and coarse-grained kinetic Monte Carlo method is applied to simulate the degradation behaviors of PLA, such as random or end scission and crystallization of PLA chains, diffusion of soluble oligomers. The CA algorithm can reveal the global changes of molecular weight, mass, reaction number and soluble oligomer number generated by hydrolysis as well as the local distributions of molecular weight, soluble oligomer number and cellular state. The calculation result of experiment demonstrates that such a CA model can accurately simulate the change of molecular weight and mass loss simultaneously. The effects of hydrolysis mode, reaction rate constant, diffusion coefficient, device size and pore structure on the degradation behaviors of PLA especially the change in the molecular weight and the famous autocatalysis effect are comprehensively investigated. A novel classification method of molecular weight change curve is presented and the effects of various factors are concluded. In general, big reaction rate constant, small diffusion coefficient, big device size, solid or low porosity, and no or few initial soluble oligomers can more easily generate a type I curve, which corresponds to a strong autocatalysis hydrolysis.
Keyphrases
  • lactic acid
  • machine learning
  • monte carlo
  • deep learning
  • molecular dynamics
  • big data
  • dna methylation
  • artificial intelligence
  • mass spectrometry
  • magnetic resonance
  • single cell