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Biodegradation of polyethylene terephthalate waste using Streptomyces species and kinetic modeling of the process

Author:
Farzi, Ali, Dehnad, Alireza, Fotouhi, Afsaneh F.
Source:
Biocatalysis and agricultural biotechnology 2019 v.17 pp. 25-31
ISSN:
1878-8181
Subject:
Streptomyces, biodegradation, bottles, byproducts, culture media, drinking, gas chromatography-mass spectrometry, manufacturing, microorganisms, models, particle size, plastics, polyethylene terephthalates, solid wastes
Abstract:
Polyethylene terephthalate (PET) is one of the most widely used plastics in manufacture of fibers, films, and drinking bottles, etc. It is one of solid wastes which pollutes urban and marine area and gets a lot of sacrifices from creatures. Thus, its removal from the environment is very important for protecting marine life. Different physical, chemical, and biological methods are studied by authors, but because of environmental and economic reasons, biological methods are preferred. These methods are slow and must combined with one or more physical or chemical methods. In this study, biodegradation of PET by Streptomyces species was assessed. Drinking bottles as PET wastes were firstly powdered and classified into four particle sizes. Then 50 mg of samples of each particle size were taken and treated with a fixed number of microorganisms in a culture medium for 18 days at 28 °C within an incubator, and degradation values of the samples were calculated on certain days. Also, a PET film was prepared from bottles and was exposed to biodegradation to show and compare differences between degradation of powdered and film samples. Results showed that final biodegradation percentages for PET particles sizes of 500, 420, 300 and 212 µm were 49.2%, 57.4%, 62.4%, and 68.8%, respectively. We showed that particle size and reaction time were the most important parameters on biodegradation. Also, by-products of biodegradation were analyzed by GC-MS to verify biodegradation process. Kinetic modeling of biodegradation showed that Michaelis-Menten activation or inhibition model can predict experimental results, more precisely.