Exploring structure-activity relationships for polymer biodegradability by microorganisms

Research on the environmental biodegradation or microbial biodegradation of polymers has substantially increased recently due to growing demand for biodegradable polymers for certain applications. Environmental biodegradation of a polymer depends on the intrinsic biodegradability of the polymer and the characteristics of the receiving environment. The intrinsic biodegradability of a polymer is determined by the chemical structure and resulting physical properties (e.g., glass transition temperature, melting temperature, modulus of elasticity, crystallinity, and crystal structure) of the polymer. Quantitative structure-activity relationships (QSARs) on biodegradability have been well-established for discrete (non-polymeric) organic chemicals, but not for polymers due to the absence of adequate biodegradability data based on consistent and standardized biodegradation tests with appropriate characterization and reporting of the polymers tested. This review summarizes empirical structure-activity relationships (SARs) for biodegradability of polymers in laboratory studies involving various environmental matrices. In general, polyolefins with carbon-carbon chain are not biodegradable, while polymers containing labile bonds such as ester, ether, amide, or glycosidic bonds in their polymer chain may be favorable for biodegradation. Under a univariate scenario, polymers with higher molecular weight, higher crosslinking, lower water solubility, higher degree of substitution (i.e., higher average number of substituted functional groups per monomer unit), and higher crystallinity may result in reduced biodegradability. This review paper also highlights some of the challenges that hamper QSAR development for polymer biodegradability, stresses the need for better characterization of polymer structures used in biodegradation studies, and emphasizes the necessity for consistent testing conditions for the ease of cross-comparison and quantitative modeling analysis during future QSAR development.