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Current breakthroughs in green nanotechnology are capable of transforming many of the existing processes and products that enhance environmental quality, reduce pollution, and conserve natural and nonrenewable resources. Successful use of metal nanoparticles and nanocomposites in various catalytic applications, electronics, biology and biomedical applications, material science, physics, environmental remediation and interdisciplinary fields as well as their toxicity essentially depends on the structural features such as size, shape, composition and the surface chemistry of nanomaterials. Moreover, to prolong the life span of metal nanoparticles and avoid undesired effects such as aggregation in aqueous solutions and organic solvents, to prevent contamination of the environment as well as to reuse and recycle nanoparticles, it is vital to select stabilizing agents and functionalization pathways that are environmentally friendly, non toxic and easy to implement. In recent years, stabilization and surface functionalization of metal nanoparticles became 'greener' to the extent that biocompatible stabilizing agents, e.g. biodegradable polymers and enzymes among others were introduced. These agents were able to produce a great variety of extremely stable spherical-, rod-or flower-shaped metal nanoparticles that opened up vast opportunities for their utilization and potential mass production. This review summarizes the state-of-the-art in the use of biocompatible and biodegradable homo-and copolymers as well as enzymes for the production of stable, environmentally benign, selective and active metal nanoparticles for desired applications.

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The homolysis of peracetic acid (PAA) as a relevant source of free radicals (e.g., *OH) was studied in detail. Radicals formed as a result of chain radical reactions were detected with electron spin resonance and nuclear magnetic resonance spin trapping techniques and subsequently identified by means of the simulation-based fitting approach. The reaction mechanism, where a hydroxyl radical was a primary product of O-O bond rupture of PAA, was established with a complete assessment of relevant reaction thermochemistry. Total energy analysis of the reaction pathway was performed by electronic structure calculations (ab initio and semiempirical methods) at different levels and basis sets [e.g., HF/6-311G(d), B3LYP/6-31G(d)]. Furthermore, the heterogeneous MnO2/PAA system was tested for the elimination of a model aromatic compound, phenol from aqueous solution. An artificial neural network (ANN) was designed to associate the removal efficiency of phenol with relevant process parameters such as concentrations of both the catalyst and PAA and the reaction time. Results were used to train and test ANN to identify an optimized network structure, which represented the correlations between the operational parameters and removal efficiency of phenol.

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