Toward Catalyst-Free Synthesis of Tough Cyclic Poly(Ester Amide)s: Alternating Copolymerization of Aziridines and Phthalic Anhydride

Polymerization using aziridines as building blocks is an attractive route to acquire N-containing polymers with various architectures. Despite the diverse functionalities and applications of the resulting polymers, achieving control over the polymerization process has proven challenging. Here, we report a facile and efficient approach for synthesizing cyclic poly­(ester amide)­s (PEAs) through copolymerizing N-substituted aziridines and phthalic anhydride. The copolymerization of readily available monomers proceeded efficiently via a catalyst-free strategy, affording copolymers with high molar mass and alternating selectivity. Kinetic studies of copolymerization revealed a first-order dependence on the monomer pair. By modification of the substituents on nitrogen, five cyclic PEAs with distinct thermal and mechanical properties were prepared. Moreover, phosphazene was utilized to achieve perfect alternating sequences and adjustable molar masses. Given the abundance of aziridines and cyclic anhydrides, this study proposes a simple and atom-economical method for synthesizing cyclic PEAs with diverse backbone or side group structures

One-Component Multichannel Sensor Array for Rapid Identification of Bacteria

Bacterial infections routinely cause serious problems to public health. To mitigate the impact of bacterial infections, sensing systems are urgently required for the detection and subsequent epidemiological control of pathogenic organisms. Most conventional approaches are time-consuming and highly instrument- and professional operator-dependent. Here, we developed a novel one-component multichannel array constructed with complex systems made from three modified polyethyleneimine as well as negatively charged graphene oxide, which provided an information-rich multimode response to successfully identify 10 bacteria within minutes via electrostatic interactions and hydrophobic interactions. Furthermore, the concentration of bacteria (from OD600 = 0.025 to 1) and the ratio of mixed bacteria were successfully achieved with our smart sensing system. Our designed sensor array also exhibited huge potential in biological samples, such as in urine (OD600 = 0.125, 94% accuracy). The way to construct a sensor array with minimal sensor element with abundant signal outputs tremendously saves cost and time, providing a powerful tool for the diagnosis and assessment of bacterial infections in the clinic