Non Euclidean geometry model for chemo mechanical coupling in self assembled polymers towards dynamic elasticity

Self-assembly plays a fundamental role to determine thermodynamic properties of polymer systems, e.g., resulting in the formation of dynamically cross-linked networks with varied elasticity. However, the working principle of chemo-mechanical coupling between the self-assembly and elasticity of polymers is complex and has not been well understood. In this study, a non-Euclidean geometry model incorporating thermodynamics of microphase separation is proposed to understand the chemo-mechanical coupling in self-assembled triblock polymers. The thermodynamic separation of microphases, which is resulted from the self-assembly of polymeric molecules, is formulated using a non-Euclidean geometry equation, of which the geometrical parameters are applied to characterize the topologies of self-assembled and cross-linked networks. The non-Euclidean geometry model is further employed to describe chemo-mechanical coupling between the self-assembled network and dynamic elasticity of the triblock polymers, based on the rubber elasticity theory. Effectiveness of the proposed model is verified using both finite-element analysis and experimental results reported in literature. This study provides a new geometrical approach to understand the mechanochemistry and thermodynamics of self-assembled block polymers

Machine-learning assisted handwriting recognition using graphene oxide-based hydrogel

Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of currently reported handwriting recognition systems are lacked in flexible sensing and machine learning capabilities, both of which are essential for implementations of intelligent systems. Herein, assisted by machine learning, we develop a new handwriting recognition system, which can be applied as both a recognizer for written texts and an encryptor i for confidential nformation. This flexible and intelligent handwriting recognition system combines a printed circuit board with graphene oxide and good sensitivity, and allows high-- based hydrogel sensors. It offers fast response precision recognitions of handwritten conten ts from a single letter to words and signatures. By analyzing 690 acquired handwritten signatures obtained from 7 participants, we successfully demonstrate a fast recognition time (less than 1 s) and a high recognition rate (~91.30). Our developed handwri has great potentials in advanced humanting recognition system machine interactions, wearable communication devices, soft robotics manipulators, and augmented virtual reality