材料科学
灵活性(工程)
晶体工程
有机统一
纳米技术
超分子化学
晶体结构
化学
结晶学
数学
统计
认识论
哲学
作者
Xue‐Hua Ding,Lizhi Wang,Yongzheng Chang,Chuanxin Wei,Jinyi Lin,Man‐Hua Ding,Wei Huang
出处
期刊:Aggregate
[Wiley]
日期:2024-01-23
卷期号:5 (3)
被引量:14
摘要
Abstract The emergence of flexible organic crystals changed the perception of molecular crystals that were regarded as brittle entities over a long period of time, and sparked a great interest in exploring mechanically compliant organic crystalline materials toward next‐generation smart materials during the past decade. Schiff base compounds are considered to be one of the most promising candidates for flexible organic crystals owing to their easy synthesis, high yield, stimuli responsiveness and good mechanical properties. This paper gives an overview of the recent development of Schiff base flexible organic crystals (including elastic organic crystals, plastic organic crystals, and flexible organic crystals integrating elasticity and plasticity) from serendipitous discovery to design strategies and versatile applications such as stimuli responses, optical waveguides, optoelectronic devices, biomimetic soft robots, and organic photonic integrated circuits. Notably, atomic force microscopy‐micromanipulation technique has been utilized to bring the multifunctional applications of flexible organic crystals from the macroscopic level to the microscopic world. Since understanding mechanical flexibility at the molecular level through crystal engineering can assist us to trace down the structural origin of mechanical properties, we focus on the packing structures of various Schiff base flexible organic crystals driven by non‐covalent intermolecular interactions and their close correlation with mechanical behaviors. We hope that the information given here will help in the design of novel flexible organic crystals combined with other unique properties, and promote further research into the area of mechanically compliant organic crystalline materials toward multifunctional applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI