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具有响应性皱纹和荧光图案的光动力图案记忆表面

Photodynamic Pattern Memory Surfaces with Responsive Wrinkled and Fluorescent Patterns.

作者信息

Chen Shuai, Ma Tianjiao, Bai Jing, Ma Xiaodong, Yin Jie, Jiang Xuesong

机构信息

School of Chemistry & Chemical Engineering Frontiers Science Center for Transformative Molecules State Key Laboratory for Metal Matrix Composite Materials Shanghai Jiao Tong University Shanghai 200240 P. R. China.

出版信息

Adv Sci (Weinh). 2020 Oct 14;7(22):2002372. doi: 10.1002/advs.202002372. eCollection 2020 Nov.

Abstract

Reversible pattern systems, namely pattern memory surfaces, possessing tunable morphology play an important role in the development of smart materials; however, the construction of these surfaces is still extensively challenging because of complicated methodologies or chemical reactions. Herein, a functionalized basement is strategically integrated with a multi-responsive supramolecular network based on hydrogen bonding between aggregation-induced emission luminogens (AIEgens) and copolymers containing amidogen (poly(St--Dm) to establish a bilayer system for near-infrared (NIR)-driven memory dual-pattern, where both the fluorescence emission and wrinkled structures can be concurrently regulated by a noninvasive NIR input. The motion of the AIEgens and photo-to-thermal expansion of the modified base allow temporal erasing of the fluorescent wrinkling patterns. Meanwhile, when exposed to 365 nm UV radiation, the fluorescent patterns can be independently regulated through photocyclization. The fluorescent wrinkling pattern presented herein is successfully demonstrated to promote the level of information security and capacity. This strategy provides a brand-new approach for the development of smart memory interfaces.

摘要

可逆图案系统,即图案记忆表面,具有可调节的形态,在智能材料的发展中起着重要作用;然而,由于方法复杂或化学反应,这些表面的构建仍然极具挑战性。在此,一种功能化基底与基于聚集诱导发光剂(AIEgens)和含氨基的共聚物(聚(St-Dm))之间氢键的多响应超分子网络进行策略性整合,以建立用于近红外(NIR)驱动的记忆双图案的双层系统,其中荧光发射和皱纹结构都可以通过非侵入性的近红外输入同时调节。AIEgens的运动和改性基底的光热膨胀允许对荧光皱纹图案进行临时擦除。同时,当暴露于365nm紫外辐射时,荧光图案可以通过光环化独立调节。本文展示的荧光皱纹图案成功地提高了信息安全水平和容量。该策略为智能记忆界面的开发提供了一种全新的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0830/7675060/c3a63c6b03ed/ADVS-7-2002372-g001.jpg

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