Feng GAO, Xinyu Jiang, Junjun Qiu, Tong An, Manyao Zhang, Xiaokun Song, Nan Shi, Xiuhong Li, Tongxiang Fan, Qibin Zhao
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引用次数: 0
Abstract
Mechanoresponsive colloidal photonic crystals embedded in elastic solid matrices exhibit tunable optical properties under mechanical force, showing great potential for various applications. However, the response of colloidal crystals embedded in a liquid matrix remains largely unexplored. In this study, we investigate the structural and optical transitions of colloidal crystals composed of particles suspended in a liquid oligomer under pressing and shear forces. We observe that pressing induces a transition from an ordered to a disordered particle arrangement, while subsequent bending shear, such as simple hand-flipping, restores the ordered structure. This reversible transition produces press-induced optical traces that can be erased by subsequent shear, making this material a promising candidate for applications in reversible direct-writing photonic paper and anti-counterfeiting technologies. Our work provides new insights into the structural dynamics of liquid colloidal photonic crystals under mechanical force and highlights their potential in mechanoresponsive applications.
期刊介绍:
Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions.
The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.