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Emergence of large-scale patterns in soft quasicrystals
Dean Chen1, Nitesh Arora2,3, Yuhai Xiang4
1Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA, USA.
Mechanical loading triggers large-scale pattern formation in soft quasicrystals. These transformative materials exhibit switchable self-similarity across scales, controllable via initial material properties.
Area of Science:
- Materials Science
- Soft Matter Physics
- Mechanical Engineering
Background:
- Soft quasicrystals are materials exhibiting properties between crystalline solids and amorphous liquids.
- Understanding pattern formation in response to mechanical stimuli is crucial for designing advanced materials.
Purpose of the Study:
- To experimentally investigate large-scale pattern formation in soft quasicrystals under mechanical loading.
- To explore the relationship between microscale properties and macroscale pattern emergence.
- To demonstrate the designability of these patterns through controlled initial configurations.
Main Methods:
- Utilizing a soft material system capable of localized transformations.
- Applying mechanical loading to activate pattern formation.
- Analyzing microscale order loss and subsequent reemergence in large-scale patterns.
- Investigating the influence of initial local chirality and porosity.
Main Results:
- Observed large-scale organized pattern formation driven by localized transformations.
- Demonstrated initial loss of microscale quasicrystalline order, followed by its reemergence in patterns with rotational symmetry.
- Showcased pre-design capability of large-scale patterns by tuning initial chirality and porosity.
- Identified discrete characteristic lengths governed by the silver ratio in patterns with high shape similarity.
Conclusions:
- Soft quasicrystals offer a new class of transformative materials with switchable self-similarity across length scales.
- Mechanical loading is an effective trigger for controlled pattern formation.
- Initial microscale design parameters significantly influence emergent macroscale behavior.
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