Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Step-Growth Polymerization: Overview

Step-Growth Polymerization: Overview

Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Anomalous wrinkle propagation in polycrystalline graphene with tilt grain boundaries.

Physical chemistry chemical physics : PCCP·2023

Same author

Helical micro-swimmer: hierarchical tail design and propulsive motility.

Soft matter·2022

Same author

Groundwater quality assessment and hydrogeochemical processes in typical watersheds in Zhangjiakou region, northern China.

Environmental science and pollution research international·2021

Same author

A CPD photolyase gene PnPHR1 from Antarctic moss Pohlia nutans is involved in the resistance to UV-B radiation and salinity stress.

Plant physiology and biochemistry : PPB·2021

Same author

Spectroscopic Perception of Trap States on the Performance of Methylammonium and Formamidinium Lead Iodide Perovskite Solar Cells.

Advanced materials (Deerfield Beach, Fla.)·2021

Same author

Toward broader applications of iron ore waste in pollution control: Adsorption of norfloxacin.

Journal of hazardous materials·2021

Search research articles

Related Experiment Video

Updated: May 6, 2026

Stretching Micropatterned Cells on a PDMS Membrane

Stretching Micropatterned Cells on a PDMS Membrane

Published on: January 22, 2014

Deep learning-driven ultra-stretchable kirigami metamaterials: towards surface texture modulation via buckling.

Yunce Zhang^1,2, Li Lin^1,2, Yafei Wang³

¹School of Architectural Engineering, Harbin University of Science and Technology, Harbin, 150080, P. R. China.

|November 17, 2025

Summary

This summary is machine-generated.

This study explores kirigami structures, using deep learning to predict mechanical performance and enabling programmable designs. Symmetry-breaking in kirigami broadens applications, including information transmission via metasurfaces.

More Related Videos

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

Fabrication of Three-Dimensional Graphene-Based Polyhedrons via Origami-Like Self-Folding

Fabrication of Three-Dimensional Graphene-Based Polyhedrons via Origami-Like Self-Folding

Published on: September 23, 2018

Related Experiment Videos

Last Updated: May 6, 2026

Stretching Micropatterned Cells on a PDMS Membrane

Stretching Micropatterned Cells on a PDMS Membrane

Published on: January 22, 2014

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

Fabrication of Three-Dimensional Graphene-Based Polyhedrons via Origami-Like Self-Folding

Fabrication of Three-Dimensional Graphene-Based Polyhedrons via Origami-Like Self-Folding

Published on: September 23, 2018

Area of Science:

Mechanics of Materials
Computational Materials Science
Metamaterials Design

Background:

Kirigami structures exhibit unique buckling instability under tensile stress, offering stretchability and design versatility.
Conventional kirigami design often focuses on unidirectional cuts and symmetric geometric configurations.
Exploring symmetry-disrupted kirigami is crucial for understanding buckling behavior and enhancing programmability.

Purpose of the Study:

To analyze buckling instability mechanisms in tessellated kirigami structures with disrupted geometric symmetry.
To develop an innovative, programmable kirigami design strategy using deep learning.
To predict mechanical performance and optimize kirigami patterns for specific tensile strain requirements.

Main Methods:

Analysis of buckling instability in tessellated kirigami structures.
Application of deep learning techniques to predict nonlinear constitutive relationships.
Development of a programmable design framework for kirigami pattern identification.

Main Results:

Achieved 94.29% accuracy in predicting kirigami mechanical performance.
Demonstrated that breaking geometric symmetry significantly expands the kirigami design space.
Showcased the potential for information encoding and transmission using kirigami metasurfaces.

Conclusions:

The proposed deep learning-based strategy enables accurate prediction of kirigami mechanical performance.
Symmetry-breaking in kirigami design enhances adaptability and minimizes trial-and-error.
Kirigami metasurfaces offer a novel platform for functional configuration and information transmission.