Related Experiment Video
Updated: Mar 15, 2026
12:20
Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions
Published on: July 22, 2013
18.8K
Evolution of Electronic Circuits using Carbon Nanotube Composites
M K Massey1, A Kotsialos1, D Volpati2
1School of Engineering and Computing Sciences, Durham University, South Road, Durham, DH1 3LE, UK.
Scientific Reports
|August 26, 2016
Summary
This study demonstrates evolving a composite material of carbon nanotubes and liquid crystal using computer-controlled voltages. The material reconfigures to solve a simple computational classification task.
Area of Science:
- Materials Science
- Computational Materials Science
- Soft Matter Physics
Background:
- Evolution-in-materio involves training material systems with external stimuli for functional adaptation.
- Composite materials offer tunable properties through constituent interactions and arrangements.
- Liquid crystal-based composites provide dynamic platforms for reconfigurable matter.
Purpose of the Study:
- To demonstrate the computer-controlled evolution of a disordered composite material.
- To show that the evolved material can perform a simple computational task.
- To investigate the role of electric fields in material morphology changes.
Main Methods:
- Utilized a composite material of single-walled carbon nanotubes in a liquid crystal host.
- Applied computer-controlled voltages as external stimuli to manipulate the material.
- Analyzed material morphology changes in response to applied electric fields.
Main Results:
- Demonstrated significant changes in material morphology under computer-controlled electrical stimulation.
- Showed that the reconfigured material network effectively solves a classification task.
- Identified a correlation between applied electric fields and favorable material reorganization.
Conclusions:
- Evolution-in-materio is a viable approach for creating functional materials from disordered states.
- Composite materials of carbon nanotubes in liquid crystals can be trained to perform computations.
- Electrical stimuli can guide material self-organization for specific computational functions.
