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Updated: Mar 1, 2026

Ultrahigh Density Array of Vertically Aligned Small-molecular Organic Nanowires on Arbitrary Substrates
Published on: June 18, 2013
Self-Assembled Molecular Nanowires for High-Performance Organic Transistors
Luke R Fleet, James Stott1, Byron Villis1
1London Centre for Nanotechnology, University College London , London WC1H 0AH, U.K.
Researchers developed a scalable method to create high-performance organic transistors using self-assembled molecular nanowires. This breakthrough enhances speed and reliability for flexible electronics, enabling advanced applications like all-organic chips.
Area of Science:
- Materials Science
- Organic Electronics
- Nanotechnology
Background:
- Organic semiconductors offer potential for low-cost, flexible electronics but suffer from poor transistor performance (speed, reliability).
- Self-assembly can create ordered nanostructures with better transport properties, but scalable device fabrication remains a challenge.
Purpose of the Study:
- To develop a scalable fabrication method for high-performance organic transistors using self-assembled molecular nanowires.
- To demonstrate improved performance and stability of these nanowire-based transistors compared to traditional thin-film devices.
Main Methods:
- Utilized a scalable gradient sublimation technique for fabricating transistors from self-assembled molecular nanowires.
- Employed copper phthalocyanine as the organic semiconductor material.
Main Results:
- Achieved transistors with low threshold voltages (-2.1 V), high on/off ratios (10^5), small subthreshold swings (0.9 V/decade), and mobilities (0.6 cm^2/V s).
- Demonstrated lower trap energies, indicating improved stability and performance comparable to leading organic semiconductors.
- Fabrication method is compatible with flexible substrates.
Conclusions:
- The scalable gradient sublimation technique enables high-performance, stable organic transistors from self-assembled molecular nanowires.
- This advancement paves the way for integrated all-organic chips for conventional and neuromorphic computing, sensors, and displays.
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