Exploring the alignment of carbon nanotubes dispersed in a liquid crystal matrix using coplanar electrodes
- Submitting institution
-
University of Durham
- Unit of assessment
- 12 - Engineering
- Output identifier
- 96187
- Type
- D - Journal article
- DOI
-
10.1063/1.4916080
- Title of journal
- Journal of Applied Physics
- Article number
- 125303
- First page
- -
- Volume
- 117
- Issue
- 12
- ISSN
- 00218979
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
https://doi.org/10.1063/1.4916080
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
9
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Unlike reservoir computing which uses artificial materials, we demonstrate that real materials are best suited to perform unconventional computation. Using liquid crystal nanocomposites helps exploit dynamic charge transport properties to correlate material properties to their computational responses. This enabled us to achieve better material evolution and training and to demonstrate that carbon nanotube processors can be produced using evolutionary algorithms. The work, presented at IEEE International Conference on Rebooting Computing (Washington 2017), was highlighted as one of the “4 strange new ways to compute [News]” by IEEE Spectrum. A recent review article, highlighted its relevance for DC/RF microswitching technologies.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
