Evolution of Electronic Circuits using Carbon Nanotube Composites
- Submitting institution
-
University of Durham
- Unit of assessment
- 12 - Engineering
- Output identifier
- 104553
- Type
- D - Journal article
- DOI
-
10.1038/srep32197
- Title of journal
- Scientific Reports
- Article number
- 32197
- First page
- -
- Volume
- 6
- Issue
- -
- ISSN
- 20452322
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2016
- URL
-
https://doi.org/10.1038/srep32197
- 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
- To the best of our knowledge, this is the first demonstration of “computational matter”; which can be physically evolved to perform a desired electronic function. This opens up new approaches to machine learning which are unfettered by the limitations of conventional Silicon microprocessors. Our subsequent work has demonstrated that this technology platform can be trained to diagnose disease, such as breast cancer and diabetes, at a speed better than some algorithmic routes. This paper has been highlighted by news outlets including Electronics Weekly, ChemEurope and FAPESP in Brazil.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
