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Outputs (5)

Single-Walled Carbon-Nanotubes-Based Organic Memory Structures (2016)
Journal Article
Fakher, S., Nejm, R., Ayesh, A., AL-Ghaferi, A., Zeze, D., & Mabrook, M. (2016). Single-Walled Carbon-Nanotubes-Based Organic Memory Structures. Molecules, 21(9), Article 1166. https://doi.org/10.3390/molecules21091166

The electrical behaviour of organic memory structures, based on single-walled carbon-nanotubes (SWCNTs), metal–insulator–semiconductor (MIS) and thin film transistor (TFT) structures, using poly(methyl methacrylate) (PMMA) as the gate dielectric, are... Read More about Single-Walled Carbon-Nanotubes-Based Organic Memory Structures.

Evolution of Electronic Circuits using Carbon Nanotube Composites (2016)
Journal Article
Massey, M., Kotsialos, A., Volpati, D., Vissol-Gaudin, E., Pearson, C., Bowen, L., …Petty, M. (2016). Evolution of Electronic Circuits using Carbon Nanotube Composites. Scientific Reports, 6, Article 32197. https://doi.org/10.1038/srep32197

Evolution-in-materio concerns the computer controlled manipulation of material systems using external stimuli to train or evolve the material to perform a useful function. In this paper we demonstrate the evolution of a disordered composite material,... Read More about Evolution of Electronic Circuits using Carbon Nanotube Composites.

Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms (2016)
Presentation / Conference Contribution
Vissol-Gaudin, E., Kotsialos, A., Massey, M., Zeze, D., Pearson, C., Groves, C., & Petty, M. (2016, July). Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms. Presented at 15th International Conference on Unconventional Computation and Natural Computation, Manchester, UK

Evolution-in-Materio uses evolutionary algorithms (EA) to exploit the physical properties of unconfigured, physically rich materials, in effect transforming them into information processors. The potential of this technique for machine learning proble... Read More about Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms.

Direct growth of Si nanowires on flexible organic substrates (2016)
Journal Article
Tian (田琳), L., Di Mario, L., Minotti, A., Tiburzi, G., Mendis, B. G., Zeze, D. A., & Martelli, F. (2016). Direct growth of Si nanowires on flexible organic substrates. Nanotechnology, 27(22), Article 225601. https://doi.org/10.1088/0957-4484/27/22/225601

A key characteristic of semiconductor nanowires (NWs) is that they grow on any substrate that can withstand the growth conditions, paving the way for their use in flexible electronics. We report on the direct growth of crystalline silicon nanowires o... Read More about Direct growth of Si nanowires on flexible organic substrates.