Evolution of Electronic Circuits using Carbon Nanotube Composites

Massey, Mark K.; Kotsialos, Apostolos; Volpati, Diogo; Vissol-Gaudin, Eléonore; Pearson, Christopher; Bowen, Leon; Obara, Bogusław; Zeze, Dagou A.; Groves, Chris; Petty, Michael C.

doi:10.1038/srep32197

Cited by 20 publications

(23 citation statements)

References 19 publications

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“…These two contributions continue the work reported in [12], [20], [21] and [22]. The hardware platform and material are discussed in section II.…”

Section: Introductionmentioning

confidence: 80%

“…Previous papers reported results for simple, proof-ofconcept datasets [12,21,22]. The first part of our investigations focus on the training of un-configured SWCNT/LC mixture for medical datasets selected from the UCI repository.…”

Section: A Medical Datasetsmentioning

confidence: 99%

“…It has been observed in [23] that the nanotubes tend to bundle and form complex percolation paths between electrodes under an applied electric field. The LC provides a liquid medium within which the resulting conductive networks can be modified [12], adding a level of complexity to the search space as compared to solid CNT-based samples.…”

Section: Hardware Implementation and Materialsmentioning

confidence: 99%

“…In [9], [11], and [17] a dry mix of Single-Walled Carbon Nanotubes (SWCNT) with a polymer were used as the computational material and its electrical conductance was used as the manipulated property for solving the problem of calculating Boolean functions using a threshold interpretation scheme; the same material is used in [14] and [15] for solving optimisation problems. In [12], [22], [21] and [20] the material used is a solution of SWCNT with LC in liquid rather than in dry solid state. The physical property used for evolving the material is its electrical conductivity and the ability of the SWCNT to form percolation paths within the LC.…”

Section: Introductionmentioning

confidence: 99%

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Computing Based on Material Training: Application to Binary Classification Problems

Vissol-Gaudin¹,

Kotsialos²,

Groves³

et al. 2017

2017 IEEE International Conference on Rebooting Computing (ICRC)

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any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-Evolution-in-materio is a form of unconventional computing combining materials' training and evolutionary search algorithms. In previous work, a mixture of single-walled-carbonnanotubes (SWCNTs) dispersed in a liquid crystal (LC) was trained so that its morphology and electrical properties were gradually changed to perform a computational task. Materialbased computation is treated as an optimisation problem with a hybrid search space consisting of the voltages used for creating the electrical field and the material's infinitely possible SWCNT arrangements in LC. In this paper, we study solutions using synthetic data with a non-linear separating boundary. In addition, results for two real life datasets with partly merged classes are presented. The training process is based on a differential evolution (DE) algorithm, which subjects the SWCNT/LC material to repeated electrical charging, leading to progressive morphological and electric conductivity modifications. It is shown that the material configuration the DE algorithm converges to form a non-negligible part of the solution. Furthermore, the problem's complexity is relevant to the properties of the resulting physical solver. The material structures created when training for a problem allow the retraining for a less complex one. The result is a doubly-trained material that keeps the memory of the original more complex problem. This is not the case for doubly-trained materials where initial training is for the less complex problem. The optimal electric field found by the DE algorithm is also a necessary solution component for the material's output to be interpreted as a computation.

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“…These two contributions continue the work reported in [12], [20], [21] and [22]. The hardware platform and material are discussed in section II.…”

Section: Introductionmentioning

confidence: 80%

Section: A Medical Datasetsmentioning

confidence: 99%

Section: Hardware Implementation and Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computing Based on Material Training: Application to Binary Classification Problems

Vissol-Gaudin¹,

Kotsialos²,

Groves³

et al. 2017

2017 IEEE International Conference on Rebooting Computing (ICRC)

Self Cite

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“…This might be the reason why there are so many research papers presenting new materials and new experiments regarding their behavior in different external electric, magnetic or laser fields [1–8]. In many cases, the chemical or physical properties of microparticles or nanoparticles are different from those of the corresponding bulk materials.…”

Section: Introductionmentioning

confidence: 99%

Dynamic behavior of a nematic liquid crystal with added carbon nanotubes in an electric field

Petrescu¹,

Cîrtoaje²

2018

Beilstein J. Nanotechnol.

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The dynamic behavior of a nematic liquid crystal with added carbon nanotubes (CNTs) in an electric field was analyzed. A theoretical model based on elastic continuum theory was developed and the relaxation times of nematic liquid crystals with CNTs were evaluated. Experiments made with single-walled carbon nanotubes dispersed in nematic 4-cyano-4’-pentylbiphenyl (5CB) indicated a significant difference of the relaxation time when compared to pure liquid crystal. We also noticed that the relaxation time when the field is switched off depends on how long the field was applied. It is shorter when the field is switched off immediately after application and longer when the field was applied for at least one hour.

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Simulation of Tunneling Conductivity and Controlled Percolation In 3D Nanotube-Insulator Composite System

Karbovnyk

Olenych

Chalyy

et al. 2019

Springer Proceedings in Physics

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Evolution of Electronic Circuits using Carbon Nanotube Composites

Cited by 20 publications

References 19 publications

Computing Based on Material Training: Application to Binary Classification Problems

Computing Based on Material Training: Application to Binary Classification Problems

Dynamic behavior of a nematic liquid crystal with added carbon nanotubes in an electric field

Simulation of Tunneling Conductivity and Controlled Percolation In 3D Nanotube-Insulator Composite System

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