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Phs: iontronics, memristors and neuronal nanofluidic devices (H/F)

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Date Limite Candidature : vendredi 8 juillet 2022

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General information

Reference : UMR8023-LYDBOC-016
Workplace : PARIS 05
Date of publication : Friday, June 17, 2022
Scientific Responsible name : Lydéric Bocquet
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 October 2022
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

Miniaturizing computing and information systems has been the great work of the last century. Feynman had formalized the concept as early as 1959 by pushing the revolutionary idea of 'There is plenty of room at the bottom', pointing out the emergence of new properties at small scales. But, if electronic flows are 'strange' at small scales, what about fluids? The question is all the more relevant since our favorite computer, the brain, does not use electrons to perform its operations or store memory, but rather ions, suspended in water: our memories are simple salt water. In the course of evolution, Nature has developed an ionic and aqueous - rather than electronic - machinery, making full use of a circuitry made of protein channels, and capable of incredible technological feats.
It is the objective of this thesis to develop nanofluidic neural systems - using ions suspended in water as information carriers -, an artificial and biomimetic analogue of brain neurons.
The Micromegas team has a recognized expertise in nanofluidics, the field studying fluid transport at the nanoscale. For more than 10 years, the team has developed original experimental approaches, and demonstrated many unexpected behaviors for fluid transport at nanoscale. Recently, we have designed 2D nanofluidic devices behaving like memristors. These systems consist of a solution of water and ions, confined in a 2D nanochannel (a few angstroms to nanometers thick), fabricated from various two-dimensional materials such as graphene. The ions then demonstrate a functioning similar to biological synapses, with a long-term memory (from a few minutes to several hours).

The objective of the thesis is first to establish a complete fundamental understanding of the properties of nanofluidic memristors, their learning ability, the demonstration/understanding of ionic Hebb rules, all this in connection with the nanofluidic transport properties that will be characterized and quantified [2]. In the longer term, we will integrate several ionic memristors in a nanofluidic neural network. One of the objectives will be to create an advanced iontronic circuitry on a nanofluidic chip, having the capacity to learn an ionic network by approximating logic gates and capable of implementing artificial intelligence algorithms. A reflection will be conducted throughout the thesis on the energy consumption of the artificial networks created, in order to reach the performances of the brain.
[1] L. Bocquet, Nanofluidics coming of age, Nature Materials, 19, 254-256 (2020).
[2] P. Robin, N. Kavokine, and L. Bocquet, Modeling of emergent memory and voltage spiking in ionic transport through angström-scale slits, Science, 373, 687-691 (2021).

Work Context

The Micromegas team has a recognized expertise in nanofluidics, the field studying fluid transport at the nanoscale. For more than 10 years, the team has developed original experimental approaches, and demonstrated many unexpected behaviors for fluid transport at nanoscale.
https://www.lpens.ens.psl.eu/recherche/flu-int/micromegas-nano-fluidique/

Additional Information

supported by the Water program of CNRS

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