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Myline Cottance

Researcher at ESIEE Paris

Publications -  7
Citations -  176

Myline Cottance is an academic researcher from ESIEE Paris. The author has contributed to research in topics: Microelectrode & Retinal implant. The author has an hindex of 4, co-authored 6 publications receiving 146 citations. Previous affiliations of Myline Cottance include University of Paris.

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3D-nanostructured boron-doped diamond for microelectrode array neural interfacing.

TL;DR: 3D-nanostructured boron doped diamond (BDD), an innovative material consisting in a chemically stable material with a high aspect ratio structure obtained by encapsulation of a carbon nanotube template within two BDD nanolayers, allows neural cell attachment, survival and neurite extension.
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Boron doped diamond biotechnology: from sensors to neurointerfaces.

TL;DR: Boron doped nanocrystalline diamond is known as a remarkable material for the fabrication of sensors, taking advantage of its biocompatibility, electrochemical properties, and stability, which enable structured diamond devices such as microelectrode arrays (MEAs) to probe neuronal activity distributed over large populations of neurons or embryonic organs.
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Monitoring the evolution of boron doped porous diamond electrode on flexible retinal implant by OCT and in vivo impedance spectroscopy.

TL;DR: It is shown that it is possible to follow in vivo the evolution of the electric contact between the diamond electrodes and the retina over 4months by using electrochemical impedance spectroscopy.
Proceedings Article

Diamond micro-electrode arrays (MEAs): A new route for in-vitro applications

TL;DR: The fabrication of in vitro 8×8 and 4×15 planar boron-doped nanocrystalline diamond (BNCD) MEAs using microtechnology show that these devices offer good recording properties as compared to other standard electrode materials (such as Ti-Pt or Au).
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Impedance-based sensors discriminate among different types of blood thrombi with very high specificity and sensitivity

TL;DR: Combining EIS measurements with machine learning provides a highly effective approach for discriminating among different thrombus types and liquid blood and raises the possibility of developing a probe-like device (eg, a neurovascular guidewire) integrating an impedance-based sensor.