Electropolymerization processing of ...
Type de document :
Article dans une revue scientifique: Article original
Titre :
Electropolymerization processing of side-chain engineered EDOT for high performance microelectrode arrays
Auteur(s) :
Ghazal, Mahdi [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Susloparova, Anna [Auteur]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Lefebvre, Camille [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Daher Mansour, Michel [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Ghodhbane, Najami [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Melot, Alexis [Auteur]
Laboratoire Nanotechnologies et Nanosystèmes [Sherbrooke] [LN2]
Scholaert, Corentin [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Guérin, David [Auteur]
Centrale de Micro Nano Fabrication - IEMN [CMNF - IEMN]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Janel, Sebastien [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 [CIIL]
Barois, Nicolas [Auteur]
Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 [CIIL]
Colin, Morvane [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Buee, Luc [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Yger, Pierre [Auteur]
Institut de la Vision
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Halliez, Sophie [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Coffinier, Yannick [Auteur]
NanoBioInterfaces - IEMN [NBI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Pecqueur, Sebastien [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Alibart, Fabien [Auteur]
Laboratoire Nanotechnologies et Nanosystèmes [Sherbrooke] [LN2]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]

Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Susloparova, Anna [Auteur]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Lefebvre, Camille [Auteur]
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Daher Mansour, Michel [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Ghodhbane, Najami [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Melot, Alexis [Auteur]
Laboratoire Nanotechnologies et Nanosystèmes [Sherbrooke] [LN2]
Scholaert, Corentin [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Guérin, David [Auteur]

Centrale de Micro Nano Fabrication - IEMN [CMNF - IEMN]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Janel, Sebastien [Auteur]

Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 [CIIL]
Barois, Nicolas [Auteur]
Centre d’Infection et d’Immunité de Lille - INSERM U 1019 - UMR 9017 - UMR 8204 [CIIL]
Colin, Morvane [Auteur]

Lille Neurosciences & Cognition - U 1172 [LilNCog]
Buee, Luc [Auteur]

Lille Neurosciences & Cognition - U 1172 [LilNCog]
Yger, Pierre [Auteur]

Institut de la Vision
Lille Neurosciences & Cognition - U 1172 [LilNCog]
Halliez, Sophie [Auteur]

Lille Neurosciences & Cognition - U 1172 [LilNCog]
Coffinier, Yannick [Auteur]

NanoBioInterfaces - IEMN [NBI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Pecqueur, Sebastien [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Alibart, Fabien [Auteur]

Laboratoire Nanotechnologies et Nanosystèmes [Sherbrooke] [LN2]
Nanostructures, nanoComponents & Molecules - IEMN [NCM - IEMN]
Titre de la revue :
Biosensors and Bioelectronics
Pagination :
115538
Éditeur :
Elsevier
Date de publication :
2023-10
ISSN :
0956-5663
Discipline(s) HAL :
Physique [physics]
Sciences de l'ingénieur [physics]
Sciences de l'ingénieur [physics]
Résumé en anglais : [en]
Microelectrode Arrays (MEAs) are popular tools for in vitro extracellular recording. They are often optimized by surface engineering to improve affinity with neurons and guarantee higher recording quality and stability. ...
Lire la suite >Microelectrode Arrays (MEAs) are popular tools for in vitro extracellular recording. They are often optimized by surface engineering to improve affinity with neurons and guarantee higher recording quality and stability. Recently, PEDOT:PSS has been used to coat microelectrodes due to its good biocompatibility and low impedance, which enhances neural coupling. Herein, we investigate on electro-co-polymerization of EDOT with its triglymated derivative to control valence between monomer units and hydrophilic functions on a conducting polymer. Molecular packing, cation complexation, dopant stoichiometry are governed by the glycolation degree of the electro-active coating of the microelectrodes. Optimal monomer ratio allows fine-tuning the material hydrophilicity and biocompatibility without compromising the electrochemical impedance of microelectrodes nor their stability while interfaced with a neural cell culture. After incubation, sensing readout on the modified electrodes shows higher performances with respect to unmodified electropolymerized PEDOT, with higher signal-to-noise ratio (SNR) and higher spike counts on the same neural culture. Reported SNR values are superior to that of state-of-the-art PEDOT microelectrodes and close to that of state-of-the-art 3D microelectrodes, with a reduced fabrication complexity. Thanks to this versatile technique and its impact on the surface chemistry of the microelectrode, we show that electro-co-polymerization trades with many-compound properties to easily gather them into single macromolecular structures. Applied on sensor arrays, it holds great potential for the customization of neurosensors to adapt to environmental boundaries and to optimize extracted sensing features.Lire moins >
Lire la suite >Microelectrode Arrays (MEAs) are popular tools for in vitro extracellular recording. They are often optimized by surface engineering to improve affinity with neurons and guarantee higher recording quality and stability. Recently, PEDOT:PSS has been used to coat microelectrodes due to its good biocompatibility and low impedance, which enhances neural coupling. Herein, we investigate on electro-co-polymerization of EDOT with its triglymated derivative to control valence between monomer units and hydrophilic functions on a conducting polymer. Molecular packing, cation complexation, dopant stoichiometry are governed by the glycolation degree of the electro-active coating of the microelectrodes. Optimal monomer ratio allows fine-tuning the material hydrophilicity and biocompatibility without compromising the electrochemical impedance of microelectrodes nor their stability while interfaced with a neural cell culture. After incubation, sensing readout on the modified electrodes shows higher performances with respect to unmodified electropolymerized PEDOT, with higher signal-to-noise ratio (SNR) and higher spike counts on the same neural culture. Reported SNR values are superior to that of state-of-the-art PEDOT microelectrodes and close to that of state-of-the-art 3D microelectrodes, with a reduced fabrication complexity. Thanks to this versatile technique and its impact on the surface chemistry of the microelectrode, we show that electro-co-polymerization trades with many-compound properties to easily gather them into single macromolecular structures. Applied on sensor arrays, it holds great potential for the customization of neurosensors to adapt to environmental boundaries and to optimize extracted sensing features.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Projet ANR :
Source :