Publication
Electronic micro and nano-devices are suitable tools to monitor the activity of many individual neurons over mesoscale networks. However the inorganic materials currently used in microelectronics are barely accepted by neural cells and tissues, thus limiting both the sensor lifetime and efficiency. In particular, penetrating intracortical probes face high failure rate because of a wide immune response of cells and tissues. This adverse reaction called gliosis leads to the rejection of the implanted probe after few weeks and prevent long-lasting recordings of cortical neurons. Such acceptance issue impedes the realization of many neuro-rehabilitation projects. To overcome this, graphene and related carbon-based materials have attracted a lot of interest regarding their positive impact on the adhesion and regeneration of neurons, and their ability to provide high-sensitive electronic devices, such as graphene field effect transistor (G-FET). Such devices can also be implemented on numerous suitable substrates including soft substrates to match the mechanical compliance of cells and tissues, improving further the biocompatibility of the implants. Thus, using graphene as a coating and sensing device material could significantly enhance the acceptance of intracortical probes. However, such a thin monolayer of carbon atoms could be teared off during manipulation and insertion within the brain, and could also display degradation over time. In this work, we have investigated the ability to protect graphene with a natural, biocompatible and degradable polymeric film derivated from hyaluronic acid (HA). We demonstrate that HA-based coatings can be deposited over a wide range of substrates, including intracortical probes and graphene FET arrays without altering the underlying device material, its biocompatibility and sensitivity. Moreover, we show that this coating can be monitored in situ by quantifying the number of deposited charges with the G-FET arrays. The reported graphene functionalization offers promising alternatives for improving the acceptance of various neural interfaces.