• Organic Bioelectronics
• Neuromorphic devices
• Organic electrochemical transistors
• Artficial Synapses

Neural interfaces are electronic devices that interact with the nervous system and are powerful tools in the fields of neuroscience and neurology. Most neural interfaces consist of microelectrode arrays that interact with single cells or different nervous tissues and are widely used from in vitro to in vivo and clinical settings, but their seamless integration with the nervous system from an electrochemical, physiological, and mechanical perspective is still a challenge. A seamless integration comprises not only material improvements but a complete functional integration in which artificial neuromodulation can be perceived as natural behavior by the nervous system. To achieve this interaction with the nervous system at a synaptic level, That is an electronic device that can adapt its behavior according to the feedback from nearby neurons. In other words, a class of devices capable of emulating neurons’ behavior is called neuromorphic devices.
The project is focused on engineering a glutamatergic artificial synapse exploiting organic electrochemical transistors (OECT) as a neuromorphic device. The device is fabricated by means of typical microfabrication techniques (e.g., spin coating, reactive ion etching, electrodeposition), and the glutamate enzyme is then immobilized on the surface of the OECT to investigate glutamate detection as well as the neuromorphic response.
This work is part of a wider interdisciplinary project called Neurohybrid Synapse Array for Functional Electronic and Chemical Interaction with Neural Tissue (NeuroWin), which aims to couple the resulting artificial synapse with flexible microelectrode arrays (MEAs) to allow the combination of neurotransmitter sensing and voltage measurements on the same implantable device, thus resulting in a glutamatergic biohybrid synapse. This high-gain, high-risk project will certainly contribute to the development of a new class of in vitro platforms that could be further exploited for the study of neurodegenerative diseases and the implementation of smart adaptive implantable interfaces.