ORGANIC MIXED IONIC–ELECTRONIC CONDUCTORS
Organic electrochemical transistors (OECTs) are versatile electronic devices with a wide range of applications in bioelectronics, sensors, and flexible electronics. The key component of an OECT is the conducting polymer channel, which interacts with electrolytes to modulate the flow of ions, enabling low-voltage operation and high amplification. Their inherent compatibility with biological systems and ability to operate in aqueous environments make OECTs promising for interfacing with biological tissues, creating biosensors, and enabling advancements in wearable and implantable technology. Our research group is focused on a comprehensive understanding of fundamental principles to practical applications. We delve into elucidating the underlying mechanisms governing OECT operation, investigating material properties, device architectures, ion-electron interactions, and physicochemical modeling. Concurrently, our interest extends from theoretical modeling to the design and development of novel electronic devices.
ORGANIC ELECTROCHEMICAL TRANSISTORS
SYTHESIS OF PEDOT:PSS INKS
Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), PEDOT:PSS is a complex polyelectrolyte (PEC), resulting from the combination of two distinct polymers: PEDOT, a conductive conjugated polymer, and PSS, an anionic polyelectrolyte that enables the formation of aqueous dispersions of PEC. Since its introduction, three decades ago, PEDOT:PSS has played a crucial role in organic electronics, thanks to its properties that allow its application in several devices. Our research group has been studying the polymerization reaction of PEDOT:PSS, with special attention to the electrostatic interactions that arise between the reagents during synthesis, which have proven to be key factors in the final properties of the inks. Additionally, we are also interested in obtaining water-free PEDOT:PSS inks for applications in solar cells.
Reservoir computing is an idea that emerged in the early 2000s, leveraging the non-linear dynamics of physical systems, termed reservoirs, as a neural network. Unlike classical neural networks requiring extensive pre-training of all elements, the reservoir functions without explicit training, capitalizing on the innate non-linear dynamics of its components to process information. Only the output layer of the reservoir necessitates training for specific regression or classification tasks. Within our research group, we delve into exploring the fabrication, dynamics, physics, and applications of reservoirs based on conjugated polymers fibers.
RESERVOIR COMPUTING
POLYMER-BASED NEUROMORPHIC DEVICES
The Electrochemical Neuromorphic Device (ENODe) demonstrates the ability to emulate neuron-like functionalities, including multiple memory levels and Pavlovian Learning. It is designed as a battery-like organic electrochemical transistor (OECT). ENODe comprises a pre-synaptic electrode and a post-synaptic electrode connected by an electrolyte bridge. The pre-synaptic electrode features a single contact for the application of programming pulses, while the post-synaptic electrode is equipped with two contacts, resembling the structure of a transistor channel, where the post-synaptic potential is applied. The magnitude of current flowing through the "transistor-channel" is contingent upon the doping level of the post-synaptic electrode, regulated by the programming pulse at the pre-synaptic contact. In our research group, we are deeply engaged in the exploration of ENODe's fabrication, dynamics, physics, and applications.