In-operando growth of organic mixed ionic-electronic conductors for brain-inspir...
In-operando growth of organic mixed ionic-electronic conductors for brain-inspired electronics
The development of advanced brain-computer interfaces, wearable and implantable bioelectronic devices, prosthetics, and soft robotics requires the ability to process signals in a highly individualized and localized manner. To achi...
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Descripción del proyecto
The development of advanced brain-computer interfaces, wearable and implantable bioelectronic devices, prosthetics, and soft robotics requires the ability to process signals in a highly individualized and localized manner. To achieve this, new materials and devices must be developed that can sense their surroundings, process information locally, and translate it into a format our body can interpret. Currently, (bio-)electronic devices rely on remote and energy-intensive cloud processing, but electronic devices that mimic the design of the human brain offer a solution. However, silicon-based devices have limitations such as rigidity, poor biocompatibility, and operating principles that differ from the ion signal modulation of biology. Organic mixed ionic-electronic conductors (OMIECs) have emerged as a promising option in the field of bioelectronics, as they are solution processable, potentially biocompatible, and can transport both electronic and ionic signals.
The goal of INFER is to create next-generation intelligent bioelectronic devices using in-operando electropolymerization of OMIEC monomers. The proposed research activities aim to 1) understand how the molecular properties of OMIEC monomers impact their in-operando electropolymerization and the learning capabilities of the resulting biomimetic devices, 2) achieve biorealistic speeds, memory functionalities, and energy efficiencies without the use of auxiliary devices, and 3) prototype devices that can locally sense, process, and actuate/stimulate. The long-term goal is to create a brain-inspired intelligent bioelectronic platform that brings a new paradigm for in-sensor computing at the interface with biology.
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