The diversity of network disruptions that occur in patients with neuropsychiatric disorders creates a strong demand for personalized medicine. Such approaches often take the form of implantable bioelectronic devices that are capable of monitoring pathophysiological activity for identifying biomarkers to allow for local and responsive delivery of intervention. They are also required to transmit this data outside of the body for evaluation of the treatment’s efficacy.
However, the ability to perform these demanding electronic functions in the complex physiological environment with minimum disruption to the biological tissue remains a big challenge. An optimal fully implantable bioelectronic device would require each component from the front-end to the data transmission to be conformable and biocompatible. For this reason, organic material-based conformable electronics are ideal candidates for components of bioelectronic circuits due to their inherent flexibility, and soft nature.
In this work, first an organic mixed-conducting particulate composite material (MCP) able to form functional electronic components and non-invasively acquire high–spatiotemporal resolution electrophysiological signals by directly interfacing human skin is presented. Secondly, we introduce organic electrochemical internal ion-gated transistors (IGTs) as a high-density, high-amplification sensing component as well as a low leakage, high-speed processing unit.
Finally, a novel wireless, battery-free strategy for electrophysiological signal acquisition, processing, and transmission that employs IGTs and an ionic communication circuit (IC) is introduced. We show that the wirelessly-powered IGTs are able to acquire and modulate neurophysiological data in-vivo and transmit them transdermally, eliminating the need for any hard Si-based electronics in the implant.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/kxs8-c338 |
| Date | January 2023 |
| Creators | Cea, Claudia |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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