PEDOT-containing Implantable Neuroelectrodes in Inflammation Control and Neural Signal Transduction

Implantable neuroelectrodes are used to facilitate the treatments of neurological injuries and diseases such as depression, Parkinson’s disease, and epilepsy. However, since the body perceives the neuroelectrodes as exogenous materials, immune responses are triggered, resulting in formation of gliosis around the electrodes. Formation of the scar would impede the transfer of signals and reduce long-term stability. Thus, neuroelectrodes with enhanced biocompatibility, stability, and low impedance are essential for inflammation control and neural signal transduction. This work combines MADA@PEDOT with GelMA to create a composite material. Compared with traditional neuroelectrodes, this combination provides several advantages: 1) the natural origins of DA and gelatin in MADA and GelMA gives them good biocompatibility to support cellular activities, reduce inflammatory responses. 2) GelMA/MADA@PEDOT has strong adhesion to cells and tissues. 3) GelMA hydrogels provide high mechanical flexibility and they can be tuned flexibly to apply to different settings, allowing a smoother and more tailored interface. This work provides a novel implantable conductive hydrogel to reduce inflammatory responses, solve problems with traditional materials, completing the preparation of reliable neuroelectrodes, and providing new insights for the neuroelectrode field.

Authors

Institutions

Publication Details

Journal
Highlights in Science Engineering and Technology
Published
2025-06-27
DOI
https://doi.org/10.54097/7kebnw67
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PEDOT-containing Implantable Neuroelectrodes in Inflammation Control and Neural Signal Transduction

Shiyuan Li
Highlights in Science Engineering and Technology
Neuroscience and Neural Engineering
article

PEDOT-containing Implantable Neuroelectrodes in Inflammation Control and Neural Signal Transduction

Shiyuan Li
article en

Abstract

Implantable neuroelectrodes are used to facilitate the treatments of neurological injuries and diseases such as depression, Parkinson’s disease, and epilepsy. However, since the body perceives the neuroelectrodes as exogenous materials, immune responses are triggered, resulting in formation of gliosis around the electrodes. Formation of the scar would impede the transfer of signals and reduce long-term stability. Thus, neuroelectrodes with enhanced biocompatibility, stability, and low impedance are essential for inflammation control and neural signal transduction. This work combines MADA@PEDOT with GelMA to create a composite material. Compared with traditional neuroelectrodes, this combination provides several advantages: 1) the natural origins of DA and gelatin in MADA and GelMA gives them good biocompatibility to support cellular activities, reduce inflammatory responses. 2) GelMA/MADA@PEDOT has strong adhesion to cells and tissues. 3) GelMA hydrogels provide high mechanical flexibility and they can be tuned flexibly to apply to different settings, allowing a smoother and more tailored interface. This work provides a novel implantable conductive hydrogel to reduce inflammatory responses, solve problems with traditional materials, completing the preparation of reliable neuroelectrodes, and providing new insights for the neuroelectrode field.

Highlights in Science Engineering and TechnologyVol. 144
Cranbrook Institute of Science (US)
Openalex Percentile: Top 84%
Neuroscience and Neural Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.