Implantable neural probes enable high-resolution, multi-unit recordings and are essential tools for studying neurological disorders and developing brain-machine interface (BMI) technologies. However, conventional metal- or silicon-based probes exhibit significant mechanical mismatch with brain tissue, both of which elicit inflammatory responses and compromise long-term recording stability. Here, we introduce a flexible neural probe fabricated through a commercial flexible printed circuit board (FPCB) process and functionalized with a biocompatible lubricant coating to overcome these challenges. The inherent flexibility of the FPCB minimizes mechanical mismatch with brain tissue, while the coating enhances surface hydrophobicity and reduces insertion friction, thereby minimizing tissue damage during implantation. Its resistance to water ingress contributes to maintaining the probe's electrical insulation stability, supporting stable long-term performance. In chronic mouse hippocampal implants, lubricant-coated probes maintained consistent neural signal quality for several weeks, while immunohistochemical analysis revealed markedly reduced astrocytic and microglial activation (GFAP/Iba1) compared with uncoated controls, indicating effective mitigation of neuroinflammation. In vitro cell viability assays further confirmed the high biocompatibility of the coated devices. Importantly, because this approach leverages scalable and cost-effective FPCB manufacturing, it enables the production of flexible neural interfaces that combine long-term electrical and biological stability with manufacturing practicality. This work establishes a broadly applicable strategy for next-generation neural probes, offering durable, minimally invasive, and scalable solutions for chronic recordings in BMI systems, deep brain stimulation, and neurological disease models.
Building similarity graph...
Analyzing shared references across papers
Loading...
Haeyun Lee
Seungjun Lee
Kyeong Seob Hwang
ACS Applied Bio Materials
Kyungpook National University
Korea Institute of Science and Technology
Graduate School USA
Building similarity graph...
Analyzing shared references across papers
Loading...
Lee et al. (Fri,) studied this question.
synapsesocial.com/papers/69bf8692f665edcd009e8f07 — DOI: https://doi.org/10.1021/acsabm.5c02232