Anisotropic nanorod-integrated magneto-mechano-electric cascade system for neural electrical stimulation
Source abstract
Abstract
Piezoelectric materials are now being considered as a potential treatment system for neural electrical stimulation. However, their therapeutic efficacy is limited by insufficient electrical outputs, which are generated by cellular forces and/or tissue motions. Herein, we constructed a high-efficiency magneto-mechano-electric cascade system with magnetic field (MF)-driven wireless electrical stimulation. The anisotropic Fe3O4 nanorod (RFO)-integrated poly(vinylidene fluoride-co-trifluoroethylene) copolymer (PVDF-TrFE) fibrous scaffold (PT-RFO) with enhanced piezoelectric β-phase content was prepared via electrospinning technique. Under a remote pulsed MF, the high-efficiency electrical outputs within PT-RFO scaffold could be generated by converting pressure force and deflection force within the contact interface between anisotropic RFO and PVDF-TrFE matrix. As a result, such PT-RFO scaffold could accelerate the repair of injured sciatic nerves and promote the recovery of damaged motor functions in rat models. This work highlights the potential application of a wireless controllable cascade stimulation system integrated multiple designable cues for neural repair and modulation. STATEMENT OF SIGNIFICANCE: A magneto-mechano-electric cascade scaffold via the integration of anisotropic Fe3O4 nanorods and a poly(vinylidene fluoride-co-trifluoroethylene) copolymer (PVDF-TrFE) was developed, with an enhanced piezoelectric phase, coupling area and deflection force. This cascade system can produce high-efficiency electrical signal output, which shows good potential in neural electrical stimulation applications.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Neuroscience and Neural Engineering · Planarian Biology and Electrostimulation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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