ArticleNational science review2024
Ultra-low frequency magnetic energy focusing for highly effective wireless powering of deep-tissue implantable electronic devices.
Article in National science review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- The potential of soft robotics for the restoration of urinary voiding.Nature reviews. Urology · 2026Review
- Evaluation of the synergistic and antagonistic antibacterial effects of pulsed electromagnetic fields combined with ciprofloxacin and nanochitosan.Scientific reports · 2026Article
- Self-powered intelligence for personalized healthcare.National science review · 2026Review
- Galvanic-Cell-Based Self-Powered Bioelectronic Devices.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Next-Generation Piezoelectric Materials in Wearable and Implantable Devices for Continuous Physiological Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Wireless nonresonant stimulation of neurons on a magnetoelectric film surface.Science advances · 2025Article
- Wireless powering solution for implantable electronics based on ultra-low frequency magnetic energy focusing.National science review · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The limited lifespan of batteries is a challenge in the application of implantable electronic devices. Existing wireless power technologies such as ultrasound, near-infrared light and magnetic fields cannot charge devices implanted in deep tissues, resulting in energy attenuation through tissues and thermal generation. Herein, an ultra-low frequency magnetic energy focusing (ULFMEF) methodology was developed for the highly effective wireless powering of deep-tissue implantable devices. A portable transmitter was used to output the low-frequency magnetic field (<50 Hz), which remotely drives the synchronous rotation of a magnetic core integrated within the pellet-like implantable device, generating an internal rotating magnetic field to induce wireless electricity on the coupled coils of the device. The ULFMEF can achieve energy transfer across thick tissues (up to 20 cm) with excellent transferred power (4-15 mW) and non-heat effects in tissues, which is remarkably superior to existing wireless powering technologies. The ULFMEF is demonstrated to wirelessly power implantable micro-LED devices for optogenetic neuromodulation, and wirelessly charged an implantable battery for programmable electrical stimulation on the sciatic nerve. It also bypassed thick and tough protective shells to power the implanted devices. The ULFMEF thus offers a highly advanced methodology for the generation of wireless powered biodevices.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.