ArticleNano letters2017
Scalable Fabrication Framework of Implantable Ultrathin and Flexible Probes with Biodegradable Sacrificial Layers.
Article in Nano letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 16 citations in OpenAlex.
- Temporally Delayed Deployment of Photo-Responsive Liquid Crystal Polymer Networks Toward Neural Interfaces.Advanced healthcare materials · 2026Article
- Design, Fabrication, and Implantation of Invasive Microelectrode Arrays as in vivo Brain Machine Interfaces: A Comprehensive Review.Journal of manufacturing processes · 2024Article
- Influence of Implantation Depth on the Performance of Intracortical Probe Recording Sites.Micromachines · 2021Article
- Recent Progress on Non-Conventional Microfabricated Probes for the Chronic Recording of Cortical Neural Activity.Sensors (Basel, Switzerland) · 2019Review
- Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces.Materials (Basel, Switzerland) · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
For long-term biocompatibility and performance, implanted probes need to further reduce their size and mechanical stiffness to match that of the surrounding cells, which, however, makes accurate and minimally invasive insertion operations difficult due to lack of rigidity and brings additional complications in assembling and surgery. Here, we report a scalable fabrication framework of implantable probes utilizing biodegradable sacrificial layers to address this challenge. Briefly, the integrated biodegradable sacrificial layer can dissolve in physiological fluids shortly after implantation, which allows the in situ formation of functional ultrathin film structures off of the initial small and rigid supporting backbone. We show that the dissolution of this layer does not affect the viability and excitability of neuron cells in vitro. We have demonstrated two types of probes that can be used out of the box, including (1) a compact probe that spontaneously forms three-dimensional bend-up devices only after implantation and (2) an ultraflexible probe as thin as 2 μm attached to a small silicon shaft that can be accurately delivered into the tissue and then get fully released in situ without altering its shape and position because the support is fully retracted. We have obtained a >93% yield of the bend-up structure, and its geometry and stiffness can be systematically tuned. The robustness of the ultraflexible probe has been tested in tissue-mimicking agarose gels with <1% fluctuation in the test resistance. Our work provides a general strategy to prepare ultrasmall and flexible implantable probes that allow high insertion accuracy and minimal surgical damages with the best biocompatibility.
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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.