ArticleNano research2022
Flexible, anti-damage, and non-contact sensing electronic skin implanted with MWCNT to block public pathogens contact infection.
Article in Nano research, 2022. 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, 26 citations in OpenAlex.
- Versatile applications of LIG-based biosensors: from environmental monitoring to biomedical innovations.Mikrochimica acta · 2026Review
- Flexible TiPolymers · 2024Article
- Proximity Sensing Electronic Skin: Principles, Characteristics, and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Lignocellulosic Bionanomaterials for Biosensor Applications.Micromachines · 2023Review
- The Emergence of Carbon Nanomaterials as Effective Nano-Avenues to Fight against COVID-19.Materials (Basel, Switzerland) · 2023Review
- Gaussian bare-bone slime mould algorithm: performance optimization and case studies on truss structures.Artificial intelligence review · 2023Article
- Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization.Computers in biology and medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
If a person comes into contact with pathogens on public facilities, there is a threat of contact (skin/wound) infections. More urgently, there are also reports about COVID-19 coronavirus contact infection, which once again reminds that contact infection is a very easily overlooked disease exposure route. Herein, we propose an innovative implantation strategy to fabricate a multi-walled carbon nanotube/polyvinyl alcohol (MWCNT/PVA, MCP) interpenetrating interface to achieve flexibility, anti-damage, and non-contact sensing electronic skin (E-skin). Interestingly, the MCP E-skin had a fascinating non-contact sensing function, which can respond to the finger approaching 0-20 mm through the spatial weak field. This non-contact sensing can be applied urgently to human-machine interactions in public facilities to block pathogen. The scratches of the fruit knife did not damage the MCP E-skin, and can resist chemical corrosion after hydrophobic treatment. In addition, the MCP E-skin was developed to real-time monitor the respiratory and cough for exercise detection and disease diagnosis. Notably, the MCP E-skin has great potential for emergency applications in times of infectious disease pandemics. Electronic Supplementary Material: Supplementary material (fabrication of MCP E-skin, laser confocal tomography, parameter optimization, mechanical property characterization, finite element simulation, sensing mechanism, signal processing) is available in the online version of this article at 10.1007/s12274-021-3831-z.
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.