ArticleBiosensors2022
Thermally Drawn CNT-Based Hybrid Nanocomposite Fiber for Electrochemical Sensing.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 16 citations in OpenAlex.
- Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI-Enabled Smart Textiles.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Reversible thermochromic fibers with excellent elasticity and hydrophobicity for wearable temperature sensors.RSC advances · 2024Article
- Microelectronic fibers for multiplexed sweat sensing.Analytical and bioanalytical chemistry · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
Abstract
Nowadays, bioelectronic devices are evolving from rigid to flexible materials and substrates, among which thermally-drawn-fiber-based bioelectronics represent promising technologies thanks to their inherent flexibility and seamless integration of multi-functionalities. However, electrochemical sensing within fibers remains a poorly explored area, as it imposes new demands for material properties-both the electrochemical sensitivity and the thermomechanical compatibility with the fiber drawing process. Here, we designed and fabricated microelectrode fibers made of carbon nanotube (CNT)-based hybrid nanocomposites and further evaluated their detailed electrochemical sensing performances. Carbon-black-impregnated polyethylene (CB-CPE) was chosen as the base material, into which CNT was loaded homogeneously in a concentration range of 3.8 to 10 wt%. First, electrical impedance characterization of CNT nanocomposites showed a remarkable decrease of the resistance with the increase in CNT loading ratio, suggesting that CNTs notably increased the effective electrical current pathways inside the composites. In addition, the proof-of-principle performance of fiber-based microelectrodes was characterized for the detection of ferrocenemethanol (FcMeOH) and dopamine (DA), exhibiting an ultra-high sensitivity. Additionally, we further examined the long-term stability of such composite-based electrode in exposure to the aqueous environment, mimicking the in vivo or in vitro settings. Later, we functionalized the surface of the microelectrode fiber with ion-sensitive membranes (ISM) for the selective sensing of Na+ ions. The miniature fiber-based electrochemical sensor developed here holds great potential for standalone point-of-care sensing applications. In the future, taking full advantage of the thermal drawing process, the electrical, optical, chemical, and electrochemical modalities can be all integrated together within a thin strand of fiber. This single fiber can be useful for fundamental multi-mechanistic studies for biological applications and the weaved fibers can be further applied for daily health monitoring as functional textiles.
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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.