ReviewGels (Basel, Switzerland)2026
Toward Skin-like Sensors: Stretchable Conductive Gels for Triboelectric Applications.
Review in Gels (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Hybrid Conductive Hydrogels Reinforced by Core-Shell PANi@PAN Nanofibers for Resilient Electromechanical Stability at Subzero Temperatures.Gels (Basel, Switzerland) · 2026Article
- Dual-Mode Electrical-Optical Nanocomposite Hydrogel with Enhanced Upconversion Luminescence for Strain and pH Sensing.Gels (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
With the rapid development of artificial intelligence and wearable electronics, there is an increasing demand for skin-like, flexible, and self-powered sensors capable of continuously perceiving mechanical stimuli and human motions. Triboelectric nanogenerator (TENG)-based sensors incorporating stretchable conductive gels represent a promising approach to meet these requirements by combining soft mechanical compliance with efficient electromechanical signal transduction. However, conventional metallic or composite electrodes often suffer from mechanical mismatch with soft skin-like systems, motivating the exploration of intrinsically soft and stretchable conductive gels. In this review, we present a comprehensive and structured overview with comparative perspectives of stretchable skin-like conductive gel-based triboelectric devices. First, different classes of conductive gels, including hydrogels, organogels, ionogels, and other emerging gel systems, are systematically summarized and compared in terms of their composition, crosslinking strategies, conductivity, and mechanical characteristics. Next, the pivotal role of conductive gels in bridging skin-like sensing functions and triboelectric applications is elucidated, highlighting how their intrinsic softness, stretchability, self-healing capability, and interfacial conformability enable intimate skin contact and reliable electromechanical coupling. The key performance attributes of gel-based skin-like triboelectric sensors, including stretchability, self-healing behavior, optical and thermal tolerance, electrical durability, and environmental stability, are critically discussed with representative examples and comparative analysis. Typical device configurations, such as thin-film, fiber-shaped, and textile-based architectures, are further reviewed to illustrate structure-function relationships and application-oriented design strategies. Finally, current challenges, limitations, and future research directions for stretchable conductive gel-based triboelectric systems are outlined, aiming to provide practical guidelines and insights for the rational design of high-performance skin-like triboelectric sensors based on conductive gels.
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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.