ArticleScientific reports2026
AI-integrated bionic fingertip E-Skin for precision slippage detection in wet environments.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Electronic artificial skin (E-skin) replicates human tactile sensations with exceptional sensitivity and accuracy, enabling the detection of physical properties, including the shape, material, and texture of objects. Current technologies effectively detect slippage on dry surfaces but not on oil- or water-coated wet surfaces. This paper presents a wearable slip sensor featuring a micropatterned structure inspired by human fingerprints, capable of detecting slippage under all surface wetness conditions. The proposed sensor incorporates a randomly patterned fingerprint design, laser-etched onto the topmost layer of a multilayer film. It effectively detects surface slippage, even on oil film-coated low-friction surfaces. Additionally, the sensor captures intricate geometric features of microtextures, including microvibrations and ultrafast signal changes. Its applicability in soft robotic hands is demonstrated by its high-speed detection of the sliding motion of various objects. The findings will aid in advancing digital-on-demand technologies by enabling the precise reconstruction of digital tactile data within cyber-physical systems.
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.