ArticleNature materials2026
Unisensory processing of interleaving memristive nanowires enabling multimodal sensing at human-scale resolution.
Article in Nature materials, 2026. 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
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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.
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Who cites it
3 citing papers in PubMed.
- Bioinspired ionic thermoreceptors with anisotropic architecture for thermotactile perception in robots.Science advances · 2026Article
- Micro-Texturized and Ultra-Soft Dry Electrode for Motion-Artifact Tolerant and Long-Term Stable Wearable Electrophysiological Monitoring.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- A Smart Headband for Multimodal Physiological Monitoring in Human Exercises.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Numerous attempts have been made to emulate the skin's multimodal capabilities using different device architectures, but most suffer from slow response due to reactive components and limited scalability from stacking multiple elements, which restricts their practical use. Here we report a multimodal receptor based on a single memristive nanowire network that captures both thermal and mechanical properties of interacting objects through memristive switching. The device switches between thermal and mechanical sensing at 16 Hz, whereas its intrinsic response times reach submicrosecond (mechanical) and millisecond (thermal) levels due to the nanoscale thickness. To demonstrate practicality, we integrated the receptor with a wireless switching board for daily use, combined it with a machine learning model to identify 20 household objects with 83% accuracy using a single fingertip-mounted sensor, and performed multiarray measurements for spatially distributed sensing. This approach highlights the potential of memristive networks for compact and versatile multimodal sensing in wearable and interactive devices.
Identifiers
41136660What 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.