Evidence map›Paper›PMID 42397916›Full record

ArticleScience advances2026

High-resolution real-time mechanochromic tactile sensors.

Giacomo Sasso, Alessandro Pagani, Aaron M Duncan, Gianni Pedrizzetti, Nicola Pugno, James J C Busfield, Federico Carpi

Abstract read
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Giacomo SassoSchool of Engineering and Materials Science, Queen Mary University of London, London, UK.ORCID 0009-0001-1582-7188
Alessandro PaganiDepartment of Industrial Engineering, University of Florence, Florence, Italy.ORCID 0009-0009-5376-1683
Aaron M DuncanSchool of Engineering and Materials Science, Queen Mary University of London, London, UK.
Gianni PedrizzettiDepartment of Engineering and Architecture, University of Trieste, Trieste, Italy.ORCID 0000-0003-2886-7653
Nicola PugnoSchool of Engineering and Materials Science, Queen Mary University of London, London, UK.ORCID 0000-0003-2136-2396
James J C BusfieldSchool of Engineering and Materials Science, Queen Mary University of London, London, UK.ORCID 0000-0002-8304-7140
Federico CarpiDepartment of Industrial Engineering, University of Florence, Florence, Italy.ORCID 0000-0001-8496-5085

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-resolution, real-time tactile sensing is essential for robotic tasks that demand accurate, dynamic detection of contact morphology and pressure distribution, such as grasping and manipulation of delicate, slippery, or irregularly shaped objects. Existing technologies, however, face a fundamental trade-off between spatial resolution and response speed. Taxel-based sensors (e.g., capacitive, resistive, or piezoelectric) operate in real time but are intrinsically limited in resolution by taxel size, spacing, wiring, and cross-talk; even deep learning-based tactile super-resolutions rarely surpass ∼1 millimeter. Finer resolutions can be achieved with vision-based tactile sensors using just a camera, although the computation required to transform raw images into three-dimensional contact maps inherently introduces latency. Here, we present mechanochromic tactile sensors that directly encode mechanical strain into spatially resolved structural colors, enabling vision-based tactile sensing with an unprecedented combination of high resolution, real-time operation, and intrinsic simplicity. The devices consist of a stretchable mechanochromic Bragg reflector embedded between two soft silicone layers, whose thickness can be tailored to precisely map contact pressure or strain. As an example, we present topological maps of a fingertip, a one-penny coin, and a leaf, with ∼100 micrometer resolution. In comparison to the most performing vision-based tactile sensors, this was achieved without requiring any deep learning-based data enhancement and without introducing any computational latency. The straightforward applicability of this mechanochromic strategy to enhance vision-based tactile sensing in a simple yet powerful way underscores its transformative potential for uses as diverse as robotic gripping and handling, tactile product inspection, and enhanced human-robot interaction.

Identifiers

PMID42397916
PMCPMC13330820

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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.