Evidence map›Paper›PMID 41486669›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Visualizing and Quantifying Impact with Mechanochromic Sensing Paints Based on Self-Assembled Polydiacetylene-Silk Core-Shell Vesicles.

Marco Lo Presti, Giulia Guidetti, Elisabetta Ruggeri, Terri Lyne Carrington, Fiorenzo G Omenetto

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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

5 authors.

Marco Lo PrestiSilklab, Department of Biomedical Engineering, Tufts University, Medford, Massachusetts, USA.
Giulia GuidettiSilklab, Department of Biomedical Engineering, Tufts University, Medford, Massachusetts, USA.ORCID https://orcid.org/0000-0002-6065-3359
Elisabetta RuggeriSilklab, Department of Biomedical Engineering, Tufts University, Medford, Massachusetts, USA.
Terri Lyne CarringtonBerklee College of Music, Boston, Massachusetts, USA.
Fiorenzo G OmenettoDepartment of Physics, Tufts University, Medford, Massachusetts, USA.ORCID https://orcid.org/0000-0002-0327-853X

Funding

Keck FoundationOffice of Naval Research N00014-22-1-2429
6 · The paper itself

Abstract

Rapid detection and quantification of mechanical impact on surfaces is relevant for many fields, as it enables monitoring of material stresses and integrity or to assess the physiological effects of force on humans for concussions, gait, or blast dosimetry, among others. Accurate and quantitative detection of impact force, especially at high values (100-770 N), is challenging and often relies on electronic transduction and interfaces, with associated limitations in integration, shaping, and scalability. We present here a mechanochromic paint as a simple alternative for impact detection and visualization. The paint is based on the self-assembly of polydiacetylene (PDA) and regenerated silk fibroin protein (SF) into core-shell vesicles, which exhibit an irreversible mechanochromic transition from blue to red upon impact. The paint enables the tracking of the impact history on the applied material with a colorimetric response that is directly proportional to the impact energy across a broad sensing range of 100-770 N (corresponding to 100-1000 MPa) and has wide utility given by its ability to be interfaced or integrated in a wide variety of material formats, as illustrated by through several demonstrator devices.

Indexed as

core–shell vesiclesforce sensorimpact sensormechanochromic sensorspolydiacetyleneresponsive coatingssilk fibroin

Identifiers

PMID41486669
PMCPMC13045206

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.