Evidence map›Paper›PMID 40184466›Full record

ArticleScience advances2025

An autonomous snapper featuring adaptive actuation and embodied intelligence.

Duygu S Polat, Zihua Chen, Samüel A M Weima, Satoshi Aya, Danqing Liu

Abstract read
In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Duygu S PolatHuman Interactive Materials, Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, Netherlands.ORCID 0000-0002-0420-775X
Zihua ChenSouth China Advanced Institute for Soft Matter Science and Technology (AISMST), School of Emergent Soft Matter, South China University of Technology, Guangzhou, China.ORCID 0009-0004-4042-5879
Samüel A M WeimaHuman Interactive Materials, Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, Netherlands.
Satoshi AyaSouth China Advanced Institute for Soft Matter Science and Technology (AISMST), School of Emergent Soft Matter, South China University of Technology, Guangzhou, China.ORCID 0000-0001-5873-1701
Danqing LiuHuman Interactive Materials, Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, Den Dolech 2, 5612 AZ Eindhoven, Netherlands.ORCID 0000-0001-8830-0443

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developing artificial systems with autonomous motion is essential for creating devices that emulate nature's adaptive mechanisms. Here, we introduce a light-driven liquid crystalline network snapper that integrates both sensing and actuation capabilities, enabling adaptive responses to environmental conditions. Under constant light illumination, the snapper undergoes spontaneous snap-through transformation driven by the elastic instability embedded within the material. The snapper achieves out-of-equilibrium motion through continuous energy transfer with the environment, enabling it to sustain dynamic, reversible cycles of snapping without external control. We demonstrate the ability of the liquid crystalline network snapper to detect environmental changes-such as shifts in temperature, surface roughness, and color-demonstrating a form of embodied intelligence. This work offers a distinctive strategy for designing biomimetic devices that merge embodied intelligence with autonomous motion, opening pathways for advanced, adaptive systems for soft robotics.

Identifiers

PMID40184466
PMCPMC11970476

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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.