Evidence map›Paper›PMID 40269148›Full record

ArticleNature materials2025

Double-network-inspired mechanical metamaterials.

James Utama Surjadi, Bastien F G Aymon, Molly Carton, Carlos M Portela

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Article in Nature materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Review
  2. Article
  3. Polymer-inspired mechanical metamaterials.Nature communications · 2026
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  9. Review
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  11. Physical Intelligence in Small-Scale Robots and Machines.Advanced materials (Deerfield Beach, Fla.) · 2026
    Review
  12. Article
  13. Review
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  16. A conductive folding metamaterial via laser-induced biomimetic electrospinning.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  17. Article
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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

4 authors.

James Utama SurjadiDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Bastien F G AymonDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-1964-7328
Molly CartonDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Carlos M PortelaDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. cportela@mit.edu.ORCID http://orcid.org/0000-0002-2649-4235

Funding

National Science Foundation (NSF) CMMI-2142460
6 · The paper itself

Abstract

Mechanical metamaterials can achieve high stiffness and strength at low densities, but often at the expense of low ductility and stretchability-a persistent trade-off in materials. In contrast, double-network hydrogels feature interpenetrating compliant and stiff polymer networks, and exhibit unprecedented combinations of high stiffness and stretchability, resulting in exceptional toughness. Here we present double-network-inspired metamaterials by integrating monolithic truss (stiff) and woven (compliant) components into a metamaterial architecture, which achieves a tenfold increase in stiffness and stretchability compared to its pure counterparts. Nonlinear computational mechanics models elucidate that enhanced energy dissipation in these double-network-inspired metamaterials stems from increased frictional dissipation due to entanglements between networks. Through introduction of internal defects, which typically degrade mechanical properties, we demonstrate a threefold increase in energy dissipation for these metamaterials via failure delocalization. This work opens avenues for developing metamaterials in a high-compliance regime inspired by polymer network topologies.

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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.