Evidence map›Paper›PMID 41816987›Full record

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

Active Metamaterials with Tunable Shear Nonreciprocity and Nonlinear Dynamics.

Xin Fang, Miao Yu, Dianlong Yu, Li Cheng

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

4 authors.

Xin FangNational Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, Changsha, Hunan, China.ORCID https://orcid.org/0000-0002-7979-803X
Miao YuNational Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, Changsha, Hunan, China.
Dianlong YuNational Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, Changsha, Hunan, China.
Li ChengDepartment of Mechanical Engineering, Hong Kong Polytechnic University, Hong Kong, China.

Funding

National Natural Science Foundation of China 52241103National Natural Science Foundation of China 52322505
6 · The paper itself

Abstract

Actively and smoothly tunable mechanical metamaterials are in high demand for adaptive, variable-stiffness structures in smart machines. However, existing designs are largely restricted to tunable transverse deformation, reciprocal response, and linear dynamics. Here, we propose a novel gear-based design paradigm-using Taiji planar gears and planetary gear assemblies as building blocks-that overcomes these limitations by enabling simultaneous control of translational and torsional stiffnesses, shear nonreciprocity, and programmable nonlinear dynamics. Our metamaterials achieve in situ, continuous tuning of shear stiffness by 30-100×, break reciprocity under positive versus negative loads, and allow the nonreciprocity ratio to be tuned by over 100×. Meta-resonators constructed from these units showcase an application example exhibit broadly tunable transverse and torsional resonant frequencies. Furthermore, we demonstrate that static nonreciprocity serves as a precise control knob for dynamic nonlinearity-a property traditionally fixed and nearly impossible to tune in conventional materials. Analytical models and analyses elucidate the underlying mechanisms and extendable design freedoms. This work bridges the critical gaps in mechanical metamaterials and dynamics, offering a practical pathway to control both linear and nonlinear structural deformations, elastic waves, and vibrations.

Identifiers

PMID41816987
PMCPMC13185870

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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.