Evidence map›Paper›PMID 42495868›Full record

ArticleSmall (Weinheim an der Bergstrasse, Germany)2026

Geometry-Encoded Programmable Mechanics in Single-Material Dual-Phase Metamaterials.

Miao Zhao, Na Qiu, Xuhui Zhang, Bei Peng, Zhi Zeng, Xinwei Li, Qing Li

Abstract read
In one paragraph

Article in Small (Weinheim an der Bergstrasse, 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

7 authors.

Miao ZhaoSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan, China.
Na QiuSchool of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, Australia.
Xuhui ZhangSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan, China.
Bei PengSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan, China.ORCID https://orcid.org/0000-0003-2486-6776
Zhi ZengSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan, China.
Xinwei LiNewcastle University in Singapore, Faculty of Science, Agriculture, and Engineering, Newcastle University, Newcastle upon Tyne, UK.ORCID https://orcid.org/0000-0002-1702-8670
Qing LiSchool of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, Australia.

Funding

Fundamental Research Funds for the Central Universities ZYGX2024XJ015National Natural Science Foundation of China 52505248Sichuan Science and Technology Program 2025ZNSFSC1272
6 · The paper itself

Abstract

Programmable metamaterials that exhibit prescribed mechanical responses and adaptive deformation under external loading are highly desirable for multifunctional engineering applications. However, most existing designs rely on multi-material systems, which pose significant fabrication challenges with conventional additive manufacturing. Inspired by the unique soft-hard heterogeneous architecture of nacre, this study introduces a novel class of dual-phase (DP) metamaterials where spatially encoded soft and hard phases are realized through bending-dominated and stretching-dominated lattice architectures, respectively. By systematically varying the spatial coding patterns of soft-hard phases, representative DP metamaterials are shown to exhibit programmable nonlinear mechanical responses and tailored failure processes, achieved through geometry-based mechanical encoding governed by phase interactions and internal stress redistribution. Notably, the engineered sequenced failure processes and phase-coupling-induced strengthening effects lead to significantly enhanced energy absorption compared with the constituent architectures, while enabling customizable plateau stress. To efficiently explore the vast design space of DP metamaterials, a data-driven framework is then developed to model the relationship between spatial encodings and nonlinear mechanical responses. The trained model enables rapid and accurate inverse design of DP metamaterials matching the complex target responses for multifunctional applications. Overall, this work establishes a new geometry-based strategy for achieving highly programmable mechanical responses in single-material metamaterials.

Indexed as

additive manufacturinginverse designmachine learningmetamaterialprogrammable mechanical property

Identifiers

PMID42495868
PMCPMC13548926

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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.