Toggle Main Menu Toggle Search

Open Access padlockePrints

The Newcastle University research output collection, currently available on ePrints, will shortly be moving to a new open repository platform, Figshare. To prepare for the data migration we have paused adding new content to ePrints, and will resume once the new repository is launched. During this time you will continue to have access to ePrints (but no new content will appear). We will share updates here when available.

The metamaterial paradigm in wearable electronics

Lookup NU author(s): Dr Xinwei LiORCiD

Downloads


Licence

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

© 2026 The Authors. Metamaterials transcend the limits of traditional materials by achieving unprecedented performance through architectural design. This paradigm has catalyzed the emergence of meta-wearables—wearable electronics whose functions are encoded in structurally engineered matter, enabling compliant mechanics, tailored waves, and thermal transport. In this article, we summarize state-of-the-art meta-wearables and organize these systems into a three-class taxonomy. We then explain why these architectures matter by analyzing the performance gains from unique architectural designs, and elucidating how their physics are made superior by a dual mechanism: interface programming at the biointerface and transport steering of relevant flows. Moving to a system view, we propose the concept of constraint domains to formalize the essential in situ requirements any on-body system must satisfy. As such, we develop a generalized boundary-programmed design framework that translates near-body uncertainties into architectural rules, and which is transferable across materials, scales, and body sites. Finally, we present a design blueprint tailored to different body areas and discuss future prospects and challenges. The framework and insights we provide open a path for engineering future meta-wearables that are capable of adaptive co-evolution with the human body through embedded intelligence.


Publication metadata

Author(s): Li Z, Wang X, Guo Z, Wang Z, Li X, Teng Y, Ramakrishna S

Publication type: Article

Publication status: Published

Journal: eScience

Year: 2026

Volume: 6

Issue: 4

Print publication date: 01/07/2026

Online publication date: 22/01/2026

Acceptance date: 16/01/2026

Date deposited: 12/05/2026

ISSN (print): 2097-2431

ISSN (electronic): 2667-1417

Publisher: KeAi Communications Co.

URL: https://doi.org/10.1016/j.esci.2026.100537

DOI: 10.1016/j.esci.2026.100537


Altmetrics

Altmetrics provided by Altmetric


Funding

Funder referenceFunder name
National Natural Science Foundation of China (Project No. 52505299)
Newcastle University Research Support Allowance

Share