Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 192

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

High‐Performance Nanocomposites Inspired by Nature

Peng, Jingsong, Cheng, Qunfeng·2017·Advanced Materials

Abstract

Natural materials, including nacre, bone, and the lobster cuticle, exhibit excellent mechanical properties, combining high strength and toughness. Such materials have the added benefit of being light in weight. These advantageous features are due to such natural materials' orderly hierarchical architectures and abundant interface interactions. How to utilize these design principles created by nature to fabricate high‐performance bioinspired nanocomposites remains a great research challenge. A logical roadmap for developing these nanocomposites can be described as “discovery, invention, and creation.” Here, the discovery of the relationship between natural materials' design principles and such materials' extraordinary mechanical properties is discussed. Then, the invention of bioinspired strategies for mimicking natural materials is considered and representative strategies addressed. Next, the creation of multifunctional nanocomposites is discussed and bioinspired nanocomposites, including fiber nanocomposites, 2D film nanocomposites, and 3D bulk nanocomposites reviewed. Finally, a perspective and outlook for future directions in making bioinspired nanocomposites is provided to offer inspiration to the community and a clear vision for future research.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/adma.201702959

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Metadata

Authors
Peng, J., & Cheng, Q.
Venue
Advanced Materials
Publisher
Wiley
Published
2017-10-23
Volume
29
Issue
45
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
OpenAlex
W2766516438
Cited by
192
References
2
FWCI
5.83
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: High‐Performance Nanocomposites Inspired by Nature. https://app.cochat.ai/papers/10.1002/adma.201702959
The paper
Peng, J., & Cheng, Q. (2017). High‐Performance Nanocomposites Inspired by Nature. Advanced Materials. https://doi.org/10.1002/adma.201702959

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.