The DOI matches its Crossref record.
Nanoparticle Polymer Composites: Where Two Small Worlds Meet
Abstract
The mixing of polymers and nanoparticles is opening pathways for engineering flexible composites that exhibit advantageous electrical, optical, or mechanical properties. Recent advances reveal routes to exploit both enthalpic and entropic interactions so as to direct the spatial distribution of nanoparticles and thereby control the macroscopic performance of the material. For example, by tailoring the particle coating and size, researchers have created self-healing materials for improved sustainability and self-corralling rods for photovoltaic applications. A challenge for future studies is to create hierarchically structured composites in which each sublayer contributes a distinct function to yield a mechanically integrated, multifunctional material.
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.
Integrity signals
Registry
Cites
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.
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
Cited by
Polymer/Silica Nanocomposites: Preparation, Characterization, Properties, and Applications
Zou, H., Wu, S., & Shen, J. (2008) · Chemical Reviews · doi:10.1021/cr068035q · Cited by 2,121
Yuk, H., Lu, B., & Zhao, X. (2019) · Chemical Society Reviews · doi:10.1039/c8cs00595h · Cited by 2,108
Self-Healing Polymers and Composites
Blaiszik, B. J., Kramer, S. L. B., Olugebefola, S. C., Moore, J. S., Sottos, N. R., & White, S. R. (2010) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070909-104532 · Cited by 1,505
Shape-memory polymers and their composites: stimulus methods and applications
Leng, J., Lan, X., Liu, Y., & Du, S. (2011) · Progress in Materials Science · doi:10.1016/j.pmatsci.2011.03.001 · Cited by 1,495
Directing the self-assembly of block copolymers
Darling, S. B. (2007) · Progress in Polymer Science · doi:10.1016/j.progpolymsci.2007.05.004 · Cited by 1,019
Metadata
- Authors
- Balazs, A. C., Emrick, T., & Russell, T. P.
- Venue
- Science
- Published
- 2006-11-17
- Volume
- 314
- Issue
- 5802
- Pages
- 1107-1110
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2008342341
- Cited by
- 2,593
- References
- 0
- FWCI
- 86.30
- 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: Nanoparticle Polymer Composites: Where Two Small Worlds Meet. https://app.cochat.ai/papers/10.1126/science.1130557
- The paper
- Balazs, A. C., Emrick, T., & Russell, T. P. (2006). Nanoparticle Polymer Composites: Where Two Small Worlds Meet. Science. https://doi.org/10.1126/science.1130557
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.
