The DOI matches its Crossref record.
Polymer nanocomposites
Abstract
Polymer nanocomposites are polymer matrices reinforced with nano-scale fillers. This new class of composite materials has shown enhanced optical, electrical and dielectric properties. This important book begins by examining the characteristics of the main types of polymer nanocomposites and then reviews their diverse applications. Part one focuses on polymer/nanoparticle composites, their synthesis, optical properties and electrical conductivity. Part two describes the electrical, dielectric and thermal behaviour of polymer/nanoplatelet composites, whilst polymer/nanotube composites are the subject of Part three. The processing and industrial applications of these nanocomposite materials are discussed in Part four, including uses in fuel cells, bioimaging and sensors as well as the manufacture and applications of electrospun polymer nanocomposite fibers, nanostructured transition metal oxides, clay nanofiller/epoxy nanocomposites, hybrid epoxy-silica-rubber nanocomposites and other rubber-based nanocomposites. Polymer nanocomposites: physical properties and applications is a valuable reference tool for both the research community and industry professionals wanting to learn about these materials and their applications in such areas as fuel cell, sensor and biomedical technology. Gives a comprehensive review of polymer nanocomposites and their propertiesA standard reference on this areaWritten by distinguished editors and a international team of authors
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 Nanocomposites with Prescribed Morphology: Going beyond Nanoparticle-Filled Polymers
Vaia, R. A., & Maguire, J. F. (2007) · Chemistry of Materials · doi:10.1021/cm062693+ · Cited by 481
Dynamics of entangled chains in polymer nanocomposites
Schneider, G. J., Nusser, K., Willner, L., Falus, P., & Richter, D. (2011) · Macromolecules · doi:10.1021/ma200899y · Cited by 151
Metadata
- Authors
- Mai, Y.-W., & Yu, Z.-Z.
- Venue
- Woodhead Publishing Limited eBooks
- Publisher
- Woodhead Publishing Limited
- Published
- 2006
- OpenAlex
- W4255993621
- Cited by
- 336
- References
- 0
- FWCI
- 23.92
- Type
- book
- 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: Polymer nanocomposites. https://app.cochat.ai/papers/10.1533/9781845691127
- The paper
- Mai, Y.-W., & Yu, Z.-Z. (2006). Polymer nanocomposites. Woodhead Publishing Limited eBooks. https://doi.org/10.1533/9781845691127
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.