Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 171

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

Monodisperse magnetic nanoparticles for biomedical applications

Xu, Chenjie, Sun, Shouheng·2007·Polymer International

Abstract

Magnetic nanoparticles that are superparamagnetic with high saturation moment have great potential for biomedical applications. Solution‐phase syntheses have recently been applied to make various kinds of monodisperse magnetic nanoparticles with standard deviation in diameter of less than 10%. However, the surface of these nanoparticles is coated with a layer of hydrocarbon molecules due to the use of lipid‐like carboxylic acid and amine in the syntheses. Surface functionalization leads to the formation of water‐soluble nanoparticles that can be further modified with various biomolecules. Such functionalization has brought about several series of monodisperse magnetic nanoparticle systems that have shown promising applications in protein or DNA separation, detection and magnetic resonance imaging contrast enhancement. The goal of this mini review is to summarize the recent progress in the synthesis and surface modification of monodisperse magnetic nanoparticles and their applications in biomedicine. Copyright © 2007 Society of Chemical Industry

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/pi.2251

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

Cited by

See all 171

Metadata

Authors
Xu, C., & Sun, S.
Venue
Polymer International
Publisher
Wiley
Published
2007-02-28
Volume
56
Issue
7
Pages
821-826
ISSN
  • 0959-8103 (ISSN-L)
  • 1097-0126
OpenAlex
W2090631578
Cited by
171
References
2
FWCI
13.26
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: Monodisperse magnetic nanoparticles for biomedical applications. https://app.cochat.ai/papers/10.1002/pi.2251
The paper
Xu, C., & Sun, S. (2007). Monodisperse magnetic nanoparticles for biomedical applications. Polymer International. https://doi.org/10.1002/pi.2251

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.