Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 295

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

A New Class of Silica Cross-Linked Micellar Core−Shell Nanoparticles

Huo, Qisheng, Liu, Jun, Wang, Li-Qiong, Jiang, Yingbing, Lambert, Timothy N., Fang, Erica·2006·Journal of the American Chemical Society

Abstract

Micellar nanoparticles made of surfactants and polymers have attracted wide attention in the materials and biomedical community for controlled drug delivery, molecular imaging, and sensing; however, their long-term stability remains a topic of intense study. Here we report a new class of robust, ultrafine silica core-shell nanoparticles formed from silica cross-linked, individual block copolymer micelles. Compared with pure polymeric micelles, the main advantage of the new core-shell nanoparticles is that they have significantly improved stability and do not break down during dilution. We also studied the drug loading and release properties of the silica cross-linked micellar particles, and we found that the new core-shell nanoparticles have a slower release rate which allows the entrapped molecules to be slowly released over a much longer period of time under the same experimental conditions. A range of functional groups can be easily incorporated through co-condensation with the silica matrix. The potential to deliver hydrophobic agents into cancer cells has been demonstrated. Because of their unique structures and properties, these novel core-shell nanoparticles could potentially provide a new nanomedicine platform for imaging, detection, and treatment, as well as novel colloidal particles and building blocks for mutlifunctional materials.

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 · 10.1021/ja060367p

Cites

4 of 4 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

Cited by

See all 295

Metadata

Authors
Huo, Q., Liu, J., Wang, L.-Q., Jiang, Y., Lambert, T. N., & Fang, E.
Venue
Journal of the American Chemical Society
Published
2006-04-25
Volume
128
Issue
19
Pages
6447-6453
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W1990302960
Cited by
295
References
4
FWCI
7.68
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

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.