Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 542

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

pH-Induced Aggregation of Gold Nanoparticles for Photothermal Cancer Therapy

Nam, Jutaek, Won, Nayoun, Jin, Ho, Chung, Hyokyun, Kim, Sungjee·2009·Journal of the American Chemical Society

Abstract

We report a "smart" gold nanoparticle that is designed to aggregate in mild acidic intracellular environments by its hydrolysis-susceptible citraconic amide surface. With a relatively small size of 10 nm, the "smart" gold nanoparticles can be efficiently internalized into cancerous cells. Triggered by pH change, the nanoparticle surfaces are engineered to have both positive and negative charges. Electrostatic attractions between the nanoparticles can rapidly form aggregates inside the cells, and the aggregates accumulate as the exocytosis is blocked by the increased size. Endocytosis of gold nanoparticles and the aggregation are monitored real-time by dark field optical microscopy. The pH-induced formation of aggregates shifts the absorption to far-red and near-infrared. The absorption shift to longer wavelength is used for photothermal cancer therapy as it guarantees maximal tissue penetration for potential therapeutic applications. The gold nanoparticles show selective and efficient destruction of cancerous cells with an intensity threshold of 5 W/cm(2) to induce the thermal destruction. In the intensity range 5-13 W/cm(2), the circular area of damaged cells increases linearly with the irradiation power density. This shows a new proof-of-concept for photothermal cancer therapy that exploits collective plasmon modes of metal nanoparticles.

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/ja902062j

Cites

1 of 1 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 542

Metadata

Authors
Nam, J., Won, N., Jin, H., Chung, H., & Kim, S.
Venue
Journal of the American Chemical Society
Published
2009-09-08
Volume
131
Issue
38
Pages
13639-13645
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2052067429
Cited by
542
References
1
FWCI
17.86
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.