Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 224

The DOI matches its Crossref record.

Ultrasmall Biocompatible Bi2Se3 Nanodots for Multimodal Imaging-Guided Synergistic Radiophotothermal Therapy against Cancer

Mao, Fangxin, Wen, Ling, Sun, Caixia, Zhang, Shaohua, Wang, Guanglin, Zeng, Jianfeng, Wang, Yong, Ma, Jianmin, Gao, Mingyuan, Li, Zhen·2016·ACS Nano

Abstract

Sub-3 nm ultrasmall Bi 2 Se 3 nanodots stabilized with bovine serum albumin were successfully synthesized through a reaction of hydroxyethylthioselenide with bismuth chloride in aqueous solution under ambient conditions. These nanodots exhibit a high photothermal conversion efficiency (η = 50.7%) due to their strong broad absorbance in the near-infrared (NIR) window and serve as a nanotheranostic agent for photoacoustic imaging and photothermal cancer therapy. In addition, they also display radioenhancement with a ratio of 6% due to their sensitivity to X-rays, which makes them a potential sensitizer for radiotherapy. These nanodots were also labled with radioactive 99m Tc for quantification of their biodistribution by single-photon-emission computed tomography (SPECT)/computed tomography (CT) imaging. Our work demonstrates the potential of ultrasmall Bi 2 Se 3 nanodots in multimodal imaging-guided synergetic radiophotothermal therapy of cancer.

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/acsnano.6b06067

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.

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

Cited by

See all 224

Metadata

Authors
Mao, F., Wen, L., Sun, C., Zhang, S., Wang, G., Zeng, J., Wang, Y., Ma, J., Gao, M., & Li, Z.
Venue
ACS Nano
Published
2016-12-06
Volume
10
Issue
12
Pages
11145-11155
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2549648393
Cited by
224
References
0
FWCI
14.29
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: Ultrasmall Biocompatible Bi2Se3 Nanodots for Multimodal Imaging-Guided Synergistic Radiophotothermal Therapy against Cancer. https://app.cochat.ai/papers/10.1021/acsnano.6b06067
The paper
Mao, F., Wen, L., Sun, C., Zhang, S., Wang, G., Zeng, J., Wang, Y., Ma, J., Gao, M., & Li, Z. (2016). Ultrasmall Biocompatible Bi2Se3 Nanodots for Multimodal Imaging-Guided Synergistic Radiophotothermal Therapy against Cancer. ACS Nano. https://doi.org/10.1021/acsnano.6b06067

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.