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Cited by 203

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Recent advances of semiconducting polymer nanoparticles in in vivo molecular imaging

Open access
Pu, Kanyi, Chattopadhyay, Niladri, Rao, Jianghong·2016·Journal of Controlled Release

Abstract

Semiconducting polymer nanoparticles (SPNs) emerge as attractive molecular imaging nanoagents in living animals because of their excellent optical properties including large absorption coefficients, tunable optical properties and controllable dimensions, high photostability, and the use of organic and biologically inert components without toxic metals. This review summarizes the recent advances of these new organic nanoparticles in in vivo molecular imaging. The in vivo biocompatibility of SPNs is discussed first in details, followed by examples of their applications ranging from sentinel lymph node mapping and tumor imaging to long-term cell tracking, to drug toxicity and bacterial infection imaging for fluorescence, bioluminescence, chemiluminescence and photoacoustic imaging in living animals. The utility of SPNs for designing smart activatable probes for real-time in vivo imaging is also discussed.

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Crossref · 10.1016/j.jconrel.2016.01.004

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Metadata

Authors
Pu, K., Chattopadhyay, N., & Rao, J.
Venue
Journal of Controlled Release
Publisher
Elsevier BV
Published
2016-10
Volume
240
Pages
312-322
ISSN
0168-3659 (ISSN-L)
OpenAlex
W2230063920
Cited by
203
References
6
FWCI
16.30
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Recent advances of semiconducting polymer nanoparticles in in vivo molecular imaging. https://app.cochat.ai/papers/10.1016/j.jconrel.2016.01.004
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Pu, K., Chattopadhyay, N., & Rao, J. (2016). Recent advances of semiconducting polymer nanoparticles in in vivo molecular imaging. Journal of Controlled Release. https://doi.org/10.1016/j.jconrel.2016.01.004

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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.