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  5. Engineered NIR-II fluorophores with ultralong-distance molecular packing for high-contrast deep lesion identification

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Article
en
2023

Engineered NIR-II fluorophores with ultralong-distance molecular packing for high-contrast deep lesion identification

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en
2023
Vol 14 (1)
Vol. 14
DOI: 10.1038/s41467-023-40728-6

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Ben Zhong Tang
Ben Zhong Tang

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Zhe Feng
Yuanyuan Li
Siyi Chen
+11 more

Abstract

The limited signal of long-wavelength near-infrared-II (NIR-II, 900-1880 nm) fluorophores and the strong background caused by the diffused photons make high-contrast fluorescence imaging in vivo with deep tissue disturbed still challenging. Here, we develop NIR-II fluorescent small molecules with aggregation-induced emission properties, high brightness, and maximal emission beyond 1200 nm by enhancing electron-donating ability and reducing the donor-acceptor (D-A) distance, to complement the scarce bright long-wavelength emissive organic dyes. The convincing single-crystal evidence of D-A-D molecular structure reveals the strong inhibition of the π-π stacking with ultralong molecular packing distance exceeding 8 Å. The delicately-designed nanofluorophores with bright fluorescent signals extending to 1900 nm match the background-suppressed imaging window, enabling the signal-to-background ratio of the tissue image to reach over 100 with the tissue thickness of ~4-6 mm. In addition, the intraluminal lesions with strong negatively stained can be identified with almost zero background. This method can provide new avenues for future long-wavelength NIR-II molecular design and biomedical imaging of deep and highly scattering tissues.

How to cite this publication

Zhe Feng, Yuanyuan Li, Siyi Chen, Jin Li, Tianxiang Wu, Yanyun Ying, Junyan Zheng, Yuhuang Zhang, Jianquan Zhang, Xiaoxiao Fan, Xiaoming Yu, Dan Zhang, Ben Zhong Tang, Jun Qian (2023). Engineered NIR-II fluorophores with ultralong-distance molecular packing for high-contrast deep lesion identification. , 14(1), DOI: https://doi.org/10.1038/s41467-023-40728-6.

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Publication Details

Type

Article

Year

2023

Authors

14

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1038/s41467-023-40728-6

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