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  4. Energy Gap Law-Harnessing Design of Highly Second Near-Infrared Emissive 34π-Annulated Porphyrinoids for In Vivo Imaging
 
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Energy Gap Law-Harnessing Design of Highly Second Near-Infrared Emissive 34π-Annulated Porphyrinoids for In Vivo Imaging

Journal
Journal of the American Chemical Society
Journal Volume
147
Journal Issue
25
Start Page
21940
End Page
21949
ISSN
0002-7863
1520-5126
Date Issued
2025-06-11
Author(s)
Tsai, Yi-Chen
Chen, Yan-Chang
Lu, Hsiu-Feng
Chan, Kai-Min
Lin, Syue-Liang
Lin, Pin-Xuan
Rotomskis, Ricardas
Steponkiene, Simona
Wu, Tung-Kung
Chan, Ming-Hsien
JA-AN ANNIE HO  
Huang, Yu-Fen
Hsu, Chao-Ping
Chan, Yang-Hsiang
DOI
10.1021/jacs.5c05151
URI
https://www.scopus.com/record/display.uri?eid=2-s2.0-105007845913&origin=resultslist
https://scholars.lib.ntu.edu.tw/handle/123456789/730585
Abstract
NIR-II fluorophores (1000–1700 nm) are pivotal for biomedical imaging, offering deep-tissue penetration and high signal-to-noise ratios but suffer from low quantum yields (QY < 0.01%) beyond 1200 nm. To date, most reported NIR-II small-molecule dyes are derived from polymethine and xanthene frameworks. However, achieving NIR-II chromophores with sufficient QYs remains challenging, as the energy gap law dictates that internal conversion-governed by the emission energy gap and reorganization energy-dominates nonradiative decay. To address this, we designed a novel pseudo-2D molecular framework: 34 π-electron annulated porphyrinoids (Scheme 1), engineered to minimize reorganization energy. These structures achieve emission wavelengths up to 1290 nm with QYs of 1.10–6.14%. Density functional theory (DFT) calculations were performed to unravel the photophysical mechanisms underlying these behaviors, showing that the reorganization energy is as small as 10.5 meV for these dyes, which validates our design. The optimized molecular structures and the stacking geometry of these porphyrinoids in the nanoparticle form were also elaborated by DFT. The intense NIR-II fluorescence (>1200 nm) enables high-resolution in vivo vascular imaging, further enhanced by AI-driven imaging algorithms to significantly improve image quality.
SDGs

[SDGs]SDG3

Publisher
American Chemical Society (ACS)
Type
journal article

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