Dual-Emissive Iridium(III) Complex with Aggregation-Induced Emission: Mechanistic Insights into Electron Transfer for Enhanced Hypoxia Detection in 3D Tumor Models
Annu Agarwal (20571506), Shrikant Kirwale (20571509), Ajeet Singh (2203483), Bharat Kaushik (20571512), Vishal Kachwal (7245431), Aniruddha Roy (1428772), Inamur Rahaman Laskar (1522936)
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
Accurate oxygen detection and measurement of its concentration is vital in biological and industrial applications, necessitating highly sensitive and reliable sensors. Optical sensors, valued for their real-time monitoring, nondestructive analysis, and exceptional sensitivity, are particularly suited for precise oxygen measurements. Here, we report a dual-emissive iridium(III) complex, IrNPh2, featuring “aggregation-induced emission” (AIE) properties and used for sensitive oxygen sensing. IrNPh2 exhibits dual emissions at 450 and 515 nm, with 515 nm triplet-state emission demonstrating remarkable oxygen sensitivity due to its long-lived excited state (12.12 μs) and high quantum yield (68%). Stern–Volmer analysis reveals a notable quenching constant (Ksv = 12.44%–1) and an ultralow detection limit of 0.0397%, emphasizing its superior performance. The oxygen quenching mechanism is driven by electron transfer (ET), supported by computational studies showing the lowest-unoccupied molecular orbital (LUMO) alignment of IrNPh2 with the πg* orbitals of triplet oxygen, leading to superoxide radical (O2•–) formation. Electron paramagnetic resonance (EPR) studies further confirm this pathway. Biological evaluations using a three-dimensional (3D) U87-MG glioma spheroid model highlight the ability of IrNPh2 to detect hypoxic regions, with significant fluorescence enhancement under hypoxia and minimal cytotoxicity (>80% viability at 100 μM). With high sensitivity, low detection limits, and biocompatibility, IrNPh2 emerges as a promising candidate for oxygen sensing in environmental and biomedical applications, especially tumor hypoxia detection.
逐年被引趋势
暂无年度引用数据
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
计算机 / AIEducation, Technology, and Economics
Regional Development and Environment · E-commerce and Technology Innovations