journal article·2021-02

Review of long wavelength luminescent carbon-based nanomaterials: preparation, biomedical application and future challenges

Feng Huo, Wenqiong Li, Yuhang Liu, Xiaohong Liu, Chong-Yew Lee, Wei Zhang

Journal of Materials Science· Volume 56· Issue 4
Publisher page

Abstract

Carbon-based luminescent nanomaterials have attracted much attention in the biomedical field. Compared to traditional photoluminescent materials (such as rare earth-based semiconductors and organic dyes), they have excellent biocompatibility, low toxicity, excellent optical stability and site-targeting properties in addition to their low cost and environmentally friendly features. Carbon dots, a typical carbon-based luminescent material, have great application potential in biomedicine, biological imaging and optoelectronics. Actual biological imaging, however, uses low-level photon scattering and light absorption to avoid self-fluorescence by the target tissues, requiring the light waves to have strong tissue penetration ability. Long wavelength (red and near-infrared) is known to have strong tissue penetration. However, carbon dots in the long wavelength region give a low quantum yield. Thus, obtaining long wavelength-emitting carbon dots with high quantum yield is a big challenge in practical bioimaging and phototherapy. In this paper, recent progress in the development of long wavelength, strongly luminescence-emitting carbon dots, the mechanisms and methods to regulate their optical properties and improve their quantum yield are reviewed. Recent applications of carbon dots in biosensing, bioimaging and therapy are described. Finally, the future challenges and prospects of long wavelength carbon-based luminescent materials are discussed.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Carbon and Quantum Dots Applications · Nanocluster Synthesis and Applications · Luminescence and Fluorescent Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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