journal article·2009-09Open access

A dual-emissive-materials design concept enables tumour hypoxia imaging

Guoqing Zhang, Gregory M. Palmer, Mark W. Dewhirst, Cassandra L. Fraser

Nature Materials· Volume 8· Issue 9

Abstract

Luminescent materials are widely used for imaging and sensing owing to their high sensitivity, rapid response and facile detection by many optical technologies. Typically materials must be chemically tailored to achieve intense, photostable fluorescence, oxygen-sensitive phosphorescence or dual emission for ratiometric sensing, often by blending two dyes in a matrix. Dual-emissive materials combining all of these features in one easily tunable molecular platform are desirable, but when fluorescence and phosphorescence originate from the same dye, it can be challenging to vary relative fluorescence/phosphorescence intensities for practical sensing applications. Heavy-atom substitution alone increases phosphorescence by a given, not variable amount. Here, we report a strategy for modulating fluorescence/phosphorescence for a single-component, dual-emissive, iodide-substituted difluoroboron dibenzoylmethane-poly(lactic acid) (BF(2)dbm(I)PLA) solid-state sensor material. This is accomplished through systematic variation of the PLA chain length in controlled solvent-free lactide polymerization combined with heavy-atom substitution. We demonstrate the versatility of this approach by showing that films made from low-molecular-weight BF(2)dbm(I)PLA with weak fluorescence and strong phosphorescence are promising as 'turn on' sensors for aerodynamics applications, and that nanoparticles fabricated from a higher-molecular-weight polymer with balanced fluorescence and phosphorescence intensities serve as ratiometric tumour hypoxia imaging agents.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Luminescence and Fluorescent Materials · Nanoplatforms for cancer theranostics · Lanthanide and Transition Metal Complexes

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

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