Plasmonic quenching and enhancement: metal-quantum dot nanohybrids for fluorescence biosensing
Abstract
Plasmonic metal nanoparticles and semiconductor quantum dots (QDs) are two of the most widely applied nanomaterials for optical biosensing and bioimaging. While their combination for fluorescence quenching via nanosurface energy transfer (NSET) or Förster resonance energy transfer (FRET) offers powerful ways of tuning and amplifying optical signals and is relatively common, metal-QD nanohybrids for plasmon-enhanced fluorescence (PEF) have been much less prevalent. A major reason is the competition between fluorescence quenching and enhancement, which poses important challenges for optimizing distances, orientations, and spectral overlap toward maximum PEF. In this feature article, we discuss the interplay of the different quenching and enhancement mechanisms (a mixed distance dependence of quenching and enhancement - "quenchancement") to better understand the obstacles that must be overcome for the development of metal-QD nanohybrid-based PEF biosensors. The different nanomaterials, their combination within various surface and solution based design concepts, and their structural and photophysical characterization are reviewed and applications toward advanced optical biosensing and bioimaging are presented along with guidelines and future perspectives for sensitive, selective, and versatile bioanalytical research and biomolecular diagnostics with metal-QD nanohybrids.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.