Quantum Dots in Bioanalysis: A Review of Applications across Various Platforms for Fluorescence Spectroscopy and Imaging
Bibliographic record
Abstract
Semiconductor quantum dots (QDs) are brightly luminescent nanoparticles that have found numerous applications in bioanalysis and bioimaging. In this review, we highlight recent developments in these areas in the context of specific methods for fluorescence spectroscopy and imaging. Following a primer on the structure, properties, and biofunctionalization of QDs, we describe select examples of how QDs have been used in combination with steady-state or time-resolved spectroscopic techniques to develop a variety of assays, bioprobes, and biosensors that function via changes in QD photoluminescence intensity, polarization, or lifetime. Some special attention is paid to the use of Förster resonance energy transfer-type methods in bioanalysis, including those based on bioluminescence and chemiluminescence. Direct chemiluminescence, electrochemiluminescence, and charge transfer quenching are similarly discussed. We further describe the combination of QDs and flow cytometry, including traditional cellular analyses and spectrally encoded barcode-based assay technologies, before turning our attention to enhanced fluorescence techniques based on photonic crystals or plasmon coupling. Finally, we survey the use of QDs across different platforms for biological fluorescence imaging, including epifluorescence, confocal, and two-photon excitation microscopy; single particle tracking and fluorescence correlation spectroscopy; super-resolution imaging; near-field scanning optical microscopy; and fluorescence lifetime imaging microscopy. In each of the above-mentioned platforms, QDs provide the brightness needed for highly sensitive detection, the photostability needed for tracking dynamic processes, or the multiplexing capacity needed to elucidate complex systems. There is a clear synergy between advances in QD materials and spectroscopy and imaging techniques, as both must be applied in concert to achieve their full potential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".