Blinking effect in quantum dots, its suppression mechanism, and applications in medical imaging and biosensing: A review
Bibliographic record
Abstract
Semiconductor quantum dots (QDs), being an auspicious outcome of nanotechnology, have wide technological applications based on their simple synthetic procedures, tunable photoluminescent properties, and effective optical stability. However, their utilization in sensing, imaging, and optoelectronic applications is limited due to their intrinsic drawback of fluorescence intermittency, which not only hinders precise biological imaging due to challenges in tracking individual target molecules but also gives inaccurate measurements and creates complications in data analysis due to long dark (off) states that remain on the time scale of milliseconds to minutes. In order to resolve this problem, research work is being carried out on a large scale to elucidate the mechanism following blinking and approaches to suppress it. This review explicitly highlights the key mechanisms: A type (Auger), B type, near band edge carrier (C type), and D type, responsible for the blinking effect in QDs, and explores the effective methods for its suppression including shell engineering, halide vacancy filling, and passivating the surface with ligands, polymers, noble metals, and plasmonic as well as N-Type semiconductor substrates to enhance their efficiency on practical grounds. Nearly non-blinking QDs with an on-state for 99% of the time have been synthesized by shell engineering. The suppression of blinking leads to improved performance and enhanced efficiency of QD-based devices. According to the literature reports, these methodologies efficiently suppress the blinking phenomenon, even approaching near unity (≈1) photoluminescence quantum yield, which is notable. Finally, the current review discusses the advantages of blinking suppression specifically in biomedical applications such as single particle tracking, in vivo and in vitro imaging, and biosensing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".