Near infrared quantum dots for biosensing and bioimaging
Bibliographic record
Abstract
Quantum dots (QDs) possess unique optoelectronic properties, which make them very attractive to be used as optical probes in biosensing and bioimaging applications. The strong absorbance and light scattering of biological compounds like tissue and blood in the visible range pose a problem. However, if optical probes emitting in the near-infrared (NIR) range are used, scattering, absorption, and autofluorescence of biological components are strongly reduced. This allows for an increased light penetration depth and higher spatial and temporal resolution for the investigation of biological processes. The synthesis and application of NIR emitting QDs is a fast-growing research field and the benefits of using QDs were demonstrated for a variety of applications, such as photoelectrochemical biosensor , in vivo vascular imaging, and fluorescence-guided surgery. This article reviews the state-of-the-art developments in the preparation of NIR/IR QDs and highlights the latest research about their utilization in biosensing and bioimaging applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".