How Functionalization Affects the Detection Ability of Quantum Dots
Bibliographic record
Abstract
Quantum dots (QDs) have outstanding optical, physiochemical, and chemical properties that make them an extraordinary alternative to fluorescent organic dyes. Recently, they have become excellent photoluminescent labels for detection and diagnosis in medical sciences; they are also used for the detection of target analytes in a variety of scientific fields, such as agricultural, food, and environmental sciences. These extensive applications are made possible by QDs’ high potential for surface state changes when coupled with macromolecules, such as antibodies, aptamers, proteins, lipids, and other small molecules. QDs can be functionalized by complicated or simple procedures depending on their type. The bioconjugation of carbon QDs (CQDs) is more facile due to the possibility of one-pot synthesis and functionalization with carboxylic and amine groups through the accurate selection of carbon precursors. Bioconjugation and functionalization protocols for semiconductor QDs (SQDs) are more complicated compared with those reported for carbon-based QDs and organic dyes. The functionalization of QDs affects their photoluminescence and chemical characteristics, size distribution, in vivo and in vitro detection abilities, and toxicity. Functionalized QDs may also act as antioxidants and scavenge reactive oxygen species. This chapter briefly reviews several functionalization methods and shows how QDs’ surface chemistry determines their target applications. Conjugated QDs’ applications in cell and tissue imaging, disease diagnosis and treatment, and biomedical sensing are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".