Towards quantum dot and FRET-based optical DNA biosensor technology: surface chemistry and photoluminescence of CdSe/ZnS and Si quantum dots
Bibliographic record
Abstract
Quantum dots (QDs) are nanostructures that are highly attractive to optical biosensing. We have developed a nucleic acid biosensing strategy based on the use of quantum dots as energy donors in FRET. One of the challenges in such an approach is avoiding the non-specific adsorption of oligonucleotides. In this report, we describe our efforts to develop poly(ethylene glycol) (PEG)-based hydrophilic surface chemistry and hexanethiol based hydrophobic surface chemistry to alleviate non-specific adsorption. With respect to the former, it was found that the PEG surface chemistry strongly quenched the band-edge luminescence of CdSe/ZnS QDs and yielded significant band-gap luminescence. Furthermore, the PEG chemistry proved ineffective in preventing adsorption. With respect to hexanethiol capped CdSe/ZnS QDs, it was found that good QD luminescence was retained in organic solvent but was quenched in aqueous solution. The use of hydrophobic hexanethiol QDs in aqueous solution required the immobilization of QDs. To achieve this, we used thiol modified biotin and avidin coated fused silica optical fibers. Despite the quenching of the QDs, minimal adsorption was observed suggesting the methodology has good potential. In addition, we describe the development of a one-pot method for both the synthesis and capping of silicon QDs. Our approach also allows versatile post-synthetic modification of the silicon QD capping to produce a variety of functional groups. Silicon QDs are of interest in biosensing due to their biocompatibility and much lower toxicity compared to II-VI semiconductors.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".