Energy Transfer Assays Using Quantum Dot–Gold Nanoparticle Complexes: Optimizing Oligonucleotide Assay Configuration Using Monovalently Conjugated Quantum Dots
Bibliographic record
Abstract
The energy transfer between quantum dots (QDs) and gold nanoparticles (AuNPs) represents a popular transduction scheme in analytical assays that use nanomaterials. The impact of the spatial arrangement of the two types of nanoparticles on analytical performance has now been evaluated using a nucleic acid strand displacement assay. The first spatial arrangement (configuration 1) involved the assembly of a number of monovalently functionalized QD-oligonucleotide conjugates around a single central AuNP that was functionalized with complementary oligonucleotide sequences. The assembly of these complexes, and subsequent disassembly via target oligonucleotide-mediated displacement, were used to evaluate energy transfer efficiencies. Furthermore, the inner filter effect of AuNPs on the fluorescence intensity of the QD was studied. AuNPs of three different diameters (6, 13, and 30 nm) were used in these studies. Configuration 2 was based on the placement of monovalently functionalized AuNP-oligonucleotide conjugates around a single QD that was functionalized with a complementary oligonucleotide. The optimal assay configuration, established by evaluating energy transfer efficiencies and inner filter effects, was obtained by arranging at most 15 QDs around the 13 nm AuNP (configuration 1). These assays provided a 2.5-fold change in fluorescence intensity in the presence of target oligonucleotides. To obtain the same response with configuration 2 required the placement of three 6 nm AuNPs around the QD. This resulted in configuration 2 having a 5-fold lower fluorescence intensity when compared to configuration 1. The use of low-cost detection systems (digital camera) further emphasized the higher analytical performance of configuration 1. Response curves obtained using these detection systems demonstrated that configuration 1 had a 10-fold higher sensitivity when compared to configuration 2. This study provides an important framework for the development of sensitive assays using gold nanoparticles and quantum dots.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".