Immobilization of Quantum Dot-DNA Conjugates Templated by DNA Self-Assembled Monolayers on Single-Crystal Gold Bead Electrodes Toward Advanced Sensing
Bibliographic record
Abstract
The controlled assembly of quantum dots (QDs) on electrode surfaces is challenging but of interest for the development of sensors and other bioanalytical platforms. Here, we demonstrate the DNA-templated assembly of QDs on a gold electrode. QDs conjugated with complementary DNA were hybridized with a fluorescently labeled single-stranded DNA self-assembled monolayer (SAM) on a single-crystal gold bead electrode. Colocalization of QD and fluorophore photoluminescence from the modified surface indicated that the QD coverage was correlated to the facet-dependent density of the underlying DNA-SAM. AFM imaging of the assembled QD-DNA SAM on the Au(111) facet showed QDs at a density of ∼1 × 10 10 particles/cm 2 or roughly 100 nm between QDs, consistent with the mobility of the DNA-templated QD SAMs measured using potential-induced reorientation. QDs did not assemble on a dsDNA SAM, DNA-free SAMs, nor if the QDs lacked conjugated complementary DNA. QDs desorbed with the DNA-SAM during reductive desorption of the thiol, and with the chemical and thermal denaturation of the dsDNA SAM. These observations supported a DNA-mediated assembly process. However, at low ionic strength, the anionic QDs were also removed from the surface, pointing to a competition between DNA hybridization and electrostatic repulsion between the QDs and the DNA SAM. Additionally, QDs lacking conjugated DNA were found to aggregate on mercaptohexanol-coated gold and on clean gold. These aggregated QDs were not removed via reductive electrochemistry and required very high forces to be displaced using AFM. Interfacial DNA was thus critical to the controlled and reversible binding of QDs at the gold surface. Overall, we have shown the preparation of QD-SAMs using a DNA-templated approach, and developed in situ methodology to assess the modified interface and the stability on the surface-bound QDs. This insight will guide the rational preparation of well-defined QD-modified electrode surfaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".