Engineering DNA Nanocube SAM Scaffolds for FRET-Based Biosensing: Interfacial Characterization and Sensor Demonstration
Bibliographic record
Abstract
Decorating a gold surface with molecular-level control over the positioning of DNA probes was demonstrated using a self-assembled monolayer (SAM) of wireframe DNA nanocube structures. The DNA nanocubes were specifically adsorbed and oriented using thiol-modified DNA on one face of the cube. The DNA nanocube SAM had a uniform coverage over the gold single crystal bead electrode with a separation of 20–30 nm measured by AFM. The face of the nanocube furthest from the gold surface was designed to hybridize with two different sequences of a 50 base single-stranded DNA probe that was modified with a fluorophore. The first 20 bases were hybridized with the DNA nanocube. One of a pair of FRET fluorophores was used for each probe strand. The dimensions of the nanocube controlled the relative spacing between these fluorophores. When the DNA probes were single-stranded, a FRET signal was observed. FRET decreased to background levels when a complementary DNA target was hybridized to either probe, resulting in a turn-off sensor with little cross-talk between the individual hybridization events. Hybridization isotherms for one target gave K A = 170 pM and a detection limit <50 pM. In addition, the DNA nanocube SAM was configured to be used as a turn-on NeutrAvidin sensor using biotinylated DNA targets hybridized to each probe resulting in an increase in FRET. We show that the wireframe DNA nanocube can be an effective scaffold for preparing biosensors with controlled separation between surface-bound probes facilitating precise sensor surface design and enabling a wide range of sensing modalities with more than one signal available for correlative confirmation of the target binding.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".