Capture and Translocation Characteristics of Short Branched DNA Labels in Solid-State Nanopores
Bibliographic record
Abstract
The challenge when employing solid-state nanopores as single-molecule sensors in a given assay is the specificity of the ionic current signal during the translocation of target molecules. Here we present the capture and translocation characteristics of short structurally defined DNA molecules that could serve as effective surrogate labels in biosensing applications. We produced T-shaped or Y-shaped DNA molecules with a 50 bp double-stranded DNA (dsDNA) backbone and a 25 bp dsDNA branch in the middle, as improved labels over short linear DNA fragments. We show that molecular topologies can be distinguished from linear DNA by analyzing ionic current blockades produced as these DNA labels translocate through nanopores fabricated by controlled breakdown on 10-nm-thick SiN membranes and ranging in diameter from 4 to 10 nm. Event signatures are shown to be a direct result of the structure of the label and lead to an increased signal-to-noise ratio over that of short linear dsDNA, in addition to well resolved dwell times for the pore size in this range. These results show that structurally defined branched DNA molecules can be robustly detected for a broad range of pore size, and thus represent promising candidates as surrogate labels in a variety of nanopore-based molecular or immunoassay schemes.
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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) | 0.000 | 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) | 0.000 | 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".