Convex lens-induced nanoscale templating
Bibliographic record
Abstract
We demonstrate a new platform, convex lens-induced nanoscale templating (CLINT), for dynamic manipulation and trapping of single DNA molecules. In the CLINT technique, the curved surface of a convex lens is used to deform a flexible coverslip above a substrate containing embedded nanotopography, creating a nanoscale gap that can be adjusted during an experiment to confine molecules within the embedded nanostructures. Critically, CLINT has the capability of transforming a macroscale flow cell into a nanofluidic device without the need for permanent direct bonding, thus simplifying sample loading, providing greater accessibility of the surface for functionalization, and enabling dynamic manipulation of confinement during device operation. Moreover, as DNA molecules present in the gap are driven into the embedded topography from above, CLINT eliminates the need for the high pressures or electric fields required to load DNA into direct-bonded nanofluidic devices. To demonstrate the versatility of CLINT, we confine DNA to nanogroove and nanopit structures, demonstrating DNA nanochannel-based stretching, denaturation mapping, and partitioning/trapping of single molecules in multiple embedded cavities. In particular, using ionic strengths that are in line with typical biological buffers, we have successfully extended DNA in sub-30-nm nanochannels, achieving high stretching (90%) that is in good agreement with Odijk deflection theory, and we have mapped genomic features using denaturation analysis.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".