Fabrication and Characterization of Gold Nano-gaps for ssDNA Immobilization and Hybridization Detection
Bibliographic record
Abstract
We develop a method for fabricating the nano-gaps directly by using just photolithography and wet etching processes without any nano lithography or difficult techniques. It shows that this resolution enhancement allows one to fabricate metal electrodes with separation from arbitrarily large to fewer than one hundred nanometers. Furthermore, because these nano-gaps are on a thin film, they can be imaged with high-resolution transmission electron microscopy (HRTEM). Efforts toward achieving electrical contact to nanostructures have been active for over a decade. Even though several devices based on “nano-gaps” – two gaps separated by a nanometer-scale distance - have been demonstrated, their realization has remained a significant challenge. Even the best methods are highly labor-intensive and suffer from low yield and poor geometrical control. Most nano-gaps are also incompatible with high resolution transmission electron microscopy (HRTEM) and scanning electron microscopy (SEM). As a consequence, the proof of the nano-gap quality and content in past studies has been indirect. High-resolution imaging is therefore required to ensure the quality of nano-gaps and to be able to identify possible artifacts. This project presents a unique vertical nano-gap biosensor that can detect changes in DNA structure. Using a size reduction to interrogate samples between the nano-scale gaps, this biosensor will be sensitive enough to record the conformational changes for ss-DNA.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".