Kinetics of nanopore fabrication during controlled breakdown of dielectric membranes in solution
Bibliographic record
Abstract
Nanopore fabrication by controlled breakdown (CBD) overcomes many of the challenges of traditional nanofabrication techniques, by reliably forming solid-state nanopores sub-2 nm in size in a low-cost and scalable way for nucleic acid analysis applications. Herein, the breakdown kinetics of thin dielectric membranes immersed in a liquid environment are investigated in order to gain deeper insights into the mechanism of solid-state nanopore formation by high electric fields. For various fabrication conditions, we demonstrate that nanopore fabrication time is Weibull-distributed, in support of the hypothesis that the fabrication mechanism is a stochastic process governed by the probability of forming a connected path across the membrane (i.e. a weakest-link problem). Additionally, we explore the roles that various ions and solvents play in breakdown kinetics, revealing that asymmetric pH conditions across the membrane can significantly affect nanopore fabrication time for a given voltage polarity. These results, characterizing the stochasticity of the nanopore fabrication process and highlighting the parameters affecting it, should assist researchers interested in exploiting the potential of CBD for nanofluidic channel fabrication, while also offering guidance towards the conceivable manufacturing of solid-state nanopore-based technologies for DNA sequencing applications.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".