Highly accurate random DNA sequencing using inherent interlayer potential traps of bilayer MoS <sub>2</sub> nanopores
Bibliographic record
Abstract
Abstract Solid-state MoS 2 nanopores are emerging as potential real-time DNA sequencers due to their ultra-thinness and pore stability. One of the major challenges in determining random nucleotide sequence (unlike polynucleotide strands) is the non-homogeneity of the charge interaction and velocity during DNA translocation. This results in varying blockade current for the same nucleotide, reducing the sequencing confidence. In this work, we studied the inherent impedance-tunability (due to vertical interlayer potential gradient and ion accumulation) of multilayered MoS 2 nanopores along with its effect on improving analyte capture and charge interaction, for more sensitive and confident sensing. Experimentally we demonstrate that 2-3 nm diameter bilayer MoS 2 pores are best suited for high accuracy (~90%) sequencing of mixed nucleotides with signal-to-noise-ratio greater than 11 in picomolar concentration solutions. High temporal resolution demonstrated by bilayer MoS 2 nanopores can help detect neutral proteins in future. The high accuracy detection in low concentration analyte can hence be applied for control and prevention of hereditary diseases and understanding health effects of rare microbial strains.
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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 | 0.000 | 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".