Wafer-Scale Fabrication of Uniform, Micrometer-Sized, Triangular Membranes on Sapphire for High-Speed Protein Sensing in a Nanopore
Bibliographic record
Abstract
Ultra-low-noise solid-state nanopores are attractive for high-accuracy single-molecule sensing. A conventional silicon platform introduces acute capacitive noise to the system, which seriously limits the recording bandwidth. Recently, we have demonstrated the creation of thin triangular membranes on an insulating crystal sapphire wafer to eliminate the parasitic device capacitance. Uniquely different from the previous triangular etching window designs, here hexagonal windows were explored to produce triangular membranes by aligning to the sapphire crystal within a large tolerance of alignment angles (10–35°). Interestingly, sapphire facet competition serves to suppress the formation of more complex polygons but creates stable triangular membranes with their area insensitive to the facet alignment. Accordingly, a new strategy was successfully established on a 2 in. sapphire wafer to produce chips with an average membrane side length of 4.7 μm, an area of <30 μm 2 for 81% chips, or estimated calculated membrane capacitance as low as 0.06 pF. We finally demonstrated <4 μs high-speed and high-fidelity low-noise protein detection under 250 kHz high bandwidth.
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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) | 0.001 | 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".