Characterizing Surface Charge Density of Solid-State Nanopore Sensors for Improved Biosensing Applications
Bibliographic record
Abstract
Solid-state nanopores have shown great potential as single-molecule sensors for various applications, such as DNA sequencing, biomarker detection, and protein identification. The performance of these nanopores can be affected by their surface charge density, which influence the capture and passage of biomolecules through nanopores. Controlled breakdown fabrication [1] has emerged as a reliable approach for creating solid-state nanopores with precise control over pore size, but further research is needed to optimize their design for specific applications. In this study, a pressure control system was designed and developed to characterize the surface charge density of the nanopore walls via streaming current measurements. A 3D printed flow cell housing a solid-state nanopore was used to independently pressurize each side of the membrane up to 0.3 MPa, and the surface charge density of SiNxmembranes was measured based on the streaming current method. A surface charge density of$-5.3\pm 0.4$mC/m2was measured in a 0.1 M KCI pH 8 solution for the nanopores tested ranging in size from 10 nm to 11 nm. This study provides valuable insights for further optimizing the design of solid-state nanopores for specific 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 | 0.000 | 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 | 0.000 | 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".