Characterizing Solid-State Nanopore Surface Potential and the Impact of Selective Polymer Coatings to Unravel Clogging
Bibliographic record
Abstract
Solid-state nanopores have emerged as powerful tools for single-molecule analysis, with applications ranging from genomics to diagnostics. However, challenges such as surface interactions and clogging have limited their applicability, and a better characterization of the nanopore wall surfaces would provide valuable information to tackle these problems. Here, we present a platform capable of performing high-pressure streaming current measurements in the picoampere range, to characterize the surface potential of solid-state nanopores. By examining different operating conditions, such as pore diameter, salt concentration, and pH, we discuss optimizations for precise streaming current measurements. We validate the performance of the setup by studying both bare SiN and polymer-coated nanopores fabricated by the controlled breakdown method. To address the issue of nanopore clogging, we investigate two distinct coating strategies, which involve functionalizing the surface either prior to or after pore formation, and thus targeting only the membrane outer surface, or also the pore interior, respectively. Selective coating is confirmed by characterizing the pores’ surface potential and conductance. Our results allow us to establish that membrane coating is sufficient to provide antifouling properties during biosensing of long single-stranded DNA. These findings highlight the importance of surface characterization and chemical modification in enhancing nanopore performance for single-molecule detection.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".