Improving the Conductance Blockage Model of Cylindrical Nanopores – from 2D to Thick Membranes
Bibliographic record
Abstract
The ionic current blockage from a nanopore sensor is a fundamental metric for characterizing its dimensions and for sizing and identifying molecules translocating through. Yet, models for precisely predicting the conductance of a nanopore in both an open and a blocked state are lacking, which leads to significant errors in the determination of the expected blockage depth from a given translocating molecule and of the pore diameter and length. Here, using oblate spheroidal coordinates as a framework to study the conductance of a nanopore, we demonstrate that the widely used Kowalczyk et al. model significantly overestimates the contribution from the access region in the presence of a cylindrical obstruction. We present a highly precise analytical model for the blocked conductance of 2D nanopores and extend it to cylindrical pores of varying membrane thicknesses. Using finite element simulations, our results show that errors in the calculation of the conductance blockage are maximal for pores with aspect ratios of d/L≈5, but are minimal for both d/L≫1 and d/L≪1. The model presented is especially precise for ultra-thin membranes, with prediction errors below 3% for all pore sizes tested with a membrane thickness of 0.3 nm. By improving on the conductance model of the nanopore system, more accurate estimates of the expected blockage depth from a translocating molecule and of pore dimensions can be obtained, with great practical value for many biosensing 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".