Novel PMMA Polymer-Based Nanopores Capable of Detection and Discrimination Between Structurally Different Biomolecules
Bibliographic record
Abstract
We report, for the first time, the use of single poly (methyl-methacrylate) (PMMA) polymer-based nanopores to not only detect the translocating biomolecules of dsDNA and BSA in individual populations, but also distinguish between them in a mixed population, based on the obtained depths (iBlock) and durations (tD) of the ionic current blockade events. Quantitative analyzes of current blockade events induced by separate populations of dsDNA and BSA molecules translocating through single PMMA-based nanopores revealed a meaniBlock value of 22.4 ± 0.69 pA with a tDof 0.202 ± 0.009 ms, and a meaniBlock value of 60.14 ± 0.19 pA with a tDof 0.59 ± 0.08 ms for dsDNA and BSA molecules, respectively. Evaluation of translocation events by dsDNA and BSA molecules in a mixed population through single nanopores indicated two distinct groups of current blockade events, extrapolation of which could perfectly correlate the group demonstratingiBlock values of 13-38 pA to the dsDNA molecules, whereas the group demonstratingiBlock values of 52-73 pA to the BSA protein molecules transporting through the nanopores. Analysis of the data suggests that the majority of dsDNA or BSA protein molecules translocating through the nanopores are in their partially folded conformation. This paper is an important milestone in the development of polymer-based solid-state nanopore devices for fast and accurate biomolecule detection, differentiation/discrimination, and future structural characterization.
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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 | 0.001 |
| Open science | 0.001 | 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".