Instrumentation for low noise nanopore-based ionic current recording under laser illumination
Bibliographic record
Abstract
We describe a nanopore-based optofluidic instrument capable of performing low-noise ionic current recordings of individual biomolecules under laser illumination. In such systems, simultaneous optical measurements generally introduce significant parasitic noise in the electrical signal, which can severely reduce the instrument sensitivity, critically hindering the monitoring of single-molecule events in the ionic current traces. Here, we present design rules and describe simple adjustments to the experimental setup to mitigate the different noise sources encountered when integrating optical components to an electrical nanopore system. In particular, we address the contributions to the electrical noise spectra from illuminating the nanopore during ionic current recording and mitigate those effects through control of the illumination source and the use of a PDMS layer on the SiNx membrane. We demonstrate the effectiveness of our noise minimization strategies by showing the detection of DNA translocation events during membrane illumination with a signal-to-noise ratio of ∼10 at 10 kHz bandwidth. The instrumental guidelines for noise minimization that we report are applicable to a wide range of nanopore-based optofluidic systems and offer the possibility of enhancing the quality of synchronous optical and electrical signals obtained during single-molecule nanopore-based analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".