Evaluation of nanopores as candidates for electronic analyte detection
Bibliographic record
Abstract
In an effort to increase throughput and decrease the cost of electrophoretic separation of DNA and proteins, various groups are developing highly parallel, miniaturized separation devices based on capillaries etched into silicon, glass or plastic substrates. To date, these miniaturized devices have relied on optical detectors, thus placing a lower limit on instrument size, and complicating the incorporation of an entire DNA analyzer instrument on a chip. To address this limitation, we are evaluating nanopores as candidate Coulter counters for purely electronic detection of analytes in miniaturized electrophoresis and similar separation devices. To establish feasibility of this detection scheme, we have investigated the detection sensitivity of a nanopore sensor through experiments with the alpha-hemolysin (alpha-HL) ion channel, and through a Monte Carlo (MC) model of polymer capture rate with a cylindrical nanopore under an applied voltage. Experimental and model results are extrapolated to predict the capture rate of synthetic pores operating at higher voltages than presently achievable with protein pores.
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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 | 0.002 |
| 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 | 0.000 | 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".