Maximizing the Signal Gain of Electrochemical-DNA Sensors
Bibliographic record
Abstract
Electrochemical DNA (E-DNA) sensors have emerged as a promising class of biosensors capable of detecting a wide range of molecular analytes (nucleic acids, proteins, small molecules, inorganic ions) without the need for exogenous reagents or wash steps. In these sensors, a binding-induced conformational change in an electrode-bound "probe" (a target-binding nucleic acid or nucleic-acid-peptide chimera) alters the location of an attached redox reporter, leading to a change in electron transfer that is typically monitored using square-wave voltammetry. Because signaling in this class of sensors relies on binding-induced changes in electron transfer rate, the signal gain of such sensors (change in signal upon the addition of saturating target) is dependent on the frequency of the square-wave potential pulse used to interrogate them, with the optimal square-wave frequency depending on the structure of the probe, the nature of the redox reporter, and other features of the sensor. Here, we show that, because it alters the driving force of the redox reaction and thus electron transfer kinetics, signal gain in this class of sensors is also strongly dependent on the amplitude of the square-wave potential pulse. Specifically, we show here that the simultaneous optimization of square-wave frequency and amplitude produces large (often more than 2-fold) increases in the signal gain of a wide range of E-DNA-type sensors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".