Dual-Chronoamperometry Drift Correction for Electrochemical Sensors
Bibliographic record
Abstract
Accurate sensing of biomolecular targets is crucial for diagnosing diseases and developing technologies for personalized medicine. However, measuring biomarker levels with high precision is often challenging due to signal drift caused by biofouling and monolayer instability. We demonstrate a novel continuous dual-chronoamperometry method with faradaic current extraction to enable accurate and reliable detection of biomarkers in the presence of drift. We apply two sequential chronoamperometry pulses, a reference (-500 mV) and a test (+500 mV), to capture all capacitive and faradaic currents in the range. In the absence of the target, the drift in the reference and test currents is multilinear, and this relationship can be used to predict the contribution of the target current. As a proof-of-concept, we demonstrate that signal drift can be corrected using our molecular pendulum for IFN-γ detection. Importantly, we show that this technique is broadly applicable to other amperometry-based systems such as a monolayer transporter sensor, an electrochemical DNA sensor, and electrochemical aptamer-based sensors. Moreover, we train a linear regression machine learning model and use its error to quantify target concentrations with dual-chronoamperometry data. This novel method enhances the reliability and sensitivity of chronoamperometry, paving the way for its application in real-time monitoring scenarios.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".