Dual-Chronoamperometry Drift Correction for Electrochemical Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it