Calibration-Free Electrochemical Biosensors Supporting Accurate Molecular Measurements Directly in Undiluted Whole Blood
Bibliographic record
Abstract
The need to calibrate to correct for sensor-to-sensor fabrication variation and sensor drift has proven a significant hurdle in the widespread use of biosensors. To maintain clinically relevant (±20% for this application) accuracy, for example, commercial continuous glucose monitors require recalibration several times a day, decreasing convenience and increasing the chance of user errors. Here, however, we demonstrate a "dual-frequency" approach for achieving the calibration-free operation of electrochemical biosensors that generate an output by using square-wave voltammetry to monitor binding-induced changes in electron transfer kinetics. Specifically, we use the square-wave frequency dependence of their response to produce a ratiometric signal, the ratio of peak currents collected at responsive and non- (or low) responsive square-wave frequencies, which is largely insensitive to drift and sensor-to-sensor fabrication variations. Using electrochemical aptamer-based (E-AB) biosensors as our test bed, we demonstrate the accurate and precise operation of sensors against multiple drugs, achieving accuracy in the measurement of their targets of within better than 20% across dynamic ranges of up to 2 orders of magnitude without the need to calibrate each individual sensor.
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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) | 0.001 | 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".