High-Linearity Hydrogel-Based Capacitive Sensor Based on Con A–Sugar Affinity and Low-Melting-Point Metal
Bibliographic record
Abstract
Continuous glucose monitoring (CGM) plays an important role in the treatment of diabetes. Affinity sensing based on the principle of reversible binding to glucose does not produce intermediates, and the specificity of concanavalin A (Con A) to glucose molecules helps to improve the anti-interference performance and long-term stability of CGM sensors. However, these affinity glucose sensors have some limitations in their linearity with a large detection range, and stable attachment of hydrogels to sensor electrodes is also challenging. In this study, a capacitive glucose sensor with high linearity and a wide detection range was proposed based on a glucose-responsive DexG–Con A hydrogel and a serpentine coplanar electrode made from a low-melting-point metal. The results show that within the glucose concentration range of 0–20 mM, the sensor can achieve high linearity (R2 = 0.94), with a sensitivity of 33.3 pF mM−1, and even with the larger glucose concentration range of 0–30 mM the sensor can achieve good linearity (R2 = 0.84). The sensor also shows resistance to disturbances of small molecules, good reversibility, and long-term stability. Due to its low cost, wide detection range, high linearity, good sensitivity, and biocompatibility, the sensor is expected to be used in the field of continuous monitoring of blood glucose.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".