A paper-based microfluidic biosensor integrating zinc oxide nanowires for electrochemical glucose detection
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Bibliographic record
Abstract
Abstract This paper reports an electrochemical microfluidic paper-based analytical device (EμPAD) for glucose detection, featuring a highly sensitive working electrode (WE) decorated with zinc oxide nanowires (ZnO NWs). In addition to the common features of μPADs, such as their low costs, high portability/disposability, and ease of operation, the reported EμPAD has three further advantages. (i) It provides higher sensitivity and a lower limit of detection (LOD) than previously reported μPADs because of the high surface-to-volume ratio and high enzyme-capturing efficiency of the ZnO NWs. (ii) It does not need any light-sensitive electron mediator (as is usually required in enzymatic glucose sensing), which leads to enhanced biosensing stability. (iii) The ZnO NWs are directly synthesized on the paper substrate via low-temperature hydrothermal growth, representing a simple, low-cost, consistent, and mass-producible process. To achieve superior analytical performance, the on-chip stored enzyme (glucose oxidase) dose and the assay incubation time are tuned. More importantly, the critical design parameters of the EμPAD, including the WE area and the ZnO-NW growth level, are adjusted to yield tunable ranges for the assay sensitivity and LOD. The highest sensitivity that we have achieved is 8.24 μA·mM −1 ·cm −2 , with a corresponding LOD of 59.5 μM. By choosing the right combination of design parameters, we constructed EμPADs that cover the range of clinically relevant glucose concentrations (0−15 mM) and fully calibrated these devices using spiked phosphate-buffered saline and human serum. We believe that the reported approach for integrating ZnO NWs on EμPADs could be well utilized in many other designs of EμPADs and provides a facile and inexpensive paradigm for further enhancing the device performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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