A paper-based microfluidic biosensor integrating zinc oxide nanowires for electrochemical glucose detection
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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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".