Xurography-fabricated copper thin film functionalized with electrodeposited dendritic gold for sweat glucose sensing
Bibliographic record
Abstract
Glucose monitoring is essential for effective diabetes management and for reducing the risk of long-term complications. In this work, we present a low-cost, enzyme-free electrochemical platform for sweat glucose sensing, fabricated using rapid xurography and 3D printing. This portable benchtop sensing platform is constructed with a polydimethylsiloxane (PDMS) microfluidic chip, a PDMS encapsulation layer, a copper thin film electrode, and electrodeposited dendritic gold nanostructures, all embedded in a polylactic acid polymer holder. The two-inlet microfluidic configuration enables pump-free operation and provides in situ sweat pH regulation, ensuring stable glucose detection under physiologically relevant conditions. For the first time, copper oxide/copper thin film electrodes were functionalized with dendritic Au nanostructures via electrodeposition, significantly enhancing electrochemical performance. Under optimized conditions, the sensor achieved a wide linear range of 50 μM-1 mM, high sensitivity of 2889.3 μA·mM −1 ·cm −2 , excellent reproducibility (RSD % = 3.36 %), good reusability (~ 91 % signal retention after four cyclic voltammetry cycles), and long-term stability (89.6 % retention after four weeks of storage). The sensor also demonstrated robust selectivity in artificial sweat, maintaining ~87–90 % of its glucose signal in the presence of common interferents such as ascorbic acid and sodium chloride. Moreover, the platform exhibited reusability, portability, easy scalability, and a high sensitivity of 2007.9 μA·mM −1 ·cm −2 in artificial sweat, highlighting its potential for real-time, non-invasive glucose monitoring. Given its affordability, simplicity, and strong analytical performance, this sensing system represents a promising point-of-care technology, particularly relevant for low- and middle-income countries, where accessible diabetes management tools are urgently needed.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".