Platinum-based electrochemical sensors for glucose detection: a mini-review
Bibliographic record
Abstract
This mini-review provides a comprehensive overview of platinum-based electrochemical sensors for glucose detection, focusing on recent advancements in material design, fabrication techniques, and the application of single-atom catalysts. Platinum's exceptional electrocatalytic properties and inherent stability have made it a cornerstone material for developing sensitive, selective, and stable glucose sensors. Performance evaluations from the literature reveal sensors with sensitivities exceeding 850 μA/mM cm² and detection limits as low as 3.6 μM. This review examines various approaches to enhancing sensor performance, including the use of different platinum nanostructures (e.g., nanoparticles, nanowires), the incorporation of conductive polymers or metal oxides, and the application of various electrochemical techniques (e.g., amperometry, cyclic voltammetry). Despite these advancements, challenges remain in achieving improved selectivity, stability, and cost-effectiveness. Future research directions include exploring novel platinum-based materials, developing advanced fabrication techniques such as 3D printing, integrating microfluidic platforms, and leveraging single-atom catalysis to enhance sensor performance further. Developing reliable and efficient platinum-based electrochemical glucose sensors is crucial for advancing diabetes management, biomedical research, and point-of-care diagnostics. This review aims to inspire continued research and innovation in this promising field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".