Design and Electrochemical Study of Platinum-Based Nanomaterials for Sensitive Detection of Nitric Oxide in Biomedical Applications
Bibliographic record
Abstract
The extensive physiological and regulatory roles of nitric oxide (NO) have spurred the development of NO sensors, which are of critical importance in neuroscience and various medical applications. The development of electrochemical NO sensors is of significant importance, and has garnered a tremendous amount of attention due to their high sensitivity and selectivity, rapid response, low cost, miniaturization, and the possibility of real-time monitoring. Nanostructured platinum (Pt)-based materials have attracted considerable interest regarding their use in the design of electrochemical sensors for the detection of NO, due to their unique properties and the potential for new and innovative applications. This review focuses primarily on advances and insights into the utilization of nanostructured Pt-based electrode materials, such as nanoporous Pt, Pt and PtAu nanoparticles, PtAu nanoparticle/reduced graphene oxide (rGO), and PtW nanoparticle/rGO-ionic liquid (IL) nanocomposites, for the detection of NO. The design, fabrication, characterization, and integration of electrochemical NO sensing performance, selectivity, and durability are addressed. The attractive electrochemical properties of Pt-based nanomaterials have great potential for increasing the competitiveness of these new sensors and open up new opportunities in the creation of novel NO-sensing technologies for biological and medical applications.
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How this classification was reachedexpand
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) | 0.002 | 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 | 0.001 | 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 teacher head, 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".