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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".