Sensitive electrochemical detection of nitric oxide based on AuPt and reduced graphene oxide nanocomposites
Bibliographic record
Abstract
Since nitric oxide (NO) plays a critical role in many biological processes, its precise detection is essential toward an understanding of its specific functions. Here we report on a facile and environmentally compatible strategy for the construction of an electrochemical sensor based on reduced graphene oxide (rGO) and AuPt bimetallic nanoparticles. The prepared nanocomposites were further employed for the electroanalysis of NO using differential pulse voltammetry (DPV) and amperometric methods. The dependence of AuPt molar ratios on the electrochemical performance was investigated. Through the combination of the advantages of the high conductivity from rGO and highly electrocatalytic activity from AuPt bimetallic nanoparticles, the AuPt-rGO based NO sensor exhibited a high sensitivity of 7.35 μA μM(-1) and a low detection limit of 2.88 nM. Additionally, negligible interference from common ions or organic molecules was observed, and the AuPt-rGO modified electrode demonstrated excellent stability. Moreover, this optimized electrochemical sensor was practicable for efficiently monitoring the NO released from rat cardiac cells, which were stimulated by l-arginine (l-arg), showing that stressed cells generated over 10 times more NO than normal cells. The novel sensor developed in this study may have significant medical diagnostic applications for the prevention and monitoring of disease.
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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 | 0.001 |
| 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 | 0.001 | 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".