Sensitive Electrochemical Detection of Nitric Oxide Release from Cardiac and Cancer Cells via a Hierarchical Nanoporous Gold Microelectrode
Bibliographic record
Abstract
The importance of nitric oxide (NO) in many biological processes has garnered increasing research interest in the design and development of efficient technologies for the sensitive detection of NO. Here we report on a novel gold microelectrode with a unique three-dimensional (3D) hierarchical nanoporous structure for the electrochemical sensing of NO, which was fabricated via a facile electrochemical alloying/dealloying method. Following the treatment, the electrochemically active surface area (ECSA) of the gold microelectrode was significantly increased by 22.9 times. The hierarchical nanoporous gold (HNG) microelectrode exhibited excellent performance for the detection of NO with high stability. On the basis of differential pulse voltammetry (DPV) and amperometric techniques, the obtained sensitivities were 21.8 and 14.4 μA μM –1 cm –2, with detection limits of 18.1 ± 1.22 and 1.38 ± 0.139 nM, respectively. The optimized HNG microelectrode was further utilized to monitor the release of NO from different cells, realizing a significant differential amount of NO generated from the normal and stressed rat cardiac cells as well as from the untreated and treated breast cancer cells. The HNG microelectrode developed in the present study may provide an effective platform in monitoring NO in biological processes and would have a great potential in the medical diagnostics.
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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 | 0.001 | 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".