(Invited) Design of Nanomaterial-Based Electrochemical Sensor for Sensitive Detection of Nitric Oxide
Bibliographic record
Abstract
Nitric oxide (NO) plays important roles in myriad biological processes and is considered as a biomarker of cardiovascular disease, hypertension, symptoms of vaginitis, and cancer. Sensitive detection of NO molecules is vital toward the understanding of cell functionality and pathology as well as to the diagnosis of disease, drug discovery, and biological research [1-4]. Here, we report on the development of high-performance NO electrochemical sensors based on the nanocomposite of reduced graphene oxide (rGO) and AuPt bimetallic nanoparticles (rGO-AuPt) and the nanoporous Au microelectrode. The rGO-AuPt nanocomposites were formed a glassy carbon electrode (GCE) using an electrochemical method. The prepared nanocomposites were tested for the electrochemical detection 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 rGO-AuPt 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 r-GO-AuPt 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 nanoporous gold microelectrode was fabricated via an electrochemical alloying/dealloying method. It exhibited a high electrochemically active surface area and excellent performance for the detection of NO with high stability. Based on DPV and amperometric techniques, extremely high sensitivities (21.9 μA μM-1 cm-2 and 14.3 μA μM-1 cm-2) with very low detection limits of 17.0 nM and 1.43 nM, respectively, have been achieved. Moreover, the developed nanoporous Au microelectrode provides a new approach to monitor NO release from different cells, revealing that a significant differential amount of NO can be generated from the normal and stressed rat cardiac cells as well as from the untreated and treated breast cancer cells, promising for the elucidation of cellular stress responses and medical diagnostics. References [1] Z.G. Liu, H. Forsyth, N.Khaper, A. Chen. Analyst 141 (2016) 4074-4083. [2] M. Govindhan, A. Chen. Microchim. Acta 183 (2016) 2879-2887 [3] M. Govindhan, Z. Liu, A. Chen. Nanomater. 6 (2016) 211. [4] Z Liu, A Nemec-Bakk, N Khaper, A Chen. Anal. Chem. 89 (2017) 8036-8043
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".