Evidence map›Paper›PMID 33817466›Full record

ArticleACS omega2021

Facile Green Approach for Developing Electrochemically Reduced Graphene Oxide-Embedded Platinum Nanoparticles for Ultrasensitive Detection of Nitric Oxide.

Georgeena Mathew, Naresh Narayanan, Daniel Arulraj Abraham, Mrinmoy De, Bernaurdshaw Neppolian

Erratum issuedAbstract read
In one paragraph

Article in ACS omega, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Georgeena MathewSRM Research Institute, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu 603203, India.
Naresh NarayananSRM Research Institute, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu 603203, India.
Daniel Arulraj AbrahamNational Laboratory of Solid State Microstructures and Department of Materials Science and Engineering, College of Engineering and Applied Sciences, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, China.
Mrinmoy DeDepartment of Organic Chemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India.
Bernaurdshaw NeppolianSRM Research Institute, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu 603203, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nitric oxide (NO) plays a crucial and important role in cellular physiology and also acts as a signaling molecule for cancer in humans. However, conventional detection methods have their own limitations in the detection of NO at low concentrations because of its high reactivity and low lifetime. Herein, we report a strategy to fabricate Pt nanoparticle-decorated electrochemically reduced graphene oxide (erGO)-modified glassy carbon electrode (GCE) with efficiency to detect NO at a low concentration. For this study, Pt@erGO/GCE was fabricated by employing two different sequential methods [first GO reduction followed by Pt electrodeposition (SQ-I) and Pt electrodeposition followed by GO reduction (SQ-II)]. It was interesting to note that the electrocatalytic current response for SQ-I (184 μA) was ∼15 and ∼3 folds higher than those of the bare GCE (11.7 μA) and SQ-II (61.5 μA). The higher current response was mainly attributed to a higher diffusion coefficient and electrochemically active surface area. The proposed SQ-I electrode exhibited a considerably low LOD of 52 nM (S/N = 3) in a linear range of 0.25-40 μM with a short response time (0.7 s). In addition, the practical analytical applicability of the proposed sensor was also verified.

Identifiers

PMID33817466
PMCPMC8014916

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.