Evidence map›Paper›PMID 35498093›Full record

ArticleRSC advances2021

Non-enzymatic electrochemical determination of salivary cortisol using ZnO-graphene nanocomposites.

Sherin Rison, Rijo Rajeev, Vinay S Bhat, Agnus T Mathews, Anitha Varghese, Gurumurthy Hegde

Abstract read
In one paragraph

Article in RSC advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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. Article
  4. Nanomaterials for Cortisol Sensing.Nanomaterials (Basel, Switzerland) · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sherin RisonChrist Academy Institute For Advanced Studies Christ Nagar Bangalore 560083 India.
Rijo RajeevDepartment of Chemistry, CHRIST (Deemed to be University) Bangalore 560029 India anitha.varghese@christuniversity.in murthyhegde@gmail.com.
Vinay S BhatCentre for Nano-materials and Displays, B.M.S College of Engineering Bull Temple Road Bangalore 560019 India.ORCID https://orcid.org/0000-0003-0428-0321
Agnus T MathewsDepartment of Chemistry, CHRIST (Deemed to be University) Bangalore 560029 India anitha.varghese@christuniversity.in murthyhegde@gmail.com.
Anitha VargheseDepartment of Chemistry, CHRIST (Deemed to be University) Bangalore 560029 India anitha.varghese@christuniversity.in murthyhegde@gmail.com.ORCID https://orcid.org/0000-0001-7188-8420
Gurumurthy HegdeDepartment of Chemistry, CHRIST (Deemed to be University) Bangalore 560029 India anitha.varghese@christuniversity.in murthyhegde@gmail.com.ORCID https://orcid.org/0000-0002-1200-5664

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electrochemically deposited ZnO nanoparticles on a pencil graphite electrode (PGE) coated with graphene generate a noteworthy conductive and selective electrochemical sensing electrode for the estimation of cortisol. Electrochemical techniques such as cyclic voltammetry (CV) analysis and electrochemical impedance spectroscopic (EIS) tests were adopted to analyze and understand the nature of the modified sensor. Surface morphological analysis was done using various spectroscopic and microscopic techniques like X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). Structural characterization was conducted by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The effect of scan rate, concentration, and cycle numbers was optimized and reported. Differential pulse voltammetric (DPV) analysis reveals that the linear range for the detection of cortisol is 5 × 10

Identifiers

PMID35498093
PMCPMC9043917

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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.