Evidence map›Paper›PMID 33804997›Full record

ReviewInternational journal of molecular sciences2021

Graphene-Based Sensors for the Detection of Bioactive Compounds: A Review.

Carlos Sainz-Urruela, Soledad Vera-López, María Paz San Andrés, Ana M Díez-Pascual

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Review
  19. Review
  20. 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

4 authors.

Carlos Sainz-UrruelaUniversidad de Alcalá, Facultad de Ciencias, Departamento de Química Analítica, Química Física e Ingeniería Química, Ctra. Madrid-Barcelona Km. 33.6, 28805 Alcalá de Henares, Madrid, España (Spain).ORCID 0000-0003-3228-2324
Soledad Vera-LópezUniversidad de Alcalá, Facultad de Ciencias, Departamento de Química Analítica, Química Física e Ingeniería Química, Ctra. Madrid-Barcelona Km. 33.6, 28805 Alcalá de Henares, Madrid, España (Spain).
María Paz San AndrésUniversidad de Alcalá, Facultad de Ciencias, Departamento de Química Analítica, Química Física e Ingeniería Química, Ctra. Madrid-Barcelona Km. 33.6, 28805 Alcalá de Henares, Madrid, España (Spain).ORCID 0000-0002-1500-4528
Ana M Díez-PascualUniversidad de Alcalá, Facultad de Ciencias, Departamento de Química Analítica, Química Física e Ingeniería Química, Ctra. Madrid-Barcelona Km. 33.6, 28805 Alcalá de Henares, Madrid, España (Spain).ORCID 0000-0001-7405-2354

Funding

Ministerio de Ciencia, Innovación y Universidades PGC2018-093375-B-I00
6 · The paper itself

Abstract

Over the last years, different nanomaterials have been investigated to design highly selective and sensitive sensors, reaching nano/picomolar concentrations of biomolecules, which is crucial for medical sciences and the healthcare industry in order to assess physiological and metabolic parameters. The discovery of graphene (G) has unexpectedly impulsed research on developing cost-effective electrode materials owed to its unique physical and chemical properties, including high specific surface area, elevated carrier mobility, exceptional electrical and thermal conductivity, strong stiffness and strength combined with flexibility and optical transparency. G and its derivatives, including graphene oxide (GO) and reduced graphene oxide (rGO), are becoming an important class of nanomaterials in the area of optical and electrochemical sensors. The presence of oxygenated functional groups makes GO nanosheets amphiphilic, facilitating chemical functionalization. G-based nanomaterials can be easily combined with different types of inorganic nanoparticles, including metals and metal oxides, quantum dots, organic polymers, and biomolecules, to yield a wide range of nanocomposites with enhanced sensitivity for sensor applications. This review provides an overview of recent research on G-based nanocomposites for the detection of bioactive compounds, providing insights on the unique advantages offered by G and its derivatives. Their synthesis process, functionalization routes, and main properties are summarized, and the main challenges are also discussed. The antioxidants selected for this review are melatonin, gallic acid, tannic acid, resveratrol, oleuropein, hydroxytyrosol, tocopherol, ascorbic acid, and curcumin. They were chosen owed to their beneficial properties for human health, including antibiotic, antiviral, cardiovascular protector, anticancer, anti-inflammatory, cytoprotective, neuroprotective, antiageing, antidegenerative, and antiallergic capacity. The sensitivity and selectivity of G-based electrochemical and fluorescent sensors are also examined. Finally, the future outlook for the development of G-based sensors for this type of biocompounds is outlined.

Indexed as

Biosensing TechniquesElectric ConductivityAntioxidantsAscorbic AcidCurcuminElectrodesFree RadicalsGallic AcidGraphiteHumansIridoid GlucosidesMelatoninMetalsNanocompositesNanostructuresOxides3,4-dihydroxyphenylethanolAntioxidantsAscorbic AcidCurcuminFree RadicalsGallic Acidgraphene oxideGraphiteIridoid GlucosidesMelatoninMetalsoleuropeinOxidesPhenylethyl AlcoholResveratrolTanninsTocopherolsascorbic acidbioactive compoundcurcumingallic acidgraphenegraphene oxidehydroxytyrosolmelatoninoleuropeinresveratroltannic acidtocopherol

Identifiers

PMID33804997
PMCPMC8037795

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.