Evidence map›Paper›PMID 37170993›Full record

ReviewCurrent medicinal chemistry2024

Carbon Nanocomposites-based Electrochemical Sensors and Biosensors for Biomedical Diagnostics.

Palanisamy Kannan, Govindhan Maduraiveeran

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current medicinal chemistry, 2024. 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. Article
  3. Review
  4. 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

2 authors.

Palanisamy KannanCollege of Biological, Chemical Sciences and Engineering, Jiaxing University, Jiaxing, 314001, China.
Govindhan MaduraiveeranMaterials Electrochemistry Laboratory, Department of Chemistry, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603 203, India.ORCID 0000-0003-1190-7022

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detection of emergent biomolecules or biomarkers remains crucial for early diagnosis in advancing healthcare monitoring and biomedicine. The possibility for rapid detection, real-time monitoring, high sensitivity, low detection limit, good selectivity, and low cost is central, among other significant issues for advancing point-of-care diagnosis. Carbon-based nanocomposites have been employed as sensing materials for various biomarkers due to their high surface-to-volume ratio, high electrical conductivity, chemical stability, and biocompatibility. The carbon nanomaterials, such as carbon nanotubes (CNTs), graphene (GR), carbon quantum dots (CQDs), carbon fibres (CFs), and their nanocomposites have broadly integrated with numerous sensing electrode materials for the detection of biomarkers under various experimental settings. The present review includes the recent advances in the development of carbon nanomaterials-based electrochemical sensors and biosensors for biomedical applications. The preparation, electrode preparation, effective utilization of carbon-derived nanomaterials, and their sensing performances towards numerous biomarkers have been highlighted. The state-of-the-merit, challenges, and prospects for designing carbon nanocomposites-based electrochemical sensor/biosensor platforms for biomedical diagnostics have also been described.

Indexed as

Biosensing TechniquesElectrochemical TechniquesNanocompositesBiomarkersCarbonGraphiteHumansNanotubes, CarbonQuantum DotsBiomarkersCarbonGraphiteNanotubes, Carbonbiomarkers detectionbiomedical diagnosticsCarbon nanocompositesCQDs.electroanalytical methodelectrochemical sensors

Identifiers

What OpenQuestion holds

Textmetadata
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.