Evidence map›Paper›PMID 36831949›Full record

ReviewBiosensors2023

Carbon Nanotube-Based Biosensors Using Fusion Technologies with Biologicals & Chemicals for Food Assessment.

Jinyoung Lee

Abstract readReview
In one paragraph

Review in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

1 author.

Jinyoung LeeDepartment of Green Chemical Engineering, Sangmyung University, Cheonan 31066, Republic of Korea.ORCID 0000-0002-2908-8240

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-sensitivity sensors applied in various diagnostic systems are considered to be a promising technology in the era of the fourth industrial revolution. Biosensors that can quickly detect the presence and concentration of specific biomaterials are receiving research attention owing to the breakthroughs in detection technology. In particular, the latest technologies involving the miniaturization of biosensors using nanomaterials, such as nanowires, carbon nanotubes, and nanometals, have been widely studied. Nano-sized biosensors applied in food assessment and in in vivo measurements have the advantages of rapid diagnosis, high sensitivity and selectivity. Nanomaterial-based biosensors are inexpensive and can be applied to various fields. In the present society, where people are paying attention to health and wellness, high-technology food assessment is becoming essential as the consumer demand for healthy food increases. Thus, biosensor technology is required in the food and medical fields. Carbon nanotubes (CNTs) are widely studied for use in electrochemical biosensors. The sensitive electrical characteristics of CNTs allow them to act as electron transfer mediators in electrochemical biosensors. CNT-based biosensors require novel technologies for immobilizing CNTs on electrodes, such as silicon wafers, to use as biosensor templates. CNT-based electrochemical biosensors that serve as field-effect transistors (FET) increase sensitivity. In this review, we critically discuss the recent advances in CNT-based electrochemical biosensors applied with various receptors (antibodies, DNA fragments, and other nanomaterials) for food evaluation, including pathogens, food allergens, and other food-based substances.

Indexed as

Biological ProductsBiosensing TechniquesNanotubes, CarbonElectron TransportHumansTechnologyBiological ProductsNanotubes, Carbonantibodyaptamercarbon nanotubeelectrochemistryfood pathogenmicroorganism

Identifiers

PMID36831949
PMCPMC9953396

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.