Evidence map›Paper›PMID 34770730›Full record

ReviewSensors (Basel, Switzerland)2021

Metal-Organic-Framework- and MXene-Based Taste Sensors and Glucose Detection.

Ha Huu Do, Jin Hyuk Cho, Sang Mok Han, Sang Hyun Ahn, Soo Young Kim

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. 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. Article
  2. Review
  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

5 authors.

Ha Huu DoSchool of Chemical Engineering and Materials Science, Chung-Ang University, Dongjak-gu, Seoul 06974, Korea.
Jin Hyuk ChoDepartment of Materials Science and Engineering, Institute of Green Manufacturing Technology, Korea University, Seongbuk-gu, Seoul 02841, Korea.
Sang Mok HanKorea Institute of Geoscience and Mineral Resources, Yuseong-gu, Pohang 37559, Korea.ORCID 0000-0003-2250-5950
Sang Hyun AhnSchool of Chemical Engineering and Materials Science, Chung-Ang University, Dongjak-gu, Seoul 06974, Korea.ORCID 0000-0001-8906-5908
Soo Young KimDepartment of Materials Science and Engineering, Institute of Green Manufacturing Technology, Korea University, Seongbuk-gu, Seoul 02841, Korea.ORCID 0000-0002-0685-7991

Funding

Korea Agency for Infrastructure Technology Advancement 20IFIP-B133622-04National Research Foundation of Korea 2021R1A4A3027878
6 · The paper itself

Abstract

Taste sensors can identify various tastes, including saltiness, bitterness, sweetness, sourness, and umami, and have been useful in the food and beverage industry. Metal-organic frameworks (MOFs) and MXenes have recently received considerable attention for the fabrication of high-performance biosensors owing to their large surface area, high ion transfer ability, adjustable chemical structure. Notably, MOFs with large surface areas, tunable chemical structures, and high stability have been explored in various applications, whereas MXenes with good conductivity, excellent ion-transport characteristics, and ease of modification have exhibited great potential in biochemical sensing. This review first outlines the importance of taste sensors, their operation mechanism, and measuring methods in sensing utilization. Then, recent studies focusing on MOFs and MXenes for the detection of different tastes are discussed. Finally, future directions for biomimetic tongues based on MOFs and MXenes are discussed.

Indexed as

Biosensing TechniquesMetal-Organic FrameworksBiomimeticsGlucoseTasteGlucoseMetal-Organic Frameworkselectrochemical sensingmetal–organic frameworksMXenestaste sensors

Identifiers

PMID34770730
PMCPMC8587148

What OpenQuestion holds

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Registered trials

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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.