Evidence map›Paper›PMID 36354436›Full record

ReviewBiosensors2022

An Overview of the Design of Metal-Organic Frameworks-Based Fluorescent Chemosensors and Biosensors.

Ning Xia, Yong Chang, Qian Zhou, Shoujie Ding, Fengli Gao

Abstract readReview
In one paragraph

Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Microcrystalline Luminescent (EuMolecules (Basel, Switzerland) · 2024
    Article
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.

Ning XiaCollege of Chemistry and Chemical Engineering, Anyang Normal University, Anyang 455000, China.ORCID 0000-0003-0366-7524
Yong ChangCollege of Chemistry and Chemical Engineering, Anyang Normal University, Anyang 455000, China.ORCID 0000-0001-7807-9730
Qian ZhouCollege of Chemistry and Chemical Engineering, Anyang Normal University, Anyang 455000, China.
Shoujie DingCollege of Chemistry and Chemical Engineering, Anyang Normal University, Anyang 455000, China.
Fengli GaoCollege of Chemistry and Chemical Engineering, Anyang Normal University, Anyang 455000, China.

Funding

Program for Innovative Research Team of Science and Technology in the University of Henan Province 21IRTSTHN005
6 · The paper itself

Abstract

Taking advantage of high porosity, large surface area, tunable nanostructures and ease of functionalization, metal-organic frameworks (MOFs) have been popularly applied in different fields, including adsorption and separation, heterogeneous catalysis, drug delivery, light harvesting, and chemical/biological sensing. The abundant active sites for specific recognition and adjustable optical and electrical characteristics allow for the design of various sensing platforms with MOFs as promising candidates. In this review, we systematically introduce the recent advancements of MOFs-based fluorescent chemosensors and biosensors, mainly focusing on the sensing mechanisms and analytes, including inorganic ions, small organic molecules and biomarkers (e.g., small biomolecules, nucleic acids, proteins, enzymes, and tumor cells). This review may provide valuable references for the development of novel MOFs-based sensing platforms to meet the requirements of environment monitoring and clinical diagnosis.

Indexed as

Biosensing TechniquesMetal-Organic FrameworksNucleic AcidsAdsorptionCatalysisMetal-Organic FrameworksNucleic Acidsbiosensorschemosensorsfluorescencemetal-organic frameworks

Identifiers

PMID36354436
PMCPMC9688172

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.