Evidence map›Paper›PMID 38138637›Full record

ArticleMolecules (Basel, Switzerland)2023

Graphitic Carbon Nitride/MOFs Hybrid Composite as Highly Selective and Sensitive Electrodes for Calcium Ion Detection.

Ahmed S Abou-Elyazed, Shilin Li, Gehad G Mohamed, Xiaolin Li, Jing Meng, Safa S El-Sanafery

Open access · goldAbstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2023. 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
2.4field-weighted citation impact, top 9% of its field
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, 14 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. 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

6 authors at 5 institutions in 2 countries.

Ahmed S Abou-ElyazedInstitute of Intelligent Manufacturing Technology, Shenzhen Polytechnic University, Shenzhen 518055, China.ORCID 0000-0003-1607-4496
Shilin LiInstitute of Intelligent Manufacturing Technology, Shenzhen Polytechnic University, Shenzhen 518055, China.
Gehad G MohamedChemistry Department, Faculty of Science, Cairo University, Giza 12613, Egypt.
Xiaolin LiInstitute of Intelligent Manufacturing Technology, Shenzhen Polytechnic University, Shenzhen 518055, China.
Jing MengSchool of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
Safa S El-SanaferyChemistry Department, Faculty of Science, Menoufia University, Shebin El-Kom 32512, Egypt.ORCID 0000-0002-4222-0373
Shenzhen Polytechnic University · CNCairo University · EGHarbin Institute of Technology · CNHarbin University of Science and Technology · CNMenoufia University · EG

Funding

Guangdong Basic and Applied Basic Research Foundation 2021A1515011808Post-doctoral Later-stage Foundation Project of Shenzhen Polytechnic University 4103-6021271018K1Scientific Research Start-up Project of Shenzhen Polytechnic 4103-6023330010KShenzhen Science and Technology Program RCBS20210609104444087the financial support from the National Natural Science Foundation of China 52105307
6 · The paper itself

Abstract

The metal-organic framework (MOF) is a class of materials that exhibits a notable capacity for electron transfer. This unique framework design offers potential applications in various fields, including catalysis, gas storage, and sensing. Herein, we focused on a specific type of MOF called Ti-MOF. To enhance its properties and functionality, the composite material was prepared by incorporating graphitic carbon nitride (g-C

Indexed as

carbon-paste electrodesgraphitic carbon nitrideHPLCmetal-organic frameworkMPMSSM

Identifiers

PMID38138637
PMCPMC10746060
OpenAlexW4389890314

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.