Evidence map›Paper›PMID 36697945›Full record

ReviewCommunications chemistry2022

Materials discovery of ion-selective membranes using artificial intelligence.

Reza Maleki, Seyed Mohammadreza Shams, Yasin Mehdizadeh Chellehbari, Sima Rezvantalab, Ahmad Miri Jahromi, Mohsen Asadnia, Rouzbeh Abbassi, Tejraj Aminabhavi, Amir Razmjou

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Communications chemistry, 2022. 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
2.0field-weighted citation impact, top 14% 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, 27 citations in OpenAlex.

  1. Article
  2. Review
  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

9 authors at 7 institutions in 3 countries.

Reza Maleki *Department of Chemical Engineering, Shiraz University, Shiraz, Iran.
Seyed Mohammadreza Shams *Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Yasin Mehdizadeh ChellehbariDepartment of Chemical Engineering, Sharif University of Technology, Tehran, Iran.
Sima RezvantalabRenewable Energies Department, Faculty of Chemical Engineering, Urmia University of Technology, 57166-419, Urmia, Iran.
Ahmad Miri JahromiDepartment of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Mohsen AsadniaSchool of Engineering, Macquarie University, Sydney, NSW, 2109, Australia.
Rouzbeh AbbassiSchool of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia.
Tejraj AminabhaviSchool of Engineering, Macquarie University, Sydney, NSW, 2109, Australia.ORCID http://orcid.org/0000-0002-5613-3916
Amir RazmjouMineral Recovery Research Center (MRRC), School of Engineering, Edith Cowan University, Joondalup, Perth, WA, 6027, Australia. amirr@unsw.edu.au.ORCID http://orcid.org/0000-0002-3554-5129
Amirkabir University of Technology · IRMacquarie University · AUEdith Cowan University · AUKLE Technological University · INSharif University of Technology · IRShiraz University · IRUrmia University of Technology · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.

Identifiers

PMID36697945
PMCPMC9814132
OpenAlexW4306871176

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.