Evidence map›Paper›PMID 40813432›Full record

ArticleScientific reports2025

Advanced intelligent techniques for modeling oxygen storage in zeolite-based porous materials.

Arefeh Naghizadeh, Ahmadreza Jafari-Sirizi, Behnam Amiri-Ramsheh, Saeid Atashrouz, Dragutin Nedeljkovic, Ahmad Mohaddespour, Abdolhossein Hemmati-Sarapardeh

Abstract read
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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Arefeh NaghizadehDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Ahmadreza Jafari-SiriziDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Behnam Amiri-RamshehDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Saeid AtashrouzDepartment of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. s.atashrouz@gmail.com.
Dragutin NedeljkovicCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait.
Ahmad MohaddespourDepartment of Chemical Engineering, McGill University, Montreal, QC, H3A 0C5, Canada. ahmad.mohaddespour@mail.mcgill.ca.
Abdolhossein Hemmati-SarapardehDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. hemmati@uk.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oxygen and nitrogen are essential gases with extensive applications in industrial and medical fields, making their separation from air a critical yet complex challenge. Zeolites, renowned for their highly porous structures, have garnered significant attention as promising materials for enhancing gas storage and separation processes. In this paper, advanced machine learning (ML) methods, including Generalized Regression Neural Network (GRNN), Cascade Forward Neural Network, and Multilayer Perceptron were utilized to forecast the O

Indexed as

Air separationArtificial intelligence techniquesGRNNMachine learningO2/N2 separationO2 storageZeolite

Identifiers

PMID40813432
PMCPMC12354734

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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.