Evidence map›Paper›PMID 40804440›Full record

ArticleScientific reports2025

Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics.

Heyder Mhohamdi, Usama S Altimari, Krunal Vaghela, V Vivek, Sarbeswara Hota, Devendra Singh, Mahesh Manchanda, Shirin Shomurotova, Prakhar Tomar, Mohammad Mahtab Alam

Abstract read
PubMed Full text at PMC Publisher
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Heyder MhohamdiDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq. haideralabdeli@iunajaf.edu.iq.
Usama S AltimariDepartment of Medical Laboratories Technology, AL-Nisour University College, Baghdad, Iraq.
Krunal VaghelaDepartment of Computer Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi University, Rajkot, Gujarat, India.
V VivekDepartment of Computer Science and Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India.
Sarbeswara HotaDepartment of Computer Application, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, Odisha, India.
Devendra SinghDepartment of Computer Science & Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, Uttarakhand, India.
Mahesh ManchandaComputer Science and Engineering, Graphic Era Hill University, Dehradun, India.
Shirin ShomurotovaDepartment of Chemistry Teaching Methods, Tashkent State Pedagogical University named after Nizami, Bunyodkor street 27, Tashkent, Uzbekistan.
Prakhar TomarCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
Mohammad Mahtab AlamDepartment of Basic Medical Sciences, College of Applied Medical Science, King Khalid University, Abha, 61421, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A combination of artificial intelligence (AI) and computational fluid dynamics was carried out to advance the modeling of adsorption separation processes. A comparative examination of three AI-based regression models including Gaussian Process Regression (GPR), Multi-layer Perceptron (MLP), and Polynomial Regression (PR) was carried out to predict chemical concentrations of solute in a dataset with two input variables (x and y) and one output feature (C in mol/m

Indexed as

AdsorptionMachine learningMLP modelPorous materialsSeparation

Identifiers

PMID40804440
PMCPMC12350729

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.