Numerical and machine learning based evaluation of ethylene glycol based hybrid nano-structured (TiO

Hijaz Ahmad, Kamel Guedri, Sohail Ahmad, Kashif Ali, Gulnaz Atta, Syed M Hussain, Mustafa Bayram, Assmaa Abd-Elmonem

Abstract read

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.

Machine and deep learning models for predicting high pressure density of heterocyclic thiophenic compounds based on critical properties.Scientific reports · 2025
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

8 authors.

Hijaz AhmadOperational Research Center in Healthcare, Near East University, TRNC Mersin 10, 99138, Nicosia, Turkey.

Kamel GuedriMechanical Engineering Department, College of Engineering and Architecture, Umm Al-Qura University, P. O. Box 5555, 21955, Makkah, Saudi Arabia.

Sohail AhmadDepartment of Basic Sciences and Humanities, Muhammad Nawaz Sharif University of Engineering and Technology, Multan, 60000, Pakistan.

Kashif AliDepartment of Basic Sciences and Humanities, Muhammad Nawaz Sharif University of Engineering and Technology, Multan, 60000, Pakistan.

Gulnaz AttaUniversity of Education Lahore, DG Khan Campus, Dera Ghazi Khan, Pakistan.

Syed M HussainOperational Research Center in Healthcare, Near East University, TRNC Mersin 10, 99138, Nicosia, Turkey.

Mustafa BayramDepartment of Computer Engineering, Biruni University, 34010, Istanbul, Turkey.

Assmaa Abd-ElmonemDepartment of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia. aahaasn@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A better mechanical resistance and improved thermal conductivity, as compared to mono nano-liquids, can be attained by the ethylene glycol-based hybrid nanofluids. These fluids have substantial uses in several engineering systems. The main focus, in the recent work, is to assess the dynamics of the ethylene glycol-based hybrid nano-structured fluid via the computational fluid dynamics (CFD) and machine learning (ML) approaches. The nano-composition of single-walled carbon nanotubes (SWCNTs) and titanium dioxide (TiO

Indexed as

CFDEthylene glycolMachine learningSingle-walled carbon nanotubesTitanium dioxide

Identifiers

PMID39971987

DOI10.1038/s41598-025-88789-5

PMCPMC11840152

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.