Evidence map›Paper›PMID 42867576›Full record

ArticleDiscover nano2026

Stability and Thermophysical Properties of Silicon Carbide and Carbon Nanofibers Thermal Oil Hybrid Nanofluids.

Suhaib Umer Ilyas, Lim Li Shan, Nawal Noshad, Haris Naseer, Mustafa Alsaady, Aymn Abdulrahman, Anas Ahmed, Rajashekhar Pendyala

Abstract read
In one paragraph

Article in Discover nano, 2026. 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

8 authors.

Suhaib Umer IlyasDepartment of Chemical Engineering, College of Engineering, University of Jeddah, Jeddah, 23890, Kingdom of Saudi Arabia. suilyas@uj.edu.sa.ORCID http://orcid.org/0000-0002-7690-0668
Lim Li ShanChemical Engineering Department, Universiti Teknologi PETRONAS, Perak Darul Ridzuan, 32610, Seri Iskandar, Malaysia.
Nawal NoshadSchool of Chemical and Materials Engineering, National University of Sciences and Technology, 44000, Islamabad, Pakistan.
Haris NaseerMechanical Engineering Department, Universiti Teknologi PETRONAS, Perak Darul Ridzuan, 32610, Seri Iskandar, Malaysia.
Mustafa AlsaadyDepartment of Chemical Engineering, College of Engineering, University of Jeddah, Jeddah, 23890, Kingdom of Saudi Arabia.
Aymn AbdulrahmanDepartment of Chemical Engineering, College of Engineering, University of Jeddah, Jeddah, 23890, Kingdom of Saudi Arabia.
Anas AhmedDepartment of Industrial and Systems Engineering, College of Engineering, University of Jeddah, Jeddah, 23890, Kingdom of Saudi Arabia.
Rajashekhar PendyalaCompound Research LLC, 1170 Windbrooke Dr Apt 202, Buffalo Grove, IL, 60089, USA.

Funding

University of Jeddah UJ-24-SUTU-20832
6 · The paper itself

Abstract

Hybrid Nanofluids (HNFs) have been widely investigated to improve the thermal efficiency of energy systems, thereby reducing overall energy consumption. Silicon Carbide (SiC) nanoparticles offer excellent thermal stability, and Carbon Nanofibers (CNF) provide superior phonon-mediated thermal conduction. However, their synergistic combination as HNFs remains largely unexplored. In this study, SiC and CNF are dispersed in thermal oil (THO) at concentrations of 0.1%, 0.3%, 0.5%, 1.0%, and 2.0% by mass, using ultrasonication and the surfactant Span 85. The batch sedimentation stability analysis is performed on four stages to achieve the long-term stability of SiC-CNF HNFs. The thermophysical properties of the SiC-CNF HNFs are investigated over the temperature range 25 -75 °C. SiC-CNF HNFs exhibit a maximum thermal conductivity enhancement of 32.3% at 0.5 wt% compared to THO at 55 °C. A maximum viscosity and density enhancement of 18.7% and 1.55%, respectively, is calculated. SiC-CNF HNFs followed both the power law and the Herschel-Bulkley model and exhibited non-Newtonian behavior. Artificial neural network (ANN) models are developed to predict the thermophysical properties of SiC-CNF HNFs effectively. The optimized models achieved coefficients of determination (R²) values of 0.7606 for thermal conductivity, 0.9844 for viscosity, and 0.9999 for density, respectively. These experimental and modeling results indicate the significance of SiC-CNF-based HNFs for effective thermal management systems integration.

Indexed as

artificial neural networkscarbon nanofibershybrid nanofluidssilicon carbidethermophysical properties

Identifiers

PMID42867576
PMCPMC13661872

What OpenQuestion holds

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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.