Evidence map›Paper›PMID 41653227›Full record

ArticleDiscover nano2026

Mathematical modeling for nonlinear slippery-hybrid nanofluid flow with radiation impacts and thermophoretic diffusion via a porous medium.

Nawal Odah Al-Atawi

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

1 author.

Nawal Odah Al-AtawiDepartment of Mathematics, Faculty of Science, University of Tabuk, 71491, Tabuk, Saudi Arabia. n.oalatawi@ut.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study addresses the growing need for advanced thermal regulation by investigating the nonlinear flow behavior of a hybrid nanofluid within a porous medium under realistic physical conditions. The primary objective is to evaluate how slip effects, variable viscosity, and thermophoresis influence heat and mass transfer performance when multiple physical mechanisms act simultaneously. A comprehensive mathematical model is developed to describe the momentum, thermal, and concentration transport of a water-based hybrid nanofluid containing two distinct nanoparticle species, subjected to magnetic field effects, thermal radiation, and internal heat generation. The governing partial differential equations are transformed into a system of coupled nonlinear ordinary differential equations using similarity transformations and solved numerically via a shooting technique under appropriate boundary conditions. The novelty of this work lies in the combined consideration of slip velocity, thermophoresis-driven nanoparticle migration, and variable viscosity within a magnetohydrodynamic porous framework. The present study is relevant to practical applications, including microelectronics cooling, advanced heat exchangers, and maritime thermal management. The results indicate that increasing thermophoretic effects and internal heat generation significantly influence transport behavior, as enhanced particle migration reduces concentration gradients while internal heating shifts the heat transfer mechanism toward convection-dominated regimes. Further, the findings reveal that hybrid nanofluids exhibit enhanced thermal control capabilities, with thermophoresis and slip mechanisms playing a key role in improving heat and mass transfer rates, highlighting their potential for high-performance cooling applications.

Indexed as

Buongiorno modelDissipative hybrid nanofluidPorous mediumSlip phenomenonThermophoretic phenomenonVariable viscosity.

Identifiers

PMID41653227
PMCPMC12882931

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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.