Evidence map›Paper›PMID 40783582›Full record

ArticleScientific reports2025

Mathematical modeling of blood flow with copper and graphene nanoparticles in inclined stenotic arteries.

Pooriya Majidi Zar, Zahra Poolaei Moziraji, Ali Ahmadi Azar

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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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

3 authors.

Pooriya Majidi ZarDepartment of Mechanical Engineering, NT.C., Islamic Azad University, Tehran, Iran.
Zahra Poolaei MozirajiDepartment of Mechanical Engineering, NT.C., Islamic Azad University, Tehran, Iran. z.poolaei@iau-tnb.ac.ir.
Ali Ahmadi AzarDepartment of Mechanical Engineering, NT.C., Islamic Azad University, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research investigates hemodynamic behavior in stenosed arteries using a rheological model that integrates hybrid nanoparticles (copper and graphene) suspended in blood. A mathematical framework is developed to analyze flow dynamics in an inclined artery with mild stenosis, incorporating electromagnetic fields, Hall currents, heat generation, and porous media effects governed by Darcy's law. Simplifications under mild stenosis and low Reynolds number conditions enable analytical solutions via the homotopy perturbation method (HPM) and Akbari Ganji Method (AGM). The minimal error observed for axial velocity is [Formula: see text], while that for temperature is [Formula: see text]. Key findings reveal that hybrid nanoparticle enrichment reduces blood flow resistance, and elevated Hall parameters significantly decrease wall shear stress at the arterial boundary. Additionally, an increase in the Darcy number leads to higher axial velocity in all cases. Streamline visualizations demonstrate altered flow patterns in stenosed regions under varying nanoparticle volumes and electromagnetic inputs. Notably, Hall currents exert a pronounced influence on nanoparticle-enhanced flow behavior, underscoring their relevance in biomedical contexts. The efficacy of HPM and AGM in resolving nonlinear momentum equations is validated, supporting their utility in modeling complex bio-nanofluid systems. These insights advance applications in targeted drug delivery, bio-nanofluid mechanics, and therapeutic device design. They offer pathways for optimizing nanoparticle-mediated treatments in cardiovascular diseases and oncology.

Indexed as

ArteriesCopperGraphiteModels, CardiovascularNanoparticlesBlood Flow VelocityConstriction, PathologicHemodynamicsHumansModels, TheoreticalCopperGraphiteElectro–magneto–hydrodynamics (EMHD)GrapheneHall currentNano–pharmacodynamicsWall Shear Stress

Identifiers

PMID40783582
PMCPMC12335543

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LicenceCC BY-NC-ND
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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.