Artificial neural network analysis for oxytactic microbes in hybrid nanofluid with chemical reaction and thermal radiation.

Munawar Abbas, Md Mahbub Alam, Mouloud Aoudia, Faiza Benabdallah, Ali Akgül, Murad Khan Hassani

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. Cited by 4 papers.

0numbers the graph read from it

0cells of the map it votes in

4citing 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

4 citing papers in PubMed.

Surface tension gradient effects on hybrid nanofluid with activation energy using machine learning technique: Stefan blowing phenomena.MethodsX · 2026
Article
Design optimization and thermal analysis of C₂H₆O₂ hybrid nanofluid flow over solar plate under solar radiation using artificial neural network.MethodsX · 2026
Article
Bayesian regularization technique for chemical reactive flow dynamics of hybrid nanomaterial using Xue model: Chemical reactor optimization.MethodsX · 2026
Article
Thermal Performance of ternary hybrid nanofluid with activation energy and surface tension gradient using HCM and YOM models.Discover nano · 2026
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

6 authors.

Munawar AbbasSchool of Robotics and Advanced Manufacturing, Harbin Institute of Technology (Shenzhen), Harbin, 518055, Guangdong, China.

Md Mahbub AlamSchool of Robotics and Advanced Manufacturing, Harbin Institute of Technology (Shenzhen), Harbin, 518055, Guangdong, China.

Mouloud AoudiaDepartment of Industrial Engineering, College of Engineering, Northern Border University, P.O. Box 1321, 91431, Arar, Saudi Arabia.

Faiza BenabdallahDepartment of Industrial and Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Ali AkgülDepartment of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamilnadu, India.

Murad Khan HassaniDepartment of Mathematics, Ghazni University, Ghazni, Afghanistan. mhassani@gu.edu.af.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this investigation is to assess the outcome of Oxytactic microorganism in chemical reactive flow of TiO

Indexed as

Artificial intelligenceChemical reactionOxytactic microorganismThermal radiationTiO2 + GO/water based hybrid nanofluid

Identifiers

PMID42113444

DOI10.1186/s11671-026-04519-4

PMCPMC13161392

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LicenceCC BY-NC-ND

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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.