ArticleDiscover nano2026
Artificial neural network optimization of gyrotactic microbes in water hybrid nanoliquid with carbon nanotubes using modified Hamilton crosser model.
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.
What it found
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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.
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.
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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 · 2026Article
- Article
- Article
- Thermal Performance of ternary hybrid nanofluid with activation energy and surface tension gradient using HCM and YOM models.Discover nano · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The artificial neural network-optimized model for exploring local thermal non-equilibrium (LTNE) influences on gyrotactic microorganisms in a chemically reactive SWCNTs-MWCNTs/water-based hybrid nanofluid has a wide range of applications in advanced engineering and biotechnology. It has the potential to upsurge heat transfer performance in microfluidic and cooling systems that incorporate hybrid nanofluid and microbial activity. In biotechnology, the model aids in the optimization of microorganism-driven mixing, sensing, and biodegradation processes in complicated heat and chemical environments. It can also help with the design of more efficient reactors and bio-suspension systems, as gyrotactic microorganisms improve fluid stability and energy transmission. Additionally, the application of neural networks enables real-time optimization and predictive control, which is advantageous for smart bio-nanofluid systems, environmental remediation, and industrial thermal management. The goal of this investigation is to use artificial neural networks to evaluate the outcome of local thermal non-equilibrium on microbes in hybrid nanofluid flow over a sheet with porous media. The liquid phase thermal profile increases as the values of the interphase heat transfer parameter grow, whereas the solid phase thermal profile decreases.
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Registered trials
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.