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

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Abbas M., Alam M. M., Aoudia M., Benabdallah F., Akgül A., Hassani M. K.

Discover Nano, vol.21, no.1, 2026 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 21 Issue: 1
  • Publication Date: 2026
  • Doi Number: 10.1186/s11671-026-04519-4
  • Journal Name: Discover Nano
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Artificial intelligence, Chemical reaction, Oxytactic microorganism, Thermal radiation, TiO2 + GO/water based hybrid nanofluid
  • Open Archive Collection: AVESIS Open Access Collection
  • Karadeniz Technical University Affiliated: Yes

Abstract

The purpose of this investigation is to assess the outcome of Oxytactic microorganism in chemical reactive flow of TiO2 + GO/water based hybrid nanofluid flow across a sheet applying artificial intelligence. and nanoparticles are combined with the base fluid, water (). There are numerous real-world uses for the concept of artificial intelligence-driven performance improvement of oxytactic microbes in hybrid nanofluid with chemical reaction and thermal radiation in a variety of sectors. By controlling the temperature and chemical conditions for improved performance and focused treatment, it is applicable in biomedical engineering to improve microbial-based drug delivery systems. The model may improve the efficacy of environmental biotechnology’s bioremediation processes, which use microorganisms to degrade pollutants under a range of chemical and temperature conditions. Additionally, the model can improve the efficacy of microbial cultures employed in fermentation or other bio-manufacturing processes in industrial processes like cooling systems by optimizing heat transfer in reactors utilizing nanofluids. The ordinary differential equations can alternatively be resolved utilizing an artificial neural network-based technique with Bayesian regularization. State training, performance, fitting plots, model response, and error histograms plots are utilized to explore the resulting network.