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Ternary hybrid nanofluids have attracted significant attention in chemical and environmental engineering due to their superior heat and mass transfer performance compared with conventional and binary hybrid nanofluids. This study investigates the coupled thermo bioconvective transport characteristics of a non-Newtonian Sutterby ternary hybrid nanofluid, driven by the growing need for efficient thermal regulation and controlled pollutant dispersion in advanced energy and bio environmental applications. The working fluid consists of aluminium oxide (Al 2 O 3 ), titanium dioxide (TiO 2 ), and ferric oxide (Fe 3 O 4 ) nanoparticles dispersed in water. The mathematical model incorporates oxytactic microbial bioconvection and nonlinear pollutant discharge effects to examine their influence on fluid momentum, temperature, species concentration, and microorganism density. The governing nonlinear partial differential equations are transformed into a dimensionless form using nonsimilar transformations and solved numerically through an implicit finite difference method combined with a quasilinearization technique, ensuring numerical stability and rapid convergence. The novelty of this work lies in the unified treatment of Sutterby rheology, ternary hybrid nanoparticles, microbial bioconvection, and nonlinear pollutant dynamics within a single framework. A comprehensive parametric study evaluates the effects of key physical parameters, including magnetic field strength, Deborah number, Richardson number, inclination angle, velocity ratio, bioconvective Rayleigh number, and pollutant growth parameters. The results demonstrate that magnetic forces and opposing buoyancy contribute to an increase in surface drag and thermal boundary layer thickness, whereas bioconvection plays a mitigating role by reducing the drag . The , experiences an approximate increase of 236% and 120% as De transitions from to , at and , respectively. Augmenting from 10 to 30 at enriches the approximately by 1.2%, 2.2%, 3.2% for , , , respectively. Regression-based surrogate models closely match numerical results, offering practical value for system design across energy, pollution control, and bioenvironmental engineering applications.
Patil et al. (Mon,) studied this question.