Neural network-based computational evaluation of periodic electroosmotic flow in propylene glycol–water ternary nanofluids with oxytactic microbes
Bibliographic record
Abstract
The rapid evolution of artificial intelligence (AI) is revolutionizing molecular-scale data analysis, transport modeling, and the prediction of dynamic behavior in complex fluids. In this study, we present a novel application of an AI-driven artificial neural network (ANN) to investigate chaotic transport dynamics in periodic electroosmotic flow (PEOF) of Sutterby ternary nanofluids containing oxytactic microbes. The working fluid, a 50:50 mixture of propylene glycol and water infused with Fe₃O₄, TiO₂, and Al₂O₃ nanoparticles, is modeled flowing across a deformable porous geometry. The nonlinear governing equations are solved numerically using the finite difference method (FDM), with ANN employed to enhance predictive capability. Model validation shows remarkable accuracy, achieving mean squared errors between 10 −7 and 10 −9 , thereby confirming the robustness of the AI-assisted framework. The findings reveal that electroosmotic and magnetic parameters exert competing effects on fluid motion, while oxytactic microbes reduce concentration distribution. Increasing the Brownian motion parameter enhances random particle movement, resulting in higher temperatures and lower concentrations. Additionally, the density of motile microbes decreases with increasing Peclet and bio-Schmidt numbers. Importantly, tri-hybrid nanofluids exhibit superior thermal distribution compared with hybrid nanofluids, single nanofluids, and base fluids. This study is the first to integrate AI-driven ANN modeling with chaotic PEOF transport in Sutterby ternary nanofluids containing oxytactic microbes. Unlike previous works, it uniquely combines advanced AI techniques with nonlinear bio-nanofluid dynamics, achieving unprecedented predictive accuracy while uncovering new insights into the coupled roles of electroosmosis, magnetism, Brownian motion, and microbial activity. The outcomes provide a new pathway for AI-assisted optimization of nanofluid-based systems in wastewater treatment, microfluidics, and energy transport, enabling more efficient and sustainable technologies. • Artificial neural network algorithm analyzed vast molecular-scale datasets generated from the simulation. • Explores how nanofluids and oxytactic microbes interact in a periodically driven electroosmotic environment. • Examines the synergistic effects of thermal diffusion and random molecular motion on a Propylene Glycol-Water nanofluid. • Oxytactic microbes' affinity for oxygen-rich zones leads to a reduction in nanoparticle concentration. • Validates the theoretical model and its applicability in wastewater treatment, advanced thermal management and drug delivery systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".