A minimal correction law for magnetic nanofluids capturing concentration curvature and field-dependent enhancements

Measured thermal conductivity in magnetic nanofluids often deviates from classical effective- medium predictions, showing sublinear dependence on particle concentration and additional enhancement under applied magnetic fields. This work introduces a compact two-factor cor- rection that explains both effects with minimal parameters and without new experiments. The effective conductivity ratio is written as:

where ? is particle volume fraction, α is a single calibration parameter estimated once from standard characterization proxies (e.g., viscosity ratio, ζ-potential, DLS size), and g(B) is a bounded function describing field-induced alignment, parameterized by a characteristic field B₀ available from published magnetization data. Using reported datasets for Fe₃O₄ and CoFe₂O₄ dispersions (20–40?C, ? ≤ 5%), the model reproduces (i) baseline curvature at B = 0 and (ii) modest field-on boosts up to ∼200 mT, while avoiding case-by-case parameter refits.

We provide a practical design chart that maps target conductivity gains against (?, B), along with a clear falsifier condition specifying where the model would fail (e.g., persis- tent super-linear field scaling after saturation). The approach is closed-form, fast to evaluate, and requires only standard inputs, making it a useful predictive tool for formulating magnetic nanofluids with minimal trial-and-error. No new laboratory work is required for this contribu- tion.

The poster will present: (i) derivation and physical interpretation of the two-factor law; (ii) validation on held-out concentrations and field strengths across Fe₃O₄/CoFe₂O₄ systems; (iii) falsifier and uncertainty bounds; and (iv) synthesis/processing guidance to achieve +10–30% conductivity gains with routine characterization.

Edwin Maina is a graduate student in Materials Science and Engineering at Portland State Uni- versity. His research focuses on nanomaterials, nanofluids, and compact predictive modeling of material properties. He develops lightweight, falsifiable laws that bridge theory and experiment, enabling practical “predict-then-make” workflows that forecast performance before fabrication. His broader interests include thermal transport, polymer nanocomposites, and next-generation materials for energy and engineering applications.