Towards a Synergy?Driven Design Paradigm for Metal?Organic Framework/Polymer Nanocomposites via Predictive Multiscale Simulations

A synergy?driven design paradigm aims to identify nanocomposites whose properties exceed rule?of?mixtures limits. This study focuses on thermal conductivity, a key factor in many technologically relevant MOF?polymer applications. Atomistic simulations identify interphases that enhance or suppress heat transport, while a multiscale framework reveals how these interphase effects can ultimately govern bulk material behavior.Experimental reports indicate that embedding metal–organic frameworks (MOFs) in polymer matrices both facilitates practical engineering applications and may yield properties surpassing the rule?of?mixtures expectations owing to interfacial synergy. Herein, computational methods are used to predict this amplification a priori, opening the door to in?silico synergy?driven design paradigm. Thermal conductivity is chosen as the model property, important because heat management governs adsorption?driven separations employing MOFs. Non?equilibrium molecular dynamics provides conductivities for the MOF, the polymer, and the interphase; a multiscale scheme then delivers the composite value. The workflow is applied to HKUST?1/polyethylene glycol(PEG) and UiO?66/PEG and benchmarked against ZIF?8/stearic acid(SA). For HKUST?1/PEG a conductivity of 0.39 W/mK is predicted, representing a 0.09 W/mK synergy above the rule?of?mixtures baseline; experimental measurements report, respectively, 0.428 and 0.14 W/mK, confirming the model predictions. UiO?66/PEG model and its experimental analogue UiO?66(CH3)/PEG display comparable agreement and, notably, outperform both bulk constituents. In contrast, ZIF?8/SA exhibits no significant enhancement. The demonstrated predictive route identifies MOF/polymer pairs with favorable thermal transport and guides rational design of advanced adsorbent composites.

» Publication Date: 21/11/2025

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737


                   




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