RESUMEN
Embedding soft matter with nanoparticles (NPs) can provide electromagnetic tunability at sub-micron scales for a growing number of applications in healthcare, sustainable energy, and chemical processing. However, the use of NP-embedded soft material in temperature-sensitive applications has been constrained by difficulties in validating the prediction of rates for energy dissipation from thermally insulating to conducting behavior. This work improved the embedment of monodisperse NPs to stably decrease the inter-NP spacings in polydimethylsiloxane (PDMS) to nano-scale distances. Lumped-parameter and finite element analyses were refined to apportion the effects of the structure and composition of the NP-embedded soft polymer on the rates for conductive, convective, and radiative heat dissipation. These advances allowed for the rational selection of PDMS size and NP composition to optimize measured rates of internal (conductive) and external (convective and radiative) heat dissipation. Stably reducing the distance between monodisperse NPs to nano-scale intervals increased the overall heat dissipation rate by up to 29%. Refined fabrication of NP-embedded polymer enabled the tunability of the dynamic thermal response (the ratio of internal to external dissipation rate) by a factor of 3.1 to achieve a value of 0.091, the largest reported to date. Heat dissipation rates simulated a priori were consistent with 130 µm resolution thermal images across 2- to 15-fold changes in the geometry and composition of NP-PDMS. The Nusselt number was observed to increase with the fourth root of the Rayleigh number across thermally insulative and conductive regimes, further validating the approach. These developments support the model-informed design of soft media embedded with nano-scale-spaced NPs to optimize the heat dissipation rates for evolving temperature-sensitive diagnostic and therapeutic modalities, as well as emerging uses in flexible bioelectronics, cell and tissue culture, and solar-thermal heating.