The interest in polyurethane rigid (PUR) foams as potent thermally insulating materials for a wide range of applications continues to grow as the minimization of CO2 emissions has become a… Click to show full abstract
The interest in polyurethane rigid (PUR) foams as potent thermally insulating materials for a wide range of applications continues to grow as the minimization of CO2 emissions has become a global issue. Controlling the thermal insulation efficiency of PUR foams starts with the control of their morphology. Although the presence of micrometric air bubbles originating from air entrainment during the blending of the PU reactive mixture has been shown to influence the final PUR foam morphology, detailed experimental investigations on how exactly they affect the final PUR foam pore size are still lacking. To fill this gap, we use a double-syringe mixing device, which allows to control the number of air bubbles generated during a first air entrainment step, before using the same device to blend the reactive components in a sealed environment, avoiding further air entrainment. Keeping all experimental parameters constant except for the air bubble density in the reactive mixture, we can correlate changes of the final PUR foam morphology with the variation of the air bubble density in the initially liquid reactive mixture. Our results confirm recent findings which suggest the presence of two different regimes of bubble nucleation and growth depending on the presence or absence of dispersed air bubbles in the liquid reactive mixture. Our study pushes those insights further by demonstrating an inverse relation between the air bubble density in the liquid reactive mixture and the final pore volume of the PUR foam. For example, at constant chemical formulation and blending conditions, we could vary the final pore size between 400–1600 μm simply by controlling the amount of pre-dispersed air bubbles within the system. We are confident that the presented approach may not only provide a valuable model experiment to scan formulations in R&D laboratories, but it may also provide suggestions for the optimization of industrial processes.
               
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