The CO2 adsorption performance of porous materials
loaded
with an amine-bearing functionality is determined by the pore network
of porous adsorbents and the dynamics of the adsorbed polymeric chains
bearing the amine functionality. Herein, we report the modifications
in the pore network of evaporation-induced self-assembled microspheres
of silica nanoparticles with varying loadings of polyethyleneimine
(PEI) using positron annihilation lifetime spectroscopy (PALS) and
small-angle X-ray scattering (SAXS). The dynamics of the loaded PEI
in the pore network of microspheres have been investigated using temperature-dependent
broadband dielectric spectroscopy. PALS investigation confirms microporosity
at two different length scales available at the interparticle regions
in pristine silica nanoparticle-based microspheres. With PEI loading
through the self-assembly process, the pore size and pore volume accessed
by PALS are observed to increase up to 20 wt % loading, followed by
a decrease due to modification in the clustering behavior of nanoparticles
and filling of pores with excess PEI. These variations are highly
consistent with the nonmonotonic jamming of silica nanoparticles,
as observed from SAXS analysis, arising due to complex interactions
between silica nanoparticles and PEI. Both the segmental and localized
relaxations of the adsorbed PEI are observed to slow down compared
to bulk PEI due to the interactions with silica nanoparticles akin
to polymer nanocomposites. The slowing down of the relaxations is
consistent with the free-volume variations of the confined PEI determined
using PALS. The segmental relaxation time of the loaded PEI in the
microspheres varies nonmonotonically with the PEI content. The observations
from complementary investigations unequivocally confirm that the packing
behavior of silica nanoparticles in the evaporation-induced self-assembled
microspheres is controlled by the PEI content. On the other hand,
the dynamics of PEI confined within the pore network of the microsphere
are determined by the clustering behavior of silica nanoparticles.