A larger
number of studies successfully prepared various polymer
materials with excellent self-healing properties, but the study on
the underlying self-healing mechanism remains comparably backward
and still unclear. In this study, we prepared a self-healing polyurethane-urea
(PUU) elastomer based on noncovalent bonds. Then, a coarse-grained
model of PUU was successfully constructed using the iteration Boltzmann
inversion (IBI) method. Microphase separation and mechanical properties
of PUU were reproduced using this model by coarse-grained molecular
dynamics (MD) simulation. The three-stage healing mechanism comprised
the following: (1) movement of the material to close the gap, (2)
interdiffusion of the polymer, and (3) bond exchange. The mechanism
was revealed by determining the effects of hard segment content on
the microstructure (chain entanglement, interactions of soft and hard
segments, chain motility) and healing capacity over healing time.
In the initial stage of healing, the polymer chains were disentangled,
and the degree of entanglement of the healed samples decreased. A
novel experimental strategy confirmed the transition of hydrogen bonds
from disorder to order during the healing process. The motility of
the cut polymer chains (low molecular weight), especially the cut
soft segment, and the disordered hydrogen bonds played a key role
in the healing capacity. The increased content of the ordered hydrogen
bonds led to the formation of a hard segment network, which was not
conducive to healing. Finally, the promoting mechanism of external
factors, such as heating and trace amount of solvent, on the healing
of PUU was explained. Our work systematically and profoundly reveals
the self-healing behavior and mechanism of microphase-separated PUU
at the molecular level.
Migration testing of cyclic organosiloxane
oligomer molecules from
silicone rubber into food simulants was performed as a function of
three factors: temperature, time, and food simulant type (i.e., n-hexane, n-heptane, and ethyl alcohol).
A back-propagation artificial neural network (BP-ANN) was proposed
to study the migration properties with an average prediction accuracy
of 0.997. The analysis of the ANN indicated that high-temperature
and n-hexane environments accelerate the migration
of cyclic organosiloxane oligomer molecules from silicone rubber.
A molecular dynamics simulation was employed to illustrate the migration
mechanism at the molecular level. The results indicated that the high-temperature
and n-hexane condition led to a high interaction
energy, fractional free volume, and diffusion coefficient and a low
difference solubility parameter of the system, which accelerate the
molecules transfer process and threaten the safety of the materials.
These fundamental studies provide a comprehensive understanding of
the migration of cyclic organosiloxane oligomer contaminants and guidance
for the failure prediction of materials.
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