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W. Qiu and X.-Y. Liu
found that silk fibroin (SF) materials have potential in multiple applications, particularly for flexible optical and electronic devices [4]. For instance, the intrinsic performance as well as functionalization (by incorporating organic molecules or inorganic
nanoparticles into the SF matrix) of SF materials make them ideal candidates for
basic optical elements, light energy conversion devices, photochemical reactions,
sensors, and bioimaging [4]. In addition, the programmable degradation behaviors
of SF materials also enable the fabrication of a variety of SF-based flexible electronics
for versatile applications including biomedical monitoring, therapy, biosensing, and
memory devices [3, 5, 17].
6.1.2 Structural Factors Correlated to Macroscopic
Properties
For many soft materials, including SF materials, the macroscopic performance is
controlled largely by the unique structures, whose size ranges from the nanoscale
to the mesoscale level (Fig. 6.2b). In general, the performance of soft materials is
highly correlated to the following four structural factors: [16, 18, 19]
(1) Topology: It demonstrates how joints/points are associated with each other. In
principle, network modifications often begin with an alternation in topology.
(2) Correlation length, ξ: It describes the average distance between two correlated joints/points. In terms of hierarchical structure, the joints/points refer to
structural units at the same level.
(3) Ordering/symmetry of structural units: In many situations, structural units in a
network are anisotropic. The ordering/symmetry enable us to understand how
these structural units are patterned.
(4) Strength of interactions: Notably, the interactions between adjacent structural
units can be of the physical, thermodynamic, or chemical type.
The stability of networks is largely controlled by the strength of these interactions.
To obtain a comprehensive understanding of the correlation between the structure
and performance of soft materials, a more precise insight into the hierarchical structures of soft materials is required; this has also inspired scientists and engineers
to fabricate advanced functionalized soft materials, by effectively manipulating the
aforementioned structural factors [20]. Although numerous attempts have been made
to characterize the hierarchical structures of silk materials, the mechanism by which
the multi-level structures of silk materials are constructed and the corresponding
structural factors affecting macroscopic performance remain debatable.
W. Qiu and X.-Y. Liu
found that silk fibroin (SF) materials have potential in multiple applications, particularly for flexible optical and electronic devices [4]. For instance, the intrinsic performance as well as functionalization (by incorporating organic molecules or inorganic
nanoparticles into the SF matrix) of SF materials make them ideal candidates for
basic optical elements, light energy conversion devices, photochemical reactions,
sensors, and bioimaging [4]. In addition, the programmable degradation behaviors
of SF materials also enable the fabrication of a variety of SF-based flexible electronics
for versatile applications including biomedical monitoring, therapy, biosensing, and
memory devices [3, 5, 17].
6.1.2 Structural Factors Correlated to Macroscopic
Properties
For many soft materials, including SF materials, the macroscopic performance is
controlled largely by the unique structures, whose size ranges from the nanoscale
to the mesoscale level (Fig. 6.2b). In general, the performance of soft materials is
highly correlated to the following four structural factors: [16, 18, 19]
(1) Topology: It demonstrates how joints/points are associated with each other. In
principle, network modifications often begin with an alternation in topology.
(2) Correlation length, ξ: It describes the average distance between two correlated joints/points. In terms of hierarchical structure, the joints/points refer to
structural units at the same level.
(3) Ordering/symmetry of structural units: In many situations, structural units in a
network are anisotropic. The ordering/symmetry enable us to understand how
these structural units are patterned.
(4) Strength of interactions: Notably, the interactions between adjacent structural
units can be of the physical, thermodynamic, or chemical type.
The stability of networks is largely controlled by the strength of these interactions.
To obtain a comprehensive understanding of the correlation between the structure
and performance of soft materials, a more precise insight into the hierarchical structures of soft materials is required; this has also inspired scientists and engineers
to fabricate advanced functionalized soft materials, by effectively manipulating the
aforementioned structural factors [20]. Although numerous attempts have been made
to characterize the hierarchical structures of silk materials, the mechanism by which
the multi-level structures of silk materials are constructed and the corresponding
structural factors affecting macroscopic performance remain debatable.