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Abstract: Nanoparticle solutions can be used as a general platform for the design of polymer-based gels
.
However, system optimization for a specific type of gel requires an understanding of how to determine the interfacial adhesion between the gel and the gel through different experimental adjustable parameters
.
Recently, the team of Professor Nicola Molinari of Harvard University and Professor Angioletti-Uberti of Imperial College used molecular dynamics simulation to study the effect of nanoparticle size and gel-nanoparticle interaction on the coarse-grained model representing hydrogel (or in other words, in swelling A cross-linked polymer network with variable stiffness in the state)
.
?? The authors show that regardless of the stiffness, the competitive effect will cause the optimal nanoparticle size for interface enhancement to always be near the polymer mesh size
.
The author also showed that after deviating from this optimal size, the glue performance will quickly drop to the point that nanoparticles will even weaken the interfacial adhesion, which implies the limitation of polydispersity in the particle size acceptable for obtaining functional glues
.
Overall, these simulations provide further steps for the rational design of nanoparticle solutions with the best adhesive properties
.
Related papers are published on "Macromolecules" with the title Designing Nanoparticles as Glues for Hydrogels: Insights from a Microscopic Model
.
[See analysis for main image] Figure 1.
The polymer beads are blue, while the nanoparticles are red
.
Panel (a) shows the basic cube unit used and the number of repetitions in each dimension
.
In Figures (b) and (c), sample structures before and after balance are shown respectively
.
Figures (d) and (e) show the contribution of two angles to the potential energy of the system
.
Note that for any choice of kθ, k?=kθ/2
.
Finally, the small figure (f) shows the relationship between the swelling degree and the angular stiffness and the polymer-polymer interaction parameter ?BB
.
The purple dot corresponds to the network configuration studied in this article
.
Figure 2.
Examples of stress-strain responses for different RNP values when the constant ?BN = 1.
6?, the network kθ = 350 and ?BB = 0.
5
.
The adhesion energy here is defined as the integral between the two zeros of each stress-strain curve
.
Although not visible, the overall uncertainty is expressed as a shaded area around the average curve
.
Figure 3.
Summary of the percentage change in adhesion energy relative to the case without nanoparticles
.
Figure 4.
Spatial distribution of polymer beads (blue) and nanoparticles (red)
.
All figures refer to the network with kθ = 350 and ?BB = 0.
5
.
From (a) to (d), R increases from R = 0.
2 to R = 1.
4
.
Figure 5.
(a) Different polymer bead-nanoparticle interactions and (b) mean square displacement of different R? values
.
All results are for the network with kθ = 350 and ?BB = 0.
5
.
Figure 6.
Diffusion coefficient analysis of the network with kθ = 350 and ?BB = 0.
5
.
Figure 7.
The relationship between particle-network interaction energy difference (interface-bulk) and particle radius (left) and polymer bead-nanoparticle affinity (right)
.
For all parameter sets, the energy of the particles at the interface is always lower than the energy in the whole, thus providing a dynamic trap that can limit them to a very long time range
.
.
However, system optimization for a specific type of gel requires an understanding of how to determine the interfacial adhesion between the gel and the gel through different experimental adjustable parameters
.
Recently, the team of Professor Nicola Molinari of Harvard University and Professor Angioletti-Uberti of Imperial College used molecular dynamics simulation to study the effect of nanoparticle size and gel-nanoparticle interaction on the coarse-grained model representing hydrogel (or in other words, in swelling A cross-linked polymer network with variable stiffness in the state)
.
?? The authors show that regardless of the stiffness, the competitive effect will cause the optimal nanoparticle size for interface enhancement to always be near the polymer mesh size
.
The author also showed that after deviating from this optimal size, the glue performance will quickly drop to the point that nanoparticles will even weaken the interfacial adhesion, which implies the limitation of polydispersity in the particle size acceptable for obtaining functional glues
.
Overall, these simulations provide further steps for the rational design of nanoparticle solutions with the best adhesive properties
.
Related papers are published on "Macromolecules" with the title Designing Nanoparticles as Glues for Hydrogels: Insights from a Microscopic Model
.
[See analysis for main image] Figure 1.
The polymer beads are blue, while the nanoparticles are red
.
Panel (a) shows the basic cube unit used and the number of repetitions in each dimension
.
In Figures (b) and (c), sample structures before and after balance are shown respectively
.
Figures (d) and (e) show the contribution of two angles to the potential energy of the system
.
Note that for any choice of kθ, k?=kθ/2
.
Finally, the small figure (f) shows the relationship between the swelling degree and the angular stiffness and the polymer-polymer interaction parameter ?BB
.
The purple dot corresponds to the network configuration studied in this article
.
Figure 2.
Examples of stress-strain responses for different RNP values when the constant ?BN = 1.
6?, the network kθ = 350 and ?BB = 0.
5
.
The adhesion energy here is defined as the integral between the two zeros of each stress-strain curve
.
Although not visible, the overall uncertainty is expressed as a shaded area around the average curve
.
Figure 3.
Summary of the percentage change in adhesion energy relative to the case without nanoparticles
.
Figure 4.
Spatial distribution of polymer beads (blue) and nanoparticles (red)
.
All figures refer to the network with kθ = 350 and ?BB = 0.
5
.
From (a) to (d), R increases from R = 0.
2 to R = 1.
4
.
Figure 5.
(a) Different polymer bead-nanoparticle interactions and (b) mean square displacement of different R? values
.
All results are for the network with kθ = 350 and ?BB = 0.
5
.
Figure 6.
Diffusion coefficient analysis of the network with kθ = 350 and ?BB = 0.
5
.
Figure 7.
The relationship between particle-network interaction energy difference (interface-bulk) and particle radius (left) and polymer bead-nanoparticle affinity (right)
.
For all parameter sets, the energy of the particles at the interface is always lower than the energy in the whole, thus providing a dynamic trap that can limit them to a very long time range
.