Block copolymer (BCP)-based supramolecules offer a versatile platform to generate hierarchically assembled nanostructures with inherent functions. However, the multicomponent supramolecular system is complex, and there is a limited understanding of the self-assembly process which is critical for precise control of the nanostructure. Here, the effect of small molecule loading and the solvent annealing condition on thin films of supramolecules on flat and patterned substrates were investigated. There is a superposition between the effect of the small molecule loading and solvent annealing condition that can be used to control the assembly rate, grain size, and feature size. The final film features are a result of the kinetic pathway taken during the assembly process. On patterned substrates with linear or circular trenches, the assembly is not driven by incommensurability as is commonly seen in BCP thin films but rather by the kinetic pathway. The present study offers insight into the importance of systematic studies in the kinetic process in self-assembly of multicomponent systems.
Nonequilibrium structures in nanocomposites provide possibilities to modulate the organization of nanofillers beyond the phase diagram and to fabricate functional materials with targeted properties. However, multicomponent systems, such as nanocomposites, have complex phase diagrams and kinetic pathways. Here, effects of two critical parameters, nanoparticle (NP) size and supramolecular morphology, were systematically evaluated in NP/supramolecule blends. NPs in the size range of 5−25 nm were assembled in cylindrical or lamellar supramolecular nanocomposite thin films with periodicities of 20−30 nm under solvent vapor annealing and rapid solvent removal conditions. The ratio of particle size to supramolecular periodicity was tuned between 0.17 and 1.25. The results showed that the vertical diffusion of NPs toward the film surface depends on the matrix morphology. NP surface migration is more prominent in cylindrical thin films than in lamellar thin films. This is mainly attributed to the higher energetic barriers for interdomain diffusion in lamellar morphology. Supramolecular nanocomposite thin films with ordered structures can be obtained by balancing the size-dependent NP diffusivity and energetic factors affecting NP diffusion during the annealing process for a range of NP sizes. Present studies give insight into how to manipulate the assembly kinetics to access targeted morphologies in nanocomposite thin films.
ABSTRACT:A model based on a combination of the solubility parameters of Hansen and the polymer solution theory of Flory-Rehner was used to predict the solubility and permeation properties of organic solvents in Viton polymeric glove material. To test the validity of the model, weight gain data were collected for 32 organic solvents versus Viton. Samples were exposed for periods of 2 to 12 weeks until each glove sample had achieved a stable, equilibrium weight. Using a nonlinear least-squares regression, the three-dimensional solubility parameter was determined for Viton to be as follows: dispersion ϭ 15.38, polar ϭ 10.49, and hydrogen bonding ϭ 2.47. Breakthrough times, lag times, and steady-state permeation rates for the solvents verses Viton were also determined and combined with results obtained from the literature. A high level of correlation was observed between the model and the properties evaluated.
Currently, devices like these are designed and refined via many cycles of trial and error. The nanocomposite design process is particularly arduous because effective medium approximations do not accurately predict the properties of materials built from nanoscopic components. [5,6] In the case of polymer-nanoparticle blends, the spatial arrangement of components determines the overall properties through nanoparticle (NP) coupling, confinement, and organic-inorganic interface effects. [7][8][9] The final NP distribution, achieved via self-assembly, is a complex function of multiple composition and processing variables. [10][11][12] Due to the abundance of non-equilibrium states, knowledge of the kinetic pathway is particularly important for processing polymer-based blends. [13][14][15][16] Reverse engineering a specific nanostructure is challenging because the parameter space of formulations and processing conditions is very large. For example, in blends of block copolymers (BCPs) and NPs, a common class of nanoscopicallystructured composites, the final arrangement is a function of the polymer chain length, the block ratio, the particle size, the relative proportions of the components, the chemical compatibility between them, and the processing conditions. [15,16] Within the last decade, it has been established that adding organic small molecules to a BCP-NP blend facilitates the incorporation of large or anisotropic particles, accelerates assembly, and produces new nanostructures. [17][18][19] These are exciting milestones in the development of functional nanocomposites but, with the addition of small molecules, the parameter space of nanocomposite compositions has grown even larger.The challenges of working with large parameter spaces are not unique to self-assembly. Humans are excellent at finding patterns in low-dimensional data but struggle to understand trends in high-dimensional systems. Machine learning (ML) methods offer ways to predict outcomes and visualize trends in high-dimensional spaces. As these methods do not rely on hard-coded relationships between parameters, they are suited to complex systems without a solid theoretical foundation. Parameter spaces considered large by experimental standards, such as the 7D space described above, are tiny compared to the capabilities of modern ML models. [20] ML methods have recently Blends of nanoparticles, polymers, and small molecules can self-assemble into optical, magnetic, and electronic devices with structure-dependent properties. However, the relationship between a multicomponent nanocomposite's formulation and its assembled structure is complex and cannot be predicted by theory. The blends can be strongly influenced by processing conditions, which can introduce non-equilibrium states. Currently, nanocomposite devices are designed through cycles of experimental trial and error. Machine learning (ML) methods are a compelling alternative because they can use existing datasets to map high-dimensional spaces. These methods do not rely on known relationships b...
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