The electrode resolution of current retinal prostheses is still far from matching the densities of retinal neurons. Decreasing electrode diameter increases impedance levels thus deterring effective stimulation of neur...The electrode resolution of current retinal prostheses is still far from matching the densities of retinal neurons. Decreasing electrode diameter increases impedance levels thus deterring effective stimulation of neurons. One solution is to increase the surface roughness of electrodes, which can be done via nanoparticle coatings. This paper explores a Lattice Gas Model of the drying-mediated self-assembly of nanoparticle mixtures. The model includes representations for different types of nanoparti- cles, solvent, vapour, substrate and the energetic relationships between these elements. The dynamical aspect of the model is determined by energy minimization, stochastic fluctuations and physical constraints. The model attempts to unravel the rela- tionships between different experimental conditions （e.g. evaporation rate, substrate characteristics and solvent viscosity） and the surface roughness of resulting assemblies. Some of the main results include the facts that the assemblies formed by nanoparticles of different sizes can boost roughness in specific circumstances and that the optimized assemblies can exhibit walled or stalagmite structures. This study provides a set of simulation modelling experiments that if confirmed in the laboratory may result in new and useful materials.展开更多
文摘The electrode resolution of current retinal prostheses is still far from matching the densities of retinal neurons. Decreasing electrode diameter increases impedance levels thus deterring effective stimulation of neurons. One solution is to increase the surface roughness of electrodes, which can be done via nanoparticle coatings. This paper explores a Lattice Gas Model of the drying-mediated self-assembly of nanoparticle mixtures. The model includes representations for different types of nanoparti- cles, solvent, vapour, substrate and the energetic relationships between these elements. The dynamical aspect of the model is determined by energy minimization, stochastic fluctuations and physical constraints. The model attempts to unravel the rela- tionships between different experimental conditions （e.g. evaporation rate, substrate characteristics and solvent viscosity） and the surface roughness of resulting assemblies. Some of the main results include the facts that the assemblies formed by nanoparticles of different sizes can boost roughness in specific circumstances and that the optimized assemblies can exhibit walled or stalagmite structures. This study provides a set of simulation modelling experiments that if confirmed in the laboratory may result in new and useful materials.
