RESUMEN
Compressed sensing is an image reconstruction technique to achieve high-quality results from limited amount of data. In order to achieve this, it utilizes prior knowledge about the samples that shall be reconstructed. Focusing on image reconstruction in nanotomography, this work proposes enhancements by including additional problem-specific knowledge. In more detail, we propose further classes of algebraic inequalities that are added to the compressed sensing model. The first consists in a valid upper bound on the pixel brightness. It only exploits general information about the projections and is thus applicable to a broad range of reconstruction problems. The second class is applicable whenever the sample material is of roughly homogeneous composition. The model favors a constant density and penalizes deviations from it. The resulting mathematical optimization models are algorithmically tractable and can be solved to global optimality by state-of-the-art available implementations of interior point methods. In order to evaluate the novel models, obtained results are compared to existing image reconstruction methods, tested on simulated and experimental data sets. The experimental data comprise one 360° electron tomography tilt series of a macroporous zeolite particle and one absorption contrast nano X-ray computed tomography (nano-CT) data set of a copper microlattice structure. The enriched models are optimized quickly and show improved reconstruction quality, outperforming the existing models. Promisingly, our approach yields superior reconstruction results, particularly when only a small number of tilt angles is available.
RESUMEN
Bifurcations in kinetic pathways decide the evolution of a system. An example is crystallization, in which the thermodynamically stable polymorph may not form due to kinetic hindrance. Here, we use confined self-assembly to investigate the interplay of thermodynamics and kinetics in the crystallization pathways of finite clusters. We report the observation of decahedral clusters from colloidal particles in emulsion droplets and show that these decahedral clusters can be thermodynamically stable, just like icosahedral clusters. Our hard sphere simulations reveal how the development of the early nucleus shape passes through a bifurcation that decides the cluster symmetry. A geometric argument explains why decahedral clusters are kinetically hindered and why icosahedral clusters can be dominant even if they are not in the thermodynamic ground state.
RESUMEN
Supraparticles are spherical colloidal crystals prepared by confined self-assembly processes. A particularly appealing property of these microscale structures is the structural color arising from interference of light with their building blocks. Here, we assemble supraparticles with high structural order that exhibit coloration from uniform, polyhedral metal-organic framework (MOF) particles. We analyse the structural coloration as a function of the size of these anisotropic building blocks and their internal structure. We attribute the angle-dependent coloration of the MOF supraparticles to the presence of ordered, onion-like layers at the outermost regions. Surprisingly, even though different shapes of the MOF particles have different propensities to form these onion layers, all supraparticle dispersions show well-visible macroscopic coloration, indicating that local ordering is sufficient to generate interference effects.
RESUMEN
The structure of finite self-assembling systems depends sensitively on the number of constituent building blocks. Recently, it was demonstrated that hard sphere-like colloidal particles show a magic number effect when confined in emulsion droplets. Geometric construction rules permit a few dozen magic numbers that correspond to a discrete series of completely filled concentric icosahedral shells. Here, we investigate the free energy landscape of these colloidal clusters as a function of the number of their constituent building blocks for system sizes up to several thousand particles. We find that minima in the free energy landscape, arising from the presence of filled, concentric shells, are significantly broadened, compared to their atomic analogues. Colloidal clusters in spherical confinement can flexibly accommodate excess particles by ordering icosahedrally in the cluster center while changing the structure near the cluster surface. In between these magic number regions, the building blocks cannot arrange into filled shells. Instead, we observe that defects accumulate in a single wedge and therefore only affect a few tetrahedral grains of the cluster. We predict the existence of this wedge by simulation and confirm its presence in experiment using electron tomography. The introduction of the wedge minimizes the free energy penalty by confining defects to small regions within the cluster. In addition, the remaining ordered tetrahedral grains can relax internal strain by breaking icosahedral symmetry. Our findings demonstrate how multiple defect mechanisms collude to form the complex free energy landscape of colloidal clusters.