In situ investigation of particle clustering dynamics in colloidal assemblies using fluorescence microscopy

Grain Crystallinity, Anisotropy, and Boundaries Govern Microscale Hydrodynamic Transport in Semicrystalline Porous Media

Polycrystallinity is often an unintended consequence of real manufacturing processes used to produce designer porous media with deterministic and periodic architectures. Porous media are widely employed as high-surface conduits for fluid transport; unfortunately, even small concentrations of defects in the long-range order become the dominant impediment to hydrodynamic transport. In this study, we isolate the effects of these defects using a microfluidic analogy to energy transport in atomic polycrystals by directly tracking capillary transport through polycrystalline inverse opals. We reveal─using high-fidelity florescent microscopy─the boundary-limited nature of flow motions, along with nonlinear impedance elements introduced by the presence of "grain boundaries" that are separating the well-ordered "crystalline grains". Coupled crystallinity, anisotropy, and linear defect density contribute to direction-dominated flow characteristics in a discretized manner rather than traditional diffusive-like flow patterns. Separating individual crystal grains' transport properties from polycrystals along with new probabilistic data sets enables demonstrating statistical predictive models. These results provide fundamental insight into transport phenomena in (poly)crystalline porous media beyond the deterministic properties of an idealized unit cell and bridge the gap between engineering models and the ubiquitous imperfections found in manufactured porous materials.

Crack-Free Structural Color Materials Prepared without Disrupting the Particle Arrangement by Controlling the Internal Stress Relaxation and Interactions of the Melanin Particles

In fabricating structural color materials with assembled colloidal particles, there is a trade-off between the internal stresses acting on the particles and the interactions between the particles during solvent volatilization. It is crucial to fabricate crack-free materials that maintain the periodic arrangements of the particles by understanding the mechanism for crack initiation. Here, we focused on the composition and additives of melanin particle dispersions to obtain crack-free structural color materials without disturbing the particle arrangements. The use of a water/ethanol mixture as a dispersant effectively reduced the internal stresses of the particles during solvent evaporation. Furthermore, the addition of low-molecular-weight, low-volatility ionic liquids ensured that the arrangement and interactions of the particles were maintained after solvent volatilization. Optimization of the composition and additives of the dispersion made it possible to achieve crack-free melanin-based structural color materials while maintaining vivid, angular-dependent color tones.

Photonic crystals with rainbow colors by centrifugation-assisted assembly of colloidal lignin nanoparticles

Photonic crystals are optical materials that are often fabricated by assembly of particles into periodically arranged structures. However, assembly of lignin nanoparticles has been limited due to lacking methods and incomplete understanding of the interparticle forces and packing mechanisms. Here we show a centrifugation-assisted fabrication of photonic crystals with rainbow structural colors emitted from the structure covering the entire visible spectrum. Our results show that centrifugation is crucial for the formation of lignin photonic crystals, because assembly of lignin nanoparticles without centrifugation assistance leads to the formation of stripe patterns rather than photonic crystals. We further prove that the functions of centrifugation are to classify lignin nanoparticles according to their particle size and produce monodispersed particle layers that display gradient colors from red to violet. The different layers of lignin nanoparticles were assembled in a way that created semi-closed packing structures, which gave rise to coherent scattering. The diameter of the lignin nanoparticles in each color layer is smaller than that predicted by a modified Bragg's equation. In situ optical microscope images provided additional evidence on the importance of dynamic rearrangement of lignin nanoparticles during their assembly into semi-closed packing structures. The preparation of lignin nanoparticles combined with the methodology for their classification and assembly pave the way for sustainable photonic crystals.

Computer vision-assisted investigation of boiling heat transfer on segmented nanowires with vertical wettability

The boiling efficacy is intrinsically tethered to trade-offs between the desire for bubble nucleation and necessity of vapor removal. The solution to these competing demands requires the separation of bubble activity and liquid delivery, often achieved through surface engineering. In this study, we independently engineer bubble nucleation and departure mechanisms through the design of heterogeneous and segmented nanowires with dual wettability with the aim of pushing the limit of structure-enhanced boiling heat transfer performances. The demonstration of separating liquid and vapor pathways outperforms state-of-the-art hierarchical nanowires, in particular, at low heat flux regimes while maintaining equal performances at high heat fluxes. A deep-learning based computer vision framework realized the autonomous curation and extraction of hidden big data along with digitalized bubbles. The combined efforts of materials design, deep learning techniques, and data-driven approach shed light on the mechanistic relationship between vapor/liquid pathways, bubble statistics, and phase change performance.

A Deep Learning Perspective on Dropwise Condensation

Condensation is ubiquitous in nature and industry. Heterogeneous condensation on surfaces is typified by the continuous cycle of droplet nucleation, growth, and departure. Central to the mechanistic understanding of the thermofluidic processes governing condensation is the rapid and high-fidelity extraction of interpretable physical descriptors from the highly transient droplet population. However, extracting quantifiable measures out of dynamic objects with conventional imaging technologies poses a challenge to researchers. Here, an intelligent vision-based framework is demonstrated that unites classical thermofluidic imaging techniques with deep learning to fundamentally address this challenge. The deep learning framework can autonomously harness physical descriptors and quantify thermal performance at extreme spatio-temporal resolutions of 300 nm and 200 ms, respectively. The data-centric analysis conclusively shows that contrary to classical understanding, the overall condensation performance is governed by a key tradeoff between heat transfer rate per individual droplet and droplet population density. The vision-based approach presents a powerful tool for the study of not only phase-change processes but also any nucleation-based process within and beyond the thermal science community through the harnessing of big data.