Self‐assembly continuously gains attention as an excellent method to create novel nanoscale structures with a wide range of applications in photonics, optoelectronics, biomedical engineering, and heat transfer applications. However, self‐assembly is governed by a diversity of complex interparticle forces that cause fabricating defectless large scale (>1 cm) colloidal crystals, or opals, to be a daunting challenge. Despite numerous efforts to find an optimal method that offers the perfect colloidal crystal by minimizing defects, it has been difficult to provide physical interpretations that govern the development of defects such as grain boundaries. This study reports the control over grain domains and intentional defect characteristics that develop during evaporative vertical deposition. The degree of particle crystallinity and evaporation conditions is shown to govern the grain domain characteristics, such as shapes and sizes. In particular, the grains fabricated with 300 and 600 nm sphere diameters can be tuned into single‐column structures exceeding ≈1 mm by elevating heating temperature up to 93 °C. The understanding of self‐assembly physics presented in this work will enable the fabrication of novel self‐assembled structures with high periodicity and offer fundamental groundworks for developing large‐scale crack‐free structures.

Capillary wicking through homogeneous porous media remains challenging to simultaneously optimize due to the unique transport phenomena that occur at different length scales. This challenge may be overcome by introducing hierarchical porous media, which combine tailored morphologies across multiple length scales to design for the individual transport mechanisms. Here, we fabricate hierarchical nanowire arrays consisting of vertically aligned copper nanowires (∼100 to 1000 nm length scale) decorated with dense copper oxide nanostructures (∼10 to 100 nm length scale) to create unique property sets that include a large specific surface area, high rates of fluid delivery, and the structural flexibility of vertical arrays. These hierarchical nanowire arrays possess enhanced capillary wicking (K/R eff = 0.004–0.023 μm) by utilizing hemispreading and are advantageous as evaporation surfaces. With the advent and acceleration of flexible electronics technologies, we measure the capillary properties of our freestanding hierarchical nanowire arrays installed on curved surfaces and observe comparable fluid delivery to flat arrays, showing the difference of 10–20%. The degree of effective inter-nanowire pore and porosity is shown to govern the capillary performance parameters, thereby this study provides the design strategy for capillary wicking materials with unique and tailored combinations of thermofluidic properties.

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It was found that the Ag electrode layer in a transmission electron microscopy (TEM) specimen of an inverted polymer solar cell structure of Ag/PEDOT:PSS/P3HT:PCBM/TiO(2)/ITO/glass (where PEDOT is poly(3,4-ethylenedioxythiophene), PSS is polystyrene sulfonate, and ITO is indium tin oxide) was broken down into particles as time passed. In order to investigate the cause of Ag particle formation and the effect of the degradation on the performance of solar cells, the temporal change of the cross-sectional TEM micrographs was examined together with energy-dispersive X-ray spectroscopy (EDS) analysis and electron tomography. Temporal degradation of Ag/Si and Ag/1 nm-Ti/PEDOT:PSS/ITO/glass structures was also studied. Absorption of water by the PEDOT:PSS layer followed by corrosion of the grain boundaries of the Ag layer by the corrosive water was thought to be the reason of Ag particle formation and fast performance lowering of the device.

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.