Lightweight reactor materials that simultaneously possess low heat capacity and large surface area are desirable for various applications such as catalytic supports, heat exchangers, and biological scaffolds. However, they are challenging to satisfy this criterion originating from their structural property in most porous cellular solids. Microlattices have great potential to resolve this issue in directing transport phenomena because of their hierarchically ordered design and controllable geometrical features such as porosity, specific surface, and tortuosity. In this study, we report hollow ceramic microlattices comprising a 10 μm thick hollow nickel oxide beam in an octet-truss architecture with low heat capacity and high specific surface area. Our microarchitected reactors exhibited a low heat capacity for a rapid thermal response with a small Biot number (Bi << 1) and large intertwined surface area for homogeneous flow mixing and chemical reactions, which made them ideal candidates for various energy applications. The hollow ceramic microlattice was fabricated by digital light three-dimensional (3D) printing, composite electroless plating, polymer removal, and subsequent thermal annealing. The transient thermal response and fluidic properties of the 3D-printed microstructures were experimentally investigated using a small-scale thermal and fluid test system, and analytically interpreted using simplified models. Our findings indicate that hollow microarchitected reactors provide a promising platform for developing multifunctional materials for thermal and fluid applications.

Unpredictable web temperature distributions in the dryer and strain deviations in the cross-machine (CMD) and machine (MD) directions could hamper the manufacture of smooth functional layers on polymer-based webs through the roll-to-roll (R2R) continuous process system. However, research on this topic is limited. In this study, we developed a structural analysis model using the temperature distribution of the web as a boundary condition to analyze the drying mechanism of the dryer used in an R2R system. Based on the results of this model, we then applied structural modifications to the flow channel and hole density of the aluminum plate of the dryer. The model successfully predicted the temperature and strain distributions of the web inside the dryer in the CMD and MD by forming a tension according to the speed difference of the driven rolls at both ends of the span. Our structural improvements significantly reduced the temperature deviation of the moving web inside the dryer by up to 74% and decreased the strain deviation by up to 46%. The findings can help prevent web unevenness during the drying process of the R2R system, which is essential to minimize the formation of defects on functional layers built over polymer-based webs.

Recently, slot-die coating based on the roll-to-roll process has been actively used to fabricate nanoparticle-based electrolyte layers because it is advantageous for high-speed processes and mass production of uniformly thick electrolyte layers. In this process, the fabricated electrolyte layer is stored as a wound roll throughout the rewinding process. We analyzed the defects and geometric changes in an electrolyte layer, i.e., gadolinium-doped cerium oxide (GDC), due to the radial stress in the wound roll. We found that the thickness of the coated layer could be decreased by increasing the radial stress, i.e., cracks can be generated in the coated layer if excessively high radial stress is applied to the wound-coated layer. More thickness changes and crack defects were generated with time due to the residual stress in the wound roll. Finally, we analyzed the effects of taper tension profiles on the defects of the coated layer in the wound roll and determined the taper tension profile to minimize defects.

Roll-to-roll manufacturing systems have been widely adopted for their cost-effectiveness, eco-friendliness, and mass-production capabilities, utilizing thin and flexible substrates. However, in these systems, defects in the rotating components such as the rollers and bearings can result in severe defects in the functional layers. Therefore, the development of an intelligent diagnostic model is crucial for effectively identifying these rotating component defects. In this study, a quantitative feature-selection method, feature partial density, to develop high-efficiency diagnostic models was proposed. The feature combinations extracted from the measured signals were evaluated based on the partial density, which is the density of the remaining data excluding the highest class in overlapping regions and the Mahalanobis distance by class to assess the classification performance of the models. The validity of the proposed algorithm was verified through the construction of ranked model groups and comparison with existing feature-selection methods. The high-ranking group selected by the algorithm outperformed the other groups in terms of training time, accuracy, and positive predictive value. Moreover, the top feature combination demonstrated superior performance across all indicators compared to existing methods.