Organic–inorganic perovskite quantum dot (PQD)–polymer composites are emerging optoelectronic materials with exceptional properties that are promising widespread application in next‐generation electronics. Advances in the utilization of these materials depend on the development of suitable fabrication techniques to create 3D architectures composed of PQD–polymer for sophisticated optoelectronics. This study introduces a straightforward and effective method for producing 3D architectures of PQD‐encapsulated high‐performance composites (PQD‐HPCs) through direct‐ink writing (DIW). This method employs an ink composed of prefabricated PQDs and hydroxypropyl cellulose (HPC) in dichloromethane (DCM). HPC, an appropriate organic‐soluble polymer, exhibits optical transparency in the highly volatile DCM and enables the formulation of a stable, room‐temperature extrudable ink. The architectures, which are printed by adjusting the halide ratios (Cl, Br, and I) for the compositions of CH₃NH₃PbBr_1.5I_1.5, CH₃NH₃PbBr₃, and CH₃NH₃PbBr_1.5Cl_1.5, exhibit single peak photoluminescence emissions of red (639 nm), green (515 nm), and blue (467 nm). Optimizing the printing parameters of DIW enables the precise fabrication of programmed and complex PQD‐HPC 3D architectures for advanced anti‐counterfeiting and information encryption. This method has the potential to enhance the functionality of modern printed electronic devices significantly.

Using energy‐saving electrochromic (EC) displays in smart devices for augmented reality makes cost‐effective, easily producible, and efficiently operable devices for specific applications possible. Prussian blue (PB) is a metal‐organic coordinated compound with unique EC properties that limit EC display applications due to the difficulty in PB micro‐patterning. This work presents a novel micro‐printing strategy for PB patterns using localized crystallization of FeFe(CN)₆ on a substrate confined by the acidic‐ferric‐ferricyanide ink meniscus, followed by thermal reduction at 120 °C, thereby forming PB. Uniform PB patterns can be obtained by manipulating printing parameters, such as the concentration of FeCl₃·K₃Fe(CN)₆, printing speed, and pipette inner diameter. Using a 0.1 M KCl (pH 4) electrolyte, the printed PB pattern is consistently and reversibly converted to Prussian white (CV potential range: −0.2–0.5 V) with 200 CV cycles. The PB‐based EC display with a navigation function integrated into a smart contact lens is able to display directions to a destination to a user by receiving GPS coordinates in real time. This facile method for forming PB micro‐patterns could be used for advanced EC displays and various functional devices.

One of the key aspects of the diagnosis and treatment of atypical femoral fractures is the early detection of incomplete fractures and the prevention of their progression to complete fractures. However, an incomplete atypical femoral fracture can be misdiagnosed as a normal lesion by both primary care physicians and orthopedic surgeons; expert consultation is needed for accurate diagnosis. To overcome this limitation, we developed a transfer learning-based ensemble model to detect and localize fractures. A total of 1050 radiographs, including 100 incomplete fractures, were preprocessed by applying a Sobel filter. Six models (EfficientNet B5, B6, B7, DenseNet 121, MobileNet V1, and V2) were selected for transfer learning. We then composed two ensemble models; the first was based on the three models having the highest accuracy, and the second was based on the five models having the highest accuracy. The area under the curve (AUC) of the case that used the three most accurate models was the highest at 0.998. This study demonstrates that an ensemble of transfer-learning-based models can accurately classify and detect fractures, even in an imbalanced dataset. This artificial intelligence (AI)-assisted diagnostic application could support decision-making and reduce the workload of clinicians with its high speed and accuracy.