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The redox activities of polyaniline (PANI) are hindered by the instability of pernigraniline salt (PS) state which degrades into oligo‐aniline. In this work, the use of protic additives is examined to mitigate capacity fading and increase utilization of PANI in nonaqueous electrolytes. The protic additive propylene glycol, with its hydrogen‐bonding capabilities, stabilizes the PS PANI and promotes reversible redox reactions, facilitating high capacity and an extended cycle lifetime for applications in metal ion supercapacitors. The use of this protic nonaqueous electrolyte in a PANI–zinc device results in an energy density of 255 Wh kg−1 at a power density of 1.8 kW kg−1 and a robust cycle lifetime of 3,850 charge/discharge cycles. The PANI at a high current density of 6.5 mA cm−2 reaches a capacity of 257 mAh g−1, equivalent to 87% of the its theoretical capacity, showcasing the effectiveness of the protic additive in improving both capacity and cycle life in electrochemical supercapacitors.

Human action recognition systems enhance public safety by detecting abnormal behavior autonomously. RGB sensors commonly used in such systems capture personal information of subjects and, as a result, run the risk of potential privacy leakage. On the other hand, privacy-safe alternatives, such as depth or thermal sensors, exhibit poorer performance because they lack the semantic context provided by RGB sensors. Moreover, the data availability of privacy-safe alternatives is significantly lower than RGB sensors. To address these problems, we explore effective cross-modality distillation methods in this paper, aiming to distill the knowledge of context-rich large-scale pre-trained RGB-based models into privacy-safe depth-based models. Based on extensive experiments on multiple architectures and benchmark datasets, we propose an effective method for training privacy-safe depth-based action recognition models via cross-modality distillation: cross-modality mixing distillation. This approach improves both the performance and efficiency by enabling interaction between depth and RGB modalities through a linear combination of their features. By utilizing the proposed cross-modal mixing distillation approach, we achieve state-of-the-art accuracy in two depth-based action recognition benchmarks. The code and the pre-trained models will be available upon publication.

Transparent heaters are gaining significant attention for applications such as antifog glass, smart windows, and smart farm greenhouses. A transparent heater basically consists of transparent conducting materials that serve as a heating area and contact pad electrode to apply power. To fabricate a transparent heater, materials with excellent light transmittance and low sheet resistance are required. Among various transparent conducting materials, such as Indium Tin Oxide (ITO), carbon nanotube (CNT), graphene, and silver nanowires (AgNWs), AgNWs are particularly favored due to their good electrical, optical, and mechanical properties. However, in order to improve the heating characteristics of transparent heaters, research is essential not only on improving the properties of transparent conducting materials but also on the design of contact pad electrodes that can uniformly improve current distribution. Here, we explore various shapes of contact pad electrodes for AgNW-based transparent heaters to improve current distribution. Shapes such as line, spot, twisted, and parallel-type contact pad electrodes are designed and investigated to optimize overall heating characteristics. We analyze the heating properties of these transparent heaters with various contact pad electrodes, demonstrating how their specific shape and size affect heating characteristics and uniformity. We also investigate the optimal shape of the contact pad electrode to minimize transmission loss through UV-VIS spectroscopy. As a result, we confirm that the shape of the contact pad electrode was important for simultaneously achieving high heating characteristics of 120 °C, good heating uniformity, and over 80% transparency in an AgNW-based transparent heater.

This study derives a uniaxial tensile flow from spherical indentation data using an artificial neural network (ANN) combined with finite element (FE) analysis. The feasibility of the FE-based simulations is confirmed through experimental indentation for various steels. Parametric studies of the FE simulation are performed to generate an ANN training database. An encoding for feature extraction and a hyperparameter optimization is implemented to design the ANN with high predictive performance. The indentation load–depth curves are converted into hardening parameters through the trained ANN. The predictive performance of the FE–ANN model using real-life indentation data is investigated in-depth with thorough error evaluation, and verified by uniaxial tensile tests. The emphasis is made that the mean absolute percentage error between the experimental and simulated indentation data is required to be meticulously controlled below 1% to accurately predict the tensile properties. The validations demonstrate that the applied FE–ANN modeling approach is very robust and captures the tensile properties well. Furthermore, the Taguchi orthogonal array (OA) method that can achieve high efficiency and fidelity with less training data is discussed. The FE–ANN model is concisely designed using the Taguchi OA method and can predict elasticity as well as plasticity.

Metal-based transparent top electrodes allow electronic devices to achieve transparency, thereby expanding their application range. Silver nanowire (AgNW)-based transparent electrodes can function as transparent top electrodes, owing to their excellent conductivity and transmittance. However, they require a high-temperature drying process, which damages the bottom functional layers. Here, we fabricated two types of AgNW-based electrodes using the following three drying methods: thermal, room-temperature, and vacuum. Thereafter, we investigated the variation in their morphological, electrical, and optical characteristics as a function of the drying method and duration. When the AgNW-exposed electrode was dried at room temperature, it exhibited a high surface roughness and low conductivity, owing to the slow solvent evaporation. However, under vacuum, it exhibited a similar electrical conductivity to that achieved by thermal drying because of the decreased solvent boiling point and fast solvent evaporation. Conversely, the AgNW-embedded electrodes exhibited similar roughness values and electrical conductivities regardless of the drying method applied. This was because the polymer shrinkage during the AgNW embedding process generated capillary force and improved the interconnectivity between the nanowires. The AgNW-based electrodes exhibited similar optical properties regardless of the drying method and electrode type. This study reveals that vacuum drying can afford transparent top electrodes without damaging functional layers.

The eukaryotic translation elongation factor eEF-1Bβ1 (EF1Bβ) is a guanine nucleotide exchange factor that plays an important role in translation elongation. In this study, we show that the EF1Bβ protein is localized in the plasma membrane and cytoplasm, and that the transcripts should be expressed in most tissue types in seedlings. Sectioning of the inflorescence stem revealed that EF1Bβ predominantly localizes to the xylem vessels and in the interfascicular cambium. EF1Bβ gene silencing in efβ caused a dwarf phenotype with 38% and 20% reduction in total lignin and crystalline cellulose, respectively. This loss-of-function mutant also had a lower S/G lignin monomer ratio relative to wild type plants, but no changes were detected in a gain-of-function mutant transformed with the EF1Bβ gene. Histochemical analysis showed a reduced vascular apparatus, including smaller xylem vessels in the inflorescence stem of the loss-of-function mutant. Over-expression of EF1Bβ in an eli1 mutant background restored a WT phenotype and abolished ectopic lignin deposition as well as cell expansion defects in the mutant. Taken together, these data strongly suggest a role for EF1Bβ in plant development and cell wall formation in Arabidopsis.