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Nanostructures are critical for improving the contact area with an electrolyte and catalytic efficiency for the CO2 reduction reaction (CO2RR). However, their hydrophobicity conflicts with the intended increase in the contact area and complicates the determination of the active contact area. Here, bismuth–nickel (BiNi) micro–nano hierarchical catalysts for the CO2RR were studied to understand the effects of electrolyte–catalyst contact area variation with the immersion duration in an aqueous electrolyte. The immersed BiNi samples showed about 13.4-fold higher formate production compared to the pristine BiNi sample. The 2-day pre-immersed BiNi sample exhibited faradaic efficiencies (FE%) of ∼80.1% for formate and ∼10% for H2 with a current density of 10.2 mA cm−2 at −1.5 V vs. Ag/AgCl. In contrast, the pristine BiNi catalysts exhibited an FE% of ∼12.9% for formate and ∼76.3% for H2 with a current density of 5.38 mA cm−2. Our experimental results reveal that the improved contact between the electrolyte and the catalyst surface through pre-immersion can lead to enhanced CO2RR efficiency for formate production using hierarchical BiNi catalysts. [ABSTRACT FROM AUTHOR]

Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications. [ABSTRACT FROM AUTHOR]

The progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed. [ABSTRACT FROM AUTHOR]

Flexible bioelectronic devices have attracted immense interest in the biomedical field because of their wearability, biocompatibility, diverse functionalities, and ease of personalization. Recently, flexible energy harvesters have been developed, and attempts have been made to integrate them with such devices, enabling them to operate without external batteries or power supply. In this review, the latest advances in flexible energy harvesters and their use in wearable devices are discussed. Energy harvesters for versatile biomedical applications are summarized. Finally, the challenges and future perspectives of self‐powered biomedical systems that enable wearable healthcare monitoring and management are outlined. [ABSTRACT FROM AUTHOR]