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Abstract Suppressing nonradiative recombination and releasing residual strain are prerequisites to improving the efficiency and stability of perovskite solar cells (PSCs). Here, long‐chain polyacrylic acid (PAA) is used to reinforce SnO2 film and passivate SnO2 defects, forming a structure similar to “reinforced concrete” with high tensile strength and fewer microcracks. Simultaneously, PAA is also introduced to the SnO2/perovskite interface as a “buffer spring” to release residual strain, which also acts as a “dual‐side passivation interlayer” to passivate the oxygen vacancies of SnO2 and Pb dangling bonds in halide perovskites. As a result, the best inorganic CsPbBr3 PSC achieves a champion power conversion efficiency of 10.83% with an ultrahigh open‐circuit voltage of 1.674 V. The unencapsulated PSC shows excellent stability under 80% relative humidity and 80°C over 120 days.

Abstract Realization of stable and industrial-level H2O2 electroproduction still faces great challenge due large partly to the easy decomposition of H2O2. Herein, a two-dimensional dithiine-linked phthalocyaninato cobalt (CoPc)-based covalent organic framework (COF), CoPc-S-COF, was afforded from the reaction of hexadecafluorophthalocyaninato cobalt (II) with 1,2,4,5-benzenetetrathiol. Introduction of the sulfur atoms with large atomic radius and two lone-pairs of electrons in the C-S-C linking unit leads to an undulated layered structure and an increased electron density of the Co center for CoPc-S-COF according to a series of experiments in combination with theoretical calculations. The former structural effect allows the exposition of more Co sites to enhance the COF catalytic performance, while the latter electronic effect activates the 2e− oxygen reduction reaction (2e− ORR) but deactivates the H2O2 decomposition capability of the same Co center, as a total result enabling CoPc-S-COF to display good electrocatalytic H2O2 production performance with a remarkable H2O2 selectivity of >95% and a stable H2O2 production with a concentration of 0.48 wt% under a high current density of 125 mA cm−2 at an applied potential of ca. 0.67 V versus RHE for 20 h in a flow cell, representing the thus far reported best H2O2 synthesis COFs electrocatalysts.

Abstract Quantitative analysis of gait parameters, such as stride frequency and step speed, is essential for optimizing physical exercise for the human body. However, the current electronic sensors used in human motion monitoring remain constrained by factors such as battery life and accuracy. This study developed a self‐powered gait analysis system (SGAS) based on a triboelectric nanogenerator (TENG) fabricated electrospun composite nanofibers for motion monitoring and gait analysis for regulating exercise programs. The SGAS consists of a sensing module, a charging module, a data acquisition and processing module, and an Internet of Things (IoT) platform. Within the sensing module, two specialized sensing units, TENG‐S1 and TENG‐S2, are positioned at the forefoot and heel to generate synchronized signals in tandem with the user's footsteps. These signals are instrumental for real‐time step count and step speed monitoring. The output of the two TENG units is significantly improved by systematically investigating and optimizing the electrospun composite nanofibers' composition, strength, and wear resistance. Additionally, a charge amplifier circuit is implemented to process the raw voltage signal, consequently bolstering the reliability of the sensing signal. This refined data is then ready for further reading and calculation by the micro‐controller unit (MCU) during the signal transmission process. Finally, the well‐conditioned signals are wirelessly transmitted to the IoT platform for data analysis, storage, and visualization, enhancing human motion monitoring.

Multimodal haptic perception is essential for enhancing perceptual experiences in augmented reality applications. To date, several artificial tactile interfaces have been developed to perceive pressure and precontact signals, while simultaneously detecting object type and softness with quantified modulus still remains challenging. Here, inspired by the campaniform sensilla on insect antennae, we proposed a hemispherical bimodal intelligent tactile sensor (BITS) array using the triboelectric effect. The system is capable of softness identification, modulus quantification, and material type recognition. In principle, due to the varied deformability of materials, the BITS generates unique triboelectric output fingerprints when in contact with the tested object. Furthermore, owing to the different electron affinities, the BITS array can accurately recognize material type (99.4% accuracy), facilitating softness recognition (100% accuracy) and modulus quantification. It is promising that the BITS based on the triboelectric effect has the potential to be miniaturized to provide real-time accurate haptic information as an artificial antenna toward applications of human-machine integration. [ABSTRACT FROM AUTHOR]