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Nanogenerators are attempting to be one of the ideal power supply devices for dealing with the global energy crisis. Nowadays, piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs) are widely studied in the energy field. Herein, a hybrid arch‐shaped nanogenerator is fabricated by combining piezoelectric and triboelectric effects. Polyvinylidene fluoride (PVDF) is used for PENGs. The titanium nitride/polydimethylsiloxane (TiN/PDMS) composite films with microporous structures are prepared for use in TENG. The maximum output peak‐to‐peak voltage and output peak‐to‐peak current of the PENG are 24 V and 17 μA, respectively, with peak power of 0.8 μW. The maximum output peak‐to‐peak voltage and peak‐to‐peak current of the TENG are 35 V and 17 μA, respectively, with peak power of 1.6 μW. The hybrid nanogenerator has a maximum output peak‐to‐peak voltage and peak‐to‐peak current of 73 V and 35 μA, respectively, and a peak power of 3.9 μW. In addition, the hybrid nanogenerator has a stable charging voltage of DC 4 V, which is better than a single TENG (DC 0.3 V) or PENG (DC 2.2 V). The hybrid nanogenerator shows excellent output performance and energy conversion efficiency among existing nanogenerators of similar size. [ABSTRACT FROM AUTHOR]

An effective strategy to optimize the gas separation performance of hyperbranched polyimide (hyper‐PI) membranes by tuning their average cavity sizes is reported. Triphenylamine (TAPA) and 4,4′‐(Hexafluoroisopropylidene) diphthalic anhydride (6FDA) were used to prepare the hyper‐PI membrane, and the transformation in free fraction volume (FFV) was realized by the addition of an extra B2‐type monomer, 2,4,6‐Trimethylbenzene‐1,3‐diamine (DAM). In hyperbranched networks, DAM, in conjunction with 6FDA, generates longer linear parts and reduces the density of dendritic architectures formed by TAPA. The conformational freedom of hyperbranched TAPA‐6FDA is restricted, and chain packing is hindered. The ratio of DAM:TAPA predominantly influences both the average cavity sizes and the FFVs of the membranes, leading to the variation in gas permeabilities. DAM5:TAPA1‐6FDA shows the best performance in both O2/N2 and CO2/N2 separations. [ABSTRACT FROM AUTHOR]

Knowledge graph (KG) has been fully considered in natural language generation (NLG) tasks. A KG can help models generate controllable text and achieve better performance. However, most existing related approaches still lack explainability and scalability in large‐scale knowledge reasoning. In this work, we propose a novel CogNLG framework for KG‐to‐text generation tasks. Our CogNLG is implemented based on the dual‐process theory in cognitive science. It consists of two systems: one system acts as the analytic system for knowledge extraction, and another is the perceptual system for text generation by using existing knowledge. During text generation, CogNLG provides a visible and explainable reasoning path. Our framework shows excellent performance on all datasets and achieves a BLEU score of 36.7, which increases by 6.7 compared to the best competitor. [ABSTRACT FROM AUTHOR]