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Monitoring the driver's physical and mental state based on wearable EEG acquisition equipment, especially the detection and early warning of fatigue, is a key issue in the research of the brain–computer interface in human–machine intelligent fusion driving. Comparing and analyzing the waking (alert) state and fatigue state by simulating EEG data during simulated driving, this paper proposes a brain functional network construction method based on a phase locking value (PLV) and phase lag index (PLI), studies the relationship between brain regions, and quantitatively analyzes the network structure. The characteristic parameters of the brain functional network that have significant differences in fatigue status are screened out and constitute feature vectors, which are then combined with machine learning algorithms to complete classification and identification. The experimental results show that this method can effectively distinguish between alertness and fatigue states. The recognition accuracy rates of 52 subjects are all above 70%, with the highest recognition accuracy reaching 89.5%. Brain network topology analysis showed that the connectivity between brain regions was weakened under a fatigue state, especially under the PLV method, and the phase synchronization relationship between delta and theta frequency bands was significantly weakened. The research results provide a reference for understanding the interdependence of brain regions under fatigue conditions and the development of fatigue driving detection systems. [ABSTRACT FROM AUTHOR]

The development of efficient photocatalysts for artificial photocatalytic energy conversion is an intriguing strategy. Promisingly, conjugated polymers (CPs) have been actively investigated as alternatives to traditional inorganic semiconductors for photocatalysis due to their molecularly tunable optoelectronic properties, thus providing a great platform for molecular design. Incorporating donor (D) and acceptor (A) units into the backbone of CPs ensures an adequate D−A interface, which is essential for facilitating charge separation. This approach also allows for tunable bandgaps and optoelectronic properties, leading to significant progress in photocatalytic energy conversions in recent years. Here, the fundamentals of D–A type CPs for photocatalysis are initially outlined, followed by advanced experimental methods and density functional theory (DFT) calculations for investigating carrier dynamics. Then, a detailed exposition of the synthetic strategies for D−A type CPs is carried out. Their extensive applications in diverse energy‐related photocatalytic conversions, such as hydrogen evolution, oxygen evolution, overall water splitting, CO2 reduction, N2 reduction, and H2O2 evolution are comprehensively presented. This review provides new and comprehensive insights into the molecular‐level design of D−A type CPs catalysts for boosted photocatalytic energy conversion, which is expected to further advance the development of CPs in photocatalysis. [ABSTRACT FROM AUTHOR]

Motor imagery, as a paradigm of brainmachine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.

Covalent triazine frameworks (CTFs) are emerging as a promising molecular platform for photocatalysis. Nevertheless, the construction of highly effective charge transfer pathways in CTFs for oriented delivery of photoexcited electrons to enhance photocatalytic performance remains highly challenging. Herein, a molecular engineering strategy is presented to achieve highly efficient charge separation and transport in both the lateral and vertical directions for solar-to-formate conversion. Specifically, a large π-delocalized and π-stacked Schottky junction (Ru-Th-CTF/RGO) that synergistically knits a rebuilt extended π-delocalized network of the D-A 1 -A 2 system (multiple donor or acceptor units, Ru-Th-CTF) with reduced graphene oxide (RGO) is developed. It is verified that the single-site Ru units in Ru-Th-CTF/RGO act as effective secondary electron acceptors in the lateral direction for multistage charge separation/transport. Simultaneously, the π-stacked and covalently bonded graphene is regarded as a hole extraction layer, accelerating the separation/transport of the photogenerated charges in the vertical direction over the Ru-Th-CTF/RGO Schottky junction with full use of photogenerated electrons for the reduction reaction. Thus, the obtained photocatalyst has an excellent CO 2 -to-formate conversion rate (≈11050 µmol g -1 h -1 ) and selectivity (≈99%), producing a state-of-the-art catalyst for the heterogeneous conversion of CO 2 to formate without an extra photosensitizer., (© 2023 Wiley-VCH GmbH.)