1e:T46c,Tactile perception includes the direct response of tactile corpuscles to environmental stimuli and psychological parameters associated with brain recognition. To date, several artificial haptic-based sensing techniques can accurately measure physical stimuli. However, quantifying the psychological parameters of tactile perception to achieve texture and roughness identification remains challenging. Here, we developed a smart finger with surpassed human tactile perception, which enabled accurate identification of material type and roughness through the integration of triboelectric sensing and machine learning. In principle, as each material has different capabilities to gain or lose electrons, a unique triboelectric fingerprint output will be generated when the triboelectric sensor is in contact with the measured object. The construction of a triboelectric sensor array could further eliminate interference from the environment, and the accuracy rate of material identification was as high as 96.8%. The proposed smart finger provides the possibility to impart artificial tactile perception to manipulators or prosthetics.1f:T4a9,A multifunctional wearable tactile sensor assisted by deep learning algorithms is developed, which can realize the functions of gesture recognition and interaction. This tactile sensor is the fusion of a triboelectric nanogenerator and piezoelectric nanogenerator to construct a hybrid self-powered sensor with a higher power density and sensibility. The power generation performance is characterized with an open-circuit voltage V_OC of 200 V, a short-circuit current I_SC of 8 μA, and a power density of 0.35 mW cm^-2 under a matching load. It also has an excellent sensibility, including a response time of 5 ms, a signal-to-noise ratio of 22.5 dB, and a pressure resolution of 1% (1-10 kPa). The sensor is successfully integrated on a glove to collect the electrical signal output generated by the gesture. Using deep learning algorithms, the functions of gesture recognition and control can be realized in real time. The combination of tactile sensor and deep learning algorithms provides ideas and guidance for its applications in the field of artificial intelligence, such as human-computer interaction, signal monitoring, and smart sensing.20:T54c,Abstract Ocean wave energy is one of the important renewable energy sources. However, random, low‐frequency, and micro‐amplitude characteristics of ocean waves make it difficult for traditional electromagnetic generators to collect wave energy efficiently. The emergence of triboelectric nanogenerator (TENG) provides an extremely effective technical means for the collection of low‐frequency and micro‐amplitude wave energy. In this work, a non‐contact turntable‐structured oscillating water column TENG (OWC‐TENG) is designed by combining an OWC wave energy conversion mechanism with a TENG for the first time. The OWC is optimized through simulation experiments according to the principles of ship hydrostatics and wave theory. The output performances of the OWC‐TENGs with different structural parameters under different water wave excitation conditions are then tested. The TENG delivers a maximum output current of 55.45 µA and an output power of 5.28 mW, which corresponds to a power density of 114.8 W m −3 , enabling a stable power supply for small sensors at sea. This work provides a new solution to the efficient collection of low‐frequency micro‐amplitude ocean wave energy for powering various offshore equipments, presenting broad application prospects in ocean blue energy development and offshore Internet of Things.21:T4e0,Due to the high application value in intelligent robots, tactile sensors with large sensing area and multi-dimensional sensing ability have attracted the attention of researchers in recent years. Inspired by bionics of hairs on human skin, a flexible tactile sensor based on magnetic cilia array is developed, showing extremely high sensitivity and stability. The upper layers of the

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