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Keggin-type polyoxometalate (POM) cluster based non-volatile memory has been investigated, and the molecular reconfiguration induced by the reduction process of POM molecules is proposed to initialize the resistive switching behavior.

To quantitatively evaluate the output performance of triboelectric nanogenerators, figures of merit have been developed. However, the current figures of merit, without considering the breakdown effect that seriously affects the effective maximized energy output, are limited for application. Meanwhile, a method to evaluate output capability of nanogenerators is needed. Here, a standardized method that considers the breakdown effect is proposed for output capability assessment of nanogenerators. Contact separation and contact freestanding-triboelectric-layer modes triboelectric nanogenerators are used to demonstrate this method, and the effective maximized energy output and revised figures of merit are calculated based on the experimental results. These results are consistent with those theoretically calculated based on Paschen’s law. This method is also conducted to evaluate a film-based piezoelectric nanogenerator, demonstrating its universal applicability for nanogenerators. This study proposes a standardized method for evaluating the effective output capability of nanogenerators, which is crucial for standardized evaluation and application of nanogenerator technologies.

Through years of development, the triboelectric nanogenerator (TENG) has been demonstrated as a burgeoning efficient energy harvester. Plenty of efforts have been devoted to further improving the electric output performance through material/surface optimization, ion implantation or the external electric circuit. However, all these methods cannot break through the fundamental limitation brought by the inevitable electrical breakdown effect, and thus the output energy density is restricted. Here, a method for enhancing the threshold output energy density of TENGs is proposed by suppressing the breakdown effects in the high-pressure gas environment. With that, the output energy density of the contact-separation mode TENG can be increased by over 25 times in 10 atm than that in the atmosphere, and that of the freestanding sliding TENG can also achieve over 5 times increase in 6 atm. This research demonstrates the excellent suppression effect of the electric breakdown brought by the high-pressure gas environment, which may provide a practical and effective technological route to promote the output performance of TENGs.

The conversion and transmission of blue energy in the ocean are critical issues. By employing triboelectric nanogenerators (TENGs), blue energy can be harvested but the corresponding electricity transmission and storage are still great challenges. In this work, an automatic high‐efficiency self‐powered energy collection and conversion system is proposed that converts blue energy to chemical energy. A gear‐driven unidirectional acceleration TENG is designed to convert disordered and low‐frequency water wave energy to low voltage and high current DC output. The output bias from the TENG can be used to drive a Ti–Fe2O3/FeNiOOH based photoelectrochemical cell under sunlight to produce hydrogen. Moreover, under the situation without sunlight, the self‐powered system can be automatically switched to another working state to charge a Co3O4 based lithium‐ion battery. The hydrogen production rate reaches to 4.65 µL min‐1 under sunlight at the rotation speed of 120 rpm. The conversion efficiency of the whole system is calculated to be 2.29%. The system triggered by photoswitches can automatically switch between two working states with or without sunlight and convert the blue energy to either hydrogen energy or battery energy for easy storage and transmission, which widens the future applications for blue energy.

Nanogenerators have been demonstrated as a high-efficiency energy harvesting technology, while methods to evaluate merits of nanogenerators are being focused. Energy density, as a common way for evaluating energy devices, is believed to be strongly related to the output performance limit of nanogenerators. Hence, this report introduces an evaluation standard, the output energy density, to evaluate the performance of nanogenerators. With the sliding freestanding mode TENG as an example, theoretical simulations are conducted and experimental methods are developed to understand and optimize the maximal output energy density, with the breakdown effect considered. By comparing the output energy density of TENGs and other nanogenerators, sliding-triggered TENGs are demonstrated with the highest output energy density, which is approaching 1 × 10 4 J/m 3 . This study demonstrates the advantages of sliding-triggered TENGs in output energy density due to the suppressed breakdown effect and further confirmed the "killer application" of TENGs in harvesting low-frequency and small-scale mechanical energy.

Inspired by the highly parallel processing power and low energy consumption of the biological nervous system, the development of a neuromorphic computing paradigm to mimic brain-like behaviors with electronic components based artificial synapses may play key roles to eliminate the von Neumann bottleneck. Random resistive access memory (RRAM) is suitable for artificial synapse due to its tunable bidirectional switching behavior. In this work, a biological spiking synapse is developed with solution processed Au@Ag core-shell nanoparticle (NP)-based RRAM. The device shows highly controllable bistable resistive switching behavior due to the favorable Ag ions migration and filament formation in the composite film, and the good charge trapping and transport property of Au@Ag NPs. Moreover, comprehensive synaptic functions of biosynapse including paired-pulse depression, paired-pulse facilitation, post-tetanic potentiation, spike-time-dependent plasticity, and the transformation from short-term plasticity to long-term plasticity are emulated. This work demonstrates that the solution processed bimetal core-shell nanoparticle-based biological spiking synapse provides great potential for the further creation of a neuromorphic computing system.