Atomically dispersed Zn–N–C nanomaterials are promising platinum‐free catalysts for the oxygen reduction reaction (ORR). However, the fabrication of Zn–N–C catalysts with a high Zn loading remains a formidable challenge owing to the high volatility of the Zn precursor during high‐temperature annealing. Herein, we report that an atomically dispersed Zn–N–C catalyst with an ultrahigh Zn loading of 9.33 wt % could be successfully prepared by simply adopting a very low annealing rate of 1° min−1. The Zn–N–C catalyst exhibited comparable ORR activity to that of Fe–N–C catalysts, and significantly better ORR stability than Fe–N–C catalysts in both acidic and alkaline media. Further experiments and DFT calculations demonstrated that the Zn–N–C catalyst was less susceptible to protonation than the corresponding Fe–N–C catalyst in an acidic medium. DFT calculations revealed that the Zn–N4 structure is more electrochemically stable than the Fe–N4 structure during the ORR process.

Computational imaging through scatter generally is accomplished by first characterizing the scattering medium so that its forward operator is obtained and then imposing additional priors in the form of regularizers on the reconstruction functional to improve the condition of the originally ill-posed inverse problem. In the functional, the forward operator and regularizer must be entered explicitly or parametrically (e.g., scattering matrices and dictionaries, respectively). However, the process of determining these representations is often incomplete, prone to errors, or infeasible. Recently, deep learning architectures have been proposed to instead learn both the forward operator and regularizer through examples. Here, we propose for the first time, to our knowledge, a convolutional neural network architecture called "IDiffNet" for the problem of imaging through diffuse media and demonstrate that IDiffNet has superior generalization capability through extensive tests with well-calibrated diffusers. We also introduce the negative Pearson correlation coefficient (NPCC) loss function for neural net training and show that the NPCC is more appropriate for spatially sparse objects and strong scattering conditions. Our results show that the convolutional architecture is robust to the choice of prior, as demonstrated by the use of multiple training and testing object databases, and capable of achieving higher space-bandwidth product reconstructions than previously reported.

Vibrations in the environment are usually distributed over a wide frequency spectrum in multiple directions and a weaker amplitude, which makes most of the current vibrational energy collectors limited in practical environmental applications. Herein, a triboelectric−electromagnetic hybridized nanogenerator (TEHG) for low-frequency random microvibrational energy harvesting in all directions and a wide working bandwidth is fabricated. The output peak power of a triboelectric nanogenerator (TENG) up to 3.65 mW is realized (θ = 0.4 rad, f = 1 Hz). In addition, a real self-powered seawater splitting system and electrochemical cathodic protection system are fabricated, directly converting blue energy to hydrogen energy, and the ships can achieve selfprotection against corrosion. Furthermore, relying on the linear relationship between the number of peaks and the amplitude of vibration, a highly sensitive self-powered vibration amplitude sensor system based on LabVIEW software is achieved, which can be used as an amplitude detection of bridges and earthquake monitoring, etc. This work is an important development for harvesting low-frequency random multiple direction microvibrational energy over a wide working bandwidth and the bright future of blue energy. In addition, it has been successfully applied to the power supply of portable electronic equipment, environmental monitors, and self-powered systems.

utilization. [1,3] However, harvesting such energy is rather challenging due to the limitations of traditional electromagnetic generators (EMGs), especially at low frequency. [4] How to harvest these types of energies with low cost, high stability, and high efficiency is the main barrier.A triboelectric nanogenerator (TENG) based on the friction-induced and electrostatic induction effects has been proved to be a powerful invention technology for converting random and low-frequency mechanical energy into electricity with unique merits of high power density, high efficiency, light weight, low cost, and simple fabrication. [1,4a,5] The TENG is much more effective than the EMG for harvesting energy in the frequency range of <5 Hz due to its distinct mechanism, which is ideally suited for our daily life and nature, such as the ocean waves. [4a,b] Recently, various TENGs with different structures and functions for blue energy have been developed, which can be roughly divided into two types: liquidsolid contact electrification TENGs [6] and fully enclosed solid-solid contact electrification TENGs. [7] However, robustness and packaging are critical factors for these TENGs toward practical applications, since the performance of TENG is greatly affected by environmental conditions, e.g., humidity. [1,5c] What's more, for fully enclosed solid-solid contact electrification TENG, it is difficult to disassemble after being enclosed, if there is something wrong with the devices. In addition, with the seawater immersion and corrosion, the package will also fail soon; once water infiltrates the device, it will be unable to work, especially for electrical devices.The newly invented triboelectric nanogenerator (TENG) is deemed to be a more efficient strategy than an electromagnetic generator (EMG) in harvesting low-frequency (<2 Hz) water wave energy. Various TENGs with different structures and functions for blue energy have been developed, which can be roughly divided into two types: liquid-solid contact electrification TENGs and fully enclosed solid-solid contact electrification TENGs. Robustness and packaging are critical factors in the development of TENGs toward practical applications. Furthermore, for fully enclosed TENGs, the requirements and costs of packaging are very high, and they can difficult to disassemble after enclosed, if there is something wrong with the devices. Herein, a nonencapsulative pendulum-like paper based hybrid nanogenerator for energy harvesting is designed, which mainly consists of three parts, one solar panel, two paper based zigzag multilayered TENGs, and three EMG units. This unique structure reveals the superior robustness and a maximum peak power of zigzag multilayered TENGs up to 22.5 mW is realized. Moreover, the device can be used to collect the mechanical energy of human motion in hand shaking. This work presents a new platform of hybrid generators toward energy harvesting as a portable practical power source, which has potential applications in navigation and lighting.

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Atomically dispersed Zn-N-C nanomaterials are promising platinum-free catalysts for the oxygen reduction reaction (ORR). However,the fabrication of Zn-N-C catalysts with ahigh Zn loading remains aformidable challenge owing to the high volatility of the Zn precursor during high-temperature annealing.Herein, we report that an atomically dispersed Zn-N-C catalyst with an ultrahigh Zn loading of 9.33 wt % could be successfully prepared by simply adopting av ery low annealing rate of 18 8 min À1 .T he Zn-N-C catalyst exhibited comparable ORR activity to that of Fe-N-C catalysts,a nd significantly better ORR stability than Fe-N-C catalysts in both acidic and alkaline media. Further experiments and DFT calculations demonstrated that the Zn-N-C catalyst was less susceptible to protonation than the corresponding Fe-N-C catalyst in an acidic medium. DFT calculations revealed that the Zn-N 4 structure is more electrochemically stable than the Fe-N 4 structure during the ORR process.