The aprotic Li-O battery has attracted a great deal of interest because theoretically it can store more energy than today's Li-ion batteries. However, current Li-O batteries suffer from passivation/clogging of the cathode by discharged Li O , high charging voltage for its subsequent oxidation, and accumulation of side reaction products (particularly Li CO and LiOH) upon cycling. Here, an advanced Li-O battery with a hexamethylphosphoramide (HMPA) electrolyte is reported that can dissolve Li O , Li CO , and LiOH up to 0.35, 0.36, and 1.11 × 10 m, respectively, and a LiPON-protected lithium anode that can be reversibly cycled in the HMPA electrolyte. Compared to the benchmark of ether-based Li-O batteries, improved capacity, rate capability, voltaic efficiency, and cycle life are achieved for the HMPA-based Li-O cells. More importantly, a combination of advanced research techniques provide compelling evidence that operation of the HMPA-based Li-O battery is backed by nearly reversible formation/decomposition of Li O with negligible side reactions.

Drawing inspiration from biology, neuromorphic systems are of great interest in direct interaction and efficient processing of analogue signals in the real world and could be promising for the development of smart sensors. Here, we demonstrate an artificial sensory neuron consisting of an InGaZnO 4 (IGZO 4 )-based optical sensor and NbO x -based oscillation neuron in series, which can simultaneously sense the optical information even beyond the visible light region and encode them into electrical impulses. Such artificial vision sensory neurons can convey visual information in a parallel manner analogous to biological vision systems, and the output spikes can be effectively processed by a pulse coupled neural network, demonstrating the capability of image segmentation out of a complex background. This study could facilitate the construction of artificial visual systems and pave the way for the development of light-driven neurorobotics, bioinspired optoelectronics, and neuromorphic computing.

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In computed tomography, automated detection of pulmonary nodules with a broad spectrum of appearance is still a challenge, especially, in the detection of small nodules. An automated detection system usually contains two major steps: candidate detection and false positive (FP) reduction. We propose a novel strategy for fast candidate detection from volumetric chest CT scans, which can minimize false negatives (FNs) and false positives (FPs). The core of the strategy is a nodule-size-adaptive deep model that can detect nodules of various types, locations, and sizes from 3D images. After candidate detection, each result is located with a bounding cube, which can provide rough size information of the detected objects. Furthermore, we propose a simple yet effective CNNs-based classifier for FP reduction, which benefits from the candidate detection. The performance of the proposed nodule detection was evaluated on both independent and publicly available datasets. Our detection could reach high sensitivity with few FPs and it was comparable with the state-of-the-art systems and manual screenings. The study demonstrated that excellent candidate detection plays an important role in the nodule detection and can simplify the design of the FP reduction. The proposed candidate detection is an independent module, so it can be incorporated with any other FP reduction methods. Besides, it can be used as a potential solution for other similar clinical applications.INDEX TERMS Computed tomography, pulmonary nodule, object detection, deep-learning, convolutional neural networks.

In this paper, a MnO 2 /activated carbon (AC) composite with high electrochemical performance is synthesized through a novel synthesis method (Grafting Oxidation Method). The structure and morphology are analyzed using X-ray diffraction, Fourier transmission infrared spectra, scanning electron microscopy and transmission electron microscopy. Additionally, the electrochemical properties are evaluated through cyclic voltammetry, electrochemical impedance spectra and galvanostatic cycling measurements. The results demonstrate this MnO 2 /AC composite owes homogeneous particle size of nanometer dimension. The quasi-rectangular and symmetric cyclic voltammetry curves of the composite, which are measured under a three-electrode electrochemical system with a 0.5 mol L −1 Na 2 SO 4 solution at room temperature, indicate it has an ability of rapidly reversible Faraday reaction and good electrochemical behavior. Compared to the MnO 2 /AC prepared through liquid-phase method, the composite prepared by grafting oxidation method exhibits a much higher specific capacitance which is up to 332.6 F g −1 at scanning rate of 2 mV s −1 . A laboratory capacitor assembled with this MnO 2 /AC composite electrode shows an average capacitance attenuation rate of just 0.0068% after 2000 cycles. Besides, the impedance tests results show that the charge transfer resistance of this composite is 0.92 , which is much lower than the composite (2.52 ) synthesized through liquid-phase method. Supercapacitors, also known as electrochemical capacitors or ultracapacitors, have attracted considerable interest worldwide, primarily because of their ability to provide higher power densities than batteries and higher energy densities than conventional dielectric capacitors. 1,2Because of these advantages, supercapacitors can be widely used in applications such as hybrid vehicles, electronic devices, and digital products. 3-6The most crucial factors determining the electrochemical performance rely on the electrode materials. The electrode-active materials that are widely used for supercapacitors include carbon, conducting polymers and transition-metal oxides. Considering the investigated electrode materials, transition metal oxides are considered to be good alternatives due to their high capacity from pseudocapacitance. 26 have investigated the charge storage mechanism of manganese dioxide compounds with various structures. They discovered that the capacitance of all amorphous compounds was due to faradaic processes localized at the surface and subsurface regions of the electrode. The capacitance of the crystallized materials is clearly dependent upon the crystalline structure, especially with the size of the tunnels that could be able to provide limited cations intercalation. It's also found that the interlayer spacing of the MnO 2 birnessite structure increased upon electrochemical oxidation in the presence of Na + cations in the electrolyte due to the deintercalation of Na + and the intercalation of H 2 O between the layers. 25However, a major drawba...

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Aqueous aluminum‐ion batteries (AABs) are regarded as promising next‐generation energy storage devices, and the current reported cathodes for AABs mainly focused on inorganic materials which usually implement a typical Al3+ ions (de)insertion mechanism. However, the strong electrostatic forces between Al3+ and the host materials usually lead to sluggish kinetics, poor reversibility and inferior cycling stability. Herein, we employ an organic compound with redox‐active moieties, phenazine (PZ), as the cathode material in AABs. Different from conventional inorganic materials confined by limited lattice spacing and rigid structure, the flexible organic molecules allow a large‐size Al‐complex co‐intercalation through reversible redox active centers (‐C=N‐) of PZ. This co‐intercalation behavior can effectively reduce desolvation penalty, and substantially lower the Coulombic repulsion during the ion (de)insertion process. Consequently, this organic cathode exhibits a high capacity and excellent cyclability, which exceeds those of most reported electrode materials for AABs. This work highlights the anion co‐intercalation chemistry of redox‐active organic materials, which is expected to boost the development of high‐performance multivalent‐ion battery systems.

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.