No abstract

Lithium-sulfur batteries are a promising high energy output solution for substitution of traditional lithium ion batteries. In recent times research in this field has stepped into the exploration of practical applications. However, their applications are impeded by cycling stability and short life-span mainly due to the notorious polysulfide shuttle effect. In this work, a multifunctional sulfur host fabricated by grafting highly conductive Co 3 Se 4 nanoparticles onto the surface of an N-doped 3D carbon matrix to inhibit the polysulfide shuttle and improve the sulfur utilization is proposed. By regulating the carbon matrix and the Co 3 Se 4 distribution, N-CN-750@Co 3 Se 4 -0.1 m with abundant polar sites is experimentally and theoretically shown to be a good LiPSs absorbent and a sulfur conversion accelerator. The S/N-CN-750@ Co 3 Se 4 -0.1 m cathode shows excellent sulfur utilization, rate performance, and cyclic durability. A prolonged cycling test of the as-fabricated S/N-CN-750@Co 3 Se 4 -0.1 m cathode is carried out at 0.2 C for more than 5 months which delivers a high initial capacity of 1150.3 mAh g −1 and retains 531.0 mAh g −1 after 800 cycles with an ultralow capacity reduction of 0.067% per cycle, maintaining Coulombic efficiency of more than 99.3%. The reaction details are characterized and analyzed by ex situ measurements. This work highly emphasizes the potential capabilities of transition-metal selenides in lithium-sulfur batteries.

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection. To address this, we developed a method to decompose EEG data into 7 commonly used wavelet families, to the maximum theoretical level of each mother wavelet. Wavelets and decomposition levels providing the highest accuracy in each wavelet family were then searched in an exhaustive selection of frequency bands, which showed optimal accuracy and low computational cost. The selection of frequency bands and features removed approximately 40% of redundancies. The developed algorithm achieved promising performance on two well-tested EEG datasets (accuracy >90% for both datasets). The experimental results of the developed method have demonstrated that the settings of DWT affect its performance on seizure detection substantially. Compared with existing seizure detection methods based on wavelet, the new approach is more accurate and transferable among datasets.

Fabricating flexible pressure sensors with high sensitivity in a broad pressure range is still a challenge. Herein, a flexible pressure sensor with engineered microstructures on polydimethylsiloxane (PDMS) film is designed. The high performance of the sensor derives from its unique pyramid‐wall‐grid microstructure (PWGM). A square array of dome‐topped pyramids and crossed strengthening walls on the film forms a multiheight hierarchical microstructure. Two pieces of PWGM flexible PDMS film, stacked face‐to‐face, form a piezoresistive sensor endowed with ultrahigh sensitivity across a very broad pressure range. The sensitivity of the device is as high as 383 665.9 and 269 662.9 kPa−1 in the pressure ranges 0–1.6 and 1.6–6 kPa, respectively. In the higher pressure range of 6.1–11 kPa, the sensitivity is 48 689.1 kPa−1, and even in the very high pressure range of 11–56 kPa, it stays at 1266.8 kPa−1. The pressure sensor possesses excellent bending and torsional strain detection properties, is mechanically durable, and has potential applications in wearable biosensing for healthcare. In addition, 2 × 2 and 4 × 4 sensor arrays are prepared and characterized, suggesting the possibility of manufacturing a flexible tactile sensor.

The existence of rechargeable lithium ion batteries with high operating voltage, high energy density, and excellent cycling performance are drawing increasing attention due to their viability to be used as portable power and in electrical applications. However, there is a considerable problem that the conductivity of the active material becomes poor due to the volume expansion under the condition of repeated circulation, which reduces the performance of the device, thus hindering its practical application. As an emerging 2D material, black phosphorus (BP) has drawn significant attention in the field of Li‐ion battery energy storage due to its large theoretical capacity of 2596 mA h g−1 and ability to absorb large amount of Li atoms. Here, a unique 3D conductive structure with the BP and carbon nanotubes (CNTs), displaying good stability and high conductivity for the fabrication of BP@CNTs hybrid‐based Li‐ion batteries is described. With strong trapping, good affinity, structure stable, and high adsorption for polyphosphorus, the developed BP@CNTs hybrid electrodes display high capacity, good electrical conductivity, and a stable cycle life. Additionally, the lithium ion batteries can illuminate the light emitting diode, proving that the materials have great potential for development of energy storage devices.