Design of dual carbon encapsulated porous micron silicon composite with compact surface for enhanced reaction kinetics of lithium-ion battery anodes.

Developing high-performance composites with fast charging and superior cycle life is paramount for lithium-ion batteries (LIBs). Herein, we synthesized a double-shell carbon-coated porous structure composite with a compact surface (P-Si@rGO@C) using low-cost commercial micron-sized silicon (Si) instead of nanoscale silicon. Results reveal that the unique P-Si@rGO@C features high adaptability to volume expansion, accelerates electron/ion transmission rate, and forms a stable solid electrolyte interphase (SEI) film. This phenomenon arises from the synergistic effect of abundant internal voids and an external double-layer carbon shell with a dense surface. Specifically, the P-Si@rGO@C anode exhibits a high initial coulombic efficiency (ICE) (88.0 %), impressive rate-capability (612.1 mAh/g at 2C), and exceptional long-term cyclability (972.2 mAh/g over 500 cycles at 0.5C). Further kinetic studies elucidate the diffusion-capacitance hybrid energy storage mechanism and reveal an improved Li+ diffusion coefficient (from 3.47 × 10-11 to 2.85 × 10-9 cm2 s-1). Ex-situ characterization confirms the crystal phase change of micron-sized Si and the formation of a stable LiF-rich SEI. Theoretical calculations support these findings by demonstrating an enhancement in the adsorption ability of Si to Li+ (from -0.89 to -0.97 eV) and a reduction in the energy migration barrier (from 0.35 to 0.18 eV). Additionally, practical LixSi powder is shown to increase the ICE of full cells from 67.4 % to 87.9 %. Furthermore, a pouch cell utilizing the prelithiated P-Si@rGO@C anode paired with LiNi1/3Co1/3Mn1/3O2 (NCM111) cathode delivers a high initial reversible capacity of 7.2 mAh and 76.8 % capacity retention after 100 cycles. This work provides insights into the application of commercial silicon-aluminum alloy powder in the anode of high-energy LIBs.

High-Performance Multi-Level Grayscale Conversion by Driving Waveform Optimization in Electrowetting Displays.

As a new type of reflective display, electrowetting display (EWD) has excellent dynamic display performance, which is based on polymer coatings. However, there are still some issues which can limit its performance, such as oil backflow and the hysteresis effect which reduces the stability and response speed of EWDs. Therefore, an effective driving waveform was proposed to overcome these drawbacks, which consisted of grayscale conversions between low gray levels and high gray levels. In the driving waveform, to stabilize the EWD at any initial grayscale (low gray levels/high gray levels), an exponential function waveform and an AC signal were used. Then, the grayscale conversion was performed by using an AC signal with a switching voltage to quickly achieve the target grayscale. Finally, another AC signal was used to stabilize the EWD at the target grayscale. A set of driving waveforms in grayscale ranging across four levels was designed using this method. According to the experimental results, oil backflow and the hysteresis effect could be effectively attenuated by the proposed driving waveforms. During conversion, the response speed of EWDs was boosted by at least 9.37% compared to traditional driving waveforms.

Editorial for the Special Issue on Advances in Optoelectronic Devices.

Optoelectronic devices are fabricated based on an optoelectronic conversion effect, which is a developing research field of modern optoelectronic technology and microelectronics technology [...].

In Situ Growth of Iron Sulfide on Fast Charge Transfer V2 C-MXene for Superior Sodium Storage Anodes.

Due to the upstream pressure of lithium resources, low-cost sodium-ion batteries (SIBs) have become the most potential candidates for energy storage systems in the new era. However, anode materials of SIBs have always been a major problem in their development. To address this, V2 C/Fe7 S8 @C composites with hierarchical structures prepared via an in situ synthesis method are proposed here. The 2D V2 C-MXene as the growth substrate for Fe7 S8  greatly improves the rate capability of SIBs, and the carbon layer on the surface provides a guarantee for charge-discharge stability. Unexpectedly, the V2 C/Fe7 S8 @C anode achieves satisfactory sodium storage capacity and exceptional rate performance (389.7 mAh g-1  at 5 A g-1 ). The sodium storage mechanism and origin of composites are thoroughly studied via ex situ characterization techniques and first-principles calculations. Furthermore, the constructed sodium-ion capacitor assembled with N-doped porous carbon delivers excellent energy density (135 Wh kg-1 ) and power density (11 kW kg-1 ), showing certain practical value. This work provides an advanced system of sodium storage anode materials and broadens the possibility of MXene-based materials in the energy storage.

Multi-user motion recognition using sEMG via discriminative canonical correlation analysis and adaptive dimensionality reduction.

The inability of new users to adapt quickly to the surface electromyography (sEMG) interface has greatly hindered the development of sEMG in the field of rehabilitation. This is due mainly to the large differences in sEMG signals produced by muscles when different people perform the same motion. To address this issue, a multi-user sEMG framework is proposed, using discriminative canonical correlation analysis and adaptive dimensionality reduction (ADR). The interface projects the feature sets for training users and new users into a low-dimensional uniform style space, overcoming the problem of individual differences in sEMG. The ADR method removes the redundant information in sEMG features and improves the accuracy of system motion recognition. The presented framework was validated on eight subjects with intact limbs, with an average recognition accuracy of 92.23% in 12 categories of upper-limb movements. In rehabilitation laboratory experiments, the average recognition rate reached 90.52%. The experimental results suggest that the framework offers a good solution to enable new rehabilitation users to adapt quickly to the sEMG interface.

Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method.

To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.

A Contention-Based Hop-By-Hop Bidirectional Congestion Control Algorithm for Ad-Hoc Networks.

Existing hop-by-hop congestion control algorithms are mainly divided into two categories: those improving the sending rate and those suppressing the receiving rate. However, these congestion control algorithms have problems with validity and limitations. It is likely that the network will be paralyzed due to the unreasonable method of mitigating congestion. In this paper, we present a contention-based hop-by-hop bidirectional congestion control algorithm (HBCC). This algorithm uses the congestion detection method with queue length as a parameter. By detecting the queue length of the current node and the next hop node, the congestion conditions can be divided into the following four categories: 0-0, 0-1, 1-0, 1-1 (0 means no congestion, 1 means congestion). When at least one of the two nodes is congested, the HBCC algorithm adaptively adjusts the contention window of the current node, which can change the priority of the current node to access the channel. In this way, the buffer queue length of the congested node is reduced. When the congestion condition is 1-1, the hop-by-hop priority congestion control (HPCC) method proposed in this paper is used. This algorithm adaptively changes the adjustment degree of the current node competition window and improves the priority of congestion processing of the next hop node. The NS2 simulation shows that by using the HBCC algorithm, when compared with distributed coordination function (DCF) without congestion control, the proposed unidirectional congestion control algorithms hop-by-hop receiving-based congestion control (HRCC) and hop-by-hop sending-based congestion control (HSCC), and the existing congestion control algorithm congestion alleviation-MAC (CA-MAC), the average saturation throughput increased by approximately 90%, 62%, 12%, and 62%, respectively, and the buffer overflow loss ratio reduced by approximately 80%, 79%, 44%, and 79%.

Modifying the microstructure of algae-based active carbon and modelling supercapacitors using artificial neural networks.

An improved activated carbon material is synthesized from nostoc flagelliforme algae (NF) using an acid immersing method. The material has more pores and lower internal resistance compared with raw NF. Hydrofluoric acid can effectively decompose cellulose fibers and remove inorganic impurities, giving the carbon materials high mesopore volumes, which makes electrolyte ions rapidly transfer to the active site on the electrode surface. The specific capacitance of the sample was increased from 200 to 283 F g-1 after immersing in hydrofluoric acid. In addition, the symmetric supercapacitor shows an excellent energy density of 22 W h kg-1 at a power density of 80 W kg-1. The capacitance remains at 101.7% after 10 000 cycles. Furthermore, in order to find the relationship between the biochar structure and electrochemical performance in supercapacitors, an artificial neural network (ANN) method is used for studying the complex synergy mechanism. The specific capacitance is modelled using various input factors like aspect ratio (r L/D), cellulose ratio (CL(%)), specific surface area (S BET), pore volume (V tot), internal resistance (R s) and so on. The Levenberg-Marquart back propagation algorithm with sigmoid and ReLu active function is adopted to train the model. Random forest is used to analyse the relative importance of every input factor on specific capacitance. Results show that the model can estimate the energy storage with a mean squared error of 4.39 for materials with specific structure. Importance analyses indicate the first three significant variables are S BET, R s and V por. The ANN model can accurately predict the electrical properties of biomass-based carbon materials, and also provide guidance for the selection of energy storage materials in the future.

Medium-sized [Formula: see text] (n = 14-20) clusters: a combined study of photoelectron spectroscopy and DFT calculations.

Size-selected anionic silicon clusters, [Formula: see text] (n = 14-20), have been investigated by photoelectron spectroscopy and density functional theory (DFT) calculations. Low-energy structures of the clusters are globally searched for by using a genetic algorithm based on DFT calculations. The electronic density of states and vertical detachment energies have been simulated by using ten DFT functionals and compared to the experimental results. We systematically evaluated the DFT functionals for the calculation of the energetics of silicon clusters. CCSD(T) single-point energies based on MP2 optimized geometries for selected isomers of [Formula: see text] are also used as benchmark for the energy sequence. The HSE06 functional with aug-cc-pVDZ basis set is found to show the best performance. Our global minimum search corroborates that most of the lowest-energy structures of [Formula: see text] (n = 14-20) clusters can be derived from assembling tricapped trigonal prisms in various ways. For most sizes previous structures are confirmed, whereas for [Formula: see text] a new structure has been found.