Interphase Engineering Enhanced Electro-chemical Stability of Prelithiated Anode.

Prelithiation is an essential technology to compensate for the initial lithium loss of lithium-ion batteries due to the formation of solid electrolyte interphase (SEI) and irreversible structure change. However, the prelithiated materials/electrodes become more reactive with air and electrolyte resulting in unwanted side reactions and contaminations, which makes it difficult for the practical application of prelithiation technology. To address this problem, herein, interphase engineering through a simple solution treatment after chemical prelithiation is proposed to protect the prelithiated electrode. The used solutions are carefully selected, and the composition and nanostructure of the as-formed artificial SEIs are revealed by cryogenic electron microscopy and X-ray photoelectron spectroscopy. The electrochemical evaluation demonstrates the unique merits of this artificial SEI, especially for the fluorinated interphase, which not only enhances the interfacial ion transport but also increases the tolerance of the prelithiated electrode to the air. The treated graphite electrode shows an initial Coulombic efficiency of 129.4%, a high capacity of 170 mAh g-1 at 3 C, and negligible capacity decay after 200 cycles at 1 C. These findings not only provide a facile, universal, and controllable method to construct an artificial SEI but also enlighten the upgrade of battery fabrication and the alternative use of advanced electrolytes.

Spin-dependent and tunable perfect absorption in a Fabry-Perot cavity containing a multi-Weyl semimetal.

Spin-dependent absorption has been widely studied in metamaterials and metasurfaces with chirality since it develops significant applications in multiplexed holograms, photodection, and filtering. Here, the one-dimensional photonic crystal Fabry-Perot (FP) cavity containing a multi-Weyl semimetal (mWSM) defect is proposed to investigate the spin-dependent perfect absorption. Results denote that the distinct refractive indices of right hand circularly polarized (RCP) and left hand circularly polarized (LCP) waves are present due to the nonzero off-diagonal term of mWSM, thus supporting the perfect absorption of RCP and LCP waves at distinct resonant wavelengths. The different perfect absorption wavelengths of RCP and LCP waves reveal the spin-dependent perfect absorption. By altering the Fermi energy, tilt degree of Weyl cones, Weyl nodes separation, topological charge, and thickness of the mWSM layer, the perfect absorption wavelength of RCP and LCP waves can be regulated conveniently. Particularly, the linear tunable perfect absorption wavelength with thickness of the mWSM layer supports the accurate determination of perfect absorption wavelength at distinct mWSM thicknesses. Our studies develop simple and effective approaches to acquire the spin-dependent and adjustable perfect absorption without the external magnetic field, and can find practical applications in spin-dependent photonic devices.

Probing the Effect of Photovoltaic Material on Voc in Ternary Polymer Solar Cells with Non-Fullerene Acceptors by Machine Learning.

The power conversion efficiency (PCE) of ternary polymer solar cells (PSCs) with non-fullerene has a phenomenal increase in recent years. However, improving the open circuit voltage (Voc) of ternary PSCs with non-fullerene still remains a challenge. Therefore, in this work, machine learning (ML) algorithms are employed, including eXtreme gradient boosting, K-nearest neighbor and random forest, to quantitatively analyze the impact mechanism of Voc in ternary PSCs with the double acceptors from the two aspects of photovoltaic materials. In one aspect of photovoltaic materials, the doping concentration has the greatest impact on Voc in ternary PSCs. Furthermore, the addition of the third component affects the energy offset between the donor and acceptor for increasing Voc in ternary PSCs. More importantly, to obtain the maximum Voc in ternary PSCs with the double acceptors, the HOMO and LUMO energy levels of the third component should be around (-5.7 ± 0.1) eV and (-3.6 ± 0.1) eV, respectively. In the other aspect of molecular descriptors and molecular fingerprints in the third component of ternary PSCs with the double acceptors, the hydrogen bond strength and aromatic ring structure of the third component have high impact on the Voc of ternary PSCs. In partial dependence plot, it is clear that when the number of methyl groups is four and the number of carbonyl groups is two in the third component of acceptor, the Voc of ternary PSCs with the double acceptors can be maximized. All of these findings provide valuable insights into the development of materials with high Voc in ternary PSCs for saving time and cost.

Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition.

Stroke is a significant cause of disability worldwide, and stroke survivors often experience severe motor impairments. Lower limb rehabilitation exoskeleton robots provide support and balance for stroke survivors and assist them in performing rehabilitation training tasks, which can effectively improve their quality of life during the later stages of stroke recovery. Lower limb rehabilitation exoskeleton robots have become a hot topic in rehabilitation therapy research. This review introduces traditional rehabilitation assessment methods, explores the possibility of lower limb exoskeleton robots combining sensors and electrophysiological signals to assess stroke survivors' rehabilitation objectively, summarizes standard human-robot coupling models of lower limb rehabilitation exoskeleton robots in recent years, and critically introduces adaptive control models based on motion intent recognition for lower limb exoskeleton robots. This provides new design ideas for the future combination of lower limb rehabilitation exoskeleton robots with rehabilitation assessment, motion assistance, rehabilitation treatment, and adaptive control, making the rehabilitation assessment process more objective and addressing the shortage of rehabilitation therapists to some extent. Finally, the article discusses the current limitations of adaptive control of lower limb rehabilitation exoskeleton robots for stroke survivors and proposes new research directions.

Transport and retention of n-hexadecane in cadmium-/naphthalene-contaminated calcareous soil sampled in a karst area.

Studying the transport of petroleum hydrocarbons in cadmium-/naphthalene-contaminated calcareous soils is crucial to comprehensive assessment of environmental risks and developing appropriate strategies to remediate petroleum hydrocarbons pollution in karst areas. In this study, n-hexadecane was selected as a model petroleum hydrocarbon. Batch experiments were conducted to explore the adsorption behavior of n-hexadecane on cadmium-/naphthalene-contaminated calcareous soils at various pH, and column experiments were performed to investigate the transport and retention of n-hexadecane under various flow velocity. The results showed that Freundlich model better described the adsorption behavior of n-hexadecane in all cases (R2 > 0.9). Under the condition of pH = 5, it was advantageous for soil samples to adsorb more n-hexadecane, and the maximum adsorption content followed the order of: cadmium/naphthalene-contaminated > uncontaminated soils. The transport of n-hexadecane in cadmium/naphthalene-contaminated soils at various flow velocity was well described by two kinetic sites model of Hydrus-1D with R2 > 0.9. Due to the increased electrostatic repulsion between n-hexadecane and soil particles, n-hexadecane was more easily able to breakthrough cadmium/naphthalene-contaminated soils. Compared to low flow velocity (1 mL/min), a higher concentration of n-hexadecane was determined at high flow velocity, with 67, 63, and 45% n-hexadecane in effluent from cadmium-contaminated soils, naphthalene-contaminated soils, and uncontaminated soils, respectively. These findings have important implications for the government of groundwater in calcareous soils from karst areas.

In Situ Detecting Thermal Stability of Solid Electrolyte Interphase (SEI).

Solid electrolyte interphase (SEI) plays an important role in regulating the interfacial ion transfer and safety of Lithium-ion batteries (LIBs). It is unstable and readily decomposed releasing much heat and gases and thus triggering thermal runaway. Herein, in situ heating X-ray photoelectron spectroscopy is applied to uncover the inherent thermal decomposition process of the SEI. The evolution of the composition, nanostructure, and the released gases are further probed by cryogenic transmission electron microscopy, and gas chromatography. The results show that the organic components of SEI are readily decomposed even at room temperature, releasing some flammable gases (e.g., H2 , CO, C2 H4 , etc.). The residual SEI after heat treatment is rich in inorganic components (e.g., Li2 O, LiF, and Li2 CO3 ), provides a nanostructure model for a beneficial SEI with enhanced stability. This work deepens the understanding of SEI intrinsic thermal stability, reveals its underlying relationship with the thermal runaway of LIBs, and enlightens to enhance the safety of LIBs by achieving inorganics-rich SEI.

A review: Music-emotion recognition and analysis based on EEG signals.

Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.

Clinical tooth segmentation based on local enhancement.

The tooth arrangements of human beings are challenging to accurately observe when relying on dentists' naked eyes, especially for dental caries in children, which is difficult to detect. Cone-beam computer tomography (CBCT) is used as an auxiliary method to measure patients' teeth, including children. However, subjective and irreproducible manual measurements are required during this process, which wastes much time and energy for the dentists. Therefore, a fast and accurate tooth segmentation algorithm that can replace repeated calculations and annotations in manual segmentation has tremendous clinical significance. This study proposes a local contextual enhancement model for clinical dental CBCT images. The local enhancement model, which is more suitable for dental CBCT images, is proposed based on the analysis of the existing contextual models. Then, the local enhancement model is fused into an encoder-decoder framework for dental CBCT images. At last, extensive experiments are conducted to validate our method.

Rational Design, Synthesis, and Biological Evaluation of Fluorine- and Chlorine-Substituted Pyrazol-5-yl-benzamide Derivatives as Potential Succinate Dehydrogenase Inhibitors.

To develop novel succinate dehydrogenase inhibitors (SDHIs), two series of novel N-4-fluoro-pyrazol-5-yl-benzamide and N-4-chloro-pyrazol-5-yl-benzamide derivatives were designed and synthesized, and their antifungal activities were evaluated against Valsa mali, Sclerotinia sclerotiorum, FusaHum graminearum Sehw, Physalospora piricola, and Botrytis cinerea. The bioassay results showed that some of the target compounds exhibited good antifungal activities in vitro against V. mali and S. sclerotiorum. Remarkably, compound 9Ip displayed good in vitro activity against V. mali with an EC50 value of 0.58 mg/L. This outcome was 21-fold greater than that of fluxapyroxad (12.45 mg/L) and close to that of the commercial fungicide tebuconazole (EC50 = 0.36 mg/L). In addition, in vivo experiments proved that compound 9Ip has good protective fungicidal activity with an inhibitory rate of 93.2% against V. mali at 50 mg/L, which was equivalent to that of the positive control tebuconazole (95.5%). The results of molecular docking indicated that there were obvious hydrogen bonds and p-π interactions between compound 9Ip and succinate dehydrogenase (SDH), which could explain the probable action mechanism. In addition, the SDH enzymatic inhibition assay was carried out to further prove its mode of action. Our studies suggest that compound 9Ip could be a fungicidal lead to discover more potent SDHIs for crop protection.

Preparation of epoxy/ZrO2 composite coating on the Q235 surface by electrostatic spraying and its corrosion resistance in 3.5% NaCl solution.

The epoxy coating containing ZrO2 nanoparticles modified with 3-aminopropyltriethoxysilane (APTES) was prepared by electrostatic spraying on the surface of Q235 mild steel. The effect of the concentration of APTES-modified ZrO2 nanoparticles on the corrosion resistance of epoxy coating was characterized and tested by FTIR spectroscopy, scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS). The results show that nano ZrO2 was successfully modified by a silane coupling agent. By adding an appropriate amount of APTES to modify nano ZrO2 in epoxy coating could significantly improve the corrosion resistance of the Q235 surface. When the mass fraction of nano ZrO2 is 2%, the composite coating shows the highest impedance value of about 1.0 × 105 Ω cm2 to achieve the best corrosion resistance.

Novel N-(1H-Pyrazol-5-yl)nicotinamide Derivatives: Design, Synthesis and Antifungal Activity.

To discover more effective antifungal agents, twenty N-(1H-pyrazol-5-yl)nicotinamide derivatives were designed, synthesized, and structurally confirmed by 1 H-NMR, 13 C-NMR, and ESI-MS. All target compounds were evaluated for their antifungal activities by mycelia growth inhibition. Preliminary screening results displayed that many of these compounds had good fungicidal activity to S. sclerotiorum and V. mali. Compound B4 exhibited antifungal activity against S. sclerotiorum and V. mali with EC50 values of 10.35 and 17.01 mg/L, respectively. The experiment in vivo identified that compound B4 was effective for suppressing rape sclerotinia rot caused by S. sclerotiorum at 50 mg/L. The molecular docking study and scanning electron microscopy preliminary clarified the possible antifungal mechanism of compound B4.

Joint segmentation and detection of COVID-19 via a sequential region generation network.

The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.

A Deep Segmentation Network of Multi-Scale Feature Fusion Based on Attention Mechanism for IVOCT Lumen Contour.

Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors' workload while ensuring diagnostic accuracy. In this paper, we proposed a deep residual segmentation network of multi-scale feature fusion based on attention mechanism (RSM-Network, Residual Squeezed Multi-Scale Network) to segment the lumen contour in IVOCT images. Firstly, three different data augmentation methods including mirror level turnover, rotation and vertical flip are considered to expand the training set. Then in the proposed RSM-Network, U-Net is contained as the main body, considering its characteristic of accepting input images with any sizes. Meanwhile, the combination of residual network and attention mechanism is applied to improve the ability of global feature extraction and solve the vanishing gradient problem. Moreover, the pyramid feature extraction structure is introduced to enhance the learning ability for multi-scale features. Finally, in order to increase the matching degree between the actual output and expected output, the cross entropy loss function is also used. A series of metrics are presented to evaluate the performance of our proposed network and the experimental results demonstrate that the proposed RSM-Network can learn the contour details better, contributing to strong robustness and accuracy for IVOCT lumen contour segmentation.

Turnable perfect absorption at infrared frequencies by a Graphene-hBN Hyper Crystal.

In this article, we have theoretically demonstrated that the perfect absorption at infrared frequencies can be achieved and controlled by using a graphene-hexagonal Boron Nitride (hBN) hyper crystal. hBN, the latest natural hyperbolic material, can be regarded as an excellent substrate to form a hyper crystal with graphene. Although the perfect absorption by a half-space of hBN crystal can be achieved due to its high optical anisotropy, but the perfect absorption can only appear at certain fixed wavenumber and incidence angle. By introducing a graphene-hBN hyper crystal, we can get perfect absorption at different wavenumbers and incidence angles by varying the Fermi energy level of graphene sheets via electrostatic biasing. We show that the perfect absorption can be realized at different Fermi energies for TM waves.

Manipulating dispersive wave generation by anomalous self-steepening effect in metamaterials.

We present a theoretical investigation of dispersive wave (DW) generation in nonlinear metamaterials (MMs). The role of the anomalous self-steepening (SS) effect, which can be either positive or negative, and the negative SS parameter can have a very large value compared to an ordinary positive-index material, in DW generation is particularly identified. It is demonstrated that the SS effect exerts a great impact on the peak power while has little effect on the frequency shift of DW. For positive third-order dispersion (TOD), the negative SS broadens the pulse spectrum and weakens the DW's peak power significantly, opposite to the case of positive SS. For negative TOD, however, the negative SS narrows the pulse spectrum and enhances the DW's peak power, also opposite to the case of positive SS. The results suggest that the DW generation in nonlinear MMs can be manipulated by SS effect to a large extent.