Layered molybdenum disulfide (MoS2) has been studied for decades for its diversity of structure and properties, where the structural dynamic evolution during lithium intercalation is an important but ...still indistinct, controversial topic. Here the electrochemical dynamic process of MoS2 nanosheets upon lithium intercalation has been systematically investigated by in situ high-resolution transmission electron microscopy. The results indicate that the lithiated MoS2 undergoes a trigonal prismatic (2H)-octahedral (1T) phase transition with a lithium ion occupying the interlayer S–S tetrahedron site in the 1T-LiMoS2. A pseudoperiodic structural modulation composed of polytype superlattices is also revealed as a consequence of the electron–lattice interaction. Furthermore, the shear mechanism of the 2H-1T phase transition has been confirmed by probing the dynamic phase boundary movement. The in situ real-time characterization at atomic scale provides a great leap forward in the fundamental understanding of the lithium ion storage mechanism in MoS2, which should be also of help for other transition metal dichalcogenides.
Microwave heating is rapidly emerging as an effective and efficient tool in various technological and scientific fields. A comprehensive understanding of the fundamentals of microwave-matter ...interactions is the precondition for better utilization of microwave technology. However, microwave heating is usually only known as dielectric heating, and the contribution of the magnetic field component of microwaves is often ignored, which, in fact, contributes greatly to microwave heating of some aqueous electrolyte solutions, magnetic dielectric materials and certain conductive powder materials,
This paper focuses on this point and presents a careful review of microwave heating mechanisms in a comprehensive manner. Moreover, in addition to the acknowledged conventional microwave heating mechanisms, the special interaction mechanisms between microwave and metal-based materials are attracting increasing interest for a variety of metallurgical, plasma and discharge applications, and therefore are reviewed particularly regarding the aspects of the reflection, heating and discharge effects. Finally, several distinct strategies to improve microwave energy utilization efficiencies are proposed and discussed with the aim of tackling the energy-efficiency-related issues arising from the application of microwave heating. This work can present a strategic guideline for the developed understanding and utilization of the microwave heating technology.
2D black phosphorene (BP) carries a stellar set of physical properties such as conveniently tunable bandgap and extremely high ambipolar carrier mobility for optoelectronic devices. Herein, the ...judicious design and positioning of BP with tailored thickness as dual‐functional nanomaterials to concurrently enhance carrier extraction at both electron transport layer/perovskite and perovskite/hole transport layer interfaces for high‐efficiency and stable perovskite solar cells is reported. The synergy of favorable band energy alignment and concerted cascade interfacial carrier extraction, rendered by concurrent positioning of BP, delivered a progressively enhanced power conversion efficiency of 19.83% from 16.95% (BP‐free). Investigation into interfacial engineering further reveals enhanced light absorption and reduced trap density for improved photovoltaic performance with BP incorporation. This work demonstrates the appealing characteristic of rational implementation of BP as dual‐functional transport material for a diversity of optoelectronic devices, including photodetectors, sensors, light‐emitting diodes, etc.
Ambipolar black phosphorene (BP) nanosheets with tailored thicknesses concurrently enhance carrier extraction at both the electron‐transport layer/perovskite and hole‐transport layer/perovskite interfaces for high‐efficiency perovskite solar cells, demonstrating the appealing implementation of BP as a dual‐functional carrier‐transport material for a diversity of optoelectronic devices, including solar cells, photodetectors, sensors, light‐emitting diodes, etc.
Motor imagery (MI) is a brain-computer interface (BCI) technique in which specific brain regions are activated when people imagine their limbs (or muscles) moving, even without actual movement. The ...technology converts electroencephalogram (EEG) signals generated by the brain into computer-readable commands by measuring neural activity. Classification of motor imagery is one of the tasks in BCI. Researchers have done a lot of work on motor imagery classification, and the existing literature has relatively mature decoding methods for two-class motor tasks. However, as the categories of EEG-based motor imagery tasks increase, further exploration is needed for decoding research on four-class motor imagery tasks. In this study, we designed a hybrid neural network that combines spatiotemporal convolution and attention mechanisms. Specifically, the data is first processed by spatiotemporal convolution to extract features and then processed by a Multi-branch Convolution block. Finally, the processed data is input into the encoder layer of the Transformer for a self-attention calculation to obtain the classification results. Our approach was tested on the well-known MI datasets BCI Competition IV 2a and 2b, and the results show that the 2a dataset has a global average classification accuracy of 83.3% and a kappa value of 0.78. Experimental results show that the proposed method outperforms most of the existing methods.
•Tetracycline (TC) photolysis followed pseudo-first-order kinetics.•Light-source-dependent TC photolysis was promoted or inhibited by NO3- or HA.•TC photolysis involved O2--mediated self-sensitized ...photolysis.•TC photolysis pathways involved hydroxylation and loss of some groups.
To elucidate the environmental fate of tetracycline (TC), we reported the light-source-dependent dual effects of humic acid (HA) and NO3- on TC photolysis. TC photolysis rate was highly pH- and concentration-dependent, and was especially enhanced at higher pH and lower initial TC concentrations. Under UV-254 and UV-365 irradiation, HA inhibited TC photolysis through competitive photoabsorption or reactive oxygen species (ROS) quenching with TC; under solar and xenon lamp irradiation, TC photolysis was enhanced at low HA concentration due to its photosensitization, whereas was suppressed at high HA concentration due to competitive photoabsorption or ROS quenching with TC. Similarly, the effect of NO3- on TC photolysis varied with light irradiation conditions. Even under the same light irradiation conditions, the effects of HA or NO3- on TC photolysis varied with their concentrations. The electron spin resonance spectrometer and ROS scavenger experiments demonstrated that TC photolysis was involved in O2--mediated self-sensitized photolysis. The photolysis pathways were involved in hydroxylation and loss of some groups. More toxic intermediates than TC were generated under different light irradiation conditions. These results can provide insight into the potential fate and transformation of TC in surficial waters.
Abstract
Recently, monolayer molybdenum disulphide (MoS
2
) has emerged as a promising and non–precious electrocatalyst for hydrogen evolution reaction. However, its performance is largely limited by ...the low density and poor reactivity of active sites within its basal plane. Here, we report that domain boundaries in the basal plane of monolayer MoS
2
can greatly enhance its hydrogen evolution reaction performance by serving as active sites. Two types of effective domain boundaries, the 2H-2H domain boundaries and the 2H-1T phase boundaries, were investigated. Superior hydrogen evolution reaction catalytic activity, long-term stability and universality in both acidic and alkaline conditions were achieved based on a multi-hierarchy design of these two types of domain boundaries. We further demonstrate that such superior catalysts are feasible at a large scale by applying this multi-hierarchy design of domain boundaries to wafer-scale monolayer MoS
2
films.
This graphic shows the structure of our network. In the preprocessing section, we used the Beer-Lambert law to convert the optical signals into hemodynamic HbR and HbO. We used an end-to-end ...structure without much preprocessing of the raw fNIRS signal. We input the signal with the number of channels C = 24 and the number of samples T = 351. the original MI and MA signals are first passed through a convolution block. The convolution block consists of a 2D time convolution, a depth convolution, and a separable convolution, each followed by a Batch Normalization layer, an ELU activation function, an average pooling layer, and a dropout layer. Afterwards, spatio-temporal feature extraction is performed by spatial attention and temporal convolutional networks, capable of reducing overfitting. Finally, the fNIRS signal is classified as MI or MA. The results show that the method using only 3.23 K training parameters has an accuracy of 85.63% (HbO) and 86.21% (HbR) in the MI task and 96.84% (HbO) and 94.83% (HbR) in the MA task.
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•fNIRS decoding performance improvement.•Using Convolutional Neural Networks for fNIRS Classification.•Spatial attention mechanisms can capture remote contextual information.•Temporal convolutional network outperforms most RNN in time-series tasks.
Brain Computer Interface (BCI) is a highly promising human–computer interaction method that can utilize brain signals to control external devices. BCI based on functional near-infrared spectroscopy (fNIRS) is considered a relatively new and promising paradigm. fNIRS is a technique of measuring functional changes in cerebral hemodynamics. It detects changes in the hemodynamic activity of the cerebral cortex by measuring oxyhemoglobin and deoxyhemoglobin (HbR) concentrations and inversely predicts the neural activity of the brain. At the present time, Deep learning (DL) methods have not been widely used in fNIRS decoding, and there are fewer studies considering both spatial and temporal dimensions for fNIRS classification. To solve these problems, we proposed an end-to-end hybrid neural network for feature extraction of fNIRS. The method utilizes a spatial–temporal convolutional layer for automatic extraction of temporally valid information and uses a spatial attention mechanism to extract spatially localized information. A temporal convolutional network (TCN) is used to further utilize the temporal information of fNIRS before the fully connected layer. We validated our approach on a publicly available dataset including 29 subjects, including left-hand and right-hand motor imagery (MI), mental arithmetic (MA), and a baseline task. The results show that the method has few training parameters and high accuracy, providing a meaningful reference for BCI development.
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