Molybdenum-Based Catalytic Materials for Li–S Batteries: Strategies, Mechanisms, and Prospects

Lithium–sulfur (Li–S) batteries are regarded as promising candidates for high-energy storage devices because of their high theoretical energy density (2600 Wh kg−1). However, their practical applications are still hindered by a multitude of key challenges, especially the shuttle effect of soluble lithium polysulfides (LiPSs) and the sluggish sulfur redox kinetics. To address these challenges, varieties of catalytic materials have been exploited to prevent the shuttle effect and accelerate the LiPSs conversion. Recently, molybdenum-based (Mo-based) catalytic materials are widely used as sulfur host materials, modified separators, and interlayers for Li–S batteries. They include the Mo sulfides, diselenides, carbides, nitrides, oxides, phosphides, borides, and metal/single atoms/clusters. Here, recent advances in these Mo-based catalytic materials are comprehensively summarized, and the current challenges and prospects for designing highly efficient Mo-based catalytic materials are highlighted, with the aim to provide a fundamental understanding of the sulfur reaction mechanism, and to guide the rational design of cathode catalysts for high-energy and long-life Li–S batteries

Investigation into the dynamic change pattern of the stress field during integral fracturing in deep reservoirs

Deep reservoirs have high temperature, high pressure, and high stress. The development of such resources is high cost. Integral fracturing applies one-time well displacement, batch drilling, and batch fracturing. Multiple wells are stimulated with zipper fracturing. It can avoid the interference of the well drilling and fracturing. In this way, the spatial stresses can be utilized to generate the complex fracture network. The dynamic change pattern of the stress field is of great value for the design of integral fracturing. Based on the displacement discontinuity method (DDM) and the fracture mechanics criteria, a whole fracture propagation program is developed to calculate the spatial stress distribution and the whole fracture geometry. The reliability of the program is verified against the classical analytical solutions. Based on the program, this work systematically investigates the effects of the fracture length, the fracturing sequence, the fracture distribution mode, and the injection pressure on the stress field. The main conclusions are as follows: 1) When the fracture half-length is 150 m and the well spacing is 300 m, the staggered fracture distribution mode can ensure uniform fracture propagation and realize the active utilization of inter-well stress field; 2) Compared with the relative fracture distribution mode, the staggered fracture distribution mode is less susceptible to the stress field induced by the adjacent hydraulic fractures, hydraulic fractures tend to propagate along the direction of the maximum horizontal principal stress; 3) The stress field is highly influenced by the in-fracture fluid pressure. The stress interference is stronger with a greater fluid injection pressure and a higher fracture deflection angle will be obtained. It can enhance the fracture propagation resistance and increase the stress value. This work discovers the stress change pattern and lays out a solid foundation for the optimization of the integral fracturing

Long-Range Temporal Correlations of Patients in Minimally Conscious State Modulated by Spinal Cord Stimulation

Spinal cord stimulation (SCS) has been shown to improve the consciousness levels of patients with disorder of consciousness (DOC). However, the underlying mechanisms of SCS remain poorly understood. This study recorded resting-state electroencephalograms (EEG) from 16 patients with minimally conscious state (MCS), before and after SCS, and investigated the mechanisms of SCS on the neuronal dynamics in MCS patients. Detrended fluctuation analysis (DFA), combined with surrogate data method, was employed to measure the long-range temporal correlations (LRTCs) of the EEG signals. A surrogate data method was utilized to acquire the genuine DFA exponents (GDFAE) reflecting the genuine LRTCs of brain activity. We analyzed the GDFAE in four brain regions (frontal, central, posterior, and occipital) at five EEG frequency bands [delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–45 Hz)]. The GDFAE values ranged from 0.5 to 1, and showed temporal and spatial variation between the pre-SCS and the post-SCS states. We found that the channels with GDFAE spread wider after SCS. This phenomenon may indicate that more cortical areas were engaged in the information integration after SCS. In addition, the GDFAE values increased significantly in the frontal area at delta, theta, and alpha bands after SCS. At the theta band, a significant increase in GDFAE was observed in the occipital area. No significant change was found at beta or gamma bands in any brain region. These findings show that the enhanced LRTCs after SCS occurred primarily at low-frequency bands in the frontal and occipital regions. As the LRTCs reflect the long-range temporal integration of EEG signals, our results indicate that information integration became more “complex” after SCS. We concluded that the brain activities at low-frequency oscillations, particularly in the frontal and occipital regions, were improved by SCS

A study on the effects of the Qihuang Needle therapy on patients with Parkinson's disease

ObjectiveThis study aimed to evaluate the effectiveness of the Qihuang Needle (QHN) in treating Parkinson's disease (PD).Design, setting, and participantsThe trial was an 8-week randomized clinical trial (4 weeks of treatment followed by 4 weeks of follow-up) conducted from January 2021 to July 2022 in outpatient settings at three clinical sites in Guangzhou, China. Thirty-four participants with PD were diagnosed based on the diagnostic criteria formulated by the brain bank of the British Parkinson's Disease Society in 1992.InterventionsPatients in the treatment and control groups received six sessions within 4 weeks of the QHN therapy or the sham acupuncture therapy (two times per week for the first two consecutive weeks and one time per week for the following two consecutive weeks).Main outcomes and measuresThe primary outcome measure was the change in the Parkinson's Disease Rating Scale-Part III Motor Examination (UPDRS III) between baseline and 8 weeks after treatments. Secondary outcome measures were the Non-Motor Symptoms Scale for Parkinson's Disease (NMSS) and Parkinson's Disease Daily Quality of Life-39 (PDQ-39). Real-time shear wave elastography (SWE) was assessed for each patient at baseline and during the 4-week period as the third outcome measure.ResultsA more significant reduction of UPDRS III score, PDQ-39, NMSS, and SWE was observed in the QHN group than in the sham acupuncture group.ConclusionsThe QHN therapy consistently demonstrated superiority and produced clinically meaningful benefits in reducing motor and non-motor symptoms, as well as significantly improving muscle stiffness, in patients with PD

Synthesis of Zinc Phosphonated Poly(ethylene imine) and Its Fire-Retardant Effect in Low-Density Polyethylene

A novel oligomeric intumescent fire-retardant chelate, zinc phosphonated poly(ethylene imine) (Zn-PEIP), with a variable Zn2+ loading, was synthesized. The chemical structure of Zn-PEIP was confirmed by FTIR, 13C NMR, and 31P NMR spectroscopies. The thermal behavior and fire retardancy of low-density polyethylene (LDPE) containing 25 wt % Zn-PEIPs with different amounts of Zn2+ were investigated by thermogravimetric analysis (TGA), limiting oxygen index (LOI) measurements, and cone calorimetry. The TGA results showed that higher concentrations of Zn2+ improved the thermal stability and increased the residue yield of LDPE. However, the data from the LOI and cone calorimetry tests showed that there is an optimum concentration of Zn2+ for the best fire-retardancy performance of LDPE. This behavior is ascribed to the high cross-link density resulting from zinc bridges, preventing normal swelling of the intumescent system. The surface morphology of the char was characterized by digital photography and scanning electron microscopy (SEM). This confirmed the optimum intumescence and coherent and strong barrier layer formation at an intermediate Zn2+ loading

Damage identification of bridges from signals measured with a moving vehicle

Identifying damage of a bridge from a vehicle moving over it is an attractive idea especially for those bridges without structural health monitoring systems as it is faster than putting sensors on the bridges. Many parts of highways and railways have been constructed on bridges and it is important to ensure that they are in good conditions. Therefore a large amount of bridges need to be monitored and for the sake of economy the monitoring should be efficient. If an instrumented vehicle can identify the occurrence and locations of damage by running over the bridges, it would save a lot of labor and time. As acceleration is easier to acquire, it is used as the main signal for damage detection. Research in this area is relatively little, not to mention the need to take into account road surface roughness and experimental verification. Frequencies can be conveniently extracted from the vehicle response. The damage can hence be identified based on the relationship between the change of frequencies and the fractional change of strain energy. A vehicle-bridge interaction system is used to simulate the process of a vehicle running over a bridge and obtain the vehicle response for investigation. The proposed method can identify damage of simply supported and multi-span continuous bridges taking into account road surface roughness and measurement noise. They are also validated in the laboratory where a simply supported bridge is modeled using an aluminum beam and the vehicle is modeled with aluminum vehicles. This method can limit the damage location to two potential locations. The multi-level multi-pass strategy makes use of the identification from the above method, applies genetic algorithm and lets the vehicle run over the bridge at various speeds. The unique damage location can then be identified. A numerical study for simply supported bridges and multi-span continuous bridges has verified its effectiveness. Continuous wavelet transform (CWT) can identify local changes in a signal as damage is assumed to cause local change to the vehicle response, which makes it suitable for damage detection from vehicle response. However, the road surface roughness and measurement noise often mask the information about damage. Smoothing technique and damage indicators are proposed to help with the identification. By validating the method with a numerical vehicle-bridge interaction system and model tests in the laboratory, the damage can be correctly identified. Additional masses and sinusoidal excitation force can help with the identification too. Repeated application of CWT involves applying the CWT to the coefficients of continuous wavelet again and again, which can also improve the results. If CWT is treated as a mathematical microscope, repeated application of CWT is like amplifying the signal several times. The effectiveness of the method has been verified numerically and experimentally. In summary, a convenient and efficient technique to test the conditions of bridges by putting sensors on a moving vehicle is proposed and the method is verified by numerical and experimental studies. It can provide an alternative or a useful complement to conventional structural health monitoring systems.published_or_final_versionCivil EngineeringDoctoralDoctor of Philosoph

Optimized pointwise convolution operation by Ghost blocks

In the lightweight convolutional neural network model, the pointwise convolutional structure occupies most of the parameters and computation amount of the model. Therefore, improving the pointwise convolution structure is the best choice to optimize the lightweight model. Aiming at the problem that the pointwise convolution in MobileNetV1 and MobileNetV2 consumes too many computation resources, we designed the novel Ghost-PE and Ghost-PC blocks. First, in order to optimize the channel expanded pointwise convolution with the number of input channels less than the output, Ghost-PE makes full use of the feature maps generated by main convolution of the Ghost module, and adds global average pooling and depth convolution operation to enhance the information of feature maps generated through cheap convolution. Second, in order to optimize the channel compressed pointwise convolution with the number of input channels more than the output, Ghost-PC adjusts the Ghost-PE block to make full use of the features generated by cheap convolution to enhance the feature channel information. Finally, we optimized MobileNetV1 and MobileNetV2 models by Ghost-PC and Ghost-PE blocks, and then tested on Food-101, CIFAR and Mini-ImageNet datasets. Compared with other methods, the experimental results show that Ghost-PE and Ghost-PC still maintain a relatively high accuracy in the case of a small number of parameters

Cortical complexity and connectivity during isoflurane-induced general anesthesia: a rat study

Objective. The investigation of neurophysiologic mechanisms of anesthetic drug-induced loss of consciousness (LOC) by using the entropy,&nbsp;complexity, and information integration theories at the mesoscopic level has been a hot topic in recent years. However, systematic research is still lacking. Approach. We analyzed electrocorticography (ECoG) data recorded from nine&nbsp;rats&nbsp;during&nbsp;isoflurane-induced unconsciousness. To characterize the&nbsp;complexity&nbsp;and&nbsp;connectivity&nbsp;changes, we investigated ECoG power, symbolic dynamic-based entropy (i.e. permutation entropy (PE)),&nbsp;complexity&nbsp;(i.e. permutation Lempel-Ziv&nbsp;complexity&nbsp;(PLZC)), information integration (i.e. permutation cross mutual information (PCMI)), and PCMI-based&nbsp;cortical&nbsp;brain networks in the frontal, parietal, and occipital&nbsp;cortical&nbsp;regions. Main results. Firstly, LOC was accompanied by a raised power in the ECoG beta (12-30 Hz) but a decreased power in the high gamma (55-95 Hz) frequency band in all three brain regions. Secondly, PE and PLZC showed similar change trends in the lower frequency band (0.1-45 Hz), declining after LOC (p &lt; 0.05) and increasing after recovery of consciousness (p &lt; 0.001). Thirdly, intra-frontal and inter-frontal-parietal PCMI declined after LOC, in both lower (0.1-45 Hz) and higher frequency bands (55-95 Hz) (p &lt; 0.001). Finally, the local network parameters of the nodal clustering coefficient and nodal efficiency in the frontal region decreased after LOC, in both the lower and higher frequency bands (p &lt; 0.05). Moreover, global network parameters of the normalized average clustering coefficient and small world index increased slightly after LOC in the lower frequency band. However, this increase was not statistically significant. Significance. The PE, PLZC, PCMI and PCMI-based brain networks are effective metrics for qualifying the effects of isoflurane.</p