Hebei University of Science and Technology

Abstract Scalable and sustainable solar hydrogen production through photocatalytic water splitting requires highly active and stable earth-abundant co-catalysts to replace expensive and rare platinum. Here we employ density functional theory calculations to direct atomic-level exploration, design and fabrication of a MXene material, Ti 3 C 2 nanoparticles, as a highly efficient co-catalyst. Ti 3 C 2 nanoparticles are rationally integrated with cadmium sulfide via a hydrothermal strategy to induce a super high visible-light photocatalytic hydrogen production activity of 14,342 μmol h −1 g −1 and an apparent quantum efficiency of 40.1% at 420 nm. This high performance arises from the favourable Fermi level position, electrical conductivity and hydrogen evolution capacity of Ti 3 C 2 nanoparticles. Furthermore, Ti 3 C 2 nanoparticles also serve as an efficient co-catalyst on ZnS or Zn x Cd 1− x S. This work demonstrates the potential of earth-abundant MXene family materials to construct numerous high performance and low-cost photocatalysts/photoelectrodes.

Oxidative stress and mitochondrial damage have been implicated in the pathogenesis of several neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. Oxidative stress is characterized by the overproduction of reactive oxygen species, which can induce mitochondrial DNA mutations, damage the mitochondrial respiratory chain, alter membrane permeability, and influence Ca(2+) homeostasis and mitochondrial defense systems. All these changes are implicated in the development of these neurodegenerative diseases, mediating or amplifying neuronal dysfunction and triggering neurodegeneration. This paper summarizes the contribution of oxidative stress and mitochondrial damage to the onset of neurodegenerative eases and discusses strategies to modify mitochondrial dysfunction that may be attractive therapeutic interventions for the treatment of various neurodegenerative diseases.

Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data.

In this review, we focus on the types of smart supramolecular gels whose self-assembly processes are affected or even triggered by physical forces including sonication and mechanical stress (mechanical force). The types of gels that are responsive to sonication and mechanical stress are examined and summarised. The gels exhibit non-covalent interactions among the gelator molecules and show dynamic and reversible properties controlled by the stimuli. Upon stimulation, the gelators cause instant and in situ gelation of organic solvents or water with different modes and outcomes of self-assembly. On the other hand, sonication and mechanical stress, as external factors, can give rise to dynamic changes in microscopic morphology, optical properties, etc. Certain thixotropic supramolecular gels exhibit perfect self-healing characteristics. The driving forces and the mechanism of the self-assembly process and the responsive outcome of morphological and spectroscopic changes are discussed. Those supramolecular gels responding to sonication and mechanical stress offer a wide range of applications in fields such as smart and adaptive materials, switches, drug control and release, and tissue engineering.

The aqueous zinc ion battery has emerged as a promising alternative technology for large-scale energy storage due to its low cost, natural abundance, and high safety features. However, the sluggish kinetics stemming from the strong electrostatic interaction of divalent zinc ions in the host crystal structure is one of challenges for highly efficient energy storage. Oxygen vacancies (VO••), in the present work, lead to a larger tunnel structure along the b axis, which improves the reactive kinetics and enhances Zn-ion storage capability in VO2 (B) cathode. DFT calculations further support that VO•• in VO2 (B) result in a narrower bandgap and lower Zn ion diffusion energy barrier compared to those of pristine VO2 (B). VO••-rich VO2 (B) achieves a specific capacity of 375 mAh g–1 at a current density of 100 mA g–1 and long-term cyclic stability with retained specific capacity of 175 mAh g–1 at 5 A g–1 over 2000 cycles (85% capacity retention), higher than that of VO2 (B) nanobelts (280 mAh g–1 at 100 mA g–1 and 120 mAh g–1 at 5 A g–1, 65% capacity retention).

The design and synthesis of metal oxide nanomaterials is one of the key steps for achieving highly efficient energy conversion and storage on an industrial scale. Solution combustion synthesis (SCS) is a time- and energy-saving method as compared with other routes, especially for the preparation of complex oxides which can be easily adapted for scale-up applications. This review summarizes the synthesis of various metal oxide nanomaterials and their applications for energy conversion and storage, including lithium-ion batteries, supercapacitors, hydrogen and methane production, fuel cells and solar cells. In particular, some novel concepts such as reverse support combustion, self-combustion of ionic liquids, and creation of oxygen vacancies are presented. SCS has some unique advantages such as its capability for in situ doping of oxides and construction of heterojunctions. The well-developed porosity and large specific surface area caused by gas evolution during the combustion process endow the resulting materials with exceptional properties. The relationship between the structural properties of the metal oxides studied and their performance is discussed. Finally, the conclusions and perspectives are briefly presented.

Abstract Oxygen evolution reaction (OER) is a pivotal reaction in many technologies for renewable energy, such as water splitting, metal–air batteries, and regenerative fuel cells. However, this reaction is known to be kinetically sluggish and proceeds at rather high overpotential due to the universal scaling relationship, namely, the adsorption energies of intermediates are linearly correlated and cannot be optimized simultaneously. Several approaches have been proposed to break the scaling relationship by introducing additional active sites; however, positive experimental results are still absent. Herein, a different solution is suggested on the basis of dynamic tridimensional adsorption of the OER intermediates at NiO/NiFe layered double hydroxide intersection, by which the adsorption energy of each intermediate can be adjusted independently, so as to bypass the scaling relationship and achieve high catalytic performance. Experimentally, the OER overpotential is reduced to ≈205 mV at current density of 30 mA cm −2 , which represents the best performance achieved by state‐of‐the‐art OER catalysts.

An in situ microwave-assisted synthesis approach has been developed to prepare N-TiO 2 /g-C 3 N 4 composites using H 2 TiO 3 as the reactant and NH 3 ·H 2 O as the N-doping source. In this way, the N-TiO 2 /g-C 3 N 4 composite catalysts have a porous structure and large surface areas, which increase the contact area of pollutants. Degradation of rhodamine B (Rh B) and methylene blue (MB) were carried out to evaluate the photocatalytic activity of samples under visible light irradiation. N-TiO 2 /g-C 3 N 4 composite with 40 wt % N-TiO 2 exhibits the highest photocatalytic activity and the optimal temperature is 400 °C. The increased photocatalytic activity of N-TiO 2 /g-C 3 N 4 composites can be attributed to the formation of the heterojunction between N-TiO 2 and g-C 3 N 4, which suppresses the recombination of photoinduced electron–hole pairs. The tests of radical scavengers confirmed that • O 2 – was the main reactive species during the photocatalytic process.

Abstract Despite the extensive developments of flexible capacitive pressure sensors, it is still elusive to simultaneously achieve excellent linearity over a broad pressure range, high sensitivity, and ultrahigh pressure resolution under large pressure preloads. Here, we present a programmable fabrication method for microstructures to integrate an ultrathin ionic layer. The resulting optimized sensor exhibits a sensitivity of 33.7 kPa −1 over a linear range of 1700 kPa, a detection limit of 0.36 Pa, and a pressure resolution of 0.00725% under the pressure of 2000 kPa. Taken together with rapid response/recovery and excellent repeatability, the sensor is applied to subtle pulse detection, interactive robotic hand, and ultrahigh-resolution smart weight scale/chair. The proposed fabrication approaches and design toolkit from this work can also be leveraged to easily tune the pressure sensor performance for varying target applications and open up opportunities to create other iontronic sensors.

The reversibility and cyclability of aqueous zinc-ion batteries (ZIBs) are largely determined by the stabilization of the Zn anode. Therefore, a stable anode/electrolyte interface capable of inhibiting dendrites and side reactions is crucial for high-performing ZIBs. In this study, we investigated the adsorption of 1,4-dioxane (DX) to promote the exposure of Zn (002) facets and prevent dendrite growth. DX appears to reside at the interface and suppress the detrimental side reactions. ZIBs with the addition of DX demonstrated a long-term cycling stability of 1000 h in harsh conditions of 10 mA cm –2 with an ultrahigh cumulative plated capacity of 5 Ah cm –2 and shows a good reversibility with an average Coulombic efficiency of 99.7%. The Zn//NH 4 V 4 O 10 full battery with DX achieves a high specific capacity (202 mAh g –1 at 5 A g –1 ) and capacity retention (90.6% after 5000 cycles), much better than that of ZIBs with the pristine ZnSO 4 electrolyte. By selectively adjusting the Zn 2+ deposition rate on the crystal facets with adsorbed molecules, this work provides a promising modulation strategy at the molecular level for high-performing Zn anodes and can potentially be applied to other metal anodes suffering from instability and irreversibility.

Abstract Piezocatalysis, converting mechanical vibration into chemical energy, has emerged as a promising candidate for water‐splitting technology. However, the efficiency of the hydrogen production is quite limited. We herein report well‐defined 10 nm BaTiO 3 nanoparticles (NPs) characterized by a large electro‐mechanical coefficient which induces a high piezoelectric effect. Atomic‐resolution high angle annular dark field scanning transmission electron microscopy (HAADF‐STEM) and scanning probe microscopy (SPM) suggests that piezoelectric BaTiO 3 NPs display a coexistence of multiple phases with low energy barriers and polarization anisotropy which results in a high electro‐mechanical coefficient. Landau free energy modeling also confirms that the greatly reduced polarization anisotropy facilitates polarization rotation. Employing the high piezoelectric properties of BaTiO 3 NPs, we demonstrate an overall water‐splitting process with the highest hydrogen production efficiency hitherto reported, with a H 2 production rate of 655 μmol g −1 h −1 , which could rival excellent photocatalysis system. This study highlights the potential of piezoelectric catalysis for overall water splitting.

In recent years, the rapid development of modern society is calling for advanced energy storage to meet the growing demands of energy supply and generation. As one of the most promising energy storage systems, secondary batteries are attracting much attention. The electrolyte is an important part of the secondary battery, and its composition is closely related to the electrochemical performance of the secondary batteries. Lithium-ion battery electrolyte is mainly composed of solvents, additives, and lithium salts, which are prepared according to specific proportions under certain conditions and according to the needs of characteristics. This review analyzes the advantages and current problems of the liquid electrolytes in lithium-ion batteries (LIBs) from the mechanism of action and failure mechanism, summarizes the research progress of solvents, lithium salts, and additives, analyzes the future trends and requirements of lithium-ion battery electrolytes, and points out the emerging opportunities in advanced lithium-ion battery electrolytes development.

Green hydrogen from electrolysis of water has attracted widespread attention as a renewable power source. Among several hydrogen production methods, it has become the most promising technology. However, there is no large-scale renewable hydrogen production system currently that can compete with conventional fossil fuel hydrogen production. Renewable energy electrocatalytic water splitting is an ideal production technology with environmental cleanliness protection and good hydrogen purity, which meet the requirements of future development. This review summarizes and introduces the current status of hydrogen production by water splitting from three aspects: electricity, catalyst and electrolyte. In particular, the present situation and the latest progress of the key sources of power, catalytic materials and electrolyzers for electrocatalytic water splitting are introduced. Finally, the problems of hydrogen generation from electrolytic water splitting and directions of next-generation green hydrogen in the future are discussed and outlooked. It is expected that this review will have an important impact on the field of hydrogen production from water.

To improve the recognition model accuracy of crop disease leaves and locating diseased leaves, this paper proposes an improved Faster RCNN to detect healthy tomato leaves and four diseases: powdery mildew, blight, leaf mold fungus and ToMV. First, we use a depth residual network to replace VGG16 for image feature extraction so we can obtain deeper disease features. Second, the k-means clustering algorithm is used to cluster the bounding boxes. We improve the anchoring according to the clustering results. The improved anchor frame tends toward the real bounding box of the dataset. Finally, we carry out a k-means experiment with three kinds of different feature extraction networks. The experimental results show that the improved method for crop leaf disease detection had 2.71% higher recognition accuracy and a faster detection speed than the original Faster RCNN.

Electronic band structure and optical properties of Cr-doped ZnO were studied using the density functional method within the generalized-gradient approximation. Three configurations with the substitution of Zn by one and two Cr atoms in different positions were considered. For the pure ZnO, the Fermi level locates at the valence band maximum, while it shifts to the conduction band and exhibits metal-like characteristic after Cr atoms are introduced into the ZnO supercell. The calculated optical properties indicate that the optical energy gap is increased after Cr doping. More importantly, strong absorption in the visible-light region is found, which originates from the intraband transition of the Cr 3d bands and the conduction bands. Our calculations provide electronic structure evidence that, in addition to usage as short-wavelength optoelectronic devices, the Cr-doped ZnO system could be a potential candidate for photoelectrochemical application due to the increase in its photocatalytic activity.

The average valence, ValO, of the oxygen anions in the perovskite oxide BaTiO3, was found using O1s photoelectron spectra to be −1.55. This experimental result is close to the theoretical value for BaTiO3 (−1.63) calculated by Cohen [Nature 358, 136 (1992)] using density functional theory. Using the same approach, we obtained values of ValO for several monoxides, and investigated the dependence of ValO and the ionicity on the second ionization energy, V(M2+), of the metal cation. We found that the dependence of the ionicity on V(M2+) in this work is close to that reported by Phillips [Rev. Mod. Phys. 42, 317 (1970)]. We therefore suggest that O1s photoelectron spectrum measurements should be accepted as a general experimental method for estimating the ionicity and average valence of oxygen anions.

By now, personal life has been invaded by online social networks (OSNs) everywhere. They intend to move more and more offline lives to online social networks. Therefore, online social networks can reflect the structure of offline human society. A piece of information can be exchanged or diffused between individuals in social networks. From this diffusion process, lots of latent information can be mined. It can be used for market predicting, rumor controlling, and opinion monitoring among other things. However, the research of these applications depends on the diffusion models and methods. For this reason, we survey various information diffusion models from recent decades. From a research process view, we divide the diffusion models into two categories—explanatory models and predictive models—in which the former includes epidemics and influence models and the latter includes independent cascade, linear threshold, and game theory models. The purpose of this paper is to investigate the research methods and techniques, and compare them according to the above categories. The whole research structure of the information diffusion models based on our view is given. There is a discussion at the end of each section, detailing related models that are mentioned in the literature. We conclude that these two models are not independent, they always complement each other. Finally, the issues of the social networks research are discussed and summarized, and directions for future study are proposed.

Cross-linked enzyme aggregate (CLEA) technology has been regarded as an effective carrier-free immobilization method. This method is very attractive due to its simplicity and robustness, as well as for the possibility of using the crude enzyme extract and the opportunity to co-immobilize multiple different enzymes. The resulting CLEAs generally exhibit high catalyst productivities, improved storage and operational stability and are easy to recycle. Nowadays, although the technology has been applied to various enzymes, some undesirable properties have limited its further application. To overcome these limitations, novel strategies have been developing in recent years. This mini-review focuses on process optimization, new improved strategies and the latest advances on CLEAs technology.

Abstract Background Jujube ( Ziziphus jujuba Mill.) fruit is one of the largest productions in China and its increasing production has drawn considerable attention from researchers. Polysaccharide is one of the most abundant components of jujube, and it represents a major group of biolotegically active constituents. This study intended to investigate the special structure of a homogeneous acidic polysaccharide (PZMP4) produced from Ziziphus Jujuba cv. Muzao fruit using novel methods, including DEAE-Sepharose Fast Flow and Sephacryl S-300 column chromatography. Results The structure of PZMP4 was determined via high-performance gel permeation chromatography (HPGPC), gas chromatography (GC), Fourier transform infrared spectroscopy (FT-IR), methylation analysis, nuclear magnetic resonance spectroscopy (NMR), scanning electron microscopy (SEM), and atomic force microscopy (AFM). The results reveal that PZMP4 with a molecular weight of 27.90 kDa was composed of rhamnose, arabinose, mannose, glucose, galactose, and galacturonic acid at a ratio of 2.32:2.21:0.22:0.88:2.08:8.83. Advanced structural analysis revealed a netted structure with molecular aggregates of PZMP4. Structural features demonstrated that the basic backbone of PZMP4 appeared to mainly consist of (1→4)-linked Gal p A with three branches bonded to O -3 of (1→3)-linked Ara f , (1→2)-linked Rha p , and terminated with Gal p A. Conclusions PZMP4’s unique structure could imply distinct bioactivities and have considerable utilization in functional food. Graphic abstract

Autonomous vehicles require constant environmental perception to obtain the distribution of obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional module as it can simultaneously predict surrounding objects' categories, locations, and sizes. Generally, autonomous vehicles are equipped with multiple sensors, including cameras and LiDARs. The fact that single-modal methods suffer from unsatisfactory detection performance motivates utilizing multiple modalities as inputs to compensate for single sensor faults. Although many multi-modal fusion detection algorithms exist, there is still a lack of comprehensive and in-depth analysis of these methods to clarify how to fuse multi-modal data effectively. Therefore, this paper surveys recent advancements in fusion detection methods. First, we present the broad background of multi-modal 3D object detection and identify the characteristics of widely used datasets along with their evaluation metrics. Second, instead of the traditional classification method of early, middle, and late fusion, we categorize and analyze all fusion methods from three aspects: feature representation, alignment, and fusion, which reveals how these fusion methods are implemented in an essential way. Third, we provide an in-depth comparison of their pros and cons and compare their performance in mainstream datasets. Finally, we further summarize current challenges and research trends for realizing the full potential of multi-modal 3D object detection.