Synthesis of MoS2 and their performance for hydrogen evolution reaction

MoS2 is one of the most important catalysts for hydrogen evolution reaction among transition metal dichalcogenides (TMD) compounds owing to its characteristic S-Mo-S triatomic structure. At present, the main research objects are the 1T phase and 2H phase of MoS2, but its development is still challenging due to its limited active sites and inert basal planes. Heteroatom modification and phase transition could enhance the electrocatalytic performance. MoS2 nanomaterials with lower onset potential and lower Tafel slope were successfully synthesized in this thesis, which increased the electrical conductivity and exhibited excellent HER performance. This thesis contains the following parts: (1) Synthesis of pure 2H-MoS2 by electrodeposition. The effects of deposition time and temperature on HER performance were explored by changing the parameters, providing a basis for the subsequent improvement of the electrocatalytic performance of MoS2. (2) One-step electrodeposition to synthesize MoS2 with Ni doping The surface area of 5 at% Ni-modified MoS2 increased, having more active sites, and the resistance of the in situ grown catalyst became smaller, which was more favorable for electron transfer so that the HER performance was significantly enhanced. (3) Hydrazine hydrate was added to convert 2H into 1T phase MoS2 during hydrothermal method, and the HER performance of MoS2 was better owing to the Co-doping. The design concept of this work can also be used to develop and design other TMDs catalysts to promote the practical application of TMDs materials in hydrogen evolution reaction

Application of multidimensional structural equations in the emergency management of coal mine accidents

The use of coal as a source of energy is crucial for the growth of the national economy, but mining poses numerous risks and a potential for significant disasters. Coal mine safety is the prerequisite and guarantee for coal industry to achieve new industrialization and sustainable development. Therefore, it is crucial to predict a safety accident in the coal mine in advance. In order to facilitate the early warning of coal mine safety accidents, this study seeks to present a prediction model based on emergency management of safety accidents, which is a fusion model of principal component analysis (PCA) and long short-term memory neural network. According to the results, the correlation coefficients of risk identification and monitoring (a11), safety inspection and warning (a12), emergency planning and training (a13), material and technical support (a15), and macroenvironmental management (a21) were 0.718, 0.653, 0.628, 0.444, and 0.553, respectively, after the PCA dimensionality reduction process, demonstrating that the previous principal component analysis had a better effect. The absolute relative errors of each evaluation index of safety accident emergency management did not exceed the limit of 5%, including a15 and a21, whose values were 4.5% and −3.8%, while the relative errors of the remaining indicators were kept at a relatively low level. In conclusion, it is clear that the algorithm model suggested in this research improved the warning capabilities of safety accident emergency risk

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route\&time prediction; (ii) model architecture, which encompasses sequence-based and graph-based models; and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field

Transient Response Characteristics Analysis of High-Power Piezoelectric Transducers

To improve suitability in applications with high dynamic performance requirements, the transient response characteristics of high-power piezoelectric transducers should be studied quantitatively. This paper proposes the vector reduction method to solve the complex transient equations and obtains a transient matching scheme clarifying the mechanism of electrical matching resistance on electromechanical damping. A matching scheme with a combination of full-bridge inverter, transformer and series LC circuit is designed and validated, which can provide suitable electrical damping without causing energy losses. Consequently, the experiment verifies the transient properties of the proposed scheme. For a typical piezoelectric cutting transducer with 100.8 ms response time, our scheme is verified to have high dynamic performance within frequency response time of 5.5 ms and vibration response time of 15.0 ms

Effects of the Transverse Deck-Roadbed Pounding on the Seismic Behaviors of the Prefabricated Frame Bridge

Pounding effects on prefabricated frame bridges are not clear, which may influence seismic behaviors a lot in some cases. Prefabricated frame bridges are emerging structures designed to solve the problem of difficult land acquisition in highway expansion and reconstruction, the deck of the prefabricated frame bridge is adjacent to the original roadbed in the transverse direction, so the pounding potential exists under the earthquake ground motions. In this study, the artificial ground motions of the different seismic intensities are selected to carry out the nonlinear time history analyses, and the pounding effects on the prefabricated frame bridge are evaluated based on the pounding forces and the components’ seismic response. It is found that the pounding effects are not obvious in all cases; some energy can be dissipated in the pounding process, which is also limited to some extent. Finally, the influences of the gap distance and seismic intensity are investigated according to the parameter sensitivity analysis. The results indicate that the gap distance and the seismic intensity are the two important factors related to the pounding effects, the seismic response of the components will decrease when the pounding effects are obvious, and the transverse deformation of the deck cannot influence the stress state of the superstructure

Social Media-Based Health Management Systems and Sustained Health Engagement: TPB Perspective

Background: With the popularity of mobile Internet and social networks, an increasing number of social media-based health management systems (SocialHMS) have emerged in recent years. These social media-based systems have been widely used in registration, payment, decision-making, chronic diseases management, health information and medical expenses inquiry, etc., and they greatly facilitate the convenience for people to obtain health services. Objective: This study aimed to investigate the factors influencing sustained health engagement of SocialHMS by combining the theory of planned behavior (TPB) with the big-five theory and the trust theory. Method: We completed an empirical analysis based on the 494 pieces of data collected from Anhui Medical University first affiliated hospital (AMU) in East China through structural equation modeling and SmartPLS (statistical analysis software). Results: Openness to new experience has a significantly positive influence on attitude (path coefficient = 0.671, t = 24.0571, R2 = 0.451), perceived behavioral control (path coefficient = 0.752, t = 32.2893, R2 = 0.565), and perceived risk (path coefficient = 0.651, t = 18.5940, R2 = 0.424), respectively. Attitude, perceived behavioral control, subjective norms, and trust have a significantly positive influence on sustained health engagement (path coefficients = 0.206, 0.305, 0.197, 0.183 respectively, t = 3.6684, 4.9158, 4.3414, and 3.3715, respectively). The explained variance of the above factors to the sustained health engagement of SocialHMS is 60.7% (R2 = 0.607). Perceived risk has a significantly negative influence on trust (path coefficient = 0.825, t = 46.9598, R2 = 0.681). Conclusions: Attitude, perceived behavioral control, subjective norm, and trust are the determinants that affect sustained health engagement. The users’ personality trait of openness to new experience and perceived risk were also found to be important factors for sustained health engagement. For hospital managers, there is the possibility to take appropriate measures based on users’ personality to further enhance the implementation and utilization of SocialHMS. As for system suppliers, they can provide the optimal design for SocialHMS so as to meet users’ needs

Advances in the optimization of central carbon metabolism in metabolic engineering

Abstract Central carbon metabolism (CCM), including glycolysis, tricarboxylic acid cycle and the pentose phosphate pathway, is the most fundamental metabolic process in the activities of living organisms that maintains normal cellular growth. CCM has been widely used in microbial metabolic engineering in recent years due to its unique regulatory role in cellular metabolism. Using yeast and Escherichia coli as the representative organisms, we summarized the metabolic engineering strategies on the optimization of CCM in eukaryotic and prokaryotic microbial chassis, such as the introduction of heterologous CCM metabolic pathways and the optimization of key enzymes or regulatory factors, to lay the groundwork for the future use of CCM optimization in metabolic engineering. Furthermore, the bottlenecks in the application of CCM optimization in metabolic engineering and future application prospects are summarized

Mining of the Catharanthus roseus Genome Leads to Identification of a Biosynthetic Gene Cluster for Fungicidal Sesquiterpenes

Characterization of cryptic biosynthetic gene clusters (BGCs) from microbial genomes has been proven to be a powerful approach to discovery of new natural products. However, such a genome mining approach to discovery of bioactive plant metabolites has been muted. The plant BGCs characterized to date encode pathways for antibiotics important in plant defense against microbial pathogens, providing a means to discover such phytoalexins by mining plant genomes. Here is reported discovery and characterization of a minimal BGC from the medicinal plant Catharanthus roseus, consisting of an adjacent pair of genes encoding a terpene synthase (CrTPS18) and cytochrome P450 (CYP71D349). These two enzymes act sequentially, with CrTPS18 acting as a sesquiterpene synthase, producing 5-epi-jinkoh-eremol (1), which CYP71D349 further hydroxylates to debneyol (2). Infection studies with maize revealed that 1 and 2 exhibit more potent fungicidal activity than validamycin. Accordingly, this study demonstrates that characterization of such cryptic plant BGCs is a promising strategy for discovery of potential agrochemical leads. Moreover, despite the observed absence of 1 and 2 in C. roseus the observed transcriptional regulation is consistent with their differential fungicidal activity, suggesting that such conditional co-expression may be sufficient to drive BGC assembly in plants.This is a manuscript of an article published as Liang, Jincai, Tianyue An, Jian-Xun Zhu, Shan Chen, Jian-Hua Zhu, Reuben J. Peters, Rongmin Yu, and Jiachen Zi. "Mining of the Catharanthus roseus Genome Leads to Identification of a Biosynthetic Gene Cluster for Fungicidal Sesquiterpenes." Journal of Natural Products 84, no. 10 (2021): 2709-2716. doi:10.1021/acs.jnatprod.1c00588. Posted with permission

Engineering work functions of cobalt-doped manganese oxide based electrocatalysts for highly efficient oxygen evolution reaction

The crystalline and electronic structures are two important factors for the design of electrocatalysts. In this work, Co-doped MnO electrocatalysts grown on nickel foam (NF) were prepared by a facile hydrothermal reaction, followed by H2 treatment process. The electrocatalytic performance of MnO was significantly improved after doping with Co and the Co0.1Mn0.9O-NF sample achieved excellent oxygen evolution reaction (OER) performance with low overpotential (370 mV at 10 mA cm−2) and reasonable Tafel slope (85.6 mV dec-1). Significantly, the low work function was obtained in the Co0.1Mn0.9O-NF sample (4.37 eV), which could accelerate the charge transfer process of the OER activity. The excellent OER performance of the Co0.1Mn0.9O-NF sample is also attributed to the rich active sites, which improved electrical conductivity and enlarged electrochemical surface areas.</p

Nickel-Doped Manganese Dioxide Electrocatalysts with MXene Surface Decoration for Oxygen Evolution Reaction

Electrochemical water splitting (EWS) has been considered as an ideal strategy to produce renewable hydrogen energy. However, the application of EWS is hindered by its sluggish kinetics of oxygen evolution half-reaction. In this work, we successfully prepared an efficient MXene-Ni0.075Mn0.925O2/CC catalyst for oxygen evolution reaction (OER) enhanced by a novel electrodeposition process. By corroborating from characterization results, the Ni element has been successfully doped into the MnO2crystal. In addition, electron microscopy images visualized that MXene firmly cooperated with the Ni-doped MnO2. With the proper amount of Ni doping in the pristine MnO2, more defects were induced. In addition, the two-dimensional (2D) MXene cooperation collaboratively provided more mass transport channels for OER. Therefore, the prepared MXene-Ni0.075Mn0.925O2/CC catalyst exhibited an outstanding catalytic performance with an overpotential of ?410 mV at a constant current density of 50 mA cm-2, about 105 mV smaller than that of the pristine MnO2/CC catalyst. The proposed electrodeposition method may pave the way for future designing of binder-free electrocatalytic materials for EWS