Showing papers by "Jiangxi University of Science and Technology published in 2019"

A ZnII-Based Metal-Organic Framework with a Rare tcj Topology as a Turn-On Fluorescent Sensor for Acetylacetone.

Abstract: A ZnII-based metal-organic framework (MOF) with a rare tcj topology has been solvothermally synthesized and displays relatively good thermal and chemical stabilities. Interestingly, the MOF can sensitively and selectively sense acetylacetone (acac) via a fluorescence enhancement effect with a detection limit of 0.10 ppm and good reusability, which demonstrates the first example of a MOF-based turn-on fluorescent sensor for acac.

Carbon‐Nanomaterial‐Based Flexible Batteries for Wearable Electronics

Abstract: Wearable electronics have received considerable attention in recent years. These devices have penetrated every aspect of our daily lives and stimulated interest in futuristic electronics. Thus, flexible batteries that can be bent or folded are desperately needed, and their electrochemical functions should be maintained stably under the deformation states, given the increasing demands for wearable electronics. Carbon nanomaterials, such as carbon nanotubes, graphene, and/or their composites, as flexible materials exhibit excellent properties that make them suitable for use in flexible batteries. Herein, the most recent progress on flexible batteries using carbon nanomaterials is discussed from the viewpoint of materials fabrication, structure design, and property optimization. Based on the current progress, the existing advantages, challenges, and prospects are outlined and highlighted.

Data Security Sharing and Storage Based on a Consortium Blockchain in a Vehicular Ad-hoc Network

Abstract: A vehicular ad-hoc network (VANET) can improve the flow of traffic to facilitate intelligent transportation and to provide convenient information services, where the goal is to provide self-organizing data transmission capabilities for vehicles on the road to enable applications, such as assisted vehicle driving and safety warnings. VANETs are affected by issues such as identity validity and message reliability when vehicle nodes share data with other nodes. The method used to allow the vehicle nodes to upload sensor data to a trusted center for storage is susceptible to security risks, such as malicious tampering and data leakage. To address these security challenges, we propose a data security sharing and storage system based on the consortium blockchain (DSSCB). This digital signature technique based on the nature of bilinear pairing for elliptic curves is used to ensure the reliability and integrity when transmitting data to a node. The emerging consortium blockchain technology provides a decentralized, secure, and reliable database, which is maintained by the entire network node. In DSSCB, smart contracts are used to limit the triggering conditions for preselected nodes when transmitting and storing data and for allocating data coins to vehicles that participate in the contribution of data. The security analysis and performance evaluations demonstrated that our DSSCB solution is more secure and reliable in terms of data sharing and storage. Compared with the traditional blockchain system, the time required to confirm the data block was reduced by nearly six times and the transmission efficiency was improved by 83.33%.

Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting

Abstract: With the fast expansion of renewable energy system installed capacity in recent years, the availability, stability, and quality of smart grids have become increasingly important. The renewable energy output forecasting applications have also been developing rapidly in recent years, and such techniques have particularly been applied in the fields of wind and solar photovoltaic (PV). In the case of solar PV output forecasting, many applications have been performed with machine learning and hybrid techniques. In this paper, we propose a high-precision deep neural network model named PVPNet to forecast PV system output power. The methodology behind the proposed model is based on deep neural networks, and the model is able to generate a 24-h probabilistic and deterministic forecasting of PV power output based on meteorological information, such as temperature, solar radiation, and historical PV system output data. The forecasting accuracy of PVPNet is determined by the mean absolute error (MAE) and root mean square error (RMSE) values. The results from the experiments show that the MAE and RMSE of the proposed algorithm are 109.4845 and 163.1513, respectively. The results prove that the prediction accuracy of the PVPNet outperforms other benchmark models, and the algorithm also effectively predicts complex time series with a high degree of volatility and irregularity.

Biomass-Derived Porous Carbon Materials for Supercapacitor

Abstract: The fast consumption of fossil energy accompanied by the ever-worsening environment urge the development of a clean and novel energy storage system. As one of the most promising candidates, the supercapacitor owns unique advantages, and numerous electrodes materials have been exploited. Hence, biomass-derived porous carbon materials (BDPCs), at low cost, abundant and sustainable, with adjustable dimension, superb electrical conductivity, satisfactory specific surface area (SSA) and superior electrochemical stability have been attracting intense attention and highly trusted to be a capable candidate for supercapacitors. This review will highlight the recent lab-scale methods for preparing BDPCs, and analyze their effects on BDPCs' microstructure, electrical conductivity, chemical composition and electrochemical properties. Future research trends in this field also will be provided.

Biodegradable metallic bone implants

Abstract: Biodegradable metals, such as Mg and Mg alloys, Fe and Fe alloys, and Zn and Zn alloys, are drawing increased attention as bone implant materials owing to their biodegradability. Among them, Mg and Mg alloys have similar densities and elastic moduli as compared to those of natural bone, but they degrade too quickly in human physiological environments, resulting in excessive release of hydrogen and premature loss of strength. Fe and Fe alloys are known for their outstanding mechanical properties, while their degradation rates are too slow to meet the requirements of bone repair. In comparison, Zn and Zn alloys have suitable degradation rates when compared with the growth rates of natural bone. However, their poor strength and ductility constrain their applications in bone repair. This review summarizes the current status of research on the use of biodegradable metals in bone implants. Their biodegradability, mechanical properties, and biocompatibility are systematically reviewed. On the basis of presentation, efforts made to improve the deficiencies of these biodegradable metals, such as alloying and heat treatment, are summarized. The problems and further directions are also put forward for biodegradable metallic bone implants.

3D honeycomb nanostructure-encapsulated magnesium alloys with superior corrosion resistance and mechanical properties

Abstract: Magnesium (Mg) alloys are promising biodegradable metals for biomedical applications but limited by their too fast degradation rates. In this study, selective laser melting was used to fabricate three-dimensional honeycomb nanostructure-encapsulated Mg alloys, in which the honeycomb nanostructure was constructed by graphene oxide (GO) as a second phase and the grains of Mg alloys were encapsulated in the honeycomb unit. Results showed that GO distributed along the grain boundaries and gradually wrapped ɑ-Mg grains as GO content increasing. It was worth noting that a honeycomb nanostructure was formed with ɑ-Mg grains encapsulated in the honeycomb unit at a certain GO content (1.0 wt% in this study). As a result, the corrosion resistance and mechanical properties were both improved, which might be ascribed to the following mechanisms: (I) ɑ-Mg grains were refined due to the reduced connection and promoted nucleation by GO; (II) Benefiting from the outstanding anti-permeability of GO, the honeycomb nanostructure acted as a tight barrier to restrain the propagation of corrosion; (III) GO reinforced the corrosion layer to prevent it falling off the Mg matrix; (Ⅳ) The oxygen-containing groups on GO facilitated the deposition of bone-like apatite and further hindered the invasion of corrosive medium. These findings demonstrated the multiple defensive roles against corrosion in the honeycomb nanostructure-encapsulated Mg alloys and their great potential in biomedical applications.

Glass forming, crystallization, and physical properties of MgO-Al2O3-SiO2-B2O3 glass-ceramics modified by ZnO replacing MgO

Abstract: This study focused on the glass forming, crystallization, and physical properties of ZnO doped MgO-Al2O3-SiO2-B2O3 glass-ceramics. The results show that the glass forming ability enhances first with ZnO increasing from 0 to 0.5 mol%, and then weakens with further addition of ZnO which acted as network modifier. No nucleating agent was used and the crystallization of studied glasses is controlled by a surface crystallization mechanism. The predominant phase in glass-ceramics changed from α-cordierite to spinel/gahnite as ZnO gradually replaced MgO. The phase type did not change; however, the crystallinity and grain size in glass-ceramics increased when the glasses were treated from 1030 °C to 1100 °C. The introduction of ZnO can improve the thermal, mechanical, and dielectric properties of the glass-ceramics. The results reveal a rational mechanism of glass formation, crystal precipitation, and evolution between structure and performance in the xZnO-(20-x)MgO-20Al2O3-57SiO2-3B2O3 (0 ≤ x ≤ 20 mol%) system.

An overview of plant microbial fuel cells (PMFCs): Configurations and Applications.

Abstract: The depletion of non-renewable energy resources has led to the exploitation of alternative renewable sources such as solar energy, which is mainly employed to generate electricity using conventional photovoltaic cells. In recent years, other alternative bioelectrochemical systems such as plant microbial fuel cells (PMFCs) has been developed to generate electricity via biological interactions of plants and microbes in the presence of sunlight. Compared to the photovoltaic cells, PMFCs can also generate power continuously, being implemented on agricultural lands without any obstruction to food cultivation/production processes or even in the fields unsuitable for food production. To explore and optimize the use of living plants for sustainable power generation in PMFCs, the key fundamental aspects peculiar to PMFCs were comprehensively reviewed. Subsequently, various reported configurations of PMFCs embedded with vascular plants, macrophytes and bryophytes as well as their combination with constructed wetlands were evaluated and discussed. So far, PMFCs could be applied in the fields of wastewater treatment, polluted sediment and surface water remediation, greenhouse gas mitigation and biosensing. Finally, the prospects and challenges of PMFCs for full-scale applications were also presented. Overall, PMFCs will become alternative renewable energy sources to reduce energy scarcity and related environmental issues when they are scaled up and applied in-situ.

Free vibrations of functionally graded porous rectangular plate with uniform elastic boundary conditions

Abstract: In this paper, the free vibrations of functionally graded porous (FGP) rectangular plate with uniform elastic boundary conditions is investigated by means of an improved Fourier series method (IFSM). It is assumed that the distributions of porosity are uniform or non-uniformly along a certain direction and three types of the porosity distribution are considered, among which material property of two non-uniform porous distributions was expressed as the simple cosine. The size of the pore in a rectangular plate is determined by the porosity coefficients. Using the first-order shear deformation theory(FSDT), the energy expression of FGP rectangular plate is created. In order to obtain the admissible function of displacement for functionally graded porous rectangular plate, the IFSM is employed. Then, the Rayleigh-Ritz method is used to solve coefficients in the Fourier series which determine natural frequencies and modal shapes. Convergence and comparative research are performed to prove the convergence, reliability and accuracy of the current method. On this foundation, some new results covering the influence of the geometrical parameters subject to classical and elastic boundary condition are presented, and the parametric studies are also investigated in detail, which can provide a reference for future research by other researchers.

Evaluation of Different Machine Learning Approaches to Forecasting PM2.5 Mass Concentrations

Abstract: With the rapid growth in the availability of data and computational technologies, multiple machine learning frameworks have been proposed for forecasting air pollution. However, the feasibility of these complex approaches has seldom been verified in developing countries, which generally suffer from heavy air pollution. To forecast PM2.5 concentrations over different time intervals, we implemented three machine learning approaches: multiple additive regression trees (MART), a deep feedforward neural network (DFNN) and a new hybrid model based on long short-term memory (LSTM). By capturing temporal dependencies in the time series data, the LSTM model achieved the best results, with RMSE = 8.91 µg m–3 and MAE = 6.21 µg m–3. It also explained 80% of the variability (R2 = 0.8) in the PM2.5 concentrations and predicted 75% of the pollution levels, proving that this methodology can be effective for forecasting and controlling air pollution.

In-situ EBSD study of deformation behaviour of 600 MPa grade dual phase steel during uniaxial tensile tests

Abstract: This paper studied the plastic deformation behaviour of DP600 steel subjected to uniaxial tension, by means of in-situ EBSD technique. It provides experimental evidences and detailed insight into the microstructural aspects of plastic deformation. A phase identification method based on the band slope map of EBSD was adopted to differentiate martensite from ferrite. The results show that the plastic strain localization lies mainly in the ferrite grains, fracture could usually start in ferrite grains close to hard martensite grains. With the increase of strain, average misorientation angle decreased while the fraction of LAGBs increased. Average Taylor factor for the whole microstructure became higher at high strains due to work hardening process, and plastic deformation results in soft regions with zonal distribution parallel to the loading direction. In the undeformed state, the texture orientation ( 111 ) [ 0 1 ¯ 1 ] and ( 111 ) [ 1 1 ¯ 0 ] are the major components of the γ-fibre while ( 223 ) [ 1 1 ¯ 0 ] and ( 221 ) [ 1 1 ¯ 0 ] are the main components of α-fibre. The intensity of the α-fibre slightly decreased, while the intensity of the γ-fibre increased with increasing strain. Plastic deformation occurred in some grains which were subdivided into different regions due to the activation of different slip systems. The tensile axis orientation of the grain rotated gradually to the line link − , and lattice rotation within one single grain differs from regions to regions.