Effects of Alumina Bubble Addition on the Properties of Corundum-Spinel Castables Containing Cr2O3.

Purging plugs made of corundum-spinel castables containing Cr2O3 have been widely utilized in secondary refining process. However, their poor thermal shock resistance has greatly limited the improvement of their service life. Aiming to enhance their properties, we introduced alumina bubbles (ABs) to corundum-spinel castables, and the effects of the AB addition on the properties of the castables are studied in this manuscript. The results indicate that the apparent porosity, permanent linear change, cold strength, and hot strength all increased with an increasing AB amount. The thermal shock resistance of the samples with the AB addition was improved; the residual strength and residual strength ratio of the sample with 4 wt% ABs was the best. The effects of ABs on the tabular alumina aggregate distribution and relationship between the cold strength of the samples and the AB content was evaluated via the box dimension method. With the increments of AB content, the box dimension value of the tabular alumina within the samples significantly decreased, indicating that the tabular alumina aggregate distribution was related to the amount of ABs. In addition, the relationship between the box dimension and the strength was also established.

Large-Scale Fabrication of SiC-TiC@C Powders via Modified Molten Salt Shielding Synthesis Technique and Their Effect on the Properties of Al2O3-MgO Castables.

Graphite flakes are commonly used to fabricate carbon-based refractories owing to their superior properties, including better corrosion resistance and thermal shock resistance (TSR); unfortunately, their insufficient water-wettability has remarkably hindered their application in castables. Aiming to enhance their water-wettability, a facile and low-cost technique for fabricating carbides coated in graphite was proposed in this work. Firstly, SiC-TiC coated graphite (SiC-TiC@C) powders were prepared via modified molten salt shielding synthesis in an air atmosphere using graphite flake, Si and Ti powders as raw materials and NaCl-KCl as the molten salt shielding medium. Water-wettability and oxidation resistance of SiC-TiC@C powders were significantly improved. Compared to the Al2O3-MgO castables with graphite flakes, the water demand of the castables with SiC-TiC@C was noticeably decreased from 6.85% to 4.89%, thereby decreasing the apparent porosity of the castables with 5% SiC-TiC@C (from 20.3% to 13%), enhancing the cold strength, hot strength and oxidation resistance of the castables. Such enhancements are ascribed to continuous and crack-free SiC-TiC coatings on graphite surfaces ensuring that the castables have outstanding properties.

Public Perception of COVID-19 Vaccines on Twitter in the United States.

Background: COVID-19 vaccines play a vital role in combating the COVID-19 pandemic. Social media provides a rich data source to study public perception of COVID-19 vaccines. Objective: In this study, we aimed to examine public perception and discussion of COVID-19 vaccines on Twitter in the US, as well as geographic and demographic characteristics of Twitter users who discussed about COVID-19 vaccines. Methods: Through Twitter streaming Application Programming Interface (API), COVID-19-related tweets were collected from March 5 th , 2020 to January 25 th , 2021 using relevant keywords (such as "corona", "covid19", and "covid"). Based on geolocation information provided in tweets and vaccine-related keywords (such as "vaccine" and "vaccination"), we identified COVID-19 vaccine-related tweets from the US. Topic modeling and sentiment analysis were performed to examine public perception and discussion of COVID-19 vaccines. Demographic inference using computer vision algorithm (DeepFace) was performed to infer the demographic characteristics (age, gender and race/ethnicity) of Twitter users who tweeted about COVID-19 vaccines. Results: Our longitudinal analysis showed that the discussion of COVID-19 vaccines on Twitter in the US reached a peak at the end of 2020. Average sentiment score for COVID-19 vaccine-related tweets remained relatively stable during our study period except for two big peaks, the positive peak corresponds to the optimism about the development of COVID-19 vaccines and the negative peak corresponds to worrying about the availability of COVID-19 vaccines. COVID-19 vaccine-related tweets from east coast states showed relatively high sentiment score. Twitter users from east, west and southern states of the US, as well as male users and users in age group 30-49 years, were more likely to discuss about COVID-19 vaccines on Twitter. Conclusions: Public discussion and perception of COVID-19 vaccines on Twitter were influenced by the vaccine development and the pandemic, which varied depending on the geographics and demographics of Twitter users.

Effect of the Aromatic Amine Curing Agent Structure on Properties of Epoxy Resin-Based Syntactic Foams.

Epoxy resin is one of the commonly used matrixes of syntactic foams as a buoyancy material, the curing agents of which affect some of the properties for syntactic foams. Therefore, the curing reactions of N,N,N',N'-tetraepoxypropyl-4,4'-diaminodiphenylmethane (AG-80) epoxy resin between 4,4-diaminodiphenyl methane (DDM) and the mixture of m-xylylenediamine and DDM (DDM-m-XDA) systems are studied. The DDM mixed with m-XDA enhances curing reactions with the AG-80 epoxy resin, and the mechanisms of the two curing systems are different through nonisothermal kinetics. Compared with a single curing system, there are some wrinkles on the surface of the AG-80/DDM-m-XDA matrix because of the disordered network. Composited with hollow glass microspheres (HGMs), the more flexible m-XDA structure enhances the interfacial adhesion between the matrix and HGM for syntactic foams. However, the wrinkles in the matrix increase the broken degree of HGMs; especially at HGM contents higher than 55%, the flaw increases the density and water absorption of syntactic foams; meanwhile, it decreases the compressive strength. Therefore, the properties of syntactic foams can be improved by mixing different molecular structure curing agents and the mixture liquid curing agent simplifies the preparation process to some extent.

Preparation of a DNA-Tb-MOF conjugate as a time-resolved probe for the detection of SO2 derivatives through an off-on effect.

Herein we synthesize a DNA-sensitized Tb-MOF conjugate (DNA-Tb-MOF) as a time-resolved luminescent probe to sensitively and selectively assay SO2 and their derivatives (i.e., HSO3-) through a photoluminescence off-on effect. The charge and energy transfer mechanism enables the demonstration of the effect of the photoluminescence turn-on which results from the reaction between the amino group of the DNA-Tb-MOF conjugate and SO2/HSO3-. The results demonstrate that the DNA-Tb-MOF conjugate probe can sense SO2 and their derivatives (i.e., HSO3-) with a detection limit of 0.02 ppm. Moreover, the photoluminescence off-on effect can be observed even by the naked eye.

Oxygen vacancy rich Bi2O4-Bi4O7-BiO2-x composites for UV-vis-NIR activated high efficient photocatalytic degradation of bisphenol A.

To fully utilize the solar light, photocatalyst with broad spectrum response from UV to near-infrared (NIR) is desirable. In this work, ternary mixed valent Bi2O4-Bi4O7-BiO2-x with rich oxygen vacancy has been synthesized through one-pot hydrothermal treatment of NaBiO3. The results showed that through adjusting the hydrothermal conditions, oxygen vacancy-rich Bi2O4-Bi4O7-BiO2-x nanocomposites with much higher efficiency than single or mixed bismuth oxides (Bi2O4, Bi4O7, BiO2-x and Bi4O7-BiO2-x,) can be synthesized for photocatalytic degradation of bisphenol A (BPA) under UV, visible, and NIR light irradiation. In addition, the liquid chromatography-mass spectrometer (LC-MS) characterization demonstrated that BPA was oxidized to 4-isopropenyphenol first and the rings were opened sequentially under NIR light irradiation. Further detection of reactive species indicated that holes, O2-, and OH were the main oxidizing species in the degradation system. The experimental observations and density functional theory (DFT) calculations suggested that both type-II and the Z-scheme charge transfer with oxygen vacancies as electrons and holes mediators were formed at the interfaces of Bi2O4, Bi4O7, and BiO2-x, resulting in a very efficient separation of photogenerated charge carriers in the composite. This work adds to the growing potential of mixed valent bismuth oxides based photocatalysts and is expected to accelerate the pace of the development of new-generation photocatalysts with high efficiency utilizing full-spectrum solar light.

Data Anomaly Detection for Internet of Vehicles Based on Traffic Cellular Automata and Driving Style.

The data validity of safe driving in the Internet of Vehicles (IoV) is the basis of improving the safety of vehicles. Different from a traditional information systems, the data anomaly analysis of vehicle safety driving faces the diversity of data anomaly and the randomness and subjectivity of the driver's driving behavior. How to combine the characteristics of the IOV data with the driving style analysis to provide effective real-time anomaly data detection has become an important issue in the IOV applications. This paper aims at the critical safety data analysis, considering the large computing cost generated by the real-time anomaly detection of all data in the data package. We preprocess it through the traffic cellular automata model which is built to achieve the ideal abnormal detection effect with limited computing resources. On the basis of this model, the Anomaly Detection based on Driving style (ADD) algorithm is proposed to realize real-time and online detection of anomaly data related to safe driving. Firstly, this paper designs the driving coefficient and proposes a driving style quantization model to represent the driving style of the driver. Then, based on driving style quantization model and vehicle driving state information, a data anomaly detection algorithm is developed by using Gaussian mixture model (GMM). Finally, combining with the application scenarios of multi-vehicle collaboration in the Internet of Vehicles, this paper uses real data sets and simulation data sets to analyze the effectiveness of the proposed ADD algorithm.

Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network.

Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly detection in multivariate-sensing time-series in this paper. Based on this situation, we propose RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN). First of all, we use HTM model to evaluate the real-time anomalies of each univariate-sensing time-series. Secondly, a model of anomalous state detection in multivariate-sensing time-series based on Naive Bayesian is designed to analyze the validity of the above time-series. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, the effectiveness of the methodology is demonstrated using a simulated example. Extensive simulation results show that using RADM in multivariate-sensing time-series is able to detect more abnormal, and thus can remarkably improve the performance of real-time anomaly detection.