Behavior of phase transformation of Baotou mixed rare earth concentrate during oxidation roasting

Based on the new process named “Combination Method” for metallurgy and separation of Baotou mixed rare earth concentrate (BMREC), the aim of this paper is to clearly elucidate the phase change behavior of BMREC without additives during oxidative roasting at 450–800 °C. The results indicate that the bastnaesite in BMREC is decomposed at 450–550 °C, the weight loss is about 10.3 wt%, and the activation energy (E) is 144 kJ/mol. The bastnaesite in BMREC is decomposed into rare earth fluoride, rare earth oxides (La₂O₃, Ce₇O₁₂, Pr₆O₁₁ and Nd₂O₃), and CO₂, particularly, with the increase of roasting temperature, bastnaesite in BMREC is more completely decomposed into LaF₃, which causes a decrease in leaching rate of La during the HCl leaching process. Additionally, the maximum cerium oxidation efficiency reaches about 60 wt% when the roasting temperature is equal to or above 500 °C, and the oxidation reaction rate of cerium increases with the increasing roasting temperature.     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

Influence of temperature on acoustic emission source location accuracy in underground structure

An acoustic emission (AE) experiment was carried out to explore the AE location accuracy influenced by temperature. A hollow hemispherical specimen was used to simulate common underground structures. In the process of heating with the flame, the pulse signal of constant frequency was stimulated as an AE source. Then AE signals received by each sensor were collected and used for comparing localization accuracy at different temperatures. Results show that location errors of AE keep the same phenomenon in the early and middle heating stages. In the later stage of heating, location errors of AE increase sharply due to the appearance of cracks. This provides some beneficial suggestions on decreasing location errors of structural cracks caused by temperature and improves the ability of underground structure disaster prevention and control.     

Three distinct optical-switching states in phase-change materials containing impurities:From physical origin to material design

Ge2Sb2Tes is the most widely utilized chalcogenide phase-change material for non-volatile photonic applications,which undergoes amorphous-cubic and cubic-hexagonal phase transition under external excitations.However,the cubic-hexagonal optical contrast is negligible,only the amorphous-cubic phase transition of Ge2Sb2Te5 is available.This limits the optical switching states of traditional active dis-plays and absorbers to two.We find that increasing structural disorder difference of cubic-hexagonal can increase optical contrast close to the level of amorphous-cubic.Therefore,an amorphous-cubic-hexagonal phase transition with high optical contrast is realized.Using this phase transition,we have developed display and absorber with three distinct switching states,improving the switching perfor-mance by 50％.Through the combination of first-principle calculations and experiments,we reveal that the key to increasing structural disorder difference of amorphous,cubic and hexagonal phases is to intro-duce small interstitial impurities(like N)in Ge2Sb2Tes,rather than large substitutional impurities(like Ag)previously thought.This is explained by the formation energy and lattice distortion.Based on the impurity atomic radius,interstitial site radius and formation energy,C and B are also potential suit-able impurities.In addition,introducing interstitial impurities into phase-change materials with van der Waals gaps in stable phase such as GeSb4Te7,GeSb2Te4,Ge3Sb2Te6,Sb2Te3 will produce high optical con-trast amorphous-metastable-stable phase transition.This research not only reveals the important role of interstitial impurities in increasing the optical contrast between metastable-stable phases,but also proposes varieties of candidate matrices and impurities.This provides new phase-change materials and design methods for non-volatile optical devices with multi-switching states.     

High-Throughput Screening for Phase-Change Memory Materials

Phase change memory (PCM) is an emerging non-volatile data storage technology concerned by the semiconductor industry. To improve the performances, previous efforts have mainly focused on partially replacing or doping elements in the flagship Ge-Sb-Te (GST) alloy based on experimental “trial-and-error” methods. Here, the current largest scale PCM materials searching is reported, starting with 124 515 candidate materials, using a rational high-throughput screening strategy consisting of criteria related to PCM characteristics. In the results, there are 158 candidates screened for PCM materials, of which ≈68% are not employed. By further analyses, including cohesive energy, bond angle analyses, and Born effective charge, there are 52 materials with properties similar to the GST system, including Ge₂Bi₂Te₅, GeAs₄Te₇, GeAs₂Te₄, so on and other candidates that have not been reported, such as TlBiTe₂, TlSbTe₂, CdPb₃Se₄, etc. Compared with GST, materials with close cohesive energy include AgBiTe₂, TlSbTe₂, As₂Te₃, TlBiTe₂, etc., indicating possible low power consumption. Through further melt-quenching molecular dynamic calculation and structural/electronic analyses, Ge₂Bi₂Te₅, CdPb₃Se₄, MnBi₂Te₄, and TlBiTe₂ are found suitable for optical/electrical PCM applications, which further verifies the effectiveness of this strategy. The present study will accelerate the exploration and development of advanced PCM materials for current and future big-data applications.     

Sequential alignment attention model for scene text recognition

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state-of-the-art solutions usually depend on the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. Unfortunately, there often exists severe misalignment between feature areas and text labels in real-world scenarios. To address this problem, this paper proposes a sequential alignment attention model to enhance the alignment between input images and output character sequences. In this model, an attention gated recurrent unit (AGRU) is first devised to distinguish the text and background regions, and further extract the localized features focusing on sequential text regions. Furthermore, CTC guided decoding strategy is integrated into the popular attention-based decoder, which not only helps to boost the convergence of the training but also enhances the well-aligned sequence recognition. Extensive experiments on various benchmarks, including the IIIT5k, SVT, and ICDAR datasets, show that our method substantially outperforms the state-of-the-art methods.     

Genome-Wide Analyses of the Temperature-Responsive Genetic Loci of the Pectinolytic Plant Pathogenic Pectobacterium atrosepticum

Temperature is one of the critical factors affecting gene expression in bacteria. Despite the general interest in the link between bacterial phenotypes and environmental temperature, little is known about temperature-dependent gene expression in plant pathogenic Pectobacterium atrosepticum, a causative agent of potato blackleg and tuber soft rot worldwide. In this study, twenty-nine P. atrosepticum SCRI1043 thermoregulated genes were identified using Tn5-based transposon mutagenesis coupled with an inducible promotorless gusA gene as a reporter. From the pool of 29 genes, 14 were up-regulated at 18 °C, whereas 15 other genes were up-regulated at 28 °C. Among the thermoregulated loci, genes involved in primary bacterial metabolism, membrane-related proteins, fitness-corresponding factors, and several hypothetical proteins were found. The Tn5 mutants were tested for their pathogenicity in planta and for features that are likely to remain important for the pathogen to succeed in the (plant) environment. Five Tn5 mutants expressed visible phenotypes differentiating these mutants from the phenotype of the SCRI1043 wild-type strain. The gene disruptions in the Tn5 transposon mutants caused alterations in bacterial generation time, ability to form a biofilm, production of lipopolysaccharides, and virulence on potato tuber slices. The consequences of environmental temperature on the ability of P. atrosepticum to cause disease symptoms in potato are discussed.     

Facile self-assembly approach to construct a novel MXene-decorated nano-sized phase change material emulsion for thermal energy storage

In recent years, phase change material emulsions (PCMEs) with enhanced energy storage capacities and good flow characteristics have drawn significant attention. However, due to the thermodynamically unstable nature and tiny particle confinement, the nanomaterial modification strategies at PCM/water interface to improve stabilities and reduce supercooling of nano-sized PCMEs (NPCMEs) are very limited and challenging. Herein, we report a facile strategy for constructing MXene-decorated NPCME with good stability, little supercooling, and high thermal conductivity by self-assembly of MXene nanosheets at PCM/water interface. The concentrations of MXene have great influences on the average droplet diameters, stabilities, and thermophysical properties of the NPCMEs. The results show that the PCMs have been well dispersed into the water in the form of quasi-spherical droplets, with average droplet diameters of 242–805 nm. The thermal conductivity of 10 wt% n-tetradecane/water NPCME containing 9 mg ml^-1 MXene is 0.693 W m^-1·K^-1, achieving an enhancement by 15.5%, as compared to that of water. Besides, the MXene-decorated paraffin/water NPCMEs exhibit little supercooling and enhanced heat storage capacities. More importantly, this facile self-assembly strategy opens a new platform for preparing high-performance NPCMEs, which can be used as novel heat transfer fluids for thermal energy storage systems.     

投影寻踪分类模型在常见造纸纤维原料综合评价中的应用

将投影寻踪分类（PPC）模型与基于实数编码的加速遗传算法（RAGA）相结合，同时优化多个指标参数，将高维数据指标转化为低维空间上的一维投影值，建立RAGA-PPC模型，用于造纸纤维原料分类，并对造纸纤维原料进行综合评价。结果表明，基于RAGA-PPC模型的评价结果与造纸纤维原料实际分类结果一致，此方法客观可靠，精度高，具有一定的应用前景。