Density functional theory study of the hydrogen evolution reaction in haeckelite boron nitride quantum dots

To satisfy arising energy needs and to handle the forthcoming worldwide climate transformation, the major research attention has been drawn to environmentally friendly, renewable and abundant energy resources. Hydrogen plays an ideal and significant role is such resources, due to its non-carbon based energy and production through clean energy. In this work, we have explored catalytic activity of a newly predicted haeckelite boron nitride quantum dot (haeck-BNQD), constructed from the infinite BN sheet, for its utilization in hydrogen production. Density functional theory calculations are employed to investigate geometry optimization, electronic and adsorption mechanism of haeck-BNQD using Gaussian16 package, employing the hybrid B3LYP and wB97XD functionals, along with 6–31G(d,p) basis set. A number of physical quantities such as HOMO/LUMO energies, density of states, hydrogen atom adsorption energies, Mulliken populations, Gibbs free energy, work functions, overpotentials, etc., have been computed and analysed in the context of the catalytic performance of haeck-BNQD for the hydrogen-evolution reaction (HER). Based on our calculations, we predict that the best catalytic performance will be obtained for H adsorption on top of the squares or the octagons of haeck-BNQD. We hope that our prediction of most active catalytic sites on haeck-BNQD for HER will be put to test in future experiments.     

Estimation of gradient size of interfacial strain and its optimization for effective magnetoelectric coupling in (CoFe2O4) – (0.93Na0.5Bi0.5TiO3 – 0.07BaTiO3), 2-2 nano-composites

Strain-mediated coupling between the magnetic and electrically ordered phases plays a significant role in magnetoelectric (ME) nano-composites. This study explores a method to analyse and quantify interfacial strain using a grazing angle scan (α) in a ME composite optimised for a specific microstructure. The details of strain around the interface CoFe₂O₄ (CFO) – 0.93Na_0.5Bi_0.5TiO₃ – 0.07BaTiO₃ (NBT-BT) was determined by performing ‘α’ scan, in order to gather information at various depths of the NBT-BT layer around maximum intensity (110) reflection. The strain around the interface was observed to dominate over a spatial region of ～20–30 nm away from the interface. The Piezoresponse force microscopy (PFM) studies performed near the interface reveal that the strain constrain experienced by the ferroelectric layer operates such that polarisation rotation and domain wall motion are constrained compared to the strain relaxed region of the film. For effective strain transfer, heterostructures grown with optimised thicknesses (～20–30 nm) exhibited a superior inverse piezomagnetic effect.     

The Chemistry of DPPH· Free Radical and Congeners

Since the discovery in 1922 of 2,2-diphenyl-1-(2,4,6-trinitrophenyl) hydrazyl stable free radical (DPPH^·), the chemistry of such open-shell compounds has developed continuously, allowing for both theoretical and practical advances in the free radical chemistry area. This review presents the important, general and modern aspects of the chemistry of hydrazyl free radicals and the science behind it.     

Sulfur doped biocarbon obtained from Sargassum spp. for the oxygen reduction reaction

Sulfured doped carbon electrocatalysts is synthesized from the waste biomass Sargassum spp. Two doping procedures are examined to determine which is better for Oxygen Reduction Reaction (ORR); one by doping biocarbon obtained from the pyrolysis of the biomass and the second through a process of in situ doping in autoclave. The electrocatalyst are obtained from pyrolysis of the sample at 700 °C, which is finally characterized as a metal free electrocatalyst for the ORR. The electrocatalyst are characterized by BET surface area analysis, Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS) and the electrochemical characterization is determined in 0.1 M KOH. The sample SSKPT-1 exhibits a promising electrocatalytic activity with an onset potential of 0.896 V vs RHE and a current density of 5 mA cm^?2 (at 0.2 V vs. RHE) which could be partly attributed to its high BET surface area of 2755 m² g^?1.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Resistance reduction effect of potassium bismuth titanate doping in yttrium–manganese co-doped medium Curie temperature barium titanate lead free ceramics through improved synthesis process

Through improved synthesis process, resistance reduction effect of (K_0.5Bi_0.5)TiO₃ (KBT) doping in Y–Mn co-doped BaTiO₃ (BT) lead free ceramics was investigated. By different doping methods (doping K₂O, Bi₂O₃ and TiO₂ or synthesized KBT), medium Curie temperature (around 130 °C) lead free BT ceramics were obtained with ultra-low resistivity (13.84 Ωcm) with a temperature maintaining process at 700 °C. In this contribution, effect of sintering process and doping methods is discussed in detail.     

基于微博用户兴趣话题的相似用户挖掘

相似用户挖掘是提高社交网络服务质量的重要途径，在面向大数据的社交网络时代，准确的相似用户挖掘对于用户和互联网企业等都有重要的意义，而根据用户自己的兴趣话题挖掘的相似用户更符合相似用户的要求。提出了一种基于用户兴趣话题进行相似用户挖掘的方法。该方法首先使用TextRank话题提取方法对用户进行兴趣话题提取，再对用户发表内容进行训练，计算出所有词之间的相似度。提出CP（Corresponding Position similarity）、CPW（Corresponding Position Weighted similarity）、AP（All Position similarity）、APW（All Position Weighted similarity）四种用户兴趣话题词相似度计算方法，通过用户和相似用户间关注、粉丝重合率验证相似用户挖掘效果，APW similarity的相似用户的关注/粉丝重合百分比为1.687%，优于提出的其他三种算法，分别提高了26.3%、2.8%、12.4%，并且比传统的文本相似度方法Jaccard相似度、编辑距离算法、余弦相似度分别提高了20.4%、21.2%、45.0%。因此APW方法可以更加有效地挖掘出用户的相似用户。     

基于查询的新闻多文档自动摘要技术研究

针对新闻文本领域,该文提出一种基于查询的自动文本摘要技术,更加有针对性地满足用户信息需求。根据句子的TF-IDF、与查询句的相似度等要素,计算句子权重,并根据句子指示的时间给定不同的时序权重系数,使得最近发生的新闻内容具有更高的权重,最后使用最大边界相关的方法选择摘要句。通过与基于TF-IDF、Text-Rank、LDA等六种方法的对比,该摘要方法ROUGE评测指标上优于其他方法。从结合评测结果及摘要示例可以看出,该文提出的方法可以有效地从新闻文档集中摘取核心信息,满足用户查询内容的信息需求。     

Learning keyword phrases from query logs of USPTO patent examiners for automatic query scope limitation in patent searching

Professional search in patent repositories poses several unique challenges. One key requirement is to search the entire affected space of concepts, following well-defined procedures to ensure traceability of results obtained. Several techniques have been introduced to enhance query generation, preferably via automated query term expansion, to improve retrieval effectiveness. Currently, these approaches are mostly limited to computing additional query terms from patent documents based on statistical measures. For conceptual search to solve the limitation of traditional keyword search standard dictionaries are used to provide synonyms and keyword phrases for query refinement. Studies show that these are insufficient in such highly specialized domains. In this paper, we present an approach to extract keyword phrases from query logs created during the validation procedure of the patent applications. This creates valuable domain-specific lexical databases for several specific patent classes that can be used to both expand as well as limit the scope of a patent search. This provides a more powerful means to guide a professional searcher through the search process. We evaluate the lexical databases based on real query sessions of patent examiners.