Predictive association of metal–organic systems in the solid-state: the molecular electrostatic potential based approach

Designing crystalline solids with improved properties or performances remains a challenging task, despite great strides that have been made within the field of crystal engineering since its birth several decades ago. Herein, we are bringing examples that illustrate recent successes in taking supramolecular synthetic guidelines from the organic crystal engineering and adjusting those to metal-containing systems, particularly to the lower-dimensional ones. The versatility of calculated molecular electrostatic potential (MEP) as a new crystal engineering tool is demonstrated.     

碳化二亚胺类抗水解稳定剂Bio-SW100的合成与应用

以硫脲法工艺路线合成了单体型碳化二亚胺抗水解稳定剂SW-100,通过红外光谱、元素分析以及液相色谱考察了产物的结构、纯度。红外光谱和元素分析确定了合成产物结构正确,液相色谱确定产物纯度达到99%以上。在结构和纯度确定的基础上,将合成产物添加到聚乳酸中进行水解性能测试。结果表明,在1%添加量下聚乳酸拉伸强度保持率较纯树脂提高了80%。     

多共振分量融合CNN的行星齿轮箱故障诊断

针对行星齿轮箱中各部件所激起的振动成分混叠、早期故障特征经常被较强的各级齿轮谐波成分以及环境噪声所湮没的问题，提出一种多共振分量融合卷积神经网络（multi-resonance component fusion based convolutional neural network，简称MRCF-CNN）的行星齿轮箱故障诊断方法。首先，对振动信号进行共振稀疏分解，得到包含齿轮谐波成分的高共振分量和可能包含轴承故障冲击成分的低共振分量；其次，构建多共振分量融合卷积神经网络，将得到的高、低共振分量和原始振动信号进行自适应的特征级融合，通过有监督的方式训练模型并进行行星齿轮箱故障诊断。对行星齿轮箱实验数据的分析结果表明，该方法能够有效分类行星齿轮箱中滚动轴承和齿轮的故障，成功对行星齿轮箱故障进行诊断，同时能够进一步增强卷积神经网络对振动信号所蕴含的故障信息的辨识能力。     

Room temperature response and enhanced hydrogen sensing in size selected Pd-C core-shell nanoparticles: Role of carbon shell and Pd-C interface

In the present work, the effect of carbon shell around size selected palladium (Pd) nanoparticles on hydrogen (H₂) sensing has been studied by investigating the sensing response of Pd-C core-shell nanoparticles having a fixed core size and different shell thickness. The H₂ sensing response of sensors based on Pd and Pd-C nanoparticles deposited on SiO₂ and graphene substrate has been measured over a temperature range of 25 °C–150 °C. It is observed that Pd-C nanoparticle sensor shows higher sensitivity with increase in shell thickness and faster response/recovery in comparison to that of Pd nanoparticle samples. Pd-C nanoparticles show room temperature H₂ sensitivity in contrast to Pd nanoparticles which respond only at higher temperatures. Role of carbon shell is also understood by investigating H₂ sensing properties of Pd and Pd-C nanoparticles on graphene substrates. These results show that higher catalytic activity and electronic interaction at Pd-C interface, a complete coverage and protection of Pd surface by carbon and presence of structural defects in nanoparticle core are important for room temperature and higher sensing response.     

Self-learning residual model for fast intra CU size decision in 3D-HEVC

As an extension of the High Efficiency Video Coding (HEVC) standard, 3D-HEVC requires to encode multiple texture views and depth maps, which inherits the same quad-tree coding structure as HEVC. Due to the distinct properties of texture views and depth maps, existing fast intra prediction approaches were presented for the coding of texture views and depth maps, respectively. To further reduce the coding complexity of 3D-HEVC, a self-learning residual model-based fast coding unit (CU) size decision approach is proposed for the intra coding of both texture views and depth maps. Residual signal, which is defined as the difference between the original luminance pixel and the optimal prediction luminance pixel, is firstly extracted from each CU. Since residue signal is strongly correlated with the optimal CU partition, it is used as the feature of each CU. Then, a self-learning residual model is established by intra feature learning, which iteratively learns the features of the previously encoded coding tree unit (CTU) generated by itself. Finally, a binary classifier is developed with the self-learning residual model to early terminate CU size decision of both texture views and depth maps. Experimental results show the proposed fast intra CU size decision approach achieves 33.3% and 49.3% encoding time reduction on average for texture views and depth maps with negligible loss of overall video quality, respectively.     

Catalytic hydrogenation of CO2 from 600 MW supercritical coal power plant to produce methanol: A techno-economic analysis

Electricity and water from renewable hydropower plant are used as input for electrolysis unit to generate hydrogen, while CO₂ is captured from 600 MW supercritical coal power plant using post-combustion chemical solvent based technology. The captured CO₂ and H₂ generated through electrolysis are used to synthesize methanol through catalytic thermo-chemical reaction. The methanol synthesis plant is designed, modeled and simulated using commercial software Aspen Plus^®. The reactor is analyzed for two widely adopted kinetic models known as Graaf model and Vanden-Bossche (VB) model to predict the methanol yield and CO₂ conversion. The results show that the methanol reactor based on Graaf kinetic model produced 0.66 tonne of methanol per tonne of CO₂ utilized which is higher than that of the VB kinetic model where 0.6 tonne of methanol is produced per tonne of CO₂ utilized. The economic analysis reveals that 1.2 billion USD annually is required at the present cost of both H₂ production and CO₂ abatement to utilize continuous emission of 3.2 million tonne of CO₂ annually from 600 MW supercritical coal power unit to synthesize methanol. However, sensitivity analysis indicates that methanol production becomes feasible by adopting anyone of the route such as by increasing methanol production rate, by reducing levelised cost of hydrogen production, by reducing CO₂ mitigation cost or by increasing the current market selling price of methanol and oxygen.     

Blockchain in global supply chains and cross border trade: a critical synthesis of the state-of-the-art,challenges and opportunities

Blockchain possesses the potential of transforming global supply chain management. Gartner predicts that blockchain could be able to track $2?T of goods and services in their movement across the globe by 2023, and blockchain will be a more than $3 trillion business by 2030. Nowadays, a growing number of blockchain initiatives are disrupting traditional business models in each sector. In this paper, we provide a timely and holistic overview of the state-of-the-art, challenges, gaps and opportunities in global supply chain and trade operations for both the private sector and governmental agencies, by synthesising a wide range of resources from business leaders, global international organisations, leading supply chain consulting firms, research articles, trade magazines and conferences. We then identify collaborative schema and future research directions for industry, government, and academia to jointly work together in ensuring that the full potential of blockchain is unleashed amidst the socioeconomic, geopolitical and technological disruptions that global supply chains and trade are facing.     

2-Aminobenzothiazoles Inhibit Virulence Gene Expression and Block Polymyxin Resistance in Salmonella enterica

One promising strategy to combat antibiotic-resistant bacteria is to develop compounds that block bacterial defenses against antibacterial conditions produced by the innate immune system. Salmonella enterica, which causes food-borne gastroenteritis and typhoid fever, requires histidine kinases (HKs) to resist innate immune defenses such as cationic antimicrobial peptides (CAMPs). Herein, we report that 2-aminobenzothiazoles block histidine kinase-dependent phenotypes in Salmonella enterica serotype Typhimurium. We found that 2-aminobenzothiazoles inhibited growth under low Mg²⁺, a stressful condition that requires histidine kinase-mediated responses, and decreased expression of the virulence genes pagC and pagK. Furthermore, we discovered that 2-aminobenzothiazoles weaken Salmonella’s resistance to polymyxin B and polymyxin E, which are last-line antibiotics and models for host defense CAMPs. These findings raise the possibilities that 2-aminobenzothiazoles can block HK-mediated bacterial defenses and can be used in combination with polymyxins to treat infections caused by Salmonella.     

Structural and Functional Characterization of 4-Hydroxyphenylacetate 3-Hydroxylase from Escherichia coli

The hydroxylation of phenols into polyphenols, which are valuable chemicals and pharmaceutical products, is a challenging reaction. The search for green synthetic processes has led to considering microorganisms and pure hydroxylases as catalysts for phenol hydroxylation. Herein, we report the structural and functional characterization of the flavin adenine dinucleotide (FAD)-dependent 4-hydroxyphenylacetate 3-monooxygenase from Escherichia coli, named HpaB. It is shown that this enzyme enjoys a relatively broad substrate specificity, which allows the conversion of a number of non-natural phenolic compounds, such as tyrosol, hydroxymandelic acid, coumaric acid, hydroxybenzoic acid and its methyl ester, and phenol, into the corresponding catechols. The reaction can be performed by using a simple chemical assay based on formate as the electron donor and the organometallic complex [Rh(bpy)Cp*(H₂O)]²⁺ (Cp*: 1,2,3,4,5-pentamethylcyclopentadiene, bpy: 2,2′-bipyridyl) as the catalyst for FAD reduction. The availability of a crystal structure of HpaB in complex with FAD at 1.8 Å resolution opens up the possibility of the rational tuning of the substrate specificity and activity of this interesting class of phenol hydroxylases.