Anion-controlled geometrically different Cu(II) ion-based coordination polymers and green synthetic route for copper nanoparticles: a combined experimental and computational insight

Abstract

The Cu(II) ion-based polymeric complexes [Cu(2,2′-bpy).(N₃)₂]_n (I), [Cu₂(2,2′-bpy)₂.(N₃)₄]_n (II), and monomeric complex [Cu(2,2′-bpy).(NO₃)₂].5H₂O (III) have been synthesized with rigid (–N₃) and aromatic (2,2′-bpy = 2,2′-bipyridyl) ligand. The rigid azide group is responsible for the formation of 1-D extended structures in complexes I and II where as in the case of complex III, a monomeric complex is formed due to lack of a bridging group like –N₃, resulting in limitation in dimensionality. The thermal stability of the 1-D complexes is comparatively higher than monomeric complex III. Hirshfeld surface analysis has also been applied to investigate other weak interactions and compared with the results from single-crystal X-ray data. Due to the presence of paramagnetic metal centers and long metal···metal distances in complexes I and II and presence of lattice water molecules in complex III, decrease in luminescence intensities have been observed. To attain further insights into the aforementioned interesting species, some chemical concepts such as highest occupied molecular orbital–lowest unoccupied molecular orbital gap, electronic chemical potential, chemical hardness, and electrophilicity index, identified as a derivative of electronic energy, have also been emphasized employing the quantum chemical calculations in the framework of the density functional theory method using the M06-2X/ 6-31G** level of study. Further, these complexes have been used to synthesize copper nanoparticles by applying a green synthetic route.     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Neural network modeling of hydrological systems: A review of implementation techniques

This paper presents a review of procedural steps and implementation techniques used in the development of artificial intelligence models, generally referred to as artificial neural networks (ANNs), within the water resources domain. It focusses on identifying different areas wherein ANNs have found application thereby elucidating its advantages and disadvantages as well as various challenges encountered in its use. Results from this review provide useful insights into how the performance of ANNs can be improved and potential areas of application that are yet to be explored in hydrological modeling. Recommendations for Resource Managers

• Development of integrated and hybrid artificial intelligent tools is critical to achieving improved forecasts in hydrological modeling studies.

• Further research into comprehending the internal mechanisms of neural networks is required to obtain a practical meaning of each network component deployed to solve real‐world problems.

• More robust optimization techniques and tools like differential evolution, particle swarm optimization and deep neural nets, are yet to be fully explored in the water resources analysis, and should be given more attention to enhance neural networks aptitude for modeling complex and nonlinear hydrological processes.

     

Experiment on relationship between the magnetic gradient of low-carbon steel and its stress

In geomagnetic field, a series of tensile experiments on the low-carbon steel sticks were carried out. A special homemade detector was used to measure the magnetic gradient on the material surface. The results showed that the relationship between the magnetic gradient and the stress varied with different conditions of measurement. There was no obvious correlation between the magnetic gradient and the tensile stress if the sample remained on the material test machine. If the sample was taken off from the machine, the measured magnetic gradient was linear with the prior maximum stress. In Nanjing, PR China, a place of 32°N latitude, the slope of the linear relationship was about 67 (uT/m)/MPa. This offered a new method of non-destructive stress testing by measuring the magnetic gradient on the ferromagnetic component surface. The prior maximum applied stress of the sample could be tested by measuring the present surface magnetic gradient. Actually this phenomenon was the metal magnetic memory (MMM). The magnetic gradient near the stress concentration zone of the sample, the necking point, was much larger than other area. Thus, the hidden damage in the ferromagnetic component could be detected early by measuring the magnetic gradient distribution on its surface. In addition, the magnetic memory signal gradually weakened as the sample was taken off and laid aside. Therefore, it was effective for a given period of time to detect the stress or stress concentration based on the MMM testing.     

基于小世界网络的大学生社交网络模型研究

随着大学生社交网络使用的日趋频繁,有关大学生社交网络应用的研究也日益增多,本文采用在复杂网络研究中运用广泛的小世界网络理论,通过分析大学生社交网络的静态网络统计量,提出一种构造简单的大学生社交网络模型,并对其小世界特性进行了验证。     

Mechanical,Thermal and Morphological Behaviors of Castor Oil Based PU/PS Interpenetrating Polymer Network-Metakaolin Composites

The meta kaolin (MK) clay particulate filler with different weight ratios viz., 0, 5, 10, 20 and 30 wt% were incorporated into castable polyurethane (PU)/polystyrene (PS) (90/10) interpenetrating polymer network (IPN). The effects of MK particulate filler loading on the mechanical and thermal properties of PU/PS (90/10) IPN composites have been studied. From the tensile behavior, it was noticed that a significant improvement in tensile strength and tensile modulus as an increase in MK filler content. Thermogravimetric analysis (TGA) data reveals the marginal improvement in thermal stability after incorporation of MK filler. TGA studies of the IPN composites have been performed in order to establish the thermal stability and their mode of thermal degradation. It was found that degradation of all composites takes place in two steps. Degradation kinetic parameters were obtained for the composites using three mathematical models. Tensile fractured composite specimens were used to analyze the morphology of the composites by scanning electron microscopic (SEM) technique.     

Laterally dibenzyloxy-branched nematogens with large nematic range and rich solid polymorphism

Mesogenic compounds containing four rings in the core usually have very high melting points. However, when two identical lateral benzyloxy groups are introduced on the same side of one of the central rings, the melting point is lowered dramatically and a large nematic range is retained. This range is affected by the bulkiness of the para-substituents in the lateral rings. Methyl groups can be introduced in the ortho- or meta-positions with a consequent decrease in the melting temperature without much affecting the nematic range. These compounds exhibit a rich solid polymorphism which is certainly related to the effect of the conformations of the lateral substituent on the molecular arrangment in the solid phase. Some preliminary NMR experiments on the nematic phase indicate that the molecular long axis coincides with the core axis, whereas the para-axis of the lateral fragment makes an angle close to the magic angle with respect to the molecular long axis.