Role of micro-crystalline parameters in the physical properties of cotton fibers

Karnataka state in India is very well known for its cotton cultivation and there are innumerable varieties of these cotton fibers. Although, the yield and other types of characterization have been carried out on these fibers, the structure-property relation is not well studied till today. We have examined four different raw cotton fibers using Wide Angle X-ray Scattering (WAXS) technique and also we have computed micro-crystalline parameters. This study brings out the structure-property relation in cotton fibers.     

An unusual, mild and convenient one-pot two-step access to (E)-stilbenes from hydroxy-substituted benzaldehydes and phenylacetic acids under microwave activation: a new facet of the classical Perkin reaction

A mild and convenient one-pot two-step synthesis of hydroxystilbenes with trans selectivity has been developed through a modified Perkin reaction between benzaldehydes and phenylacetic acids bearing 4- or 2-hydroxy substitution at the aromatic ring, in the presence of piperidine-methylimidazole and polyethylene glycol under microwave irradiation. The observation of a simultaneous condensation-decarboxylation leading to the unusual formation of hydroxystilbenes in lieu of α-phenylcinnamic acid reveals an interesting facet to the classical Perkin reaction. The developed protocol provides a green alternative to the prevalent methods employing a toxic decarboxylating agent in the form of quinoline/Cu salt, and the requirement for harsh protection-deprotection steps for the synthesis of hydroxylated stilbenes.     

Dinuclear copper (II) and nickel (II) systems with planar M2N2O2 bridge

The tridentate ligand systemb (abbreviated as inkR₂) readily yield copper (II) and nickel (II) species of the formula M₂ (inkR₂)₂(CLO₄)₂. 2xH₂O (x=0–1). Dinuclear formulation is based on variable temperature magnetic susceptibility and conductivity data and on the known structure of some related systems. The Cu₂ (inkR₂) ₂ ²⁺ species are strongly antiferromagnetic (?2J=600–800 cm^?1) while the Ni₂(inkR₂) ₂ ²⁺ species are diamagnetic. The major coordination sphere is planar around each metal (II). The metal ions in a dimer are linked by planar M₂N₂O₂ bridge. The copper (II) and nickel (II) species freely form solid solutions. In these statistical scrambling of copper and nickel occur among the metal ion sites of the dimeric structure. Powder epr spectra of such mixed crystals are indicative of axial geometry around copper (II) ion.     

Validated Chiral LC Method for the Enantiomeric Separation of β-Amino-β-(3-Methoxyphenyl) Propionic Acid

A chiral liquid chromatographic method for enantiomeric resolution of β-amino-β-(3-methoxyphenyl) propionic acid was developed and validated. The “hybrid” π-electron donor–acceptor based stationary phase (R,R) Whelk-01 was found to be enantiomerically selective for (R) and (S) enantiomers of β-amino-β-(3-methoxyphenyl) propionic acid with a resolution greater than 2.0. The effects of isopropyl alcohol and ethanol on enantioselectivity and resolution of enantiomers were evaluated. Calibration curves were linear over the range of 0.10–1.00, with a regression coefficient (r) of 0.999. The limit of detection (LOD) and limit of quantification (LOQ) were 300 and 1,000 ng mL⁻¹ respectively for 10 μL injection volume. The percentage RSD of the peak area of six replicate injections of (S) enantiomer at LOQ concentration was 2.8. The percentage recovery of (S) enantiomer from (R) enantiomer samples ranged from 92 to 102. The test solution was observed to be stable up to 24 h after the preparation. The developed normal phase chiral LC method can be used for the enantiomeric purity evaluation of R-β-amino-β-(3-methoxyphenyl) propionic acid.     

Sulfur K-edge XAS and DFT calculations on [Fe4S4]2+ clusters: effects of H-bonding and structural distortion on covalency and spin topology

Sulfur K-edge X-ray absorption spectroscopy of a hydrogen-bonded elongated [Fe4S4]2+ cube is reported. The data show that this synthetic cube is less covalent than a normal compressed cube with no hydrogen bonding. DFT calculations reveal that the observed difference in electronic structure has significant contributions from both the cluster distortion and from hydrogen bonding. The elongated and compressed Fe4S4 structures are found to have different spin topologies (i.e., orientation of the delocalized Fe2S2 subclusters which are antiferromagnetically coupled to each other). It is suggested that the H-bonding interaction with the counterion does not contribute to the cluster elongation. A magneto-structural correlation is developed for the Fe4S4 cube that is used to identify the redox-active Fe2S2 subclusters in active sites of HiPIP and ferredoxin proteins involving these clusters.     

Hierarchical Order Parameters for Macromolecular Assembly Simulations I: Construction and Dynamical Properties of Order Parameters

Macromolecular assemblies often display a hierarchical organization of macromolecules or their sub-assemblies. To model this, we have formulated a space warping method that enables capturing overall macromolecular structure and dynamics via a set of coarse-grained order parameters (OPs). This article is the first of two describing the construction and computational implementation of an additional class of OPs that has built into them the hierarchical architecture of macromolecular assemblies. To accomplish this, first, the system is divided into subsystems, each of which is described via a representative set of OPs. Then, a global set of variables is constructed from these subsystem-centered OPs to capture overall system organization. Dynamical properties of the resulting OPs are compared to those of our previous nonhierarchical ones, and implied conceptual and computational advantages are discussed for a 100ns, 2 million atom solvated Human Papillomavirus-like particle simulation. In the second article, the hierarchical OPs are shown to enable a multiscale analysis that starts with the N-atom Liouville equation and yields rigorous Langevin equations of stochastic OP dynamics. The latter is demonstrated via a force-field based simulation algorithm that probes key structural transition pathways, simultaneously accounting for all-atom details and overall structure.     

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.