Novel route to synthesis of allyl starch and biodegradable hydrogel by copolymerizing allyl‐modified starch with methacrylic acid and acrylamide

Maize starch was modified by allyl chloride adopting an interfacial reaction technique with cetyltrimethyl ammonium bromide as a phase‐transfer catalyst and pyridine as an acid acceptor. The degree of substitution was determined from an increasing carbon content of the modified starch. The percentage of carbon and hydrogen of the allyl‐modified starch was estimated by elemental analysis (C, H, and N), and the product characterization was done through ¹H NMR and ¹³C NMR analyses. The allyl‐modified starch was then copolymerized with methacrylic acid and a combination of methacrylic acid and acrylamide at 50 and 70 °C with potassium persulfate as an initiator. The copolymer thus formed swelled in distilled water after neutralization with sodium carbonate. The percentage of absorption capacity of the hydrogels was determined with distilled water and 0.9% NaCl solution. The highest percentage of absorption, 6500%, was achieved for the developed hydrogel containing allyl starch and acrylic monomer in a 1.7:1 w/w ratio and acrylic monomer, namely, methacrylic acid and acrylamide in a 3.2:1 w/w ratio. The study on biodegradability of the developed hydrogel showed that the hydrogel is degradable in the presence of diastase (amylase). © 2003 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 41: 1650–1658, 2003     

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.     

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.

     

An Amino‐Acid‐Based Self‐Healing Hydrogel: Modulation of the Self‐Healing Properties by Incorporating Carbon‐Based Nanomaterials

An amino‐acid‐based (11‐(4‐(pyrene‐1‐yl)butanamido)undecanoic acid) self‐repairing hydrogel is reported. The native hydrogel, as well as hybrid hydrogels, have been thoroughly characterized by using various microscopic techniques, including transmission electron microscopy (TEM), atomic force microscopy (AFM), Raman spectroscopy, fluorescence spectroscopy, FTIR spectroscopy, X‐ray diffraction, and by using rheological experiments. The native hydrogel exhibited interesting fluorescence properties, as well as a self‐healing property. Interestingly, the self‐healing, thixotropy, and stiffness of the native hydrogel can be successfully modulated by incorporating carbon‐based nanomaterials, including graphene, pristine single‐walled carbon nanotubes (Pr‐SWCNTs), and both graphene and Pr‐SWCNTs, within the native gel system. The self‐recovery time of the gel was shortened by the inclusion of reduced graphene oxide (RGO), Pr‐SWCNTs, or both RGO and Pr‐SWCNTs. Moreover, hybrid gels that contained RGO and/or Pr‐SWCNTs exhibited interesting semiconducting behavior.     

Recent advancement in inorganic-organic electron transport layers in perovskite solar cell: current status and future outlook

Organic-inorganic lead halide perovskite solar cells have captured significant attention in recent years due to low processing costs and unprecedented development in power conversion efficiency (PCE). It has appeared from 2009 with PCE of 3.8% to being claimed more than 25.2% PCE in a very short span of time, showing their future prospective toward the fabrication of less expensive and stable solar cells. The incredible advancement in this technology encourages at one end, whereas several hurdles restricting its complete utilization for commercial purposes at another end. Although the selection of perovskite structure is limited with planar and mesoporous electron transport layers (ETLs), but identification of appropriate ETLs necessitates excellent effort to improve the surface morphology of absorber and obtain enhanced PCE with higher stability. In the present review, we have investigated various inorganic-organic ETLs with different device configurations of PSCs, primarily focusing on crystallization and morphology control techniques of ETL thin films. Numerous strategies such as surface functionalization, doping, and addition of interfacial layer are adopted for ETLs, and their effect on device efficiency, performance, and hysteresis is also discussed in detail. Additionally, designs of PSCs with different device configurations are discussed as well, providing future guidelines for significant progress in PSCs structure with different ETLs.     

Diffusion and transport phenomena in a collisional magnetoplasma having both streaming and temperature anisotropy: Collisional kinetics

Boltzmann-transport equation is analytically solved for two-component magnetoplasma using Chapman-Enskog analysis to include collisional diffusion transport having anisotropies in both streaming velocity and temperature components. The modified collisional integrals are analytically solved with flux integrals and perturbed kinetic equation to arrive at drift diffusion velocity and resulting transport coefficients which are markedly affected by both streaming and temperature anisotropy. The early isotropic results are recovered in the limit V ₀ = 0 and T _‖ = T _⊥ which reduce to eqs (11.30) and (11.31) of [1] and eqs (2.7) and (2.13) of [2]. The electrical resistivity (η_⊥) diminishes sharply in fusion temperature limit kT _⊥ = 1 keV. The shape of the curves for both electrical resistivity and thermal conductivity is rectangular hyperbolic. However, for low thermal ratio (T _‖/T _⊥ < 1), the curves are raised up and for high thermal ratio (T _‖/T _⊥ > 1), they are lowered down the isotropic case (T _‖/T _⊥ > 1), showing comparatively diminished magnitudes of the quantities.      

Competitive Multi-period Pricing for Perishable Products: A Robust Optimization Approach

We study a multi-period oligopolistic market for a single perishable product with fixed inventory. Our goal is to address the competitive aspect of the problem together with demand uncertainty using ideas from robust optimization and variational inequalities. The demand function for each seller has some associated uncertainty and we assume that the sellers would like to adopt a policy that is robust to adverse uncertain circumstances. We believe this is the first paper that uses robust optimization for dynamic pricing under competition. In particular, starting with a given fixed inventory, each seller competes over a multi-period time horizon in the market by setting prices and protection levels for each period at the beginning of the time horizon. Any unsold inventory at the end of the horizon is worthless. The sellers do not have the option of periodically reviewing and replenishing their inventory. We study non-cooperative Nash equilibrium policies for sellers under such a model. This kind of a setup can be used to model pricing of air fares, hotel reservations, bandwidth in communication networks, etc. In this paper we demonstrate our results through some numerical examples.