Exogenous Application of Green Titanium Dioxide Nanoparticles (TiO2 NPs) to Improve the Germination,Physiochemical, and Yield Parameters of Wheat Plants under Salinity Stress

Agriculture is the backbone of every developing country. Among various crops, wheat (Triticum aestivum L.) belongs to the family Poaceae and is the most important staple food crop of various countries. Different biotic (viruses, bacteria and fungi) and abiotic stresses (water logging, drought and salinity) adversely affect the qualitative and quantitative attributes of wheat. Among these stresses, salinity stress is a very important limiting factor affecting the morphological, physiological, biochemical attributes and grain yield of wheat. This research work was carried out to evaluate the influence of phytosynthesized TiO₂ NPs on the germination, physiochemical, and yield attributes of wheat varieties in response to salinity. TiO₂ NPs were synthesized using TiO₂ salt and a Buddleja asiatica plant extract as a reducing and capping agent. Various concentrations of TiO₂ nanoparticles (20, 40, 60 and 80 mg/L) and salt solutions (NaCl) (100 and 150 mM) were used. A total of 20 mg/L and 40 mg/L improve germination attributes, osmotic and water potential, carotenoid, total phenolic, and flavonoid content, soluble sugar and proteins, proline and amino acid content, superoxide dismutase activity, and reduce malondialdhehyde (MDA) content at both levels of salinity. These two concentrations also improved the yield attributes of wheat varieties at both salinity levels. The best results were observed at 40 mg/L of TiO₂ NPs at both salinity levels. However, the highest concentrations (60 and 80 mg/L) of TiO₂ NPs showed negative effects on germination, physiochemical and yield characteristics and causes stress in both wheat varieties under control irrigation conditions and salinity stress. Therefore, in conclusion, the findings of this research are that the foliar application of TiO₂ NPs can help to improve tolerance against salinity stress in plants.     

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.

     

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.      

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.     

Ultrasound-Assisted Microencapsulation of Soybean Oil and Vitamin D Using Bare Glycogen Nanoparticles

Ultrasonically synthesized core-shell microcapsules can be made of synthetic polymers or natural biopolymers, such as proteins and polysaccharides, and have found applications in food, drug delivery and cosmetics. This study reports on the ultrasonic synthesis of microcapsules using unmodified (natural) and biodegradable glycogen nanoparticles derived from various sources, such as rabbit and bovine liver, oyster and sweet corn, for the encapsulation of soybean oil and vitamin D. Depending on their source, glycogen nanoparticles exhibited differences in size and ‘bound’ proteins. We optimized various synthetic parameters, such as ultrasonic power, time and concentration of glycogens and the oil phase to obtain stable core-shell microcapsules. Particularly, under ultrasound-induced emulsification conditions (sonication time 45 s and sonication power 160 W), native glycogens formed microcapsules with diameter between 0.3 μm and 8 μm. It was found that the size of glycogen as well as the protein component play an important role in stabilizing the Pickering emulsion and the microcapsules shell. This study highlights that native glycogen nanoparticles without any further tedious chemical modification steps can be successfully used for the encapsulation of nutrients.     

Excited State Intramolecular Proton Transfer in Methyl and Methoxy Substituted Salicylic Acid

The excited state intramolecular proton transfer (ESIPT) processes in 3‐methylsalicyclic acid (3‐MeSA) and 3‐methoxysalicyclic acid (3‐MeOSA) have been investigated in cyclohexane medium by emission spectroscopic techniques. The ESIPT process was characterized in 3‐MeSA from the large Stokes fluorescent band (455 nm), but it was suppressed by 3‐MeOSA in cyclohexane. The ESIPT process was found to be accelerated both in 3‐MeSA and 3‐MeOSA in the presence of a hydrogen bond accepting agent, triethylamine (TEA). Further, theoretical calculations were carried out at the ground and excited states to complement the experimental evidences.     

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.