A green technology for producing barium ferrite (BaFe12O19) nanoparticles (BFNP’s) using Acorus Calamus rhizome extract was realized. To confirm the phase of magnetoplumbite structure without any impurities, the method of powder X-ray diffraction was performed using the Rietveld analysis and the FullProf program with the P63/mmc (No. 194) space group. Unit cell parameters were a = 5.8902(2) A and c = 23.2103(7) A. According to Scherrer’s calculations, the average crystallite size was from ∼  32 to ∼  35 nm. The results of scanning electron microscopy confirmed that synthesized BFNP’s are tightly packed and have an average grain size of ∼  70 nm. Raman and IR active modes predicted by group theory are observed at ∼  175 cm−1 and ∼  677 cm−1, which corresponds to the presence of a spinel structure and trigonal pyramidal position in barium hexaferrite. It was established that the spontaneous magnetization is ∼  52 emu/g and coercive force is ∼  440 mT at room temperature. In vitro studies were performed to evaluate the antifungal activity of BFNP’s against various plant pathogenic fungi, namely: Fusarium oxysporum, Alternaria alternata, Colletotrichum gloeosporioides and Marssonina rosae. The antifungal effect of BFNPs was determined for different phytopathogenic fungi at a multiple dose of 200 mg/L, 300 mg/L, 400 mg/L, 500 mg/L and 600 mg/L. The maximum inhibition of mycelial growth (76.67%) was detected at 600 mg/L against the growth of Fusarium oxysoporum mycelium. The data obtained show that BFNP’s synthesized using Acorus Calamus rhizome extract can be applied as a potential antifungal agent.

Synthesis of barium ferrite nanoparticles using rhizome extract of Acorus Calamus: Characterization and its efficacy against different plant phytopathogenic fungi

The distance vector-hop (DV-hop) is a typical localization algorithm It estimates sensor nodes location through detecting the hop count between nodes To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be However, there has been no clear mathematical model among positioning error, hop count, and weight This paper constructs a mathematical model between the weights and hops and analyzes the convergence of this model Finally, the genetic algorithm is used to solve this mathematical weighted DV-hop (MW-GADV-hop) positioning model, the simulation results illustrate that the model construction is logical, and the positioning error of the model converges to 1/4R

/pdf/weight-convergence-analysis-of-dv-hop-localization-algorithm-3c7o8huwd9.pdf

Weight convergence analysis of DV-hop localization algorithm with GA.

Recent advances in synthesis, characterization, and applications of nanoparticles for contaminated water treatment- A review

In this paper, we proposed an improved DV-Hop localization using runner-root algorithm to overcome the existing disadvantage of traditional DV-Hop. In proposed algorithm, we have introduced a correction factor in order to modify the hop size of the anchor nodes. The proposed algorithm lessens the communication between unknown and anchor nodes by calculating hop size of all anchors at unknown nodes. To show the applicability of proposed algorithm in anisotropic network, radio irregularity model is also considered. Simulations have been carried out on MATLAB, and comparison is made for our proposed algorithm with traditional DV-Hop, genetic algorithm-based DV-Hop and particle swarm optimization-based DV-Hop. Simulation results confirm that our proposed localization algorithm minimizes the localization error, localization error variance and computational time in comparison with existing localization techniques.

DV-Hop-based range-free localization algorithm for wireless sensor network using runner-root optimization

Sm-doped BiFeO3 (BFO) material was prepared using a modified solid-state-reaction method, which used fast heating and cooling during the sintering process. The Sm doping level varied between 1 mol % to 8 mol %. Processing parameters, such as sintering temperature and annealing temperature, were optimized to obtain high-quality samples. Based on their dielectric properties, the optimum sintering and annealing temperatures were found to be 300 °C and 825 °C, respectively. Leakage-free square-shaped ferroelectric hysteresis loops were observed in all samples. The remnant polarization was maximized in the 5 mol %-doped sample (~35 μC/cm2). Furthermore, remnant magnetization was increased after the Sm doping and the 8 mol%-doped sample possessed the largest remnant magnetization of 0.007 emu/g. Our results demonstrated how the modified solid-state-reaction method proved to be an effective method for preparing high-quality BiFeO3 ceramics, as well as how the Sm dopant can efficiently improve ferroelectric and magnetic properties.

https://www.mdpi.com/1996-1944/11/11/2208/pdf

Dielectric, Ferroelectric, and Magnetic Properties of Sm-Doped BiFeO3 Ceramics Prepared by a Modified Solid-State-Reaction Method

Effect of non-magnetic Al 3+ doping on structural, optical, electrical, dielectric and magnetic properties of BiFeO 3 ceramics

Localization of sensor nodes is one of the major challenge in wireless sensor networks. It helps in identifying the geographically area where an event occurred. Most of the cases it is one time process wherein nodes are localized once in network life time. However, in real time scenario nodes may get dislocated within the network due to land slide or any event occurrence. In such events, localization of these sensor nodes is required. In this paper, we have proposed a new framework of localization for displaced sensor nodes. The proposed framework consider DV-Hop localization methods using particle swarm optimization (PSO). Radio irregularity model is also considered to show the applicability of proposed algorithm in anisotropic network. Simulation results show that our proposed localization framework effectively minimizes the elapsed time, localization error and energy consumption as compared to traditional framework of PSO DV-Hop.

DV-Hop localization methods for displaced sensor nodes in wireless sensor network using PSO

Localization of sensor nodes is of utmost importance as the information collected by sensor nodes may be meaningless without the knowledge of its origin. Due to limited battery life and non uniform data loaded in the network, some of the sensor nodes may exhaust their battery early and may cease their intended function. This may lead to partitioning of network, wherein some portion of network may not communicate its sensed data to the sink node. In such situation we need to deploy some additional new nodes in the network. Newly deployed nodes needs to be localized as they are unaware of their estimated location. In this paper, we have proposed a new framework to localize newly deployed nodes in a pre-localized network using GA DV-Hop algorithm. The proposed framework has been simulated for various network topologies such as Random topology, C-shaped topology and W-shaped topology. Simulation results show that our proposed localization framework has a trade off between localization error and energy consumption, i.e. one of the proposed method features high localization accuracy at the cost of high energy consumption. However, it has been observed that proposed methods requires less time for localization as compared to traditional methods.

DV-Hop based localization methods for additionally deployed nodes in wireless sensor network using genetic algorithm

Accurate and fast localization of randomly deployed sensor nodes is needed for many applications in wireless sensor networks. Localization also benefits in recognizing the geographically area where an event took place. There is no meaning of any event information without the knowledge of its location coordinates. DV-Hop is one of the main range free localization technique, which estimates the position of nodes using distance vector. Particle swarm optimization is suitable for the localization issues because of its fast computing speed and high precision. To further reduce the positioning error, the traditional DV-Hop localization algorithm based on single objective optimization algorithm is converted into a multi objective optimization algorithm. In our proposed scheme, we have considered six different single objective functions and three different multi objective functions. In this paper, a multi objective particle swarm optimization based DV-Hop localization is proposed in 3-dimensional wireless sensor networks. The proposed functions has been evaluated on the basis of computation time, average localization error and localization error variance. The simulation results show that our proposed multi objective function performs better as compared to traditional single objective function.

Vivek Kanwar

Papers

Effect of non-magnetic Al 3+ doping on structural, optical, electrical, dielectric and magnetic properties of BiFeO 3 ceramics

DV-Hop-based range-free localization algorithm for wireless sensor network using runner-root optimization

DV-Hop localization methods for displaced sensor nodes in wireless sensor network using PSO

DV-Hop based localization methods for additionally deployed nodes in wireless sensor network using genetic algorithm

Range Free Localization for Three Dimensional Wireless Sensor Networks Using Multi Objective Particle Swarm Optimization