Biosynthesis and characterisation of nano‐silica as potential system for carrying streptomycin at nano‐scale drug delivery

A growing trend within nanomedicine has been the fabrication of self‐delivering supramolecular nanomedicines containing a high and fixed drug content ensuring eco‐friendly conditions. This study reports on green synthesis of silica nanoparticles (Si‐NPs) using Azadirachta indica leaves extract as an effective chelating agent. X‐ray diffraction analysis and Fourier transform‐infra‐red spectroscopic examination were studied. Scanning electron microscopy analysis revealed that the average size of particles formed via plant extract as reducing agent without any surfactant is in the range of 100–170 nm while addition of cetyltrimethyl ammonium bromide were more uniform with 200 nm in size. Streptomycin as model drug was successfully loaded to green synthesised Si‐NPs, sustain release of the drug from this conjugate unit were examined. Prolong release pattern of the adsorbed drug ensure that Si‐NPs have great potential in nano‐drug delivery keeping the environment preferably biocompatible, future cytotoxic studies in this connection is helpful in achieving safe mode for nano‐drug delivery.Inspec keywords: silicon compounds, nanofabrication, nanomedicine, drug delivery systems, nanoparticles, X‐ray diffraction, Fourier transform infrared spectra, scanning electron microscopyOther keywords: nanosilica, streptomycin, nanoscale drug delivery, nanomedicine, silica nanoparticles, Azadirachta indica leaves extract, X‐ray diffraction analysis, Fourier transform‐infrared spectroscopy, scanning electron microscopy, cetyltrimethyl ammonium bromide, SiO₂      

Assessing the dependency of selection function parameters with batch mill design

The scale-up of laboratory grinding data to industrial milling operations generally relies on tests carried out in cylindrical ball mills run in batch mode. This approach imposes no restriction on the diameter and length of the laboratory mill.In this work, the breakage characteristics of a copper ore were measured using two batch mills of different designs. For each mill, a number of feed samples of similar size distributions were prepared for testing under various conditions. Product size distributions were then measured after predefined milling time intervals. Finally, the selection function and breakage function parameters of the copper ore were back-calculated from the milling data.Results showed that the breakage function parameters from the two mills are statistically similar indicative of a normalisable copper ore. It was also found that the scale-up equations for batch grinding data described well the effect of mill diameter on the selection function parameters.     

Magnetic Properties and Microstructural Modifications of Sm-Co-Hf Alloy Ribbons by B Addition

Journal of Superconductivity and Novel Magnetism - Microstructure optimization is highly desirable for improving the magnetic properties of permanent magnetic materials. To optimize the...     

A new nonparametric double exponentially weighted moving average control chart

There are many practical situations where the underlying distribution of the quality characteristic either deviates from normality or it is unknown. In such cases, practitioners often make use of the nonparametric control charts. In this paper, a new nonparametric double exponentially weighted moving average control chart on the basis of the signed-rank statistic is proposed for monitoring the process location. Monte Carlo simulations are carried out to obtain the run length characteristics of the proposed chart. The performance comparison of the proposed chart with the existing parametric and nonparametric control charts is made by using various performance metrics of the run length distribution. The comparison showed the superiority of the suggested chart over its existing parametric and nonparametric counterparts. An illustrative example for the practical implementation of the proposed chart is also provided by using the industrial data set.     

Comparisons of the Microstructure and Magnetic Properties of Anisotropic NdFeB Magnets Prepared by Hot Pressing and Spark Plasma Sintering

Journal of Superconductivity and Novel Magnetism - NdFeB hot-pressed and hot-deformed magnets were prepared by hot-pressed (HP) sintering and spark plasma sintering (SPS). The effects of sintering...     

AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.     

Large scale synthesis of single-crystal and polycrystalline boron nitride nanosheets

Boron nitride nanosheets (BNNSs) have an identical crystal structure and similar lattice parameter to those of graphene sheets. However, growing quality BNNSs consisting of only several atomic layers remains a challenge. Here, we report on the synthesis of BNNSs at a temperature of 350 °C using a CO₂ pulsed laser plasma deposition (CO₂-PLD) technique by irradiating a pyrolytic hexagonal boron nitride (h-BN) target. The deposition was performed either in vacuum at a pressure of 0.2 Pa, for which we obtained polycrystalline BN, or in hydrogen (H₂) atmosphere at a pressure of 26 Pa for which we obtained single-crystal BNNSs. The presence of H₂ seems to minimize the side effects of sputtering and the material shows higher purity and better crystallinity. High resolution transmission electron microscopy (HRTEM) showed the sheets to be mostly defect-free and to have the characteristic honeycomb structure of six-membered B₃-N₃ hexagon. HRTEM, electron diffraction, X-ray diffraction, Raman scattering, and Fourier transform infrared spectroscopy clearly identified h-BN.     

Magneto-resistance and Excess Conductivity Studies of Tl(Ba2−y Mg y )Ca 2 Cu 3 O 10−δ Samples

The flux pinning characteristics of T l(B a _2?y M g _y )C a ₂ C u ₃ O _10?δ(0.0≤y≤ 1.5) samples have been studied under the influence of various dc magnetic fields in the light of thermally activated flux flow model. It was observed form the magneto resistivity measurements that the activation energy U has been increased in the sample with Mg incorporation of y=0.5, however, it is decreased in the samples with higher Mg doping. This shows that lower Mg doping concentration promotes enhancement, whereas its higher concentration suppresses the flux pinning characteristics of Tl(Ba _2?yMg _y )Ca ₂Cu ₃ O _10?δ (0.0 ≤y≤1.5) samples. The excess conductivity analysis of Tl(Ba _2?yMg _y )Ca ₂Cu ₃ O _10?δ (0.0 ≤ y ≤ 1.5) samples has shown that the coherence length along the c-axis ξ _c(0), the interlayer coupling J, the phase relaxation time of the carriers τ _? , and the Fermi velocity v _F of the carriers are suppressed with Mg doping. However, the values of B _c0(T), B _c1(T), and J _c(0) are enhanced which complement the magnetoresistivity analysis. These results showed that the flux pinning characteristics are enhanced for lower doping concentration of Mg at the charge reservoir layer sites.     

Profiling of hydrophilic proteins from Olea europaea olive pollen by MALDI TOF mass spectrometry

The antigenic profile of Olea europaea pollen from different Mediterranean cultivars was obtained by MALDI mass spectrometry. A simple procedure of chemical fractionation of the whole antigen extract was developed, whereby less complex, or pure, fractions of antigen candidate were obtained prior to mass spectrometric analysis. Some of the features of protein structure and distribution probably depend on cultivar adaptation to the environment. The profilings of pollen proteins thus obtained allow the distinction of the analyzed cultivars into three distinct groups: (i) those characterized by a low Ole e 1 content; (ii) those over-enriched in Ole e 1 and (iii) that containing Ole e 3 and Ole e 7 only. The latter consists of at least four isoforms differing by the degree of glycosilation. These results demonstrate that the proposed experimental procedure, can supply valuable information on the antigens' micro heterogeneity.     

Autonomous Eyewitness Identification by Employing Linguistic Rules for Disaster Events

Social networking platforms provide a vital source for disseminating information across the globe, particularly in case of disaster. These platforms are great mean to find out the real account of the disaster. Twitter is an example of such platform, which has been extensively utilized by scientific community due to its unidirectional model. It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users. Research community has proposed diverse sets of techniques to identify eyewitness account. A recent state-of-the-art approach has proposed a comprehensive set of features to identify eyewitness account. However, this approach suffers some limitation. Firstly, automatically extracting the feature-words remains a perplexing task against each feature identified by the approach. Secondly, all identified features were not incorporated in the implementation. This paper has utilized the language structure, linguistics, and word relation to achieve automatic extraction of feature-words by creating grammar rules. Additionally, all identified features were implemented which were left out by the state-of-the-art model. A generic approach is taken to cover different types of disaster such as earthquakes, floods, hurricanes, and wildfires. The proposed approach was then evaluated for all disaster-types, including earthquakes, floods, hurricanes, and fire. Based on the static dictionary, the Zahra et al. approach was able to produce an F-Score value of 0.92 for Eyewitness identification in the earthquake category. The proposed approach secured F-Score values of 0.81 in the same category. This score can be considered as a significant score without using a static dictionary.