Synthesis and characterization of polyelectrolyte hydrogels with controlled swelling behaviour

Copolymerization of sulfopropyl methacrylate potassium salt (K‐SPMA) and 2‐acrylamido‐2‐methyl propane sulfonic acid (AMPS) has been studied in the range 10–90% K‐SPMA in the feedstock. The reactivity ratios have been determined for K‐SPMA/AMPS copolymers. The copolymer compositions, utilized for determining the reactivity ratio, have been determined from nitrogen content. Crosslinked poly(AMPS) and K‐SPMA/AMPS copolymers were prepared in water in the presence of potassium persulfate as initiator and N,N‐methylene bisacrylamide (MBA) as tetrafunctional crosslinker. Irradiation of K‐SPMA and AMPS with an electron‐beam was carried out at 50 wt% aqueous solution with low pH and irradiation dose 40–120 kGy. The swelling behaviour of highly crosslinked K‐SPMA/AMPS copolymer polyelectrolyte gels in aqueous medium was studied in the presence of different types of salts. The crosslink density, the average molecular weight between the crosslinks and the dissociation constant (pK_a) of the crosslinked polymer were determined from stress–strain measurements. © 2001 Society of Chemical Industry     

Removal of organic water pollutant using magnetite nanomaterials embedded with ionic copolymers of 2‐acrylamido‐2‐methylpropane sodium sulfonate cryogels

Magnetite cryogel composites as macroporous crosslinked matrices have received wide attention and attract much interest in the water purification and desalination industry. They can be used to produce effective adsorbents with high adsorption rate, capacity and desorption for water pollutants. In this work, the incorporation of magnetite nanoparticles into cryogels by the in situ method is proposed to increase the dispersion of nanoparticles in the gel composites and to produce effective magnetic materials with high adsorption capacities. Ionic sodium‐2‐acrylamido‐2‐methylpropane sulfonate (Na‐AMPS) monomer was selected to prepare cryogels as the homopolymer or copolymers with 2‐hydroxyethyl methacrylate (HEMA) or N‐vinyl pyrrolidone (VP) by the crosslinking polymerization technique in the frozen state. Magnetite nanoparticles were introduced into the cryogel by the in situ co‐precipitation method after introducing iron cations into the cryogel networks. The surface morphologies, crystal structure, magnetite content, thermal stability and magnetic properties were determined for the cryogels and their magnetite composites. The magnetite cryogel composites show significantly enhanced methylene blue dye removal in short times with higher adsorption efficiencies and good regeneration to form an effective adsorbent for water treatment. © 2017 Society of Chemical Industry     

In situ preparation of magnetic Fe3O4.Cu2O.Fe3O4/cryogel nanocomposite powder via a reduction–coprecipitation method as adsorbent for methylene blue water pollutant

Magnetic nanocomposites have attracted great attention as adsorbents for the removal of water pollutants, which respond to an external magnet that is used to remove both pollutants and composite nanomaterial traces from water. They are environmentally friendly and effective adsorbents for water treatment. In this respect, a simple in situ preparation method was used to prepare cryogel powder composite based on Fe₃O₄.Cu₂O.Fe₃O₄ nanomaterials. The ionic cryogel based on 2‐acrylamido‐2‐methylpropane sulfonate sodium salt and styrene sulfonate sodium salt was prepared by crosslinking polymerization at low temperature. The new magnetic nanoparticles based on Fe₃O₄.Cu₂O.Fe₃O₄ were successfully prepared inside the cryogel networks by a simple reduction–coprecipitation method based on reaction of Fe³⁺ with sodium sulfite and Cu²⁺ in the presence of hydroxylamine and ammonia solution. The thermal stability, accurate Fe₃O₄.Cu₂O.Fe₃O₄ content, magnetic properties, crystal lattice structure, particle sizes and morphology of the prepared cryogel composite were evaluated. The optimum conditions such as pH, contact time, adsorbate concentrations, adsorption equilibrium and adsorption kinetics were investigated to determine the efficiency of the prepared composite as an adsorbent to remove toxic methylene blue (MB) pollutant from aqueous solution. The data for MB adsorption confirmed the high ability of the prepared composite to remove more than 4.696 mmol L^?1 of MB from water during 6 min. The regeneration and reuse experiments showed excellent data for the synthesized new dye as an effective adsorbent for water treatment. © 2018 Society of Chemical Industry     

Fast responsive poly(acrylic acid‐co‐N‐isopropyl acrylamide) hydrogels based on new crosslinker

Novel dual temperature‐ and pH‐sensitive poly(acrylic acid‐co‐N‐isopropylacrylamide), AA/NIPAAm, hydrogels were successfully prepared by chemical crosslinking with crosslinkers. Copolymers of AA/NIPAAm were crosslinked in the presence of different mol % of N,N‐methylene bisacrylamide (MBA) and melamine triacrylamide (MAAm) as crosslinkers by bulk radical polymerization. The resultant xerogels were characterized by extracting the soluble fractions and measuring the equilibrium water content. Lower critical solution transition temperatures (LCST) were measured by DSC. The properties of crosslinked AA/NIPAAm series are evaluated in terms of compositional drift of polymerization, heterogeneous crosslinking, and chemical structure of the relevant components. Soluble fractions of the crosslinked networks were reduced by varying the MAAm and MBA concentrations. The influence of environmental conditions such as temperature and pH on the swelling behavior of these polymeric gels was investigated. The swelling behaviors of the resulting gels show pH sensitivity. The prepared MAAm type AA/NIPAAm hydrogels exhibited a more rapid deswelling rate than MBA type AA/NIPAAm hydrogels in ultra pure water in response to abrupt changes from 20°C to 50°C. The results of this study provide valuable information regarding the development of dual stimuli‐sensitive hydrogels with fast responsiveness. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Numerical Study of Aeroacoustic Sound on Performance of Bladeless Fan

Aeroacoustic performance of fans is essential due to their widespread application. Therefore, the original aim of this paper is to evaluate the generated noise owing to different geometric parameters. In current study, effect of five geometric parameters was investigated on well performance of a Bladeless fan. Airflow through this fan was analyzed simulating a Bladeless fan within a 2 m×2m×4 m room. Analysis of the flow field inside the fan and evaluating its performance were obtained by solving conservations of mass and momentum equations for aerodynamic investigations and FW-H noise equations for aeroacoustic analysis. In order to design Bladeless fan Eppler 473 airfoil profile was used as the cross section of this fan. Five distinct parameters, namely height of cross section of the fan, outlet angle of the flow relative to the fan axis, thickness of airflow outlet slit, hydraulic diameter and aspect ratio for circular and quadratic cross sections were considered. Validating acoustic code results, we compared numerical solution of FW-H noise equations for NACA0012 with experimental results. FW-H model was selected to predict the noise generated by the Bladeless fan as the numerical results indicated a good agreement with experimental ones for NACA0012. To validate 3-D numerical results, the experimental results of a round jet showed good agreement with those simulation data. In order to indicate the effect of each mentioned parameter on the fan performance, SPL and OASPL diagrams were illustrated.     

Corrosion inhibition, hydrogen evolution and antibacterial properties of newly synthesized organic inhibitors on 316L stainless steel alloy in acid medium

Electrochemical corrosion behavior and hydrogen evolution reaction of 316L stainless steel has been investigated, in 0.5 M sulfuric acid solution containing four novel organic inhibitors as derivatives from one family, using potentiodynamic polarization, electrochemical impedance spectroscopy (EIS) measurements and surface examination via scanning electron microscope (SEM) technique. The effect of corrosion inhibitors on the hydrogen evolution reaction was related to the chemical composition, concentration and structure of the inhibitor. The inhibition efficiency, for active centers of the four used compounds, was found to increase in the order: -Cl < -Br < -CH₃ < -OCH₃. The corrosion rate and hydrogen evolution using the compound with methoxy group as a novel compound was found to increase with either increasing temperature or decreasing its concentration as observed by polarization technique and confirmed by EIS measurements. The compound with methoxy group (newly synthesized) has very good inhibition efficiency (IE) in 0.5 M sulfuric acid (98.3% for 1.0 mM concentration). EIS results were confirmed by surface examination. Also, antibacterial activity of these organic inhibitors was studied. The results showed that the highest inhibition efficiency was observed for the compound that posses the highest antibacterial activity.     

Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

Natural Language Processing (NLP) for the Arabic language has gained much significance in recent years. The most commonly-utilized NLP task is the ‘Text Classification’ process. Its main intention is to apply the Machine Learning (ML) approaches for automatically classifying the textual files into one or more pre-defined categories. In ML approaches, the first and foremost crucial step is identifying an appropriate large dataset to test and train the method. One of the trending ML techniques, i.e., Deep Learning (DL) technique needs huge volumes of different types of datasets for training to yield the best outcomes. The current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machine-based Arabic Corpus Classification (DODHBM-ACC) model in this background. The presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding process. For Arabic text classification, the DHBM technique is utilized. This technique is a hybrid version of the Deep Boltzmann Machine (DBM) and Deep Belief Network (DBN). It has the advantage of learning the decisive intention of the classification process. To adjust the hyperparameters of the DHBM technique, the Dice Optimization Algorithm (DOA) is exploited in this study. The experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC model. The outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.     

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the ML approaches were applied, and they performed quite effectively. Still, a huge effort is required from the human side to generate the labelled training data. The recent progress of the Deep Learning (DL) methods seems to be a promising solution to tackle difficult types of Natural Language Processing (NLP) tasks, especially fake news detection. To unlock social media data, an automatic text classifier is highly helpful in the domain of NLP. The current research article focuses on the design of the Optimal Quad Channel Hybrid Long Short-Term Memory-based Fake News Classification (QCLSTM-FNC) approach. The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news. To attain this, the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glove-based word embedding process. Besides, the QCLSTM model is utilized for classification. To boost the classification results of the QCLSTM model, a Quasi-Oppositional Sandpiper Optimization (QOSPO) algorithm is utilized to fine-tune the hyperparameters. The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset. The QCLSTM-FNC approach successfully outperformed all other existing DL models under different measures.     

Interactive effects of training condition and septal lesions on perseverative responding in the rat.

In a study with 30 male Wistar rats, Ss that had received septal lesions either prior to acquisition of a position habit (Group SAR) or immediately after position habit acquisition (Group SR) were tested on 2 reversals of the task. Compared with control-operated Ss, both groups of Ss with septal lesions exhibited position-habit reversal deficits. The 2 groups with septal lesions could not be distinguished in terms of the total number of errors made during reversal learning, but could be distinguished on the basis of the type of error committed. Compared with Group SR, Group SAR made significantly more perseverative errors on the 1st reversal and significantly fewer perseverative errors on the 2nd reversal. Group SR made significantly more nonperseverative errors than Group SAR on both reversals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Modified Garden Balsan Optimization Based Machine Learning for Intrusion Detection

The Internet of Things (IoT) environment plays a crucial role in the design of smart environments. Security and privacy are the major challenging problems that exist in the design of IoT-enabled real-time environments. Security susceptibilities in IoT-based systems pose security threats which affect smart environment applications. Intrusion detection systems (IDS) can be used for IoT environments to mitigate IoT-related security attacks which use few security vulnerabilities. This paper introduces a modified garden balsan optimization-based machine learning model for intrusion detection (MGBO-MLID) in the IoT cloud environment. The presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud atmosphere. Initially, the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform format. In addition, the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of features. Moreover, the attention-based bidirectional long short-term (ABiLSTM) method can be utilized for the detection and classification of intrusions. At the final level, the Aquila optimization (AO) algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM methods. The experimental validation of the MGBO-MLID method is tested using a benchmark dataset. The extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches.