Smart nano generation of transgenic algae expressing white spot syndrome virus in shrimps for inner ear-oral infection treatments using the spotted hyena optimizer (SHO)-Long short-term memory algorithm

Nanotechnology offers a promising avenue to amplify the effectiveness and precision of using transgenic algae in managing WSSV in shrimp by possibly crafting nano-carriers for targeted therapeutic agent delivery or modifying algae cells at a molecular level. Leveraging the capabilities of nano-scale interventions, this study could explore innovative means to manipulate cellular processes, control biological interactions, and enhance treatment efficacy while minimizing undesirable impacts in aquatic environments. The White Spot Syndrome Virus (WSSV) is a double-stranded DNA virus with a tail and rod form that belongs to theNimaviridaefamily. There is no workable way to manage this illness at the moment. This research proposes a new model based on the Long Short-Term Memory (LSTM) and Spotted Hyena Optimizer (SHO) method to control the inner ear-oral infection, utilizing transgenic algae (Chlamydomonas reinhardtii). It is pretty tricky to modify the weight matrix in LSTM. The output will be more accurate if the weight of the neurons is exact. Histological examinations and nested polymerase chain reaction (PCR) testing were performed on the challenged shrimp every 4 h to assess the degree of white spot disease. The SHO-LSTM has shown the highest accuracy and Roc value (98.12% and 0.93, respectively) and the lowest error values (MSE = 0.182 and MAE = 0.48). The hybrid optimized model improves the overall inner ear-oral linked neurological diseases detection ratio. Additionally, with the slightest technical complexity, it effectively controls the forecast factors required to anticipate the ENT. Algal cells were found to be particularly well-suited for inner ear-oral infections, and shrimps fed a transgenic line had the best survival ratio in WSSV infection studies, with 87% of the shrimp surviving. This shows that using this line would effectively stop the spread of WSSV in shrimp populations.

Effect of dapagliflozin against NAFLD and dyslipidemia in type 2 diabetic albino rats: possible underlying mechanisms

OBJECTIVE

The aim was to investigate the effect of dapagliflozin on non-alcoholic fatty liver disease and dyslipidemia in type 2 diabetic rats by studying the histopathological structure of the liver and detecting possible underlying mechanisms for this impact by evaluating the potential anti-inflammatory action of dapagliflozin.

MATERIALS AND METHODS

100 albino rats were used in this work and divided into five equal groups: group I (Control group), group II (Control diabetic group), group III (was administered dapagliflozin, 0.75 mg/kg, p.o.), group IV (was administered dapagliflozin, 1.5 mg/kg, p.o.), and group V (was administered dapagliflozin, 3 mg/kg, p.o.).

RESULTS

In our study, the total body weight, liver weight, liver index, blood glucose level, insulin level, insulin resistance, total cholesterol, triglycerides, liver enzymes, IL-1 β, and MDA were significantly higher in the control diabetic group than the normal group. The dapagliflozin reduced all the above variables significantly in a dose-dependent manner compared to the control diabetic group (p-value = 0.001 for all).

CONCLUSIONS

Dapagliflozin may be a promising novel treatment strategy for treating T2DM-related non-alcoholic fatty liver disease (NAFLD), and dyslipidemia where it possesses anti-oxidative, anti-inflammatory and anti-dyslipidemic effects.

A general electroelastic analysis of piezoelectric shells based on levy-type solution and eigenvalue-eigenvector method

Eigenvalue-Eigenvector approach as well as Levy type solution are used for electroelastic analysis of a doubly curved shell made of piezoelectric material based on a shear deformable model and piezoelasticity relations. The electroelastic governing equations are derived using virtual work principle. The solution is proposed for a Levy type boundary conditions with two simply-supported boundary conditions and two clamped ones. After derivation of the governing equations, a solution satisfying two simply supported boundary conditions is assumed to arrive a system of ordinary differential equations. The latest governing equations are solved using Eigenvalue-Eigenvector method to satisfy clamped-clamped boundary conditions. The distribution of displacements, rotations, electric potential, strain and stress is presented along the planar coordinate. Accuracy of the proposed solution is justified through comparison with results of previous papers.

Latest insights on separation and storage of carbon compounds in buildings towards sustainable environment: Recent innovations, challenges, future perspectives and application of machine learning

Throughout the past few decades, scientific agencies have paid a lot of attention to environmental issues such as acid rain, water poisoning, and global warming. In order to solve these environmental problems, metal-organic frameworks (MOFs), which are made up of metal ions and/or clusters attached to organic ligands, have shown some promise. With a focus on the usage of MOFs, this paper examines the most recent developments, difficulties, and potential future directions in the separation and storage of carbon compounds in buildings for a sustainable environment. The importance of using MOFs in decarbonizing water systems and lowering environmental concerns in buildings is highlighted in the research. It addresses the most recent developments in the use of MOFs for renewable energy, such as the elimination of dangerous gases like CO2 and CH4 from water systems. The article also looks at how MOFs might be used to decarbonize water systems in structures, with a focus on how carbon-containing compounds are stored chemically and physically using artificial neural network models. MOFs are a potential solution for renewable energy and environmental remediation in buildings because they have special physical and chemical characteristics like adjustable pores, high porosity, and tiny pore size. The report offers insights into existing treatments and invites academics to investigate MOFs' potential for resolving environmental problems in order to create a sustainable environment in buildings.

Effect of nanoparticle macroalgae in the treatment of fatty liver disease using logistic regression, and support vector machine

One of the major health issues facing people worldwide is liver fibrosis. Liver fibrosis may be brought on by long-term exposure to harmful substances, medicines, and microorganisms. The development of liver fibrosis in children was particularly worrying due to their longer life-span, which was possibly related to a great risk of developing long-term complications. Marine algae species have provided a biological variety in the research phase of novel approaches to the treatment of numerous ailments. Marine macroalgae have recently been the subject of research due to their rich bioactive chemical composition and potential for the production of various nutraceuticals. Macroalgae are potentially excellent sources of bioactive substances with particular and distinct biological activity when compared to their terrestrial equivalents. Macroalgae in diverse marine cases offer a few biologically active metabolites, comprising sterols, polyunsaturated fatty acids, carotenoids, oligosaccharides, polysaccharides, proteins, polyphenols, vitamins, and minerals. Accordingly, there is great interest in their high potential for supporting immunomodulatory, antimicrobial, antidiabetic, antitumoral, anti-inflammatory, antiangiogenic, and neuroprotective properties. Using an experimental model, the current research intends to collect data on the therapeutic value of macroalgae nanoparticles for fatty liver disease. The researchers' goal of predicting illnesses from the extensive medical datasets is quite difficult. The purpose of this research is to assess the protective effects of a seaweed, Padina pavonia (PP), on liver fibrosis caused by carbon tetrachloride (CCl4). This research presents two models of logistic regression (LR) and support vector machines (SVM) for predicting the likelihood of liver disease incidence. The performance of the model was evaluated using a dataset. PP macro-algae considerably reduce the high blood concentrations of aminotransferases, alkaline phosphatases, and lactate dehydrogenases, as well as causing a considerable (p < 0.05) decrease in serum bilirubin levels. In addition to improving kidney function (urea and creatinine), algal extracts enhance fat metabolism (triglycerides and cholesterol). With an accuracy rate of 70.2%, a sensitivity of 92.3%, a specificity of 74.7%, a type I error of 9.1%, and a type II error of 21.0%, the predictive model has demonstrated excellent performance. The model validated laboratory tests' ability to predict illness (age; direct bilirubin (DB), total proteins (TP), and albumin (ALB). These classifier methods are compared on the basis of their execution time and classification accuracy.

Green synthesis of Fe3O4 nanoparticles using Alliaceae waste (Allium sativum) for a sustainable landscape enhancement using support vector regression

The synthesis of metal nanoparticles using green chemistry methods has gained significant attention in the field of landscape enhancement. Researchers have paid close attention to the development of very effective green chemistry approaches for the production of metal nanoparticles (NPs). The primary goal is to create an environmentally sustainable technique for generating NPs. At the nanoscale, ferro- and ferrimagnetic minerals such as magnetite exhibit superparamagnetic properties (Fe3O4). Magnetic nanoparticles (NPs) have received increased interest in nanoscience and nanotechnology due to their physiochemical properties, small particle size (1-100 nm), and low toxicity. Biological resources such as bacteria, algae, fungus, and plants have been used to manufacture affordable, energy-efficient, non-toxic, and ecologically acceptable metallic NPs. Despite the growing demand for Fe3O4 nanoparticles in a variety of applications, typical chemical production processes can produce hazardous byproducts and trash, resulting in significant environmental implications. The purpose of this study is to look at the ability of Allium sativum, a member of the Alliaceae family recognized for its culinary and medicinal benefits, to synthesize Fe3O4 NPs. Extracts of Allium sativum seeds and cloves include reducing sugars like glucose, which may be used as decreasing factors in the production of Fe3O4 NPs to reduce the requirement for hazardous chemicals and increase sustainability. The analytic procedures were carried out utilizing machine learning as support vector regression (SVR). Furthermore, because Allium sativum is widely accessible and biocompatible, it is a safe and cost-effective material for the manufacture of Fe3O4 NPs. Using the regression indices metrics of root mean square error (RMSE) and coefficient of determination (R2), the X-ray diffraction (XRD) study revealed the lighter, smoother spherical forms of NPs in the presence of aqueous garlic extract and 70.223 nm in its absence. The antifungal activity of Fe3O4 NPs against Candida albicans was investigated using a disc diffusion technique but exhibited no impact at doses of 200, 400, and 600 ppm. This characterization of the nanoparticles helps in understanding their physical properties and provides insights into their potential applications in landscape enhancement.

Application of aluminum oxide nanoparticles in asphalt cement toward non-polluted green environment using linear regression

In order to decrease the greenhouse gas emissions generated by regular Portland cement (OPC), additional cementitious ingredients have been frequently employed, even while building road bases. OPC's susceptibility to moisture and lack of flexibility make it ineffective for stabilizing road bases. This research used alkali-activated materials (AAM) with fly ash to investigate the mechanical properties of cold asphalt binder (freeze-thaw cycles) including the compressive, flexural strength, workability and porosity of cement. Dry specimens and specimens in distilled water have both been used in the experiments to study these temperature correlations. One sample was tested at 20 °C, and the other was frozen and thawed five times at a temperature of -5 °C (cold region environment). The resulting mixtures' morphologies and microstructures were analyzed via SEM images. During the 7 to 28-day curing period, the mixture's growth ratio rose. The combination registered both the greatest and lowest robust elastic modulus. The total compressive strength of the material decreased as the water-to-cement ratio increased due to the greater amount of free water accessible with a higher cationic asphalt emulsion (CAE) content. The moderate loss of flexural strength with increasing CAE concentration after 7 and 28 days of curing was seen. There is not a major impact on flexural strength in the materials by looking at the very modest gaps in flexural strength between 7 and 28 days curing periods. Due to the particle shape and size of this precursor, FA's inclusion allowed for a lower water to binder rate while maintaining a similar level of workability. The porosity and water absorption values rose with FA substitutions. Further studies might clarify the lower flexural strength observed in this study by adding other hybrids plus fly ash such as lime or nanoparticles.

Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability

Nanoparticles (NPs) are a promising alternative to antibiotics for targeting microorganisms, especially in the case of difficult-to-treat bacterial illnesses. Antibacterial coatings for medical equipment, materials for infection prevention and healing, bacterial detection systems for medical diagnostics, and antibacterial immunizations are potential applications of nanotechnology. Infections in the ear, which can result in hearing loss, are extremely difficult to cure. The use of nanoparticles to enhance the efficacy of antimicrobial medicines is a potential option. Various types of inorganic, lipid-based, and polymeric nanoparticles have been produced and shown beneficial for the controlled administration of medication. This article focuses on the use of polymeric nanoparticles to treat frequent bacterial diseases in the human body. Using machine learning models such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), this 28-day study evaluates the efficacy of nanoparticle therapy. An innovative application of advanced CNNs, such as Dense Net, for the automatic detection of middle ear infections is reported. Three thousand oto-endoscopic images (OEIs) were categorized as normal, chronic otitis media (COM), and otitis media with effusion (OME). Comparing middle ear effusions to OEIs, CNN models achieved a classification accuracy of 95%, indicating great promise for the automated identification of middle ear infections. The hybrid CNN-ANN model attained an overall accuracy of more than 0.90 percent, with a sensitivity of 95 percent and a specificity of 100 percent in distinguishing earwax from illness, and provided nearly perfect measures of 0.99 percent. Nanoparticles are a promising treatment for difficult-to-treat bacterial diseases, such as ear infections. The application of machine learning models, such as ANNs and CNNs, can improve the efficacy of nanoparticle therapy, especially for the automated detection of middle ear infections. Polymeric nanoparticles, in particular, have shown efficacy in treating common bacterial infections in children, indicating great promise for future treatments.

Application of logistic regression, support vector machine and random forest on the effects of titanium dioxide nanoparticles using macroalgae in treatment of certain risk factors associated with kidney injuries

The use of titanium dioxide (TiO₂) nanoparticles in many biological and technical domains is on the rise. There hasn't been much research on the toxicity of titanium dioxide nanoparticles in biological systems, despite their ubiquitous usage. In the current investigation, samples were exposed to various dosages of TiO₂ nanoparticles for 4 days, 1 month, and 2 months following treatment. ICP-AES was used to dose TiO₂ into the tissues, and the results showed that the kidney had a significant TiO₂ buildup. On the other hand, apoptosis of renal tubular cells is one of the most frequent cellular processes contributing to kidney disease (KD). Nevertheless, the impact of macroalgal seaweed extract on KD remains undetermined. In this work, machine learning (ML) approaches have been applied to develop prediction algorithms for acute kidney injury (AKI) by use of titanium dioxide and macroalgae in hospitalized patients. Fifty patients with (AKI) and 50 patients (non-AKI group) have been admitted and considered. Regarding demographic data, and laboratory test data as input parameters, support vector machine (SVM), and random forest (RF) are utilized to build models of AKI prediction and compared to the predictive performance of logistic regression (LR). Due to its strong antioxidant and anti-inflammatory powers, the current research ruled out the potential of using G. oblongata red macro algae as a source for a variety of products for medicinal uses. Despite a high and fast processing of algorithms, logistic regression showed lower overfitting in comparison to SVM, and Random Forest. The dataset is subjected to algorithms, and the categorization of potential risk variables yields the best results. AKI samples showed significant organ defects than non-AKI ones. Multivariate LR indicated that lymphocyte, and myoglobin (MB) ≥ 1000 ng/ml were independent risk parameters for AKI samples. Also, GCS score (95% CI 1.4-8.3 P = 0.014) were the risk parameters for 60-day mortality in samples with AKI. Also, 90-day mortality in AKI patients was significantly high (P < 0.0001). In compared to the control group, there were no appreciable changes in the kidney/body weight ratio or body weight increases. Total thiol levels in kidney homogenate significantly decreased, and histopathological analysis confirmed these biochemical alterations. According to the results, oral TiO₂ NP treatment may cause kidney damage in experimental samples.

Evaluation of waste management and energy saving for sustainable green building through analytic hierarchy process and artificial neural network model

A significant portion of the solid waste filling landfills worldwide is debris from construction and demolition projects. Across the world, a significant portion of the solid waste filling landfills is made up of construction and demolition waste. Recycling construction waste may help cut down on the quantity of waste sent to landfills and the requirement for energy and other natural resources. To help with construction waste reduction, a management hierarchy that begins with rethink, reduce, redesign, refurbish, reuse, incineration, composting, recycle, and eventually disposal is likely to be effective. The objective of this research is to investigate the viability of the Analytic Hierarchy Process (AHP) as a data gathering instrument for the development of a solid waste management assessment tool, followed by an examination of an artificial neural network (ANN). Using a standardized questionnaire, all data was gathered from waste management practitioners in three industry sectors. The survey data was subsequently analyzed using ANN and later AHP. The suggested framework consisted of four components: (1) the development of different level structures for fluffy AHP, (2) the calculation of weights, (3) the collection of data, and (4) the making of decisions. An ANN feedforward with error back propagation (EBP) learning computation is coupled to identify the association between the items and the store execution. It was found that the combination of AHP and ANN has emerged as a key decision support tool for landfilling, incineration, and composting waste management strategies, taking into account the environmental profile and economic and social characteristics of each choice. Composting has the highest sustainable performance when a balanced weight distribution of criteria is assumed, especially if the environmental component is considered in comparison to the other criteria. However, if social and economic features are addressed, incineration or landfilling have more favorable characteristics, respectively.

Spotlighting of microbial electrodeionization cells for sustainable wastewater treatment: Application of machine learning

Microbial electrodeionization cells (MECs) have been investigated for various potential applications, including the elimination of persistent pollutants, chemical synthesis, the recovery of resources, and the development of biosensors. Nevertheless, MEC technology is still developing, and practical large-scale applications face significant obstacles. This review aims to investigate MEC implementations in sustainable wastewater treatment. Ideas and concepts of MEC technology, the setup of the electrodeionization component, the membranes of MECs, the working mechanism of MECs, and the various microorganisms used in MECs are discussed. Additionally, difficulties and prospective outcomes were discussed. The goal of this review is to support scientists and engineers in fully grasping the most recent developments in MEC technologies and applications.

Assessment of graphene-based polymers for sustainable wastewater treatment: Development of a soft computing approach

Since graphene possesses distinct electrical and material properties that could improve material performance, there is currently a growing demand for graphene-based electronics and applications. Numerous potential applications for graphene include lightweight and high-strength polymeric composite materials. Due to its structural qualities, which include low thickness and compact 2D dimensions, it has also been recognized as a promising nanomaterial for water-barrier applications. For barrier polymer applications, it is usually applied using two main strategies. The first is the application of graphene, graphene oxide (GO), and reduced graphene oxide (rGO) to polymeric substrates through transfer or coating. In the second method, fully exfoliated GO or rGO is integrated into the material. This study provides an overview of the most recent findings from research on the use of graphene in the context of water-barrier applications. The advantages and current limits of graphene-based composites are compared with those of other nanomaterials utilized for barrier purposes in order to emphasize difficult challenges for future study and prospective applications.

Application of nano remediation of mine polluted in acid mine drainage water using machine learning model

Acid mine drainage (AMD) is the term used to describe drainage from coal mines with high sulfur-bearing rocks. The oxidative weathering of metal sulfides leads to AMD. The acidic environment corrodes more harmful compounds in the soil, which is spread throughout the working area. One such significant metal is copper, which is extracted in massive quantities from ores rich in sulfide. A copper-extraction resin might be created by combining diatomaceous earth (DE) particles with polyethyleneimine (PEI), which is shown to have great selectivity and affinity for copper. In this effort, PEI-DE particles' copper absorption level was examined by using synthetic and actual acid mine drainage samples at varied pH values. The findings of the copper uptake particles have been examined through the Support Vector Machine (SVM) model. Using the n-fold 14 cross-validation approach, the quantities of parameters and C are estimated to be 0.001 and 0.01, respectively. The SVM analysis was correct, and the findings indicated that copper could bind to the material efficiently and preferentially at pH 4. Subsequent water elution studies at a pH value of 1 confirmed the pH-reliant interaction between dissolved Cu and PEI by demonstrating full release of the adsorbed Cu. In this research, the copper absorption of PEI-DE particles from synthetic and genuine AMD specimens was studied based on several pH conditions. The findings suggest that copper may attach to the material effectively and preferentially at pH 4. Studies of filtering water at pH1 later confirmed that all of the adsorbed Cu was released. This shows that the interaction between PEI and dissolved Cu depends on PH.

Assessment in carbon-based layered double hydroxides for water and wastewater: Application of artificial intelligence and recent progress

Layered double hydroxides (LDHs) are a class of clays with brucite like layers and intercalated anions. Hybrids of carbon nanomaterials and layered double hydroxides (C-LDHs) are promising nanomaterials due to their versatile properties and the large number of composition/preparation variables available for fine-tuning. Several techniques are available for the synthesis of these novel C-LDHs nanocomposites. This article assess developments in the synthesis and applications of C-LDHs in water and wastewater treatment via using artificial intelligence approaches. In addition, current challenges and possible strategies are discussed from the viewpoint of synthesis and application. It is concluded that the use of C-LDH is expected to produce interesting results. The anisotropic properties and good dispersion ability make them suitable to be used as particulates in the dispersion phase of electro-responsive and electro-rheological fluids. Although these materials have been tested for the removal of contaminants from single component solutions in water. In addition, application of artificial intelligence in this regard is discussed. At the end, the necessity of evaluating their performance in the removal of contaminants from multi-components solutions is proposed. Finally the challenges in obtaining material with precisely controlled particle sizes and morphology must be addressed.

Risk assessment of organophosphorus pesticide residues in drinking water resources: Statistical and Monte-Carlo approach

A large part of the world's agricultural production, despite its adverse effects on human health and water resources, depends on the use of pesticides. Despite strict regulations, the use of pesticides continues around the world. This study aimed to determine the residual concentrations of malathion and diazinon in samples of drinking water resources. To achieve this goal, 384 samples from 8 various sites from January to December 2020 were analyzed using gas chromatography (GC) with an electron capture detector (ECD) and liquid-liquid extraction technique. Besides, statistical analysis and a risk-modeling approach supported by an automatic Monte-Carlo procedure were applied. The results showed that there is a high carcinogenic risk regarding malathion and that the low age population is at the most non-carcinogenic risk regarding diazinon.

Application of Polyacrylic Hydrogel in Durability and Reduction of Environmental Impacts of Concrete through ANN

While adding superabsorbent polymer hydrogel particles to fresh concrete admixtures, they act as internal curing agents that absorb and then release large amounts of water and reduce self-desiccation and volumetric shrinkage of cement that finally result in hardened concrete with increased durability and strength. The entrainment of microscopic air bubbles in the concrete paste can substantially improve the resistance of concrete. When the volume and distribution of entrained air are adequately managed, the microstructure is protected from the pressure produced by freezing water. This study addresses the design and application of hydrogel nanoparticles as internal curing agents in concrete, as well as new findings on crucial hydrogel–ion interactions. When mixed into concrete, hydrogel particles produce their stored water to power the curing reaction, resulting in less volumetric shrinkage and cracking and thereby prolonging the service life of concrete. The mechanical and swelling performance qualities of the hydrogel are very sensitive to multivalent cations found naturally in concrete mixes, such as aluminum and calcium. The interactions between hydrogel nanoparticles and alkaline cementitious mixes are described in this study, while emphasizing how the chemical structure and shape of the hydrogel particles regulate swelling behavior and internal curing efficiency to eliminate voids in the admixture. Moreover, in this study, an artificial neural network (ANN) was utilized to precisely and quickly analyze the test results of the compressive strength and durability of concrete. The addition of multivalent cations reduced swelling capacity and changed swelling kinetics, resulting in fast deswelling behavior and the creation of a mechanically stiff shell in certain hydrogel compositions. Notably, when hydrogel particles were added to a mixture, they reduced shrinkage while encouraged the creation of particular inorganic phases within the void area formerly held by the swelled particle.

Mechanical Characteristics and Self-Healing Soil-Cementitious Hydrogel Materials in Mine Backfill Using Hybridized ANFIS-SVM

The compressive strength, shrinkage, elasticity, and electrical resistivity of the cement-soil pastes (slag, fly ash) of self-healing of cementitious concrete have been studied while adding hydrogels with nano silica (NSi) in this research. Defining the hydraulic and mechanical properties of these materials requires improvement to motivate more uptake for new buildings. Initially, examining the impact of different synthesized hydrogels on cement-soil pastes showed that solid particles in the mixtures highly affected the absorption capacity of NSi, representing the importance of direct interactions between solid particles and hydrogels in a cementitious matrix. All test results were analyzed by use of a hybridized soft computing model such as the adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) for precise studying and the avoidance of few empirical tests or error percentages. Subsequently, the best RMSE of ANFIS is 0.6568 and the best RMSE of SVM is 1.2564; the RMSE of ANFIS-SVM (0.5643) in the test phase is also close to zero, showing a better performance in hypothesizing self-healing soil-cementitious hydrogel materials in mine backfill. The R2 value for ANFIS-SVM is 0.9547, proving that it is a proper model for predicting the study’s goal. Electrical resistivity and compressive strength declined in the cement-soil pastes including hydrogels according to experimental outcomes; it was lowered by the increase of NSi concentration in the hydrogel. There was a decrement in the autogenous shrinkage of cement-soil pastes while adding hydrogel, depending on the NSi concentration in the hydrogels. The findings of this research are pivotal for the internal curing of cementitious materials to define the absorption of hydrogels.

Microstructure Study and Linear/Nonlinear Optical Performance of Bi-Embedded PVP/PVA Films for Optoelectronic and Optical Cut-Off Applications

Hybrid polymer films of polyvinyl pyrrolidone (PVP)/polyvinyl alcohol (PVA) embedded with gradient levels of Bi-powder were prepared using a conventional solution casting process. XRD, FTIR, and SEM techniques have been used to examine the micro/molecular structure and morphology of the synthesized flexible films. The intensities of the diffraction peaks and transmission spectrum of the PVP/PVA gradually declined with the introduction of Bi-metal. In addition, filler changes the microstructure surface of the pure film. The modification in the microstructure leads to an enhancement in the optical absorption characteristic of the blend films. The indirect allowed transition energy was calculated via Tauc’s and ASF (Absorption Spectra Fitting) models. The decrease in the hybrid film’s bandgap returns to the localized states in the forbidden region, which led the present films to be suitable for photo-electric, solar cell, etc., applications. The relation between the transition energy and the refractive index was studied. The enhancement in the refractive index with Bi-metal concentrations led to use the as-prepared films in optical sensors. The rise of Bi-metal concentrations leads also to the improvement of the nonlinear susceptibility and refractive parameters. The optical limiting characteristics revealed that the higher concentration dopant films reduce the light transmission intensity which is appropriate for laser attenuation and optical limiting in photonic devices. The results suggest that hybrid films are promising materials in a wide range of opto-electronic applications.

Microstructure Study And Linear/Nonlinear Optical Properties of Bi-Embedded PVP/ PVA Films For Optoelectronic And Optical CUT-OFF Applications.. [DOI: 10.21203/rs.3.rs-1317913/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Hybrid polymer films of polyvinyl pyrrolidone (PVP)/ polyvinyl alcohol (PVA) embedded with gradient levels of Bi-powder were prepared using a conventional solution casting process. X-ray diffraction (XRD), Fourier transform infrared spectroscopic (FT-IR), and Scanning electron microscope (SEM) techniques have been used to examine the micro/molecular structural and morphological of the synthesized flexible films. The XRD peak intensity and FTIR spectra of the PVA gradually decline as Bi-metal is introduced. In addition, filler changes the microstructure surface of the pure film. The modification in the microstructure leads to an enhancement in the optical characteristic of the blend films. The indirect allowed transition energy was calculated via Tauc's and ASF models. The decrease in the hybrid film’s bandgap (\({E}_{gi}^{opt}\)) returns to the localized states in the forbidden region, as illustrated by the Urbach energy (Eu) values. The optical absorption parameters, including absorption coefficient (α) and edge (Ee), have also been evaluated. The relation between the transition energy and the refractive index (n) was studied. The rise of Bi-metal concentrations leads to an improvement in the nonlinear (χ(3)) susceptibility and (n2) refractive parameters. The optical limiting characteristics (OLC) revealed that the higher concentration dopant films reduce the light transmission intensity. The results suggest that hybrid films are promising materials in a wide range of applications, especially optical devices, and optoelectronics.

Investigation of the interaction mechanism of 3-allyl-2-hydantoin anti-cancer on the pristine and functionalized BC2N nanotubes as an effective drug delivery nanocarriers

One of the exciting aspects of different nanomaterials in biomedical applications is the delivery of a wide range of anti-cancer drugs to mitigate their negative side effects via precise targeting of the tumor cells. In the present study, the DFT B3LYP/6-31G (d, p) level of theory was used to evaluate the capabilities of raw BC₂N nanotubes (BC₂NNTs) and functionalized BC₂NNTs nanostructures as a carrier for an anti-cancer drug (i.e., 3-allyl-2-hydantoin (3-ASH)). It was specified that the existing interaction between 3-ASH and BC₂N nanotubes is weak (-10.97kcalmol^-1). Therefore, the functionalized BC₂NNTs were investigated for 3-ASH interaction. According to our computations, compared with raw BC₂NNTs, the role of hydrogen bonds between 3-ASH molecules' active sites and carboxyl-functionalized BC₂NNTs in the complexes' fixation, adsorption, and thermodynamic energy is of great importance. A considerable transfer of charge between 3-ASH molecule to the functionalized BC₂NNTs was detected via MEP, NBO, and fractional charge transfer analysis. Also, it was found that these nanostructures have high stability on the water medium while their solvation energies have negative values. This negative value is beneficial for the applications of 3-ASH drug delivery. The present work proposes a new method to load 3-ASH drugs with a high density on BC₂NNTs' surfaces.

Aetiological factors of obesity in children

A survey was carried out of the prevalence of obesity in 7600 schoolchildren aged 11-16 years in Cairo. Weight, height and skinfold thicknesses were measured and the children classified as underweight, normal or overweight. Overweight children were further screened for obesity, a triceps skinfold thickness of greater than 18 mm for boys and greater than 25 mm for girls being taken as the lower level of obesity. Age at menarche, birth order, social class, obesity in other members of the family, food habits and dietary intakes were all studied. The prevalence of obesity in Egyptian children was comparable to that in the US. The study emphasizes the importance of social and cultural influences in the development of obesity in Egyptian children.