Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network

Prediction and optimizing of methylene blue sequestration to activated charcoal/magnetic nanocomposites using artificial neutral network and response surface methodology

Green synthesized magnetic nanoparticles (MNPs) linked with activated charcoal (AC) (AC/Fe3O4 NCs) were exploited for methylene blue (MB) confiscation in this study. The AC/Fe3O4 NCs produced were characterized using TEM, FTIR, UV/Vis and XRD spectrometry. The Response-Surface-Methodology (RSM) was utilized to improve the experimental data for the MB sorption to AC/Fe3O4 NCs, with 20 experimental runs implemented through a central composite design (CCD) to assess the effect of sorption factors-initial MB concentration, pH and sorbent dosage effects on the response (removal-effectiveness). The quadratic model was discovered to ideally describe the sorption process, with an R2 value of 0.9857. The theoretical prediction of the experimental data using the Artificial-Neural-Network (ANN) model showed that the Levenberg-Marquardt (LM) had a better performance criterion. Comparison between the modelled experimental and predicted data showed also that the LM algorithm had a high R2 of 0.9922, which showed NN model applicability for defining the sorption of MB to AC/Fe3O4 NCs with practical precision. The results of the non-linear fitting (NLF) of both isotherm and kinetic models, showed that the sorption of MB to AC/Fe3O4 NCs was perfectly described using the pseudo-second-order (PSOM) and Freundlich (FRHM) models. The estimated optimum sorption capacity was 455 mg g-1. Thermodynamically, the sorption of MB to AC/Fe3O4 NCs was shown to be non-spontaneous and endothermic.

Flexible Hybrid and Single-Component Aerogels: Synthesis, Characterization, and Applications

The inherent disadvantages of traditional nonflexible aerogels, such as high fragility and moisture sensitivity, severely restrict their applications. To address these issues, different techniques have been used to incorporate the flexibility in aerogel materials; hence, the term "flexible aerogels" was introduced. In the case of introducing flexibility, the organic part is induced with the inorganic part (flexible hybrid aerogels). Additionally, some more modern research is also available in the fabrication of hybrid flexible aerogels (based on organic-organic), the combination of two organic polymers. Moreover, a new type (single-component flexible aerogels) are quite a new category composed of only single materials; this category is very limited, charming to make the flexible aerogels pure from single polymers. The present review is composed of modern techniques and studies available to fabricate hybrid and single-component flexible aerogels. Their synthesis, factors affecting their parameters, and limitations associated with them are explained deeply. Moreover, a comparative analysis of drying methods and their effectiveness in the development of structures are described in detail. The further sections explain their properties and characterization methods. Eventually, their applications in a variety of multifunctional fields are covered. This article will support to introduce the roadmap pointing to a future direction in the production of the single-component flexible aerogel materials and their applications.

Ultrasensitive Electrochemical Detection and Plasmon-Enhanced Photocatalytic Degradation of Rhodamine B Based on Dual-Functional, 3D, Hierarchical Ag/ZnO Nanoflowers

The sensitive detection and degradation of synthetic dyes are pivotal to maintain safety owing to the adverse side effects they impart on living beings. In this work, we developed a sensitive electrochemical sensor for the nanomolar-level detection of rhodamine B (RhB) using a dual-functional, silver-decorated zinc oxide (Ag/ZnO) composite-modified, screen-printed carbon electrode. The plasmon-enhanced photocatalytic degradation of organic pollutant RhB was also performed using this nanocomposite prepared by embedding different weight percentages (1, 3, and 5 wt%) of Ag nanoparticles on the surface of a three-dimensional (3D), hierarchical ZnO nanostructure based on the photoreduction approach. The structure and morphology of an Ag/ZnO nanocomposite were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental mapping, ultraviolet-visible (UV-vis) spectroscopy, and X-ray diffraction (XRD). The electrochemical sensor exhibited a very high sensitivity of 151.44 µAµM^-1cm^-2 and low detection limit of 0.8 nM towards RhB detection. The selectivity, stability, repeatability, reproducibility, and practical feasibility were also analyzed to prove their reliability. Furthermore, the photocatalysis results revealed that 3 wt% of the Ag/ZnO hybrid nanostructure acquired immense photostability, reusability, and 90.5% degradation efficiency under visible light. Additionally, the pseudo-first-order rate constant of Ag-3/ZnO is 2.186 min^-1 suggested promising activity in visible light photocatalysis.

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton

In this study, an artificial neural network (ANN) is used for the prediction of tensile strength of nano titanium dioxide (TiO₂) coated cotton. The coating process was performed by ultraviolet (UV) radiations. Later on, a backpropagation ANN algorithm trained with Bayesian regularization was applied to predict the tensile strength. For a comparative study, ANN results were compared with traditional methods including multiple linear regression (MLR) and polynomial regression analysis (PRA). The input conditions for the experiment were dosage of TiO₂, UV irradiation time and temperature of the system. Simulation results elucidated that ANN model provides high performance accuracy than MLR and PRA. In addition, statistical analysis was also performed to check the significance of this study. The results show a strong correlation between predicted and measured tensile strength of nano TiO2-coated cotton with small error values.

Aerogels for Biomedical, Energy and Sensing Applications

The term aerogel is used for unique solid-state structures composed of three-dimensional (3D) interconnected networks filled with a huge amount of air. These air-filled pores enhance the physicochemical properties and the structural characteristics in macroscale as well as integrate typical characteristics of aerogels, e.g., low density, high porosity and some specific properties of their constituents. These characteristics equip aerogels for highly sensitive and highly selective sensing and energy materials, e.g., biosensors, gas sensors, pressure and strain sensors, supercapacitors, catalysts and ion batteries, etc. In recent years, considerable research efforts are devoted towards the applications of aerogels and promising results have been achieved and reported. In this thematic issue, ground-breaking and recent advances in the field of biomedical, energy and sensing are presented and discussed in detail. In addition, some other perspectives and recent challenges for the synthesis of high performance and low-cost aerogels and their applications are also summarized.