Bio-photoelectrochemical degradation, and photocatalysis process by the fabrication of copper oxide/zinc cadmium sulfide heterojunction nanocomposites: Mechanism, microbial community and antifungal analysis.

https://doi.org/10.1016/j.arabjc.2022.103942

Prediction of COVID-19 manipulation by selective ACE inhibitory compounds of Potentilla reptant root: In silico study and ADMET profile

Renewable energy-to-green hydrogen: A review of main resources routes, processes and evaluation

Over the last years of research on drug delivery systems (DDSs), natural polymer-based hydrogels have shown many scientific advances due to their intrinsic properties and a wide variety of potential applications. While drug efficacy and cytotoxicity play a key role, adopting a proper DDS is crucial to preserve the drug along the route of administration and possess desired therapeutic effect at the targeted site. Thus, drug delivery technology can be used to overcome the difficulties of maintaining drugs at a physiologically related serum concentration for prolonged periods. Due to their outstanding biocompatibility, polysaccharides have been thoroughly researched as a biological material for DDS advancement. To formulate a modified DDS, polysaccharides can cross-link with different molecules, resulting in hydrogels. According to our recent findings, targeted drug delivery at a certain spot occurs due to external stimulation such as temperature, pH, glucose, or light. As an adjustable biomedical device, the hydrogel has tremendous potential for nanotech applications in involved health areas such as pharmaceutical and biomedical engineering. An overview of hydrogel characteristics and functionalities is provided in this review. We focus on discussing the various kinds of hydrogel-based systems on their potential for effectively delivering drugs that are made of polysaccharides.

/pdf/a-review-on-biomedical-application-of-polysaccharide-based-1ov3efj7.pdf

A Review on Biomedical Application of Polysaccharide-Based Hydrogels with a Focus on Drug Delivery Systems

Heat capacity is among the most well‐known thermal properties of cellulosic biomass samples. This study assembles a general machine learning model to estimate the heat capacity of the cellulosic biomass samples with different origins. Combining the uncertainty and ranking analyses over 819 artificial intelligence models from seven different categories confirmed that the least‐squares support vector regression (LSSVR) with the Gaussian kernel function is the best estimator. This model is validated using 700 laboratory heat capacities of four cellulosic biomass samples in wide temperature ranges (absolute average relative deviation = 0.32%, mean square errors = 1.88 × 10−3, and R2 = 0.999991). The data validity investigation approved that only one out of 700 experimental data is an outlier. The LSSVR model considers the effect of the cellulosic samples' crystallinity, temperature, and sulfur and ash content on their heat capacity. The overall prediction accuracy of the LSSVR is more than 62% better than the achieved accuracy using the empirical correlation.

Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

Simulation the adsorption capacity of polyvinyl alcohol/carboxymethyl cellulose based hydrogels towards methylene blue in aqueous solutions using cascade correlation neural network (CCNN) technique

Predicting the hydrogen uptake ability of a wide range of zeolites utilizing supervised machine learning methods

This study aimed at investigating the variation of heat transfer and velocity changes of the fluid flow along the vertical line on a surface drawn from both sides. In the beginning, several parameters such as Prandtl number and viscoelastic effect were evaluated for heat transfer and fluid velocity by the variation iteration method. The results were compared with the numerical method. The second part of the description relates to the use Response surface method (RSM method) in the Design Expert software. In this paper, by using the RSM, optimized the fluid velocity and heat transfer passing from the stretching sheet. By increasing the Prandtl number, the convection heat transfer by 43% increased the ratio of the minimum Prandtl number. By balanced modes for Prandtl number and viscoelastic parameter and wall temperature, the best optimization occurred for fluid velocity and fluid temperature with f = 0.67 and θ = 0.606. The results of the Variation Iteration method are accurate for the nonlinear solution. As the value of k increases, the value of fluid velocity increased and by increasing the Prandtl number, the value of temperature decreases.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/eng2.12505

Examination of warm transfer on extending sheet by variation iteration method strategy and investigation of arrangements for optimizing liquid properties

This study compares the predictive performance of different classes of adaptive neuro-fuzzy inference systems (ANFIS) in predicting the permeability of carbon dioxide (CO2) in mixed matrix membrane (MMM) containing the SAPO-34 zeolite. The hybrid neuro-fuzzy technique uses the MMM chemistry, pressure, and temperature to estimate CO2 permeability. Indeed, grid partitioning (GP), fuzzy C-means (FCM), and subtractive clustering (SC) strategies are used to divide the input space of ANFIS. Statistical analyses compare the performance of these strategies, and the spider graph technique selects the best one. As a result of the prediction of more than 100 experimental samples, the ANFIS with the subtractive clustering method shows better accuracy than the other classes. The hybrid optimization algorithm and cluster radius = 0.55 are the best hyperparameters of this ANFIS model. This neuro-fuzzy model predicts the experimental database with an absolute average relative deviation (AARD) of less than 3% and a correlation of determination higher than 0.995. Such an intelligent model is not only straightforward but also helps to find the best MMM chemistry and operating conditions to maximize CO2 separation.

/pdf/developing-a-hybrid-neuro-fuzzy-method-to-predict-carbon-mlcgeya4.pdf

Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO2) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite

In this research work, we proposed a modern structured packing (SP) with perforated sheets with a specific surface area of 860[Formula: see text][Formula: see text]/[Formula: see text], which was determined using numerical techniques. This work aims to evaluate the major characteristics of PACK-860 for instance, height equivalent to a theoretical plate (HETP), dry pressure drop (DPD) and wet pressure drop (WPD). Additionally, the flow construction was defined for specific packing via computational simulation. To evaluate the value of HETP, DPD and WPD, the three-dimensional (3D) computational fluid dynamics (CFD) simulation with the Eulerian–Eulerian (EE) multi-phase method applied in this paper. According to the findings of this research, the performance of the mass transfer (MT), WPD and DPD was enhanced with the perforated-on sheets of packing. Based on the observation, numerical results are consistent well with the theoretical data which reveals the consistency of CFD tools for modeling methods to separation applications. 

Ali Hosin Alibak

Papers

Simulation the adsorption capacity of polyvinyl alcohol/carboxymethyl cellulose based hydrogels towards methylene blue in aqueous solutions using cascade correlation neural network (CCNN) technique

Predicting the hydrogen uptake ability of a wide range of zeolites utilizing supervised machine learning methods

Examination of warm transfer on extending sheet by variation iteration method strategy and investigation of arrangements for optimizing liquid properties

Developing a Hybrid Neuro-Fuzzy Method to Predict Carbon Dioxide (CO2) Permeability in Mixed Matrix Membranes Containing SAPO-34 Zeolite

Numerical simulation of binary component separation with perforated on sheets of the structured packing