Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques.

Vacuum membrane distillation (VMD) has attracted increasing interest for various applications besides seawater desalination. Experimental testing of membrane technologies such as VMD on a pilot or large scale can be laborious and costly. Machine learning techniques can be a valuable tool for predicting membrane performance on such scales.

Data-driven models for atmospheric air temperature forecasting at a continental climate region.

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning.

Compaction Curves and Strength of Clayey Soil Modified with Micro and Nano Silica.

Some Clayey soils are generally categorized as weak soils, and structures lying on such soils have been exposed to severe damage. Therefore, the central thesis of this paper is the impact of a waste material known as a silica fume as nano and micro material on soil's behaviour. To evaluate the effects of those additives on Atterberg limits, compaction characteristics and unconfined compressive strength, clayey soil samples have been transformed using micro and nano silica fume (by-product materials).

A review of medicinal plant of Middle East and North Africa (MENA) region as source in tuberculosis drug discovery.

Tuberculosis (TB) is a disease that affects one-third of the world's population. Although currently available TB drugs have many side effects, such as nausea, headache and gastrointestinal discomfort, no new anti-TB drugs have been produced in the past 30 years. Therefore, the discovery of a new anti-TB agent with minimal or no side effects is urgently needed.

Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent.

Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed.

Optimization of the Synthesis of Superhydrophobic Carbon Nanomaterials by Chemical Vapor Deposition.

Demand is increasing for superhydrophobic materials in many applications, such as membrane distillation, separation and special coating technologies. In this study, we report a chemical vapor deposition (CVD) process to fabricate superhydrophobic carbon nanomaterials (CNM) on nickel (Ni)-doped powder activated carbon (PAC). The reaction temperature, reaction time and H/CH gas ratio were optimized to achieve the optimum contact angle (CA) and carbon yield (CY).

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function.

Computational investigation of the microstructural characteristics and physical properties of glycerol-based deep eutectic solvents.

Recently, there has been significant interest in the possibility of using deep eutectic solvents (DESs) as novel green media and alternatives to conventional solvents and ionic liquids (ILs) in many applications. Due to their attractive properties, such as their biodegradability, low cost, easy preparation, and nontoxicity, DESs appear to be very promising solvents for use in the field of green chemistry. This computational study investigated six glycerol-based DESs: DES (glycerol:methyl triphenyl phosphonium bromide), DES (glycerol:benzyl triphenyl phosphonium chloride), DES (glycerol:allyl triphenyl phosphonium bromide), DES (glycerol:choline chloride), DES (glycerol:N,N-diethylethanolammonium chloride), and DES (glycerol:tetra-n-butylammonium bromide).

Novel deep eutectic solvent-functionalized carbon nanotubes adsorbent for mercury removal from water.

Due to the interestingly tolerated physicochemical properties of deep eutectic solvents (DESs), they are currently in the process of becoming widely used in many fields of science. Herein, we present a novel Hg adsorbent that is based on carbon nanotubes (CNTs) functionalized by DESs. A DES formed from tetra-n-butyl ammonium bromide (TBAB) and glycerol (Gly) was used as a functionalization agent for CNTs.

Allyl triphenyl phosphonium bromide based DES-functionalized carbon nanotubes for the removal of mercury from water.

Recently, deep eutectic solvents (DESs) have shown their new and interesting ability for chemistry through their involvement in variety of applications. This study introduces carbon nanotubes (CNTs) functionalized with DES as a novel adsorbent for Hg from water. Allyl triphenyl phosphonium bromide (ATPB) was combined with glycerol as the hydrogen bond donor (HBD) to form DES, which can act as a novel CNTs functionalization agent.