Journal of Applied Chemical Research
Volume:14 Issue: 2, Spring 2020

Heavy  metal  pollution  is  propagating  throughout  the  world  with  the  enlargement  of  industrial activities.  The  elimination  of  heavy  metal  ions  from  industrial  wastewaters  has  drawn  much attention  because  of  the  hazardous  effects  of  the  heavy  metal  ions  on  different  organisms. According to these facts, poly (2, 2, 3, 3- tetracyanocyclopropyl) phenyl acrylate (PTCP) with multi cyanocyclopropane functionalities in the pendant group were prepared by reacting benzoyl peroxide with p-(2,2,3,  3-tetracyanocyclopropyl)  phenylacrylate  (TCP) monomer.  (TCP)  monomer was synthesized by reacting cyanogen bromide and malononitrile with p-acryloyloxybenzaldehyde at 0 °C  in  a  short  time. The  synthesized  PTCP momopolymer were  examined  in heavy  metal  ions adsorption  such  as  Ni  (II),  Cu  (II),  Cr  (III)  and  Zn  (II)  under  competitive  and  non-competitive conditions in aqueous solutions at different pH. The high adsorption rate (<65 min) was seen. The synthesized polymer and its metal chelates were investigated by thermogravimetric analysis (TGA),Fourier-transform  infrared  spectroscopy  (FT-IR),  atomic  absorption  techniques  (AAS),  UV-vis spectroscopy and scanning electron microscopy (SEM).

In this work, the artificial neural networks (ANN) technology was applied to the simulation of oleuropein extraction process. For this technology, a 3-layer network structure is applied, and the operation factors such as  amount  of  flow  intensity  ratio,  temperature,  residence  time,  and  pH  are  used  as  input  variables  of  the network,  whereas  the  extraction  yield  is  considered  as  response  value.  Performance  indicators
RMSE, SSE, R2adj, R2

have  been  used  to  determine  the  number  of  optimal  midway neurons. Neural  network trained  with  an  error  back-propagation  algorithm,  was  used  to  evaluate  the  effects  of  parameters  on extraction yield.The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with 4 input neurons, 1 hidden layer with 6 neurons and one output layer including single neuron.The trained network gave the minimum value in the RMSE of 1.6423 and the maximum value in the
=  0.9641,  which  implied  a  good  agreement  between  the  predicted  value  and  the  actual  value,  and confirmed  a  good  generalization  of  the  network.Functional  structure  of  modeling,  related  to  education,validation and test were obtained 0.99229,0.95591and 0.99224 respectively. The overall agreement between the  experimental  data  and  ANN  predictions  was  satisfactory  showing  a  determination  coefficient  of 0.9838.The  neural  network  tools  implemented  in  MATLAB  software  were  used  to  predict  the  change process.

Known as a Lewis acid which acts as a natural catalyst, bentonite can be used to produce several arylidene  (thio) barbituric  acid  derivatives  through  conducting  a  Knoevenagel  reaction  between aromatic aldehydes and (thio) barbituric acid. Water is considered as the medium for this reaction and the results are at arange of good to excellent over a reasonable reaction time. This method is natural and economic as well as convenient to work with, while the reaction time is also short. In addition  to excellent  results,  this  method  is  also environment-friendly  due  to  the  use  of  water  as solvent that broadens the domain of organic synthesis in aqueous medium.

In this work, aluminum nitride (AlN) thin films with different thicknesses were deposited on quartz and  silicon  substrates  using  single  ion  beam  sputtering  technique.  The  physical  and  chemical properties  of  prepared  films  were  investigated  by  different  characterization  technique.  X-ray diffraction (XRD) spectra revealed that all of the deposited films have an amorphous structure. The Al-N bond information of deposited films on silicon substrates was identified by Fourier transform infrared (FTIR)  spectroscopy.  FTIR  results  confirmed  the  formation  of  AlN  films  in  prepared samples. Atomic force microscopy (AFM) revealed that the surface of films was smooth with low values of roughness. The low values of roughness can be caused the low acoustic loss in AlN films, which is interesting for applications in electro-acoustic devices.

In this study, a novel acidic magnetic dicationic ionic liquid was prepared in three steps to serve as a green  catalyst  in  organic  synthesis.  The  newly  synthesized  catalyst  was  characterized UV- VIS,  and  VSM  analysis.  Additionally,  the  decomposition  steps  acatalyst  were investigated by thermal  analysis  techniques  (TGA/DSC).  The  synthesized  acidic magnetic dicationic ion liquid has a magnetization of about 0.3 emu/g, which is less than FeCl emu.g- 1). Moreover, the catalytic activity of this acidic ionic liquid was successfully tested in the straight  forward  one-pot  synthesis reaction between β-naphthol, aldehyde, and acetamide or Benzamide. The pure  products  were  determined  by  analyzing  their  physical  data  (melting  points,  IR method  has  several  advantages  such  as  easy  work yield, and high atom economy. The catalyst can be reused and recovered without losing activity.

Nickel (Ni) as a heavy metal due to its toxicity should be removed from wastewater and aquatic environments  using  efficient  technology.  The  aim  of this  study  was  to  remove  Ni  from  an aqueous  solution  using  palm  leaf  ash  produced  in  a  furnace.  To  do  so,  kinetic  and thermodynamic experiments were conducted on the adsorption process. Moreover, the effect of time, pH, adsorbent and initial concentration of Ni was studied on Ni adsorption. The results of the experiment indicated that the Ni adsorption process followed the Freundlich isotherm model. The study  of kinetic data  also displayed the  removal of  Ni ions  from  the  pseudo-second-order kinetics.  The  results  showed  that  the  percentage  removal  of  Ni  (II)  and  maximum  adsorption capacity  of  an  adsorbent  for  Ni  (II)  ions  were 94.67%  and 40.81  mgg-1,  respectively. Furthermore, the enthalpy of the adsorption process (ΔH) was 62706.8 j.mol-1.