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Mohammad Reza Hadjmohammadi

Mohammad Reza Hadjmohammadi

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId:
HIndex:
Faculty: Faculty of Chemistry
Address: Babolsar
Phone: 01135302350

Research

Title
Air-Assisted Surfactant-Enhanced-Emulsification Liquid-Liquid Microextraction with Solidification of Floating Organic Droplet Coupled with HPLC for the Determination of Clozapine in Biological Samples
Type
Presentation
Keywords
Clozapine, ASLLME-SFO, HPLC-UV
Year
2018
Researchers Seyedeh Maedeh Majidi ، Mohammad Reza Hadjmohammadi

Abstract

This Study presents a simple, rapid, sensitive, low cost, and environmentally friendly microextraction method coupled with High Performance Liquid Chromatography-Ultraviolet detection, to preconcentration, determination and analysis of Clozapine in biological samples. Clozapine, dibenzodiazepine derivative, is an effective drug in treatment of schizophrenia. However, harmful side effects of this drug in high concentrations makes it necessary to determine its concentration in biological samples including plasma and urine. To this aim, in this study a green microextraction method based on DLLME is developed, called air-assisted surfactant-enhanced-emulsification liquid-liquid with solidification of floating organic droplet (ASLLME-SFO). In this method, the extraction is carried out by forming the ion-pairs between analyte and surfactant in the sample solution and transferring them to the extraction solvent. Also, in order to decrease the toxicity and harmful effects of disperser and high-density solvents on operators and environment, surfactant as emulsifier is replaced with disperser solvent, and low-density solvent is replaced with chlorine solvent. Moreover, using a glass syringe to disperse extraction solvent as fine droplets into aqueous solution, makes it possible to reduce the probability of sample degradation, cost, and complexity of this method in comparison to ultrasonic or vertex methods. In addition to the above mentioned advantages, the most significant feature of this approach is higher extraction efficiency in a shorter time compared to DLLME method. In this study, the effective parameters on extraction efficiency such as type and volume of extraction solvents, type and concentration of surfactants, type and amount of salt, pH, numbers of aspirating/dispersing cycles and centrifuge time are investigated and optimized. According to the test results, the proposed method is shown good analytical characteristics, including low LOD, LOQ, repeatability and re