The Experts below are selected from a list of 99 Experts worldwide ranked by ideXlab platform

Raham Shadmehri - One of the best experts on this subject based on the ideXlab platform.

  • Selective determination of closantel by artificial neural network-genetic algorithm optimized molecularly imprinted polypyrrole using UV-visible spectrophotometry.
    Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2020
    Co-Authors: Azizollah Nezhadali, Raham Shadmehri, Fatemeh Rajabzadeh, Samira Sadeghzadeh
    Abstract:

    Abstract A molecularly imprinted polymer (MIP) for the selective solid-phase extraction (SPE) was prepared applying polymerization of pyrrole monomer in the presence of closantel (CLS) as a template molecule. The quantitative measurements were carried out using UV–Vis spectrophotometry. Several important parameters control the performance of polypyrrole sorbent. The influence of seven factors including loading time, polymerization time, amount of sorbent, stirring rate, desorption time, initiator concentration and monomer to template ratio were investigated. The optimization of parameters was performed using Plackett–Burman design (PBD), central composite design (CCD), artificial neural network (ANN) and genetic algorithm (GA). The Pareto Plot showed that the effects of loading time, reaction time and amount of sorbent are most important to the process. These significant factors were investigated using CCD and the obtained data were used to train the ANN. The predicted model obtained from the trained ANN was introduced to GA as the fitness function to be optimized. The calibration curve demonstrated linearity over a concentration range of 0.010–10 mM with a correlation coefficient (R2) of 0.9833 under optimal condition. The synthesized MIP sorbent showed a good selectivity and sensitivity toward CLS. The limit of detection (LOD) for CLS was obtained 1.0 μM. The real sample analysis was performed to determine CLS in pharmaceutical and human serum samples.

  • Neuro-genetic multi-objective optimization and computer-aided design of pantoprazole molecularly imprinted polypyrrole sensor
    Sensors and Actuators B: Chemical, 2014
    Co-Authors: Azizollah Nezhadali, Raham Shadmehri
    Abstract:

    A molecularly imprinted polymer (MIP) of pantoprazole (PNZ) was prepared through electropolymerization of pyrrole on a functionalized multi-walled carbon nanotube modified pencil graphite electrode. The preparation of MIP and quantitative measurements were performed by cyclic voltammetry and differential pulse voltammetry (DPV), respectively. Several important parameters controlling the performance of polypyrrole film. The factors, i.e. pH of buffer solution, cyclic voltammetric scan rate in polymerization step, number of cyclic voltammetric scans, monomer and template concentrations in prepolymerization mixture, nanotube concentration in functionalized multi-walled carbon nanotubes-coating step, uptake time after MIP preparation and uptake step stirring rate were expected to affect MIP preparation and voltammetric measurements. The optimization of parameters was performed using Plackett–Burman design, central composite design, artificial neural network and genetic algorithm. The Pareto Plot showed that effects of monomer concentration and pH are most important to the process. The best MIP to NIP response ratio was obtained 17.4. The selection of monomer was performed computationally using ab initio calculations. The calibration curve demonstrated linearity over a concentration range of 5–700 μM with a correlation coefficient (r) of 0.9980. The detection limit of PNZ was obtained 3.75 × 10−7 M. The minimum and maximum recovery (%) through the spiking 0.1–0.4 mM PNZ to a biological and some pharmaceutical matrices were obtained 95.9% (human blood serum) and 106% (PNZ tablet), respectively.

Azizollah Nezhadali - One of the best experts on this subject based on the ideXlab platform.

  • Selective determination of closantel by artificial neural network-genetic algorithm optimized molecularly imprinted polypyrrole using UV-visible spectrophotometry.
    Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2020
    Co-Authors: Azizollah Nezhadali, Raham Shadmehri, Fatemeh Rajabzadeh, Samira Sadeghzadeh
    Abstract:

    Abstract A molecularly imprinted polymer (MIP) for the selective solid-phase extraction (SPE) was prepared applying polymerization of pyrrole monomer in the presence of closantel (CLS) as a template molecule. The quantitative measurements were carried out using UV–Vis spectrophotometry. Several important parameters control the performance of polypyrrole sorbent. The influence of seven factors including loading time, polymerization time, amount of sorbent, stirring rate, desorption time, initiator concentration and monomer to template ratio were investigated. The optimization of parameters was performed using Plackett–Burman design (PBD), central composite design (CCD), artificial neural network (ANN) and genetic algorithm (GA). The Pareto Plot showed that the effects of loading time, reaction time and amount of sorbent are most important to the process. These significant factors were investigated using CCD and the obtained data were used to train the ANN. The predicted model obtained from the trained ANN was introduced to GA as the fitness function to be optimized. The calibration curve demonstrated linearity over a concentration range of 0.010–10 mM with a correlation coefficient (R2) of 0.9833 under optimal condition. The synthesized MIP sorbent showed a good selectivity and sensitivity toward CLS. The limit of detection (LOD) for CLS was obtained 1.0 μM. The real sample analysis was performed to determine CLS in pharmaceutical and human serum samples.

  • Neuro-genetic multi-objective optimization and computer-aided design of pantoprazole molecularly imprinted polypyrrole sensor
    Sensors and Actuators B: Chemical, 2014
    Co-Authors: Azizollah Nezhadali, Raham Shadmehri
    Abstract:

    A molecularly imprinted polymer (MIP) of pantoprazole (PNZ) was prepared through electropolymerization of pyrrole on a functionalized multi-walled carbon nanotube modified pencil graphite electrode. The preparation of MIP and quantitative measurements were performed by cyclic voltammetry and differential pulse voltammetry (DPV), respectively. Several important parameters controlling the performance of polypyrrole film. The factors, i.e. pH of buffer solution, cyclic voltammetric scan rate in polymerization step, number of cyclic voltammetric scans, monomer and template concentrations in prepolymerization mixture, nanotube concentration in functionalized multi-walled carbon nanotubes-coating step, uptake time after MIP preparation and uptake step stirring rate were expected to affect MIP preparation and voltammetric measurements. The optimization of parameters was performed using Plackett–Burman design, central composite design, artificial neural network and genetic algorithm. The Pareto Plot showed that effects of monomer concentration and pH are most important to the process. The best MIP to NIP response ratio was obtained 17.4. The selection of monomer was performed computationally using ab initio calculations. The calibration curve demonstrated linearity over a concentration range of 5–700 μM with a correlation coefficient (r) of 0.9980. The detection limit of PNZ was obtained 3.75 × 10−7 M. The minimum and maximum recovery (%) through the spiking 0.1–0.4 mM PNZ to a biological and some pharmaceutical matrices were obtained 95.9% (human blood serum) and 106% (PNZ tablet), respectively.

Yi-ming Chen - One of the best experts on this subject based on the ideXlab platform.

  • Multi-objective optimization for two catalytic membrane reactors—Methanol synthesis and hydrogen production
    Chemical Engineering Science, 2008
    Co-Authors: Shueh-hen Cheng, Hsi-jen Chen, Hsuan Chang, Cheng-kai Chang, Yi-ming Chen
    Abstract:

    This paper provides the triple-objective-function optimization results for the catalytic membrane reactors, including one for methanol synthesis and one for hydrogen generation. A 1-D, non-isothermal model, which takes into account the intra-particle diffusion for the catalyst, and the elitist nondominated sorting genetic algorithm (NSGA-II) for the multi-objective optimization are adopted. Optimal solutions for methanol synthesis and hydrogen generation systems show distinctive feature. One is randomly scattered and the other is linearly spread out in the Pareto Plot. Solution characteristics in terms of variable distribution are quite different for the two systems. Device size, including membrane area and membrane size, shows effects both on the optimal solutions and on the correlation relations between objective functions and variables.

Samira Sadeghzadeh - One of the best experts on this subject based on the ideXlab platform.

  • Selective determination of closantel by artificial neural network-genetic algorithm optimized molecularly imprinted polypyrrole using UV-visible spectrophotometry.
    Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2020
    Co-Authors: Azizollah Nezhadali, Raham Shadmehri, Fatemeh Rajabzadeh, Samira Sadeghzadeh
    Abstract:

    Abstract A molecularly imprinted polymer (MIP) for the selective solid-phase extraction (SPE) was prepared applying polymerization of pyrrole monomer in the presence of closantel (CLS) as a template molecule. The quantitative measurements were carried out using UV–Vis spectrophotometry. Several important parameters control the performance of polypyrrole sorbent. The influence of seven factors including loading time, polymerization time, amount of sorbent, stirring rate, desorption time, initiator concentration and monomer to template ratio were investigated. The optimization of parameters was performed using Plackett–Burman design (PBD), central composite design (CCD), artificial neural network (ANN) and genetic algorithm (GA). The Pareto Plot showed that the effects of loading time, reaction time and amount of sorbent are most important to the process. These significant factors were investigated using CCD and the obtained data were used to train the ANN. The predicted model obtained from the trained ANN was introduced to GA as the fitness function to be optimized. The calibration curve demonstrated linearity over a concentration range of 0.010–10 mM with a correlation coefficient (R2) of 0.9833 under optimal condition. The synthesized MIP sorbent showed a good selectivity and sensitivity toward CLS. The limit of detection (LOD) for CLS was obtained 1.0 μM. The real sample analysis was performed to determine CLS in pharmaceutical and human serum samples.

Volkan Patoglu - One of the best experts on this subject based on the ideXlab platform.

  • RAM - Multi-criteria Design Optimization of Parallel Robots
    2008 IEEE Conference on Robotics Automation and Mechatronics, 2008
    Co-Authors: Ramazan Unal, Güllü Kızıltaş, Volkan Patoglu
    Abstract:

    This paper presents a framework for multi-criteria design optimization of parallel mechanisms. Pareto methods characterizing the trade-off between multiple design criteria are advocated for multi-criteria optimization over widely used scalarization approaches and Normal Boundary Intersection method is applied to efficiently obtain the Pareto-front hyper-surface. The proposed framework is compared against sequential optimization and weighted sum approaches. Dimensional synthesis of a sample parallel mechanism (five-bar mechanism) is demonstrated through estimation of the relative weights of performance indices that are implicit in the Pareto Plot. The framework is computational efficient, applicable to any set of performance indices, and extendable to include any number of design criteria that is required by the application.