Selective determination of closantel by artificial neural network-genetic algorithm optimized molecularly imprinted polypyrrole using UV–visible spectrophotometry

Nezhadali, Azizollah; Shadmehri, Raham; Rajabzadeh, Fatemeh; Sadeghzadeh, Samira

doi:10.1016/j.saa.2020.118779

Cited by 11 publications

(3 citation statements)

References 36 publications

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“…The result is similar to other researcher's findings in which they suggested that, the changed π -bond chromophore of indene/pyrrole aromatic ring system in ClnPy nanomaterial is due to overlapping of phenyl π -conjugated unit and skeleton of pyrrole monomer and is possibly responsible for the blue shifted and broadened absorption peak. These additional absorptions suggest π -π* electron transitions in the band gap of the conjugated pyrrole ring system, existing in CInPy polymeric chain [18][19].…”

Section: Uv-vis Characterisation Of Ppy/dnamentioning

confidence: 89%

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2021

Nano Plus: Sci. Tech. Nanomat.

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Highlights PPy/DNA nanowires were synthesised and aligned on an oxidised silicon substrate and micro electrodes  The chemical properties of the PPy/DNA composite were investigated using different spectroscopic and microscopic techniques  Results revealed the formation of a supramolecular hybrid polymer containing DNA and polypyrrole.  The conductance of the PPy/DNA film at 20 o C was of the order of 10-100 µS.  The polymer nanomaterials were thermal stable.  PPy/DNA can be deployed in gas sensor.

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Section: Uv-vis Characterisation Of Ppy/dnamentioning

confidence: 89%

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2021

Nano Plus: Sci. Tech. Nanomat.

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“…Thus, different combinations of experimental designs and response surface modeling have been used for optimization of parameters when using MIPs in adsorption, separation or sensing applications [214,267,375,467,468,470,[475][476][477]. In some studies, the optimized parameters have been improved further by using them as input for ANN models [475,621].…”

Section: Multivariate Analysismentioning

confidence: 99%

The Use of Computational Methods for the Development of Molecularly Imprinted Polymers

et al. 2021

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Recent years have witnessed a dramatic increase in the use of theoretical and computational approaches in the study and development of molecular imprinting systems. These tools are being used to either improve understanding of the mechanisms underlying the function of molecular imprinting systems or for the design of new systems. Here, we present an overview of the literature describing the application of theoretical and computational techniques to the different stages of the molecular imprinting process (pre-polymerization mixture, polymerization process and ligand–molecularly imprinted polymer rebinding), along with an analysis of trends within and the current status of this aspect of the molecular imprinting field.

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“…Khajeh et al shows ANN-PSO achieves high extraction molybdenum extraction from water using silver nanoparticles by determining optimal volumes of the extractant and complex, pH, and adsorption time [18]. There have been other successes for optimization in pharmaceuticals, extraction, and HVAC [19][20][21][22][23][24][25][26]. While this shows ANN-PSO is widely used to optimize process inputs, newer algorithms may offer further improvements.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process

2020

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One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO to newer algorithms, like Proximal Policy Optimization (PPO). To investigate ANN-PSO’s and PPO’s performance and applicability, we compare their methodologies, apply them on steady-state economic optimization of a chemical process, and compare their results to a conventional first principles modeling with nonlinear programming (FP-NLP). Our results show that ANN-PSO and PPO achieve profits nearly as high as FP-NLP, but PPO achieves slightly higher profits compared to ANN-PSO. We also find PPO has the fastest computational times, 10 and 10,000 times faster than FP-NLP and ANN-PSO, respectively. However, PPO requires more training data than ANN-PSO to converge to an optimal policy. This case study suggests PPO has better performance as it achieves higher profits and faster online computational times. ANN-PSO shows better applicability with its capability to train on historical operational data and higher training efficiency.

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Selective determination of closantel by artificial neural network-genetic algorithm optimized molecularly imprinted polypyrrole using UV–visible spectrophotometry

Cited by 11 publications

References 36 publications

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The Use of Computational Methods for the Development of Molecularly Imprinted Polymers

Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process

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