Spectrophotometric Determination of Iron(iii) With   3-((2-Hydroxyphenyl)diazenyl)pentadione-2,4 and Diantipyrylmethane

Abiyeva, A.Y.; Kuliyeva, F.V.; Nagiyev, Kh.J.; Babayev, A.Q.; Chyragov, F. M.

doi:10.32737/0005-2531-2019-2-40-43

Cited by 3 publications

(2 citation statements)

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“…Other commonly used analytical methods include the use of spectroscopic and chromatographic techniques such as atomic absorption spectrometry and ion chromatography. These methods are used for the detection of heavy metals, but have disadvantages where they require sophisticated instrumentation, experienced personnel, and high analytical costs [6]. Electrochemical techniques have achieved significant attention in most and recent research applications in all aspects of industrial and scientific site [7].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based-Models for the Qualitative Classification of Potassium Ferrocyanide using Electrochemical Methods

Kayali

Shama

Aşır

et al. 2022

Preprint

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Iron is one of the trace elements that plays a vital role in the human immune system, especially against variants of SARS-CoV-2 virus. Electrochemical methods are convenient for the detection due to the simplicity of instrumentation available for different analysis. The square wave voltammetry (SQWV) and differential pulse voltammetry (DPV), are useful electrochemical voltametric techniques for diverse types of compounds such as heavy metals. The basic reason is the increased sensitivity by lowering the capacitive current. In this study, machine learning models were improved to classify concentrations of an analyte depending on the voltammograms obtained alone. SQWV and DPV were used to quantify the concentrations of ferrous ions (Fe+2) in potassium ferrocyanide (K4Fe(CN)6), validated by machine learning models for the data classifications. The greatest classifier algorithms models Back-propagation Neural Networks, Gaussian Naive Bayes, Logistic Regression, K-Nearest Neighbors Algorithm, K-Means clustering, and Random Forest were used as data classifiers, based on the data sets obtained from the measured chemical. Once competed to other algorithms models, used previously for the data classification, ours depend on the neural networks get greater specificity and sensitivity, maximum accuracy of 100% was obtained for each analyte in 25 seconds for the datasets.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Based-Models for the Qualitative Classification of Potassium Ferrocyanide using Electrochemical Methods

Kayali

Shama

Aşır

et al. 2022

Preprint

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“…There are a lot of well-known methods for the determination of iron (III) with oxygen-containing organic reagents in natural and industrial objects in the form of binary and mixed ligand complexes [1][2][3][4][5]. Earlier [6][7][8], were offered some methods for the determination of iron (III) in natural objects by oxygen-containing organic reagents in the form of mixed-ligand complexes.…”

Section: Introductionmentioning

confidence: 99%

Determination of Iron in Mushrooms With 3-Acetyl-4,6- Diphenylhexanedione-2,6 and Hydrophobic Amines

Abiyeva¹

2019

Chemical Problems

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Methods of spectrophotometric determination of iron (III) with 3-acetyl-4,6-diphenylhexanedione-2,6 and hydrophobic amines-1,10-phenanthroline and diantipyrilmethane were developed. The complexforming of the iron (III) with reagent were examined to discover that in the presence of hydrophobic amines there are formed mixed-ligand complexes accompanied by bathochromic shift which is observed in the absorption spectra and the optimal pH formation shifts move toward more acidic environment. Effect of concentration of reagents, time and temperature on formation of mixed-ligand complexes has been investigated and their stoichiometry determined with different methods. Highselective technique of spectrophotometric determination of micro-amounts of iron (III) in the mushrooms "Champignon" picked from the settlement of Zira, Baku, was developed.

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Machine learning-based models for the qualitative classification of potassium ferrocyanide using electrochemical methods

Kayali¹,

Shama²,

Aşır³

et al. 2023

J Supercomput

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Spectrophotometric Determination of Iron(iii) With 3-((2-Hydroxyphenyl)diazenyl)pentadione-2,4 and Diantipyrylmethane

Cited by 3 publications

References 0 publications

Machine Learning Based-Models for the Qualitative Classification of Potassium Ferrocyanide using Electrochemical Methods

Machine Learning Based-Models for the Qualitative Classification of Potassium Ferrocyanide using Electrochemical Methods

Determination of Iron in Mushrooms With 3-Acetyl-4,6- Diphenylhexanedione-2,6 and Hydrophobic Amines

Machine learning-based models for the qualitative classification of potassium ferrocyanide using electrochemical methods

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