1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning model
The current study is focused on the synthesis and evaluation of 1-Hexadecyl-3-methylimidazolium tetrachloroindate [C16 mim][In Cl4] based ionic liquid (IL) as a corrosion inhibitor for mild steel in 1M HCl. Various advanced methods were employed in this research, such as potentiodynamic polarization...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024013707 |
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| author | Ndidiamaka Martina Amadi Joseph Okechukwu Ezeugo Chukwunonso Chukwuzuluoke Okoye John Ifeanyi Obibuenyi Maduabuchi Arinzechukwu Chidiebere Dominic Okechukwu Onukwuli Valentine Chikaodili Anadebe |
| author_facet | Ndidiamaka Martina Amadi Joseph Okechukwu Ezeugo Chukwunonso Chukwuzuluoke Okoye John Ifeanyi Obibuenyi Maduabuchi Arinzechukwu Chidiebere Dominic Okechukwu Onukwuli Valentine Chikaodili Anadebe |
| author_sort | Ndidiamaka Martina Amadi |
| collection | DOAJ |
| description | The current study is focused on the synthesis and evaluation of 1-Hexadecyl-3-methylimidazolium tetrachloroindate [C16 mim][In Cl4] based ionic liquid (IL) as a corrosion inhibitor for mild steel in 1M HCl. Various advanced methods were employed in this research, such as potentiodynamic polarization (PDP), quantum chemical computations, molecular dynamics simulations, weight loss assessments, electrochemical impedance spectroscopy (EIS) and multivariate statistics via machine learning models. The ionic liquid (IL) under investigation demonstrated a notable corrosion inhibition efficiency (93.88 % weight loss, 94. % PDP, 75 % EIS). The combine electrochemical approach suggested a mechanism influenced by electron transfer, underscoring the IL's as a mixed-type inhibitor. The experimental data based on weight loss was optimized using response surface methodology (RSM). Maximum inhibition efficiency of 93.72 % was predicted by the RSM model. Also, the machine learning models based on artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) demonstrated good predictive power in analyzing the interactive effects affecting the inhibition process. The adsorption behavior of [C16 mim][In Cl4] on the mild steel surface further conformed to the Langmuir isotherm, demonstrating a monolayer adsorption process. The comprehensive nature of this approach facilitated a more in-depth adsorption process through computational modelling based on DFT and molecular dynamics. The machine learning models aligned credibly with the experimental findings with pronounced degree of accuracy. Thus, these integrated approaches unravel the potential of the studied IL as effective and sustainable corrosion inhibitor for severe acidic environments. |
| format | Article |
| id | doaj-art-5e3e723831cf478cb16bbf300894a2f7 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-5e3e723831cf478cb16bbf300894a2f72025-08-20T02:34:35ZengElsevierResults in Engineering2590-12302024-12-012410311510.1016/j.rineng.2024.1031151-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning modelNdidiamaka Martina Amadi0Joseph Okechukwu Ezeugo1Chukwunonso Chukwuzuluoke Okoye2John Ifeanyi Obibuenyi3Maduabuchi Arinzechukwu Chidiebere4Dominic Okechukwu Onukwuli5Valentine Chikaodili Anadebe6Department of Chemical Engineering, Federal University of Technology, PMB 1526, Owerri, Imo State, Nigeria; Department of Chemical Engineering, Chukwuemeka Odumegwu Ojukwu Univesity, PMB 02, Uli, Anambra State, Nigeria; Corresponding author. Department of Chemical Engineering, Federal University of Technology, PMB 1526, Owerri, Imo State, NigeriaDepartment of Chemical Engineering, Chukwuemeka Odumegwu Ojukwu Univesity, PMB 02, Uli, Anambra State, NigeriaDepartment of Chemical Engineering, Nnamdi Azikwe University, PMB 5025, Awka, Anambra State, NigeriaDepartment of Chemical Engineering, Madonna University, Akpugo Campus, 402105, Akpugo, Enugu State, NigeriaDepartment of Science Laboratory Technology, Federal University of Technology, PMB 1526, Owerri, Imo State, NigeriaDepartment of Chemical Engineering, Nnamdi Azikwe University, PMB 5025, Awka, Anambra State, Nigeria; Corresponding author.Department of Chemical Engineering, Alex Ekwueme Federal University Ndufu Alike, PMB 1010, Abakaliki, Ebonyi State, Nigeria; Corresponding author.The current study is focused on the synthesis and evaluation of 1-Hexadecyl-3-methylimidazolium tetrachloroindate [C16 mim][In Cl4] based ionic liquid (IL) as a corrosion inhibitor for mild steel in 1M HCl. Various advanced methods were employed in this research, such as potentiodynamic polarization (PDP), quantum chemical computations, molecular dynamics simulations, weight loss assessments, electrochemical impedance spectroscopy (EIS) and multivariate statistics via machine learning models. The ionic liquid (IL) under investigation demonstrated a notable corrosion inhibition efficiency (93.88 % weight loss, 94. % PDP, 75 % EIS). The combine electrochemical approach suggested a mechanism influenced by electron transfer, underscoring the IL's as a mixed-type inhibitor. The experimental data based on weight loss was optimized using response surface methodology (RSM). Maximum inhibition efficiency of 93.72 % was predicted by the RSM model. Also, the machine learning models based on artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) demonstrated good predictive power in analyzing the interactive effects affecting the inhibition process. The adsorption behavior of [C16 mim][In Cl4] on the mild steel surface further conformed to the Langmuir isotherm, demonstrating a monolayer adsorption process. The comprehensive nature of this approach facilitated a more in-depth adsorption process through computational modelling based on DFT and molecular dynamics. The machine learning models aligned credibly with the experimental findings with pronounced degree of accuracy. Thus, these integrated approaches unravel the potential of the studied IL as effective and sustainable corrosion inhibitor for severe acidic environments.http://www.sciencedirect.com/science/article/pii/S2590123024013707CorrosionIonic liquidMild steelMachine learningComputational |
| spellingShingle | Ndidiamaka Martina Amadi Joseph Okechukwu Ezeugo Chukwunonso Chukwuzuluoke Okoye John Ifeanyi Obibuenyi Maduabuchi Arinzechukwu Chidiebere Dominic Okechukwu Onukwuli Valentine Chikaodili Anadebe 1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning model Results in Engineering Corrosion Ionic liquid Mild steel Machine learning Computational |
| title | 1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning model |
| title_full | 1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning model |
| title_fullStr | 1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning model |
| title_full_unstemmed | 1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning model |
| title_short | 1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning model |
| title_sort | 1 hexadecyl 3 methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel insight from experimental computational multivariate statistics and multi quadratic regression based machine learning model |
| topic | Corrosion Ionic liquid Mild steel Machine learning Computational |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024013707 |
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