Adsorption Isotherm and Activation Energy of Inhibition of Alkaloids on Mild Steel Surface in Acidic Medium

Oli, Hari Bhakta; Parajuli, Davi Lal; Sharma, Srijana; Chapagain, Amrita; Yadav, Amar Prasad

doi:10.3126/arj.v2i01.40738

Cited by 9 publications

(3 citation statements)

References 8 publications

(14 reference statements)

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“…The input gate updates the state of the current LSTM cell ct by inputting the hidden state of the t-1th unit ht-1 and the input of the t-th unit Rt into the sigmoid and tanh functions. the cell state of the t-th unit ct is updated by combining the output of forgetting gate ft and the cell state of t-1th ct-1, as illustrated in equation( 9), (10), and (11).…”

Section: ( [ ]mentioning

confidence: 99%

“…For instance, the equation of state for Type I isotherm can be expressed in linear form, whereas the equation of state for Type II isotherm is typically of non-linear form. In the context of soil and water purification adsorption, it is imperative to investigate the impact of varying isotherms categories on kinetic parameters, such as adsorption activation energy, reaction order, preexponential factor, and thermodynamic parameters like adsorption and desorption heat [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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An adsorption isotherm identification method based on CNN-LSTM neural network

Liu,

Xie,

Yan

et al. 2023

J Mol Model

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Context：Adsorption isotherm is integral to comprehending the adsorption mechanism and catalytic processes.Despite the well-established research on isotherm classification methods, existing techniques for isotherm identification are hampered by inefficiency, human interference, insufficient feature information extracting and the fact that specific types isotherm of identification can be achieved. To overcome these limitations, an end-to-end isotherm identification method based on a CNN-LSTM neural network is proposed, which employs a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory network (LSTM) to extract the features of adsorption isotherm. Additionally, dataset is constructed for training and validating the neural network using various theoretical adsorption isotherm equations, thereby eliminating the need for time-consuming and expensive repetitive experiments. The results indicate that the model achieves identification accuracy of 100% on both the training and validation sets constructed using theoretical adsorption equations. The model's F1-score on the test set, which consists of five categories actual adsorption isotherms, is 88.85%, and there has been a 20% enhancement in the mean precision of isotherm identification compared to the traditional method. All these results demonstrate that the proposed method can accurately identify adsorption isotherm.Method: Pycharm was used as the experimental and testing platform, python 3.9 was used as the programming language, Tensorflow 2.11.0 and Keras 2.10.0 were used to train and test the CNN-LSTM model, numpy 1.21.5 and scipy 1.81 were used to generate the train and validation dataset.

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Section: ( [ ]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adsorption isotherm identification method based on CNN-LSTM neural network

Liu,

Xie,

Yan

et al. 2023

J Mol Model

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“…However, it may not be the significant alkaloid responsible for the inhibition. The manifestation of adsorption, i.e., monolayer or multilayer, is determined by Langmuir's adsorption isotherm equation [34][35][36]. The linear relation between the fraction of covered surface (θ) and molar concentration (C) should be known to find the adsorption isotherm.…”

Section: Adsorption Isothermmentioning

confidence: 99%

Coriaria nepalensis Stem Alkaloid as a Green Inhibitor for Mild Steel Corrosion in 1 M H2SO4 Solution

et al. 2022

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Using natural plant extracts on metallic substances is the most frequently studied green corrosion inhibition approach in corrosion science. In this work, Coriaria nepalensis Stem Alkaloid (CNSA) has been successfully extracted and characterized by qualitative chemical (Mayer’s and Dragendroff’s) test and spectroscopic (UV and FTIR) measurement. CNSA has been employed as a green inhibitor for Mild Steel (MS) corrosion subjected to 1 M H2SO4 solution. The corrosion inhibition efficacy has been assessed by weight loss and polarization measurement methods. The effect of inhibitor concentration, immersion period, and temperature on the inhibition efficiency for the MS immersed in both acid and inhibitor solutions of different concentrations have been investigated. The maximum inhibition effect observed for CNSA is 96.4% for MS immersed in 1000 ppm inhibitor solution for 6 h at 18 °C by the weight loss measurement method. Similarly, the polarization measurement method observed a 97.03% inhibition efficiency for MS immersed for 3 h. The adsorption of inhibitor molecules on the MS surface aligns with the Langmuir model. The free energy of adsorption obtained is −28.75 kJ/mol indicating physical adsorption dominance over chemical adsorption. These findings suggested that CNSA has greater potential as an efficient green inhibitor.

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