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Estimation of Adsorbed Amounts in Organoclay by Machine Learning

[Image: see text] Adsorption properties of organoclay have been investigated for decades focusing on the morphology and physicochemical properties of two-dimensional interlayers. Experimental studies have previously revealed that the adsorption mechanisms depend on the molecular species of the organ...

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Autores principales: Shobuke, Hayato, Matsumoto, Takumi, Hirosawa, Fumiya, Miyagawa, Masaya, Takaba, Hiromitsu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9835538/
https://www.ncbi.nlm.nih.gov/pubmed/36643430
http://dx.doi.org/10.1021/acsomega.2c06602
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author Shobuke, Hayato
Matsumoto, Takumi
Hirosawa, Fumiya
Miyagawa, Masaya
Takaba, Hiromitsu
author_facet Shobuke, Hayato
Matsumoto, Takumi
Hirosawa, Fumiya
Miyagawa, Masaya
Takaba, Hiromitsu
author_sort Shobuke, Hayato
collection PubMed
description [Image: see text] Adsorption properties of organoclay have been investigated for decades focusing on the morphology and physicochemical properties of two-dimensional interlayers. Experimental studies have previously revealed that the adsorption mechanisms depend on the molecular species of the organocation and adsorbate, making it difficult to estimate the adsorbed amount without experiments. Considering that the adsorption of aromatic compounds has been reported by using various clays, organocations, and adsorbates, machine learning is a promising method to overcome the difficulty. In the present study, we collected adsorption data from the literature and constructed models to estimate the adsorbed amount of the organoclay by random forest regression. The composition of the clay, molecular descriptors of the organocation and adsorbate obtained by the RDKit, and experimental conditions were used as the explanatory variables. Simple model construction by using all the experimental data resulted in low R(2) and a mean absolute error. This problem was solved by the correction of the adsorbed amount data by the Langmuir or Freundlich equation and the following model construction at various equilibrium concentrations. The plots of the adsorbed amount estimated by the latter model were located close to the corresponding adsorption isotherm, while that by the former was not. Thus, it was revealed that the adsorbed amount was estimated quantitatively without understanding the adsorption mechanisms individually. Feature importance analysis also revealed that the combination of the organocation and adsorbate is important at high equilibrium concentrations, while the clay should be selected carefully as the concentration gets lower. Our results give an insight into the rational design of the organoclay including the synthesis and adsorption properties.
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spelling pubmed-98355382023-01-13 Estimation of Adsorbed Amounts in Organoclay by Machine Learning Shobuke, Hayato Matsumoto, Takumi Hirosawa, Fumiya Miyagawa, Masaya Takaba, Hiromitsu ACS Omega [Image: see text] Adsorption properties of organoclay have been investigated for decades focusing on the morphology and physicochemical properties of two-dimensional interlayers. Experimental studies have previously revealed that the adsorption mechanisms depend on the molecular species of the organocation and adsorbate, making it difficult to estimate the adsorbed amount without experiments. Considering that the adsorption of aromatic compounds has been reported by using various clays, organocations, and adsorbates, machine learning is a promising method to overcome the difficulty. In the present study, we collected adsorption data from the literature and constructed models to estimate the adsorbed amount of the organoclay by random forest regression. The composition of the clay, molecular descriptors of the organocation and adsorbate obtained by the RDKit, and experimental conditions were used as the explanatory variables. Simple model construction by using all the experimental data resulted in low R(2) and a mean absolute error. This problem was solved by the correction of the adsorbed amount data by the Langmuir or Freundlich equation and the following model construction at various equilibrium concentrations. The plots of the adsorbed amount estimated by the latter model were located close to the corresponding adsorption isotherm, while that by the former was not. Thus, it was revealed that the adsorbed amount was estimated quantitatively without understanding the adsorption mechanisms individually. Feature importance analysis also revealed that the combination of the organocation and adsorbate is important at high equilibrium concentrations, while the clay should be selected carefully as the concentration gets lower. Our results give an insight into the rational design of the organoclay including the synthesis and adsorption properties. American Chemical Society 2022-12-27 /pmc/articles/PMC9835538/ /pubmed/36643430 http://dx.doi.org/10.1021/acsomega.2c06602 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Shobuke, Hayato
Matsumoto, Takumi
Hirosawa, Fumiya
Miyagawa, Masaya
Takaba, Hiromitsu
Estimation of Adsorbed Amounts in Organoclay by Machine Learning
title Estimation of Adsorbed Amounts in Organoclay by Machine Learning
title_full Estimation of Adsorbed Amounts in Organoclay by Machine Learning
title_fullStr Estimation of Adsorbed Amounts in Organoclay by Machine Learning
title_full_unstemmed Estimation of Adsorbed Amounts in Organoclay by Machine Learning
title_short Estimation of Adsorbed Amounts in Organoclay by Machine Learning
title_sort estimation of adsorbed amounts in organoclay by machine learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9835538/
https://www.ncbi.nlm.nih.gov/pubmed/36643430
http://dx.doi.org/10.1021/acsomega.2c06602
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