Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning
Abstract In chemistry, empirical paradigms prevail, especially within the realm of chromatography, where the selection of separation conditions frequently relies on the chemist’s experience. However, the underlying rationale for such experiential knowledge has not been established or analysed. This...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56136-x |
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| author | Hao Xu Wenchao Wu Yuntian Chen Dongxiao Zhang Fanyang Mo |
| author_facet | Hao Xu Wenchao Wu Yuntian Chen Dongxiao Zhang Fanyang Mo |
| author_sort | Hao Xu |
| collection | DOAJ |
| description | Abstract In chemistry, empirical paradigms prevail, especially within the realm of chromatography, where the selection of separation conditions frequently relies on the chemist’s experience. However, the underlying rationale for such experiential knowledge has not been established or analysed. This study explicitly elucidates how chemists use thin-layer chromatography (TLC) to determine column chromatography (CC) conditions, employing statistical analysis and machine learning techniques. An experimental dataset of the CC is generated from the automatic platform developed in this study. On this basis, an “artificial intelligence (AI) experience” is generated through a knowledge discovery framework, where the relationship between the retardation factor (RF) value from TLC and retention volume from CC is unveiled in the form of explicit equations. These equations demonstrate satisfactory accuracy and generalizability, providing a scientific basis for the selection of the experimental conditions, and contributing to a better understanding of chromatography. |
| format | Article |
| id | doaj-art-cc4b88d67d1d4d0a8471482ebc8e2f7c |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-cc4b88d67d1d4d0a8471482ebc8e2f7c2025-01-19T12:30:34ZengNature PortfolioNature Communications2041-17232025-01-0116111210.1038/s41467-025-56136-xExplicit relation between thin film chromatography and column chromatography conditions from statistics and machine learningHao Xu0Wenchao Wu1Yuntian Chen2Dongxiao Zhang3Fanyang Mo4AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate SchoolAI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate SchoolNingbo Institute of Digital Twin, Eastern Institute of TechnologyZhejiang Key Laboratory of Industrial Intelligence and Digital Twin, Eastern Institute of TechnologyAI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate SchoolAbstract In chemistry, empirical paradigms prevail, especially within the realm of chromatography, where the selection of separation conditions frequently relies on the chemist’s experience. However, the underlying rationale for such experiential knowledge has not been established or analysed. This study explicitly elucidates how chemists use thin-layer chromatography (TLC) to determine column chromatography (CC) conditions, employing statistical analysis and machine learning techniques. An experimental dataset of the CC is generated from the automatic platform developed in this study. On this basis, an “artificial intelligence (AI) experience” is generated through a knowledge discovery framework, where the relationship between the retardation factor (RF) value from TLC and retention volume from CC is unveiled in the form of explicit equations. These equations demonstrate satisfactory accuracy and generalizability, providing a scientific basis for the selection of the experimental conditions, and contributing to a better understanding of chromatography.https://doi.org/10.1038/s41467-025-56136-x |
| spellingShingle | Hao Xu Wenchao Wu Yuntian Chen Dongxiao Zhang Fanyang Mo Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning Nature Communications |
| title | Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning |
| title_full | Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning |
| title_fullStr | Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning |
| title_full_unstemmed | Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning |
| title_short | Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning |
| title_sort | explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning |
| url | https://doi.org/10.1038/s41467-025-56136-x |
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