High-throughput automated platform for thin layer chromatography analysis

Xu, Hao; Zhang, Dongxiao; Mo, Fanyang

doi:10.1016/j.xpro.2022.101893

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…In 2022, F. Mo and D. Zhang from Peking University further contributed to the development of robotic platforms by constructing a custom desktop robot system for high-throughput thin-layer chromatography (TLC) data collection [52,53], as illustrated in Figure 6B. Executing a TLC analysis requires a series of methodical steps: first the analyte is dissolved, then spotted, followed by the development of the TLC plate, and concluding with the measurement and calculation of each analyte's R f value.…”

Section: Robot-based Automationmentioning

confidence: 99%

“…Pipeline-based automation involves a series of automated processes and machines, organized sequentially (in a pipeline), to facilitate different stages of a chemical reaction or process [55]. Conversely, robot-based automation leverages robots to execute tasks traditionally performed by human operators, ranging from simple tasks, like weighing and mixing reactants, to more complex ones, like monitoring reactions, purifying products, and analyzing results [51,52,53]. Robots can be programmed to execute tasks sequentially or in parallel and can be equipped with various tools and sensors to perform diverse operations.…”

Section: Selection Of the Appropriate Automation Approachmentioning

confidence: 99%

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Transforming organic chemistry research paradigms: Moving from manual efforts to the intersection of automation and artificial intelligence

Liu,

Chen,

2023

NSO

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Organic chemistry is undergoing a major paradigm shift, moving from a labor-intensive approach to a new era dominated by automation and artificial intelligence (AI). This transformative shift is being driven by technological advances, the ever-increasing demand for greater research efficiency and accuracy, and the burgeoning growth of interdisciplinary research. AI models, supported by computational power and algorithms, are drastically reshaping synthetic planning and introducing groundbreaking ways to tackle complex molecular synthesis. In addition, autonomous robotic systems are rapidly accelerating the pace of discovery by performing tedious tasks with unprecedented speed and precision. This article examines the multiple opportunities and challenges presented by this paradigm shift and explores its far-reaching implications. It provides valuable insights into the future trajectory of organic chemistry research, which is increasingly defined by the synergistic interaction of automation and AI.

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Section: Robot-based Automationmentioning

confidence: 99%

Section: Selection Of the Appropriate Automation Approachmentioning

confidence: 99%

Transforming organic chemistry research paradigms: Moving from manual efforts to the intersection of automation and artificial intelligence

Liu,

Chen,

2023

NSO

Self Cite

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“…Therefore, high-throughput techniques combined with automation have been developed to accumulate standardized experimental data efficiently 10 , 11 . For example, our prior work 12 has created an automated platform to conduct high-throughput thin-layer chromatographic analysis. However, high-throughput systems are usually expensive and targeted at specific scenarios, which is difficult to promote to broader fields.…”

Section: Introductionmentioning

confidence: 99%

Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network

Lin

Zhang

et al. 2023

Nat Commun

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The enantioseparation of chiral molecules is a crucial and challenging task in the field of experimental chemistry, often requiring extensive trial and error with different experimental settings. To overcome this challenge, here we show a research framework that employs machine learning techniques to predict retention times of enantiomers and facilitate chromatographic enantioseparation. A documentary dataset of chiral molecular retention times in high-performance liquid chromatography (CMRT dataset) is established to handle the challenge of data acquisition. A quantile geometry-enhanced graph neural network is proposed to learn the molecular structure-retention time relationship, which shows a satisfactory predictive ability for enantiomers. The domain knowledge of chromatography is incorporated into the machine learning model to achieve multi-column prediction, which paves the way for chromatographic enantioseparation prediction by calculating the separation probability. The proposed research framework works well in retention time prediction and chromatographic enantioseparation facilitation, which sheds light on the application of machine learning techniques to the experimental scene and improves the efficiency of experimenters to speed up scientific discovery.

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Empowering research in chemistry and materials science through intelligent algorithms

Lin,

2024

Artificial Intelligence Chemistry

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High-throughput automated platform for thin layer chromatography analysis

Cited by 4 publications

References 3 publications

Transforming organic chemistry research paradigms: Moving from manual efforts to the intersection of automation and artificial intelligence

Transforming organic chemistry research paradigms: Moving from manual efforts to the intersection of automation and artificial intelligence

Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network

Empowering research in chemistry and materials science through intelligent algorithms

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