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Predicting Collision Cross Section Values for Unknown Compounds

Predicting Collision Cross Section Values for Unknown Compounds
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Measuring and Predicting collision cross sect

Researchers at Auburn University are seeking ways to identify unknown compounds using collision cross section (CCS) measurements paired with prediction methods including computational models and machine learning. Nowadays, there is a high demand for fast and reliable detection of compounds using untargeted analyses. This is especially true for the diagnosis of various diseases. Usually, the detected compounds can have a variety of different structures which will make the detection process challenging. Therefore, they can have different effects on living organisms, which is why research in "omics" (lipidomics, metabolomics, proteomics) has gained importance and is applied in many research areas such as environmental, food, clinical, etc. Many separation techniques such as chromatography and mass spectrometry have been used to separate these structures from each other. However, the separation of structurally different compounds by these techniques is not always reliable for u

Measuring and predicting collision cross section values for unknown compounds

Measuring and predicting collision cross section values for unknown compounds
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