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Evaluation of direct analysis in real time mass spectrometry for onsite monitoring of batch slurry reactions
Author(s) -
Cho David S.,
Gibson Stephen C.,
Bhandari Deepak,
McNally Mary Ellen,
Hoffman Ron M.,
Cook Kelsey D.,
Song Liguo
Publication year - 2011
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.5269
Subject(s) - slurry , dart ion source , chemistry , mass spectrometry , sampling (signal processing) , process engineering , sample (material) , sample preparation , chromatography , petrochemical , analytical chemistry (journal) , environmental science , computer science , environmental engineering , ion , organic chemistry , filter (signal processing) , electron ionization , engineering , computer vision , ionization
Batch slurry reactions are widely used in the industrial manufacturing of chemicals, pharmaceuticals, petrochemicals and polymers. However, onsite monitoring of batch slurry reactions is still not feasible in production plants due to the challenge in analyzing heterogeneous samples without complicated sample preparation procedures. In this study, direct analysis in real time mass spectrometry (DART‐MS) has been evaluated for the onsite monitoring of a model batch slurry reaction. The results suggested that automation of the sampling process of DART‐MS is important to achieve quantitative results. With a sampling technique of manual sample deposition on melting point capillaries followed by automatic sample introduction across the helium beam, relative standard deviation (RSD) of the protonated molecule signals from the reaction product of the model batch slurry reaction ranged from 6 to 30%. This RSD range is improved greatly over a sampling technique of manual sample deposition followed by manual sample introduction where the RSDs are up to 110%. Furthermore, with the semi‐automated sampling approach, semi‐quantitative analysis of slurry samples has been achieved. Better quantification is expected with a fully automated sampling approach. Copyright © 2011 John Wiley & Sons, Ltd.