“A highlight of the presentation was the results of localized (in situ) analysis on interior materials to identify the source of VOC emissions”


Barry Prince Syft Technologies Director - Global Sales

Dr. Barry Prince

BSc(Hons), PhD


Principal Scientist / Consultant
Yan Li Syft Technologies Application Specialist

Yan Li

B.Sc, ME


Application Specialist

Posted by Dr Barry Prince, Director of Global Sales at Syft Technologies and Yan Li, Automotive Applications Specialist at Syft Technologies.

Dr Barry Prince presented the latest developments of SIFT-MS in Vehicle Interior Air Quality (VIAQ) measurements to a group of world experts at the 2017 International Forum on China Automobile Material (IFAM) held in Tianjin, China on May 10th and 11th.

Hosted by China Automotive Technology & Research Center, IFAM aims to promote the idea of “Building the value of green manufacturing for the automobile industry through innovative materials”. Dr Prince and Yan Li were invited to share Syft’s latest work in this area. In particular, how the real-time nature of SIFT-MS, coupled to its ability to measure an extensive range of target compounds, is enabling component manufacturers and OEMs to improve production quality through enhanced product screening.

Dr Prince’s presentation focused on three cases studies: one with Ford Motor Company in the US, and two with imat-uve in Germany (a certified testing laboratory). The Ford example, demonstrated the equivalency of SIFT-MS for measuring the eight compounds regulated by the Chinese government (BTEX, styrene, acrolein, formaldehyde and acetaldehyde) as compared with the traditional GC-MS and HPLC techniques. More details of this work can be found in Chris Nones’ post.
A highlight of the presentation was the results of localized (in situ) analysis on interior materials to identify the source of VOC emissions. This work, carried out by imat-uve, triggered a great deal of interest among the audience as it highlighted the cost savings that are achieved with SIFT-MS due to the rapid identification of defective components

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