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Adaptive Two-Dimensional Microgas Chromatography

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journal contribution
posted on 01.05.2012, 00:00 authored by Jing Liu, Maung Kyaw Khaing Oo, Karthik Reddy, Yogesh B. Gianchandani, Jack C. Schultz, Heidi M. Appel, Xudong Fan
We proposed and investigated a novel adaptive two-dimensional (2-D) microgas chromatography system, which consists of one 1st-dimensional column, multiple parallel 2nd-dimensional columns, and a decision-making module. The decision-making module, installed between the 1st- and 2nd-dimensional columns, normally comprises an on-column nondestructive vapor detector, a flow routing system, and a computer that monitors the detection signal from the detector and sends out the trigger signal to the flow routing system. During the operation, effluents from the 1st-dimensional column are first detected by the detector and, then, depending on the signal generated by the detector, routed to one of the 2nd-dimensional columns sequentially for further separation. As compared to conventional 2-D GC systems, the proposed adaptive GC scheme has a number of unique and advantageous features. First and foremost, the multiple parallel columns are independent of each other. Therefore, their length, stationary phase, flow rate, and temperature can be optimized for best separation and maximal versatility. In addition, the adaptive GC significantly lowers the thermal modulator modulation frequency and hence power consumption. Finally, it greatly simplifies the postdata analysis process required to reconstruct the 2-D chromatogram. In this paper, the underlying working principle and data analysis of the adaptive GC was first discussed. Then, separation of a mixture of 20 analytes with various volatilities and polarities was demonstrated using an adaptive GC system with a single 2nd-dimensional column. Finally, an adaptive GC system with dual 2nd-dimensional columns was employed, in conjunction with temperature ramping, in a practical application to separate a mixture of plant emitted volatile organic compounds with significantly shortened analysis time.

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