Capillary-column gas chromatography (GC) is a commontopic introducedin second-year analytical lecture courses. However, crafting an effectiveGC laboratory exercise for students at this curricular level presentsmany barriers. Most important among them is a novice understandingof chromatography concepts (e.g., analyte migration), a natural unfamiliaritywith GC method parameters that must be fixed or controlled in thelaboratory (e.g., column type and length, carrier flow rate, and columntemperature), and physical limitations imposed by the larger classsizes often encountered in second-year courses, such as the smallnumber of high-quality GC systems typically available for teachingand the limited amount of course time to use them. We describe herea week-by-week sequence of online prelab exercises (webinar followedby simulation) that provides better incremental preparation for thesestudents to tackle the challenging hands-on learning objective ofGC temperature programming. Students submit independent reports followingeach exercise to demonstrate their level of engagement. Our evaluationof these reports showed the webinar not only helps students improvetheir conceptual understanding of GC temperature programming, butit also helps them learn about key method parameters (e.g., temperatureramp rate and ramp hold) and prepares them to tackle the simulationand hands-on exercises that follow. In the simulation exercise, studentsfamiliarize themselves with GC parameters that closely match the methodconditions of the hands-on GC system they will use in lab the followingweek. They can quickly model chromatograms that yield retention timesvery similar to experimental chromatograms, usually within 10–20%.Students noted that the close correspondence between simulated andexperimental chromatograms is a useful tool for optimizing the finalexperimental temperature program. Also important to our approach,and ultimately to students learning this method in the lab, is thecareful choice of the capillary column (nonpolar and relatively shortlength) and the test mixture composition (10 components includingsolvent). Both choices help decrease physical barriers to learningnoted above by decreasing the time per injection and yet providinga challenging temperature program to optimize.


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