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30. Collecting quality data with QCM-D – what to consider and pitfalls to avoid
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Collecting quality data with QCM-D – what to consider and pitfalls to avoid
How do you maximize your chances of a measurement being successful? And which are the main pitfalls to avoid when planning and executing experiments?
In this episode, we talk to Jennie Ringberg, Global Technical Product Manager for QSense at Biolin Scientific, to learn more about the practical aspects of data collection and experimental design with the ambition to maximize the data quality. The conversation focuses on the QCM-D measurements, but some of the principles discussed are relevant also for other types of analysis of surfaces and interfaces.
In this educational conversation, Jennie takes us through the five main steps of preparing and running QCM-D measurements. One by one, we go through the steps and discuss what’s important to consider for the measurement to be successful with the ambition to optimize the quality of the collected data. We talk about what aspects to pay particular attention to and why these are important for the result. We also cover common challenges, and what will be the consequence if important aspects are ignored.
Thanks for listening! If you are interested in surface and interface science and related topics, you should check out our blog - the Surface Science blog
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