QCM-D is a highly sensitive technology, designed to detect minute variations in frequency, f, and dissipation, D. Being a strength in the analysis of surfaces interactions and processes, the high sensitivity can at the same time ruin the reproducibility if the measurement conditions are not under control. Here we have compiled a checklist that will help you generate high-quality data and optimize the reproducibility of your QCM-D measurements by minimizing unintentional changes of f and D.
Some of what is measured may be unintentional
All processes that influence the coupled mass or the sensor properties will, more or less, be reflected in the measured signals. This means that contaminants, sample variation, temperature variation, air bubbles etc. can all influence the measured results. What perhaps is assumed to be ‘small' variations in experimental preparation and execution can, in fact, make a big difference in the measured f and D signals and thereby corrupt the result. To generate high-quality data, it is of utmost importance to keep an eye on unintentional sources of variation. To eliminate error sources and optimize reproducibility, the experimental design and measurement conditions need to be planned and thoroughly considered. Note that one specific contaminant may be catastrophic in one measurement situation but insignificant in another.
Avoid contaminants that may interfere with your measured mass signal
Contaminants which could unintentionally interact with the sensor surface and influence the measured mass should be avoided. All surfaces and solutions that are interacting with the samples and pass the sensor, such as beakers, tubings, module interior, o-rings, the deionized water bottle, etc may be sources of contamination and cleanliness thereof is of utmost importance. To eliminate possible sources of contamination, make sure you have:
A clean instrument, i.e. liquid path
Clean tools such as tweezers, beakers, etc
Clean samples and solvents. Avoid contamination, precipitation, inhomogeneity and unwanted growth (microorganisms)
Surveying the market of QCM:s, one will come across a vast range of different QCM versions, and they all seem to provide similar information. What are the differences, and does it matter which one I use?
Lipid-based systems are used in various fields of research, e.g. in the design and development of biosensor platforms, biomaterial coatings and drug delivery applications. In this overview, we present examples of how these lipid-based systems can be characterized using QSense QCM-D technology.