How to optimize the reproducibility of your QCM-D data
Malin Edvardsson Jul 18, ’23 > 4 min

How to optimize the reproducibility of your QCM-D data

QSense QCM-D is a highly sensitive technology, designed to detect minute variations in the measured parameters frequency, f, and dissipation, D. While high sensitivity is a strength in the analysis of surfaces interactions and related processes, it can make measurement reproducibility a challenge 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.

Small variations in execution can make a big difference on the result

All processes that influence the sensor properties or the mass coupled to the sensor oscillation will, more or less, be reflected in the measured QCM-D signals. This means that unintentional variations such as contaminants, sample variation, temperature variation, air bubbles etc., all can affect the measured results and obscure the process of interest.

What could be assumed to be insignificant variations in experimental preparation and execution procedures can, in fact, have major impact and make a big difference in the measured f and D signals and thereby corrupt the result. To generate high-quality data, it is therefore 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 carefully planned and thoroughly considered

Avoid contaminants that may interfere with your measured QCM-D signal

There are several sources that could give rise to unwanted variation in the measured result. One source that could cause a lot of trouble is contamination. The impact of contamination depends on the system under study. One specific contaminant may be catastrophic in one measurement situation but insignificant in another. Therefore, to be on the safe side, 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 sensors
  • Clean samples and solvents. Avoid contamination, precipitation, inhomogeneity, and unwanted growth (microorganisms)

Temperature, sample variations, air bubbles and more

In addition to contaminants, there are several other sources that could give rise to significant measurement interference and ruin the reproducibility, for example, temperature stability. Temperature stability is key for reproducible QCM-D measurements. Temperature variations influence the QCM signals. Another important source of variation is variations in the sample(s) being measured. Differences in concentration, aging, or other aspects that will result in different surface interactions or process dynamics should be avoided. Air bubbles is also a factor that can have huge impact on the measured signal, and these should be avoided.

To learn more about different error sources that could have a negative impact on the QCM-D measurement reproducibility, and to get a checklist on how to address these, download the guide below.

How to generate quality data

Download the guide to get the full checklist

Guide  How to eliminate error sources and optimize reproducibility  Download

Editors note: This post was originally published in June 2019 and has been updated for comprehensiveness.

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