When you are to invest in a QCM-system, there are several aspects of the instrument to evaluate such as price, experimental capabilities, and data quality. Price and experimental capabilities are straightforward to assess, but the data quality can be trickier to decipher and compare between suppliers. Here we guide you to what information to look for, and how to compare the numbers specified by different suppliers.
Parameters related to data quality – which are they?
One of the first aspects that comes to mind when assessing a specific QCM setup is probably the hardware capabilities. Being able to mimic certain conditions, such as certain temperature, is indeed important. Another key aspect, which is often trickier to assess, is the quality of the data generated.
The parameters related to data quality can be challenging to identify. Not only can the terminology used to describe the instrument performance vary between suppliers, but often the specifications include different sets of parameters. Also, theoretical values are frequently mixed with values that are relevant to the measurement situation. So, which of all the parameters found in an instrument specification are relevant and will have an impact on the end measurement? And - is all the key information available?
To interpret the information, and to assess a QCM specification from a data quality and reproducibility perspective, there are a few aspects to consider
Key questions to ask
Which of the parameters specified impact the data quality? The temperature has a large impact on the resonance frequency, and therefore it is important to assure a good temperature control that keeps the temperature stable throughout the measurement. Other factors that are important, and which will influence the information quality of the measured signal, are noise and drift.
Which of the parameters specified are purely theoretical, and which ones are relevant to a measurement situation? Some parameters often mentioned in the context of QCM are purely theoretical. These parameters could be irrelevant in the actual measurement situation. One example is the measurement resolution. The electronics may be able to deliver a certain number of decimals in the measured frequency, but this number does not mean that all the decimals have any meaning. Noise and drift will ultimately determine the data quality and how many of the measured decimals that in the end are significant. Another example of a theoretical number that is often mentioned is the mass sensitivity constant. This depends on the fundamental resonant frequency of the crystal.
Under what conditions are the specified numbers valid? The conditions at which a parameter is measured and how calculations are made are important information. Is the specified information relevant to your intended measurement conditions? For example, at what temperature has the specified drift been captured? How long was the measurement, and in what temperature range is the specification valid?
Comparing different suppliers
Terminology and information provided to describe the instrument performance can vary between suppliers. So how can different information be compared to make a head-to-head comparison between different instruments the information provided is not the same? A key is to look at the unit of the specified parameter that you would like to compare. Comparing the units of the parameter that you would like to assess will guide you to how to convert or recalculate it so that it can be compared for different instruments. Also, the unit can sometimes reveal whether the specified parameter is a theoretical or an actual one. One example of a parameter that is often specified in different ways is the sensitivity. Comparing the units of the specified sensitivities, for example ng/Hz, ng/cm2 or ng/(cm2∙Hz), allows you to recalculate the numbers so that they are in the same format and can be compared.
As the overall objective of any experiment is to be able to answer a predefined question, the data quality is essential to assess. Evaluating information in a QCM-specification, and comparing information provided by different suppliers, can however be tricky. One approach is to identify the parameters that are relevant to the actual measurement situation, and to look at the unit of the specified numbers to compare the information between different suppliers.
Download the guide on how to read a QCM specification to learn more about which specific parameters that are related to QCM data quality, what they mean and why they are important.
Editors note: This post was originally published in September 2018 and has been updated for comprehensiveness
Malin graduated in engineering physics in 2006, where her research focused on the QCM-D technology. Since then, she has been scrutinizing the how’s and why’s of the world in general, and the world of QCM-D in particular.