Zeta Potential: Assessing Your Data Quality Made Easy


Think about this scenario: you’ve prepared your sample, put it in the instrument and ran your zeta potential measurements and you look at the result. The value looks reasonable but you aren’t sure. To check you have to spend time to look into the different charts from your result to see if there may be any issues but you also have plenty more samples to measure today.

We know this happens and to ensure the data quality is good you may have to put off running more measurements till the next day which can put you behind schedule. So to help we have added a new feature to ZS Xplorer to make it easier and quicker to assess the data quality of your zeta potential results.

ZS Xplorer now includes Data Quality Guidance for zeta potential which takes many of the principles from this technical note to assess the quality of your data, identify potential issues, recommend how the data can be used and also guide you on ways to improve the measurement.

Assessing the Quality

Data Quality Guidance for zeta potential takes the same approach as the Data Quality Guidance for size measurements. Next to your zeta potential records you can see the Quality icon which shows at a quick glance the quality of the data measured and below are the three icons you can see.

image 5
Data quality icons

The icon on the left (solid green with a tick) means that no data quality issues have been found. The middle icon (hollow green) means that the data is usable. Still, advice should be followed when using this data e.g. Poor Zeta Quality factor – the result is helpful for comparisons between replicates but the repeatability may be low. The right icon (solid blue) means that the data is of poor quality and the remedial advice should be followed before using data from this sample. So instead of having to look through the different charts and plots for each measurement you can quickly run through your data to see which ones really need your attention.

If you identify records that show either the hollow green or blue icons then you can find further information in the Summary view by clicking on the drop-down arrow. Here you can see the issue that has been identified, how the data can be used and what steps can be taken to improve the quality of the data as shown in the example below.

Zeta DQG Summary
Example of Zeta Potential Data Quality Guidance

Identifying the issue

Outputs from the measurement result along with the measurement settings chosen either by you or the instrument are used to decide if an issue is present and if there is one what advice to give. So let’s run through an example using Sample A we saw previously and look below first at the Phase plot. This plot seems to be poor without a uniform shape.

High conductivity zeta phase plot
Phase plot for Sample A

Next, we can look at the parameter table for this measurement with the Zeta Potential, Conductivity and User Selected Analysis Model parameters chosen.

High conductivity zeta parameter table 1
Parameter Table for Sample A

We can see that the conductivity for this sample is high at almost 20 mS/cm and if the measurement had been run with Auto used for the analysis mode, then Monomodal would have been chosen which is suited for high conductivity samples. However, we can see that in this case the user chose General Purpose which means the electric field is being applied to the sample longer and therefore the sample is degrading causing poor data quality. By taking this information the Zeta Data Quality Guidance can run through the decision logic and identify that the issue is the sample has too high conductivity for this analysis model and that to improve the measurement switching to Monomodal is the best choice.


To know if you can rely on your zeta potential measurements use the Data Quality Guidance and all you have to do is take a quick glance to see which records don’t have a green tick, read the advice provided and if needed follow what it says to improve your measurements.

Further reading

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