Zetasizer Pro and Ultra - Anamet · • In the Zetasizer Nano, DLS measurement duration is based...

31/05/2018

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© Malvern Panalytical 2018

Zetasizer Pro and UltraOverview Video

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The New Zetasizer ProAdvance With Confidence

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Dr Mike Kaszuba

Technical Support Manager

E-mail: [email protected]

mailto:[email protected]

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The New Zetasizer Pro

31 May 20183

Key Features

Zetasizer ProBack and forward detection angles

ZS XPLORER softwareAdaptive Correlation Intelligent Data Quality Advice

Constant Current zeta potential mode

Optical filter wheel


ZS XPLORER Software

Simple, fast & flexible

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ZS XPLORER Software

• Driven by sample information, which is then used to set the method defaults

• 3-step process:• Add sample information• Choose measurement type • Press run!

Sample-centric workflow

Sample info

Measurement type Run

• Robust & reliable results, regardless of level of user expertise

• Accelerates sample throughput and frees both instrument and user for other tasks


Sample-Centric WorkflowA new way of working

Sample info Measurement type Run

• Simple• Clean• Guided • Flexible

“I found the new ZS XPLORERsoftware intuitive and easy to use.It is very simple to set up a project folder, and the search and navigation tools within the file system are excellent. All that’s needed to run an analysis is a sample name, cell type, and some basic measurement parameters.“

Stuart Reynolds,Domino Printing Sciences,Cambridge, UK

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Adaptive Correlation

Quicker, more robust size results



• A new way of making DLS measurements• Numerous, short sub-runs are made

(typically 2 seconds) • The PdI values obtained are statistically

analyzed to identify sub-runs which are not representative of the sample

• The data is classified as ‘steady state’ or ‘transient’

• Both steady state and transient data can be assessed – nothing is thrown away

• The method requires no prior knowledge of the sample – it is truly adaptive


0

10

20

30

40

0.1 1 10 100 1000 10000

Inte

nsity

(Per

cent

)

Size (d.nm)

Legacy Measurements


1 mg/mL lysozyme in pH 4 sodium acetate buffer

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• Simpler sample preparation• Quicker (up to 3 times)• No requirement for user expertise• More repeatable (up to 3-fold

improvement even for ‘easy’ samples)• Gain information on any aggregates

(both size and indication of relative abundance)

• Results from the steady state, transientand unfiltered data are available to the user (no data is thrown away)

Practical advantages

Unfiltered

Transient

Steady State


Intelligent Data Quality Advice

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Intelligent Data Quality Advice

• Uses neural network (machine learning) to assess data quality

• Neural network is trained using large datasets in a supervised learning process

• Uses icons to indicate data quality assessment in the record view

• Three levels of data quality, categorized as:• Good• Suspect• Poor

• Gives user clear and unambiguous reasons why their data might be poor, and how it can be improved

Neural network-based system for size measurements


Icon DescriptionSimple data interpretation

Records View Icon Interpretation

No data quality issues found

The data is useable, but advice should be followed when using this data

e.g. Multiple populations – the distribution analysis is a meaningful description of the results and the Z-average cannot be easily interpreted

The data is of poor quality and the remedial advice should be followed before using data from this sample

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Constant Current Zeta Potential Mode

Working in high salt conditions


• Measures electrophoretic mobility and zeta potential in high conductivity media, reducing errors associated with electrode polarization at higher ion concentrations

• Use of Constant Current Mode and Diffusion Barrier Method (patented) allows users to work at very high salinities without sample degradation and/or bubble formation (publication pending)

Constant Current Zeta Potential ModeWhat is it, and what does it do?

Normal fill of a folded capillary cell

Introduce a small aliquot of sample

Sample ready for measurement

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Optical Filter Wheel

Fluorescent samples & Depolarized Dynamic Light Scattering (DDLS)


Optical Filter Wheel (Patent Pending)

• User-selectable fluorescence filter for improving measurements of fluorescing samples (i.e. improve signal to noise)• Instrument sensitivity is maintained for samples which do not require a fluorescence filter

• User-selectable horizontal and vertical polarizing filters to make depolarized DLS measurements to differentiate translational from rotational diffusion

Fluorescent samples and Depolarized Dynamic Light Scattering (DDLS)

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Collection of DLS Data Legacy Methods

• In the Zetasizer Nano, DLS measurement duration is based upon the mean count rate• The lower the mean count rate, the longer the duration

• Measurement is then divided into a series of 10 second sub runs• E.g. if the measurement duration selected was 160 seconds, then 16 sub runs

of 10 seconds will be performed• Sub runs are ordered according to their mean count rate • Final result will contain 50% of the sub runs with the lowest mean count

rates• 50% of the data is rejected, irrespective of whether this is good or not

Dust Rejection Schemes

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Collection of DLS Data Legacy MethodsDust Rejection Schemes



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• Adaptive Correlation (AC) is a new data capture and processing method for DLS size measurements that is used in the Zetasizer Pro and Ultra

• Aims of Adaptive Correlation:• Improve the reliability of DLS measurements by reducing signal noise • Improve measurement tolerance to small amounts of “dust” • Minimise measurement time

• It captures correlation data which is used by various analyses • Cumulants• Non-negative least squared regression (General Purpose & Multiple Narrow Modes)• L-curve • MADLS

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What Do We Mean by Dust?

• Any unwanted material in a DLS sample• Aggregates• Filter spoil• Debris from dirty lab-ware

• Generally much larger than the particles/molecules being measured• They skew measurement data even when present in minute quantities

• Single large particle may completely dominate the signal from the main sample population• Dust causes number fluctuations

• Variations in the number of particles within the scattering volume during the course of a DLS measurement

Why is it a Problem?


What Do We Mean by Dust?Why is it a Problem?

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Recognising Number Fluctuations

• Number fluctuations are easy to identify and are typically manifested as elevated baselines in the correlation function

• In extreme cases, the measured intercepts are > 1

Correlation Function Baselines & Measured Intercepts


Recognising Number Fluctuations

• In Range• Overall indicator of data quality• Scaled from 0 to 100% • The higher the value the better• < 90% will give a warning message in the Size Quality Report and Expert Advice System

• Measured Intercept• Signal to noise ratio obtained from the intercept of the Y-axis of the correlation function• Scaled 0 to 1• 1 = perfect correlation (impossible to achieve!)• Values > 1 caused by number fluctuations (poor definition of the baseline of the correlation function)

Useful Parameters

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• No!• It involves a new way to capture DLS data• Processes data to handle the presence of dust• Optimises data capture for low scattering samples

Is it a new Dust Rejection Scheme?



• Shown to improve measurement data quality and reproducibility, even for clean, well defined samples when compared to the Zetasizer Nano• E.g. 60nm polystyrene latex sizing standards

Improved Data Quality and Reproducibility

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• No data is rejected –• All measured data is available to the user

• Increased measurement speed• Measurement times being as short as 25s

• Improved repeatability• Reduce the need for repeat sample preparations • Reduce training for users

• Measurements are more tolerant to the presence of “dust”• Users may find that they need to filter samples less

• Greater insight to the true nature of the sample• Being able to characterise “dust”

What Benefits to the User?



• All the optimisation and equilibration stages of the measurements are the same as the Zetasizer Nano

• Measurements are performed using sub runs • Default sub run length is 1.68 seconds

• When performing an automatic measurement, the number of sub runs is dynamic and will vary depending on the quality of the data captured

• Number of sub runs captured will be a multiple of 5 (between 15 and 100)• Data is captured and assessed in groups of 5 sub runs called a “batch”

• Measurement log sheet and progress bar refers to the number of “batches” that have been measured

• When using manual settings, the length and number of sub runs can be defined

Measurement Process

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• After every batch of the size measurement, any sub runs suspected of being transient are flagged, and a z-average size result calculated from the remaining “good” data

• z-average results of the last and previous batch are statistically tested to check the variability of sub run data

• Additionally, the results of previous batch results are used to check for trending• If the result of the last batch agrees with the previous batch result, and no change in

result is expected, the measurement will end • Minimum measurement comprises of 15 sub runs, as comparison over 3 batches is

needed for trend assessment• For most samples, less than 30 sub runs will be used

When Does the Measurement End?



• Three different correlation functions produced for a single measurement-• Steady State• Transient • Unfiltered

• Steady State - Data representative of the sample• Components that are consistently present in the sample

• Transient - Dust or other artefacts that are detected intermittently • Not statistically representative of the sample

• Unfiltered - Result derived using all of the data from the sample• No data classification

What Results Are Reported?

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Adaptive CorrelationWhat Results Are Reported?

Unfiltered

Steady StateTransient

Unfiltered



• Click on the configuration button on the chart (circle with three dots)• Then the “cog” button to reveal a tick box for which the data sets are shown

Accessing Transient and Unfiltered Data

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• All sub runs are analysed using Cumulants and subs run size results are assessed for outliers based on the Polydispersity index

• Outliers are identified using a commonly used statistical test for outliers• Test looks at a distribution and compares what the distribution would look like if

the value furthest from the mean was removed• If the distribution characteristics are unchanged, there are no outliers• If it does change, then the value removed was an outlier, and the process can

be repeated for multiple outliers

How Are Transient Events Identified?



• Adaptive correlation is not a size filter, but a measure of what is consistently present in a sample

• If a sample contains aggregates that are truly a representative component of the sample, they will be detected in the steady state result

• Allows better insight to aggregation• Isolation of transient sub runs and the subsequent analysis of this data allows better resolution

of very low proportions of material that would otherwise have their signal averaged out

Detecting Aggregates

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• A short as possible sub run length is used in order to give as many correlation functions as possible in a short duration of time• Maximise selectivity to transient effects

• Sub run lengths correspond to the groups of the correlator• Correlator in the Zetasizer Pro and Ultra has a dilation of two and sub run

lengths therefore double, i.e. 1.68s, 3.36s, 6.71s etc. • For low scattering samples (e.g. low concentration proteins), signal to noise can

be improved by extending the sub run length• Sub run length automatically increases for samples that show low scattering

• i.e. when attenuator 11 is used

Other Points



• Adaptive Correlation cannot be turned off in ZS EXPLORER • Results for all of the sub runs without any classification is provided

• Legacy method measurements (i.e. dust rejection) is not supported in the ZS XPLORER software

• Adaptive Correlation is not available for the Zetasizer Nano • New feature that is only available within the ZS XPLORER software that supports the Zetasizer

Ultra and Zetasizer Pro• Adaptive correlation does not improve DLS resolution

• Resolution improved by changing analysis mode or performing a Multi-Angle DLS (MADLS) measurement

• Adaptive correlation does not mean that samples do not have to be filtered• DLS still sensitive to dust • AC measurements more tolerant to dust and filtering requirements may be relaxed

Other Points

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• 60nm polystyrene latex standard dispersed in 10mM NaCl doped with a 1.6µm latex at different ratios

• Results demonstrate that transient data can be used to infer reliable properties for any rare large particles

Application Examples



• 1 mg/ml lysozyme which had been thermally stressed.• Similar values reported for the z-average size and are all monomeric

• Only one peak size in the steady state data • The run retention (i.e. % of sub runs included in the analysis for the steady state result)

decreases over time• Detection of transient scatterers is becoming more significant i.e. the sample is not

completely stable• However, the data gives confidence with the monomeric hydrodynamic size of the protein

Application Examples

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www.malvernpanalytical.com

Thank you for your attention.Any questions?

[email protected]

http://www.malvernpanalytical.com

mailto:[email protected]

Zetasizer Pro and Ultra - Anamet · • In the Zetasizer Nano, DLS measurement duration is based...

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