Particle characterization

Abstract

A method for investigating sample characteristics that change over time is disclosed. The method includes illuminating a sample having particles with a light beam such that interaction of the light beam with the sample produces scattered light, obtaining measurements of the scattered light over a measurement period divided into a plurality of shorter sub-runs, classifying each sub-run, and determining sample particle characteristics for the sub-run. The method may further include transforming each sub-run before classifying it. Determining the sample particle characteristics for the sub-run may occur before classifying it. The method may also include determining the change in sample particle characteristics over time for the sub-run.

Description

[Technical Field]

[0001] The present invention relates to particle characterization devices and methods that may include diffraction analysis. [Background technology]

[0002] Laser diffraction is a technique for characterizing particles by analyzing the patterns of diffracted or scattered light from a sample. It is a ubiquitous technique for noninvasively characterizing ensembles of particulate matter, particularly for obtaining particle size distributions of hard spheres (Light Scattering by Small Particles, H.C. van de Hulst, Dover (2003), ISBN-10:0486642283, ISBN-13:978-0486642284; Absorption and Scattering of Light, W.V. (2012), ISBN-10:9780471293408, ISBN-13:978-0471293408).

[0003] Laser diffraction analysis is typically performed on a suspension of particles in a liquid dispersant, which can be a liquid or a gas. The pattern of diffracted light can be used to infer the particle size distribution, for example based on Mie scattering theory.

[0004] Because it is very difficult to continuously entrain particles in a low-density dispersant such as air, single-shot, non-circulating systems are often used for dry measurements. Dry powder feed may also be used to introduce particles into the dispersant liquid.

[0005] It is not uncommon to find laser diffraction measurements included as part of larger systems, especially in combination with other processes and detection methods, such as in-line with chemical reactors or comminution equipment, through sampling from industrial processes to waste or as part of a recycle loop.

[0006] Laser diffraction measurements can suffer from sources of measurement uncertainty, which typically require expert users to identify and correct. If such uncertainties are identified incorrectly or removed incompletely, they can introduce significant, but hidden, uncertainties (or errors) into reported results. In some cases, measurement uncertainty leads to testing until measurements conform to expected values, which is unsuitable for quality assurance / quality control environments (one of the important applications of laser diffraction).

[0007] The primary particle size distribution (PSD) determined by diffraction analysis can change over time. This can be due to special causes, such as comminution, aggregation, dissolution, and swelling, which affect the sample itself (Lloyd S. Nelson, "The Shewhart Control Chart - Tests for Special Causes"). Journal of Quality Technology 16, No. 4 (October 1984), 238-39. Grinding-Bonakdar T. et al., International Journal of Pharmaceutics, Vol. 501, No. 1-2, March 30, 2016, pp. 65-74. Aggregation - Kasmerchak et al., Geoderma, Volume 338, March 15, 2019, pp. 430-444. Dissolving - Azad M. et al., Drug Development and Industrial Pharmacy, 41:10, 1617-1631), and instrument effects such as particle size-dependent accumulation of a fraction (or part) of the total PSD on the inner walls of the measurement cell, thereby biasing the remaining sample entrained within the flow path (Kupetz, M, Backes, L., Ramsauer, B. et al. Eur Food Res Technol (2019)). For example, the careful addition of surfactants may be used to minimize particle-particle and particle-environment interactions, but identifying such requirements from data has so far been based on operator training, expert knowledge, or worse, a priori knowledge of what the primary PSD should be, leaving room for improvement.

[0008] The known techniques may further include the following disadvantages: 1) Many commercially available laser diffraction instruments employ a measurement structure based on standard operating procedures, allowing users to set measurement conditions such as the flow rate of the sample dispersion accessory, measurement time, and number of measurements. A set of measurement parameters that forms the basis of a standard operating procedure (SOP) may be designed. This is a laborious, iterative process that may be based on the operator's experience with similar samples. This can introduce significant random and / or systematic uncertainties into the SOP / measurement. 2) Until now, the only way to detect spurious effects such as sample shattering, buoyancy, dissolution or aggregation was for a trained user to sit back and watch the measurement proceed. 3) Very low concentrations of particular size fractions, characterized by numbers rather than in an ensemble sense, may appear infrequently, but repeatedly, and at random times. Thus, they may be of sufficient size and concentration to interfere with the analysis of the primary particle sample of interest, but with significant uncertainty in the reported relative abundance; or they may be of sufficient abundance to interfere with the analysis of the particle size distribution of the sample of interest, but are rare enough to be below a threshold that may be present in the analysis; or they may simply be averaged over time and therefore not reported to the operator. 4) The overall measurement time for each aliquot is usually set before the measurement based on operator experience, which may result in systematic as well as random uncertainties and may lead to tests that fit stable but imprecise particle size distribution reports.

[0009] WO2017 / 051149 discloses a method for particle characterization that includes correcting for scattered light from larger particles.

[0010] Although progress has been made in improving the robustness of particle characterization by scattered light, there remains considerable room for improvement. Summary of the Invention

[0011] According to a first embodiment, a method for investigating sample properties that change over time is disclosed, the method including illuminating a particle-bearing sample with a light beam such that interaction of the light beam with the sample produces scattered light, obtaining measurements of the scattered light over a measurement period divided into multiple shorter sub-runs, classifying each sub-run, and determining sample particle properties for the sub-run.

[0012] The method may further include transforming each sub-run before classifying it. Transforming each sub-run may include performing dimensionality reduction on each sub-run to make the data more manageable. A mathematical reduction of a single segment may be beneficial because it is more robust than an average of multiple segments. The transformation of each sub-run may be performed using a processor. Determining sample particle properties in the sub-run may be performed before classifying each sub-run. Once the sub-run is classified, the sub-run may be reconstructed (e.g., by Mie inversion using non-negative least squares (NNLS)). The reconstruction may be performed using various techniques depending on the classification. The method may also include determining changes in sample particle properties over time for the sub-run.

[0013] Also provided is a method for investigating sample properties that change over time, the method including irradiating a sample having particles with a light beam such that scattered light is generated by interaction of the light beam with the sample, obtaining measurements of the scattered light over a measurement period divided into a plurality of shorter sub-runs, classifying each sub-run, and determining the change in dynamic particle properties over time, where the dynamic particle properties are determined for the sub-runs in the classification.

[0014] It will be appreciated that illuminating a sample with a light beam may more generally refer to illuminating a sample with an X-ray beam in an X-ray configuration. Similarly, a light source may refer to a light source such as a laser or an X-ray source (such as an X-ray tube) depending on the equipment utilized. Furthermore, scattered light may be diffracted or scattered X-rays in an X-ray configuration.

[0015] Measurements of scattered light may be obtained at multiple detection angles.

[0016] Analysis of specific classifications of sub-runs for varying sample characteristics (i.e., sample properties) enables robust automated analysis of sample characteristics that change over time. Classification of sub-runs allows for grouping (classification) of specific types of measurement data, and time trends within each group (classification) may reveal more about the dynamic properties of the sample than simply trending all data. For example, in some embodiments, a sample may contain contaminants that contribute to measurement error and / or uncertainty in the measurement of a major fraction of the sample. Each sub-run may be automatically classified as either containing or not containing the contaminant. Thus, analysis of sub-runs classified as not containing the contaminant can provide reliable measurements of the time-varying properties of the major fraction.

[0017] In some embodiments, each sub-run may be transformed before being classified, in which case the transformation may include dimensionality reduction to make the data more manageable.

[0018] In some embodiments, the dynamic sample characteristic may include particle size and / or particle concentration, hi other embodiments, the dynamic particle characteristic may include the intensity of scattered light received by at least one detector element.

[0019] The method may further include determining a particle size distribution and identifying a particle fraction for each subrun in the classification, wherein the particle size is the average particle size of the particle fraction and / or the particle concentration is the concentration of the particle fraction.

[0020] Classifying each sub-run may include determining a classification particle characteristic and comparing the classification particle characteristic to a threshold value for the same characteristic.

[0021] The threshold value may be determined from statistics of the classification particle characteristics for at least a portion of the sub-runs. The use of statistics for at least a portion of the sub-runs may allow the classification criteria to correspond to the sample. This may be advantageous over predetermined classification criteria.

[0022] The threshold may be a predetermined number of standard deviations away from the mean value of the classified particle characteristic.

[0023] The threshold for classification of the current sub-run may be determined from the cumulative standard deviation of at least some of the sub-runs preceding the current sub-run. The cumulative standard deviation may be used to facilitate the application of dynamic classification criteria (i.e., measurement dependent) based on sub-runs already obtained and applied before all measurements are obtained.

[0024] A control diagram of the classified particle characteristics may be used to classify the sub-runs. Classifying the sub-runs may include determining upper and lower control limits on either side of the mean value of the classified particle characteristics (e.g., n standard deviations from the mean value, where n is 2, 3, or other number or fraction). A set of classification rules may be used to classify the sub-runs. The rules may include at least one of the following: a) Classification particle characteristics are outside the control limits b) A run (of consecutive subruns) of more than m particle classification characteristics on the same side of the mean. c) A run of n particle classification characteristics that all increase or decrease d) A run of o particle classification characteristics, alternating up and down e) Runs of p particle classification characteristics that are more than two standard deviations away from the mean f) Runs of q particle classification characteristics that are more than one standard deviation away from the mean g) A run of r particle classification characteristics less than one standard deviation of the mean

[0025] Classifying each sub-run may include classifying each sub-run as transient or non-transient. In some embodiments, a sub-run may be classified as transient if the classification particle characteristic is above a threshold. For sub-runs that are not transient but whose particle classification characteristics are drifting, a further classification of trending may be used, e.g., based on rules b) and / or c) above.

[0026] The method may further (alternatively or additionally) include automatically (eg, using a processor) classifying the changes in the dynamic particle properties (eg, into trending, steady state, or other categories).

[0027] The method may further (alternatively or additionally) include automatically correlating a trend condition in the first particle fraction with a complementary trend condition in the second particle fraction.

[0028] The dynamic particle properties and / or classified particle properties may be determined by diffraction analysis or dynamic light scattering analysis.

[0029] The method may further (alternatively or additionally) include classifying the variations in the dynamic particle properties.

[0030] In some embodiments, classifying a change in dynamic particle properties may include identifying the change as due to aggregation or coalescence when there is a sub-run classified as transient and the change in the dynamic particle properties of a non-transient sub-run is classified as a trend.

[0031] If the change in the dynamic particle properties in the temporal subruns is classified as a trend, the change may be identified as due to coalescence. If the change in the dynamic particle properties in the temporal subruns is not classified as a trend, the change may be identified as due to agglomeration. In this regard, it may be possible to distinguish between coalescence and agglomeration by testing the data against models of coalescence or agglomeration or by using machine learning algorithms.

[0032] The term sample fraction (or particle fraction) may refer to the entire sample (or all of the particles) in which a single sample (particle) fraction exists.

[0033] In some embodiments, classifying the change in the dynamic particle property may include identifying the change as due to particle crushing if there is a subrun classified as a trend and the change in the dynamic particle property indicates that the average particle size of the particles is decreasing and the total mass of particles in the sample is not decreasing.

[0034] In some embodiments, classifying the change in the dynamic particle characteristic may include identifying the change as being due to particle dissolution if there is a trended subrun and the change in the dynamic particle characteristic indicates that the average particle size of the particles is decreasing and the total mass of particles in the sample is decreasing.

[0035] According to a second aspect, there is provided a non-transitory machine-readable medium comprising instructions for configuring an apparatus including a processor to perform the method of the first aspect.

[0036] According to a third aspect, there is provided an apparatus for investigating time-varying sample properties, the apparatus including a light source, a sample holder, a detector, and a processor, wherein the sample holder is configured to receive a sample containing particles, the light source is operable to illuminate the sample holder with a light beam such that scattered light is generated by interaction of the light beam with particles in the sample, the detector is configured to detect the scattered light over a measurement period, and the processor is configured to divide the measurement period into a plurality of short sub-runs, classify each sub-run, and determine the change in dynamic particle properties over time, including determining the dynamic particle properties of the sub-runs in the classification.

[0037] The apparatus may be configured to perform diffraction analysis. The apparatus may include a plurality of detectors arranged to detect light scattered at a plurality of different scattering angles relative to a light beam incident on the sample.

[0038] The device may be configured to perform dynamic light scattering analysis.

[0039] The apparatus may be configured to carry out the method according to the first aspect including any feature thereof.

[0040] Embodiments of the present invention will now be described, by way of example only, with reference to the following drawings: [Brief explanation of the drawings]

[0041] [Figure 1] Schematic diagram of the measurement setup of the light scattering particle characterization instrument [Figure 2] Schematic diagram of an X-ray diffraction device [Figure 3] Schematic diagram of a particle characterization apparatus according to an embodiment. [Figure 4] 1 is a schematic diagram of a processing means of a particle characterization apparatus according to an embodiment; [Figure 5] Flow diagram of a method according to an embodiment [Figure 6] Cumulative standard deviation plot of Dv10 and Dv90 for 70 sub-runs of laser diffraction measurements [Figure 7]X-bar graph of Dv10 for 70 sub-runs of laser diffraction measurements [Figure 8] X-bar graph of Dv90 for 70 sub-runs of laser diffraction measurements [Figure 9] Illustration of the sequence of scattered light intensities measured by different detectors, with the contribution of transient scattering events to the scattered light. [Figure 10] Reporting of both summed transient and summed steady-state data and associated reduced particle size distributions from laser diffraction measurements [Figure 11] Plot of cumulative average Dv50 for 70 sub-runs of laser diffraction measurements [Figure 12] X-bar graph of Dv50 for 70 sub-runs of laser diffraction measurements [Figure 13] Schematic plot of particle size distribution in multiple subruns of laser diffraction measurements illustrating both non-transient and transient subruns. [Figure 14] Schematic plot of particle size distribution in multiple subruns of laser diffraction measurements illustrating both non-transient and transient subruns. [Figure 15] Schematic plot of particle size distribution in multiple subruns of laser diffraction measurements illustrating both non-transient and transient subruns showing coalescing samples. [Figure 16] Schematic plots of particle size distributions in multiple subruns of laser diffraction measurements illustrating both non-transient and transient subruns indicative of samples undergoing Ostwald ripening. [Figure 17] Schematic plot of particle size distribution in multiple subruns of laser diffraction measurements illustrating both non-transient and transient subruns indicative of aggregation or agglomeration. [Figure 18] Cumulative mean and cumulative standard deviation of Dv50 for 1.0 μm latex dispersed in DI water over 70 subruns. [Figure 19] Schematic plot of particle size distribution in multiple subruns of laser diffraction measurements illustrating non-transient subruns [Figure 20]Group diagram of particle size distributions in multiple subruns of laser diffraction measurements illustrating temporal particle fractionation. [Figure 21] Schematic of an adaptive measurement setup for laser diffraction measurements based on feedback from the instrument used to perform the measurements. [Figure 22] Schematic multidimensional pictogram of particle size distribution in multiple subruns of a laser diffraction measurement illustrating transient and non-transient subruns. [Figure 23] Schematic heat map representation of particle size distribution in multiple subruns of laser diffraction measurements illustrating transient and non-transient subruns [Figure 24] (FIG. 24A) Illustrative measurement data for a multi-particle sample; (FIG. 24B) A subset of the results from FIG. 24A, a reconstruction of data from a single particle from boxed region B in FIG. 24A; (FIG. 24C) A subset of the results, a reconstruction of data from boxed region C in FIG. 24A. [Figure 25A] Scattering data for a series of decreasing aliquot concentrations [Figure 25B] Scattering data for a series of decreasing aliquot concentrations [Figure 25C] Scattering data for a series of decreasing aliquot concentrations [Figure 25D] Scattering data for a series of decreasing aliquot concentrations [Figure 26A] Example scattering data for a small sample [Figure 26B] Example scattering data for a small sample [Figure 27A] Example scattering data for a small sample [Figure 27B] Example scattering data for a small sample [Figure 27C] Example scattering data for a small sample [Figure 28] (FIG. 28A) Diagram showing an alternative analytical technique to single particle analysis. (FIG. 28B) Diagram showing an alternative analytical technique to single particle analysis. [Figure 29] Diagram showing alternative analytical techniques for single particle analysis [Figure 30A]Diagram showing analytical techniques for measuring particle photometric mass [Figure 30B] Diagram showing analytical technique for measuring total sample photometric mass [Figure 31] Schematic of determining the local background estimate for each particle event DETAILED DESCRIPTION OF THE INVENTION

[0042] FIG. 1 shows a schematic diagram of a light scattering particle characterization apparatus 10, in which a light beam 11 (e.g., from a laser) is directed through a sample holder 12 holding a sample having particles 14 suspended in a diluent 15 (sometimes referred to herein as a dispersant). In this embodiment, the sample holder 12 is a flow cell through which the sample having particles 14 passes. In other embodiments, the sample holder may be a surface on which a sample droplet is supported, a capillary, a flow cell, or a cuvette. The scattered light 13 from the interaction of the illuminating laser 11 with the particles 14 forms a far-field diffraction pattern, which is detected by a detector (not shown) positioned to receive light scattered at different angles relative to the light beam 11. The vectors of the scattered light intensity at different scattering angles may be inverted (e.g., based on Mie scattering theory) to determine the particle size distribution. Also shown in FIG. 1 are an optical flow cell front window 16 and an optical flow cell back window 17 (although one skilled in the art will understand that, depending on the configuration, these may not be required or may not be exactly as depicted).

[0043] Another technique for characterizing particles is X-ray diffraction (XRD). Figure 2 shows a schematic diagram of an X-ray diffraction instrument 31, which includes a light source 34 (i.e., an X-ray source), a collimator 35, a sample 36, and wide-angle and small-angle X-ray scattering detectors 32, 33. The light from the X-ray source 34 is collimated into an illumination beam by the collimator 35. The illumination beam is directed through the sample 36, and the light (as X-rays) is scattered by particles (e.g., atoms in a crystal lattice) in the sample 36. The diffraction pattern from the particles in the sample 36 may be used to infer properties of the sample and the arrangement of the particles within the sample. The scattering angle 2θ 37 and beam stop 38 are also shown in Figure 2.

[0044] Exemplary sources of XRD uncertainty and their mitigation according to certain embodiments are described below.

[0045] In online XRD or small-angle X-ray scattering (SAXS), a continuous stream of powder, slurry, or liquid is analyzed. This sample flow can become inhomogeneous over time. Data collection is often performed by rapid scanning and then summed over time, or, if a static detector is used, the recorded data is typically the sum of many detector frames (data snapshots) recorded over time. Averaging data over long periods of time can mask the effects of phase changes or variations in sample composition, especially if the variations occur in a small number of phases.

[0046] Especially with static detectors, these data frames can be collected in very short subruns (in the millisecond range). Classifying each subrun allows for grouping the data into distinct relative compositions. Subsequent diffraction analysis can be performed for each subrun (e.g., by combining data from subruns in a particular classification or by combining the analysis results of each subrun in a classification). Separate analysis of each classification can improve analysis, especially for minority phases. Separate analysis of diffraction images with relatively high minority phase concentrations (i.e., (relatively) low majority phase concentrations) may improve detection limits and / or structural analysis of minority phases. This approach may also improve sensitivity to small relative structural changes in minority phases. Outlier analysis may also be improved, as data from outliers can be classified as outliers and separated from the main data stream. A similar approach is possible for SAXS data analysis of time-varying sample streams. One such example is a protein sample flow undergoing changes in oligomeric state. By sorting the collected subruns (e.g., data frames) based on their similarity, a good SAXS pattern of the pure phase can be achieved instead of only the mixed / averaged SAXS pattern (e.g., a SAXS pattern close to that of the pure oligomer can be obtained instead of only the mixed state).

[0047] Randomly occurring environmental events can also affect the quality of X-ray measurements. In experiments where the sample scattering is weak, a good signal-to-noise ratio is crucial for good measurement results and accurate sample analysis. Examples of this type of application include grazing-incidence small-angle X-ray scattering (GI-SAXS) and SAXS measurements, which require long data collection times to ensure sufficient counting statistics in the data. Low air scattering and low noise in the detection system are important. However, in practice, the achievable noise level and data quality are determined by the detection of interfering signals from the environment that are not originating from the sample (e.g., cosmic rays). These signal events usually appear randomly and overlap with the scattered X-ray signal from the sample.

[0048] By classifying sub-runs (e.g., data frames / data snapshots) from the detector into those that contain interfering environmental signals and those that do not, the data quality of measurements from the sample can be significantly improved (i.e., improved noise level). It will also be appreciated that it is also possible to analyze the disturbed data frames individually.

[0049] Samples undergoing repeated, rapid changes (e.g., samples undergoing rapid cycling in-situ reactions, batteries undergoing charging / discharging, biological samples undergoing radiation damage, etc.) typically exhibit repeated, time-varying data patterns in X-ray measurements. The data quality of short-term measurements may not be sufficient for accurate analysis, and averaging data collection over longer periods may mask important sample characteristics or sample reaction effects. Classification of data (e.g., fast snapshots / data collection frames) from sub-runs resulting from similar sample states or sample conditions may enable improved analytical quality for different sample states and conditions.

[0050] To the extent that prior art methods consider the issue of contaminants, data containing scattering from the contaminants is typically discarded or included in the averaging of the data collection. This crude approach can result in erroneous discarded data or distorted average data collections, resulting in incomplete or unrepresentative results, or long run times required to obtain sufficient valid data.

[0051] Many of these uncertainties are time-resolved, such as the presence of spurious size fractions, i.e., particles that are significantly larger or smaller than the majority of particles in the sample, or trends or biases in the measurement of the particle size distribution, all of which can be partially averaged using any measurement length. Additionally, embodiments may enable more accurate characterization of a small number of spurious size fractions in the presence of a primary ensemble (i.e., a sample that is representative of the majority of particles in the sample).

[0052] 3 shows a schematic diagram of a particle characterization apparatus 40. The apparatus 40 comprises a light source 41, first, second and third optical elements 43, 44, 46, a sample holder 45 and a detector 48.

[0053] The light source 41 may be a laser light source. In another embodiment, the light source 41 may be an X-ray source. The light source 41 projects a light beam 42 through a sample holder 45 containing a sample having particles. The first optical element 43 and the second optical element 44 are configured to form the light beam 42 in an appropriate configuration within the sample (in this example, a parallel beam). Other arrangements of the optical elements may be used, and this arrangement is merely exemplary. The light beam 42 is represented schematically by the hatched portion in FIG. 3. A portion of the light beam 42 is diffracted by the sample particles, resulting in diffracted light 47. The diffracted light 47 is focused by a third optical element 46 onto a detector array 48. The detector array 48 detects the scattering pattern produced by the diffracted light 47. In other embodiments, the detector need not be an array, and individual discrete detector elements positioned at different locations may be used to obtain vectors of scattering intensity at different scattering angles. In some embodiments, the detector may have a combination of an array of detector elements and at least one discrete detector element.

[0054] Another method for measuring particle properties (e.g., particle size, zeta potential) is dynamic light scattering (DLS), also known as photon correlation spectroscopy. DLS may use a time series of measurements from one or more detectors to determine particle characteristics based on the change in the scattering signal over time. Low-frequency changes in scattering intensity may be attributed to larger particles, while high-frequency changes may be attributed to smaller particles. Such instruments typically use a fast, highly sensitive detector (e.g., an avalanche photodiode) to detect a relatively narrow range of scattering angles centered around the detection angle (e.g., a range of 5 degrees or less, or 2 degrees or less). The time history of scattering intensity from at least one detector may be inverted by correlating the scattering intensity, thereby determining at least one autocorrelation function. A fit to the autocorrelation function (e.g., CONTIN or other methods) may be performed to determine particle size (e.g., Z-average particle size) or particle size distribution.

[0055] FIG. 4 is a schematic diagram showing a detector 48, a processor 51, and an output device 52. In a diffraction-based device, the detector 48 typically has multiple detectors, such as the detector array 48 described in connection with FIG. 3, arranged to detect light scattered at multiple different scattering angles relative to the incident light beam 42 on the sample. In a DLS device, a single detector may be provided to detect scattered light at a detection angle, or a single detector with reconfigurable collection optics that can be controlled to direct light from different detection angles to the detector, or multiple detectors with different detection angles. The processor 51 receives a series of measurements of the light intensity at the detector or detectors generated by the diffracted light 47 from the detector array 48. In the case of dynamic light scattering measurements, the processor 51 may be configured to perform correlation operations on the measurements to characterize particles in the sample.

[0056] The processor 51 may store the measurements in a machine-readable storage medium, such as a memory, a solid-state storage drive, a hard disk, the cloud, etc. The processor 51 may then output the analysis results to an output device 52, which may comprise a display screen.

[0057] The processor 51 is further configured to determine changes in the dynamic particle characteristics over time by dividing the measurements into a number of shorter sub-runs, classifying each sub-run, and determining the dynamic particle characteristics of the sub-runs in a classification.

[0058] FIG. 5 shows a flowchart / diagram illustrating a method 60 according to an embodiment. The method begins in step 61 by obtaining scattered light measurements, for example, using the apparatus 40 of FIG. 3 . The measurement is divided into multiple sub-runs during the measurement (e.g., by sequentially acquiring sub-runs) or after the measurement (e.g., by dividing a time series of measurement data into sub-runs). In step 62, the method classifies each sub-run. The classification of each sub-run may be based on classification particle characteristics obtained from the sub-run. For example, the classification characteristics may be average particle size (e.g., Dv50 or Z-average) obtained by diffraction analysis or dynamic light scattering analysis. In other embodiments, classification may be performed based on environmental conditions, such as the temperature or pH of the sample, and different sub-runs may be classified. The classification of the sub-runs may be performed automatically based on criteria preset by the user.

[0059] In the example of Figure 5, each subrun is classified as either transient 63 or non-transient 64. A transient category / classification means that the subrun is significantly different (so far or overall) from the other subruns in the measurement. A non-transient category / classification means that the subrun is similar (so far or overall) to the other subruns in the measurement. If the classification is based, for example, on average particle size, a statistically based threshold of the subruns may be used as the threshold for classification. For example, an average particle size that is more than three standard deviations away from the average average particle size than the other subruns may be used to classify the subrun as transient.

[0060] It is not necessary for all sub-runs to be classified. In some embodiments, a single classification may be used, for example, to select less noisy measurements and discard other measurements. Similarly, it is not necessary to use particle size for classification; other parameters (such as polydispersity index, scattering intensity, etc.) may be used. In another example, the method may classify some or each of the sub-runs into one of three or more classes.

[0061] Referring again to the example of Figure 5, once each of the sub-runs has been classified as either transient 63 or non-transient 64, in step 65, the variability of the dynamic particle properties is obtained by determining the dynamic particle properties 66, 67 of the sub-runs in one or more of the classifications 63, 64. The variability may indicate that the dynamic particle properties are stable (i.e., no variability is reported).

[0062] For example, non-transient sub-runs may be analyzed, and the variability of the dynamic particle property may be determined for each non-transient sub-run. In one example, the dynamic particle property (i.e., the property whose change over time is being investigated) may be the average particle size. Because the non-transient sub-runs exclude transient scattering that may arise from contaminants, the average particle size in the non-transient sub-runs may be more reliable, allowing for a more accurate determination of whether there is a trend in particle size over the measurement period. This is true for many other particle properties, which may be distorted or unreliable due to special causes (e.g., as discussed above).

[0063] The transient sub-randomization may also be analyzed to determine changes in dynamic particle properties. For example, the transient sub-randomization may be due to aggregates that may grow in size during the measurement period. The change in aggregate size during the measurement period may be used to gain insight into the overall sample.

[0064] In this example, in step 68, the dynamic particle characteristics 66, 67 for each classification 63, 64 subrun are classified as trend or steady state. This is not essential to the invention, but may provide further useful insight into the sample. The dynamic particle characteristics may include, for example, the median particle size of the particle size fractions of the particle size distribution or PSD (e.g., intensity-weighted PSD).

[0065] 6 through 9 show examples of classification of sub-runs. In these examples, classification involves determining a classification particle characteristic and comparing the classification particle characteristic to a threshold value for the same characteristic. A sub-run is classified as transient if the classification particle characteristic exceeds the threshold value, and as non-transient if the classification particle characteristic does not exceed the threshold value.

[0066] In the embodiments of Figures 6-9, derived particle diameter properties such as Dv10 and Dv90 are used as classification particle properties, however, it will be appreciated that any suitable derived particle property, including surface area, volume, etc., may be used to classify sub-particles into transients in alternative embodiments.

[0067] FIG. 6 is a graph of 70 sub-runs with the cumulative standard deviation of Dv10 shown in plot 71 and the cumulative standard deviation of Dv90 shown in plot 72. In the example of FIG. 6, a sub-run is classified as transient if the difference between the cumulative standard deviation of the sub-run and the cumulative standard deviation of the immediately preceding sub-run exceeds a predetermined threshold. Three sub-runs are classified as transient. The cumulative standard deviations of Dv10 for these sub-runs are shown in 73a-c of plot 71, and the cumulative standard deviations of Dv90 for these sub-runs are shown in 74a-c of plot 72.

[0068] FIG. 7 shows a plot 81 of an X-bar graph of Dv10. In the embodiment of FIG. 7, the classification particle characteristic is Dv10. The threshold for classification using Dv10 is determined by reference to the average Dv10 value 82 for all sub-runs. A sub-run is classified as transient if the absolute value of the difference between the Dv10 of the sub-run and the average value 82 is greater than three standard deviations (shown as upper limit 83 and lower limit 84). In FIG. 7, three sub-runs are classified as transient. The Dv10s of these sub-runs are shown as 85a-c in plot 81.

[0069] Figure 8 shows a bar graph of Dv90 in plot 91. The classification particle characteristic in this example is Dv90. The classification threshold is determined by reference to the average Dv90 of all subruns, 92. A subrun is classified as transient if the difference between the Dv90 of the subrun and the average 92 is greater than three standard deviations (shown as upper limit 93 and lower limit 94). In Figure 8, three subruns are classified as transient. The Dv90 of these subruns is shown in 95a-c of plot 91.

[0070] FIG. 9 shows three graphs 101, 102, and 103 plotting the scattering intensity measured by each of multiple detectors against the detector number. Each detector may be configured to detect light at a different scattering angle (i.e., the range of scattering angles received by each detector may be different). Each graph 101, 102, and 103 includes a scattering distribution 101a, 102a, and 103a, respectively, and a plot of a fitted line 101b, 102b, and 103b. As shown by the regions of the graphs designated 101c, 102c, and 103c, data from low detector numbers (detectors 1 through approximately 20) have a large relative error (e.g., average RMS of 5% or greater) relative to the fitted line. This may also be used to identify statistically significant outliers and subsequently classify subruns as transient.

[0071] 10 is a graph 111 plotting light intensity against detector number. Graph 111 shows two plots 111a and 111b representing sub-runs classified as transient, and plot 111c representing sub-runs classified as non-transient.

[0072] FIG. 10 shows a graph 112 of particle size distributions for classified subruns. Plot 112a of graph 112 shows the particle size distribution for non-transient subruns, and plot 112b shows the particle size distribution for transient subruns. In FIG. 10, time-averaged transient data and time-averaged non-transient data for a particular sample are displayed independently of each other once classified. This is a particularly useful display method for data that contains both transient and non-transient classes with partial time resolution, e.g., data that is transient in time but stable in particle size distribution.

[0073] Returning to Figure 5, once the dynamic particle characteristics 66, 67 of the subruns in each classification 63, 64 have been determined, the dynamic particle characteristics 66, 67 (e.g., mean particle size or median particle size of a particular sample fraction) may be classified as trending or steady state. Figures 11 through 17 illustrate various ways this classification can be implemented and used.

[0074] FIG. 11 shows a plot 121 of the cumulative average Dv50 (average mass of distribution) of particles for a sample calculated over 70 sub-runs. The cumulative average Dv50 shows an upward trend starting from the 20th sub-run. This trend may be used to identify the occurrence of swelling, aggregation, clumping, coalescence, device instability, etc.

[0075] Figure 12 shows a plot of Dv50 131 on an X-bar graph using the same data as Figure 11. The X-bar graph shows the mean Dv50 132, the upper control limit 133, and the lower control limit 134. Subrun 58 is classified as transient and its Dv50 is outside the upper control limit, as indicated by 135.

[0076] The methods disclosed herein may be used to analyze waste stream samples or to monitor continuous processes, including sonication of samples and / or agitation with instrument dispersion accessories, external grinding and milling (in facilities of all sizes from laboratory to industrial), agglomeration as part of a reaction or other particle growth process, thermal, chemical or admixture titration, etc.

[0077] Combining Figures 11 and 12, it can be seen that Dv50 increases with time, indicating possible swelling or aggregation of the sample in addition to the presence of transient subruns in subrun 58. Transient subruns 58 may be due to spurious results or a very low concentration (or number) of a particular particle size class.

[0078] In FIG. 13 , particle size distributions are determined for each of six sub-runs 141-146. It should be understood that, in practice, additional sub-runs may be utilized; however, the representation of six sub-runs is purely for the purpose of simplifying the illustrative embodiment being described and facilitating understanding of the concepts disclosed herein. Sub-runs 141, 143, and 146 are shown to have two independent particle size distributions 141a, 141b, 143a, 143b, 146a, and 146b, while sub-runs 142, 144, and 145 are shown to have a single particle size distribution 142a, 144a, and 145a. In conventional time-averaged measurements, secondary particle size distributions such as those depicted by 141b, 143b, and 146b are averaged out, potentially distorting the overall particle size distribution and making it impossible to discern the evolution of distinct populations within the sample.

[0079] Particle size distributions 141a, 142a, 143a, 144a, 145a, and 146a are consistent in both particle size distribution and intensity. Particle size distributions 141b, 143b, and 146b are consistent in particle size distribution and intensity, but are larger in particle size distribution and lower in intensity than particle size distributions 141a-146a. This identifies two particle fractions: a first particle fraction (i.e., non-transient particle fraction, 141a-146a) corresponding to the non-transient portion of sub-runs 141-146a, and a second particle fraction (i.e., transient particle fraction) corresponding to the transient portion of sub-runs 141a, 143b, and 146b. In this regard, Figure 13 shows a schematic diagram of the particle size distributions of the non-transient and transient particle fractions for each sub-run. When particle size (shown on the horizontal axis) is considered as the dynamic particle property, Figure 13 shows that the dynamic particle property can be classified as steady-state in both the non-transient and transient particle fractions. That is, particle size does not change over time in both the non-transient and transient particle fractions. This is also true when particle concentration (shown on the vertical axis) is considered as the dynamic particle property. In such a scenario, the lack of change in particle size and concentration over time in both the transient and non-transient particle fractions may indicate that the sample is not changing over time.

[0080] Referring now to FIG. 14, particle size distributions have been determined for each of the six subruns 151-156. Subruns 151, 153, and 156 are shown to have two distinct particle size distributions 151a, 151b, 153a, 153b, and 156a, 156b, while subruns 152, 154, and 155 are shown to have a single particle size distribution 152a, 154a, and 155a. In this regard, each of subruns 151-156 is shown to have at least a portion of particle size distribution 151a-156a, which is consistent in both particle size distribution and intensity. Subruns 151, 153, and 156 are shown to have a secondary distribution of larger particles 151b, 153b, and 156b identified in each subrun. FIG. 14 shows the particle fractions classified similarly to FIG. 13, but in this case, transient events 151b, 153b, and 156b differ from each other in particle size, indicating a more varied sample.

[0081] In some embodiments, particle properties (such as the type of information represented by Figures 13 and 14) are reported in a time-resolved manner, which may be used in any of the following applications: i. Detection of spurious, specific, size, and shape fractions in complex environmental and medical samples, industrial processes, etc. ii. Improving food or personal care products when a rough texture in the mouth or on the skin is unpleasant iii. Improved pharmaceutical development where large particles are known to induce immune responses that mitigate the performance of active ingredients iv. Improvement of additive manufacturing, especially of structural elements where large particulate inclusions significantly weaken the structure. v. Manufacturing of pneumatic tires, where even a small amount of large particulate inclusions can seriously affect the stability of the tire. vi. Grinding of cement, calcium carbonate, silica, titania, etc. vii. Chemical Mechanical Planarization (CMP) slurries, where the presence of large transients can damage the wafer, making it expensive and time-consuming to replace and requiring process downtime. viii. Inks and toners that require large particles to be avoided to avoid damaging the transfer mechanism (laser, inkjet, etc.) and transferring particulate matter to the page.

[0082] While a number of particular applications have been identified above, it should be understood that the identification of such applications should in no way be construed as limiting the intended scope of applications in which such embodiments may be implemented.

[0083] In FIG. 15, particle size distributions are determined for each of the five sub-runs 161-165. Particle size distributions, designated 161a-165a, are determined for each of the five sub-runs classified as non-transient, and particle size distributions 161b-165b are determined for each of the five sub-runs classified as transient. As described above with reference to FIG. 13, non-transient and transient particle fractions are identified from particle size distributions 161a-165a and 161b-165b. As shown in FIG. 15, the non-transient particle fractions are steady-state with respect to particle size and trend with respect to particle mass, while the transient particle fractions are steady-state with respect to particle concentration and trend with respect to particle size. The dynamic particle characteristic changes in particle size and particle concentration within each of the non-transient and transient particle fractions may be used to determine whether the transient particle fractions are coalescing. This may be further indicated when the particle size difference between the two fractions, designated by double-headed arrow 167, is considered small.

[0084] In Figure 16, three particle fractions 171, 172, and 173 are identified. Figure 16 shows that the first particle fraction 171 has a trend with particle size and a trend with particle concentration, the second particle fraction 172 is steady state with particle concentration and a trend with particle size, and the third particle fraction 173 is steady state with particle concentration and unstable with particle size. This may indicate Ostwald ripening within the sample. Ostwald ripening is a form of coalescence, a process in which small sols dissolve and deposit onto larger sols because a large surface-to-volume ratio is energetically favorable; particles are less stable when they are part of a grain boundary than when they are enclosed within the volume of a crystal lattice, for example.

[0085] In Figure 17, a non-transient particle fraction 181 is identified that is steady-state with respect to particle size and trending with respect to particle concentration. A transient particle fraction 182 is also identified, as shown in plots 182a-d, which is unstable with respect to both particle size and particle concentration. This is indicative of aggregation within the sample; the transient particle fraction may be experiencing gelation. If the sample is exposed to an external stimulus, such as an admixture or a change in the sample's chemistry, this could indicate aggregation. These processes may occur on a longer time scale than can be obtained in the measurement. However, once measurements are obtained, appropriate classification of the sub-run data may be used to investigate the dynamics of the sample.

[0086] The techniques disclosed herein may be applicable to any diffraction-based instrument. The methods disclosed herein may be used for nanoscale characterization, for example, to investigate protein unfolding kinetics (e.g., under external stress such as thermal aggregation kinetics) and chemically induced aggregation (e.g., due to changes in pH and / or ionic conditions).

[0087] Time-resolved reporting (using subruns) may be used to determine when measurement acquisition can be stopped. Figure 18 shows the cumulative mean (plot 191) and cumulative standard deviation (plot 192) of Dv50 over 70 subruns of 1.0 μm latex dispersed in deionized (DI) water. As shown in Figure 18, the asymptotic behavior of the cumulative mean and cumulative standard deviation indicates that acquisition of time-series measurements may be stopped after approximately 20 subruns for the mean and approximately 50 subruns for the standard deviation. However, around 50 subruns for the mean and 65 subruns for the standard deviation, both begin to show a gradual trend again, likely due to instrument drift or sample perturbations caused by the dispersion process. This process is not limited to a single derived particle property, nor is it limited to non-temporal subruns.

[0088] In Figure 19, particle size distributions 201a-f are determined for each of the six non-temporally classified subruns. Error bars 202a-c, 203a-c, and 204a-c represent the errors in Dv10, Dv50, and Dv90 for subruns 201a, 201c, and 201f, respectively. Figure 20 shows that these error bars steadily decrease as a time series of measurements is obtained.

[0089] In Figure 20, a group of particle size distributions 211a-e are determined for each of the five sub-runs classified as transient. The measurement time series obtained to provide the sub-runs were acquired over a relatively long period of time from laser diffraction measurements at extremely low sample concentrations. If instrument drift can be avoided or characterized, representative sample analyses can be constructed over time for samples that are stationary and ergodic, but whose concentrations are low and, when dispersed, transient.

[0090] 19 and 20 show that the point at which acquisition of measurement time series can be stopped can be determined based on sound statistics based on time reporting data from the measurement time series.

[0091] FIG. 21 illustrates a method 220 that can be used to identify whether it is appropriate to adjust process or measurement parameters, for example, in a continuously monitored process (eg, microparticle production).

[0092] In step 221, a sub-run measurement of scattered light is obtained. In step 222, it is determined whether the current sub-run meets one or more quality criteria. If the quality criteria are met, a subsequent measurement is obtained in step 223. If the quality criteria are not met, it is determined in step 224 whether the measurement is critical to the process. If the measurement is determined to be critical to the process, a need for a process change is reported or the monitored process is stopped in step 226. If the measurement is determined not to be critical to the process, the measurement conditions are reported or changed in step 225. The method then restarts with the next measurement in the measurement time series if it is determined to obtain a next measurement.

[0093] A similar approach can be used to create an adaptive measurement approach, in which quality measurements are determined from each subrun and compared to a quality standard. Comparison of the quality measurements with the quality standard can be used to determine whether adjustments to measurement parameters are appropriate. The quality measurements can be derived from particle characteristics determined from the subrun (e.g., average particle size, polydispersity, etc.) or can be determined with reference to the pre-processing scattering data (e.g., based on the value of the maximum intensity of the scattered light). Measurement adjustments can be automatic and include changing the duration of the subrun, selecting a different algorithm used to process the subrun to determine particle characteristics, changing one or more settings of the algorithm used to process the subrun to determine particle characteristics, changing the wavelength, intensity, or modulation of the light beam, changing the spectral range or spectral sensitivity profile of the detector, changing the total number of subruns obtained, changing the flow rate, stirring speed, or sonication of the sample, adjusting the stirring amplitude or frequency of the sample, adjusting the airflow to the sample, adjusting the temperature of the sample, degassing the sample, adjusting the pH of the sample, the concentration of the sample, or the level of additives and / or admixtures in the sample.

[0094] Referring now to FIG. 22, a schematic multidimensional pictogram representation of particle size distributions for multiple subruns of a laser diffraction measurement is shown, illustrating transient and non-transient subruns. To facilitate understanding of the schematic, only some of the transient subruns are labeled "T," while non-transient subruns are not. It is understood that in this figure, the "window number" axis represents time, and each line in the pictogram represents a subrun (i.e., a point in time). Such a representation can provide a more holistic representation of the data than, for example, the sum (i.e., time-averaged) transient and non-transient data shown in FIG. 10. For example, many of the actual sample effects shown as distinguishable from the non-transient, transient, and trend classes may be visually identifiable using this type of plot. This can be beneficial, for example, if the instrument has a live display that displays measurement results during the measurement (e.g., for monitoring and / or quality control processes). It can also aid in clear and concise communication of the method, particularly in traditional and / or necessarily conservative markets such as quality assurance.

[0095] Figure 23 also shows another way of representing the obtained data, this time in the form of a schematic heat map display of particle size distributions for multiple sub-runs of laser diffraction measurements illustrating transient and non-transient sub-runs. In this figure, the transient particle fractions of the sub-runs are visible as lines on the right (some of which are indicated by arrows), and the non-transient particle fractions of the sub-runs are shown on the left (labeled "steady state" in the figure).

[0096] Although not shown in Figures 22 or 23, it should be understood that automatic, semi-automatic, or manual initiation of measurements, adapted to, for example, changes in the apparatus, sample environment, dispersion composition and / or chemistry, can be clearly represented by marking the starting points, or other time-resolved events of interest, on the plots to further simplify their interpretation.

[0097] Advantageously, the techniques disclosed herein enable the detection of single particle events during measurements. For example, single particle events can be detected in dry or wet dispersion measurements. This potentially enables the characterization of ultratrace amounts of particulate samples compared to known / currently available laser diffraction instruments. It should be noted that although single particles, by definition, must be monosized, analytical broadening of detected events occurs because the applied mathematical reduction is optimized for more typical / common characteristics of multiparticulate samples, resulting in non-zero particle size distributions in reported measurements.

[0098] Figure 24A shows the measurement data for a multi-particle sample, while Figure 24B (a reconstruction of data from a single particle from boxed area B in Figure 24A) and Figure 24C (a reconstruction of data from boxed area C in Figure 24A) are subsets of the results in Figure 24A.

[0099] Using information such as that disclosed herein, it may be possible to optimize system controls to adjust and maintain sample concentration as a single particle or limit the sample to a small number of particles (i.e., measurements are primarily transient and individual particle scattering events may be detected). Such optimization may include, but is not limited to, pump and / or agitator speeds of the stirred reactor wet sample presentation device, flow rate from the syringe driver, feed rate from the dry powder free-fall feeder or feed rate from the dry powder feeding accessory, hopper output gate width and air flow pressure or air flow rate. Furthermore, optimization by manual titration, optimization by feedback loop from measurement, and / or any combination of these, such as, for example, semi-automated manual titration, are all contemplated.

[0100] Referring again to Figure 24A, particularly in the boxed region "A," transient sub-runs / events may also occur simultaneously with scattering from multiple particles considered to be steady-state particles in the beam. In each case, i.e., sub-runs containing only transient events in addition to sub-runs containing both transient and non-transient (e.g., steady-state) events, detection of a transient event may trigger the capture of a spatial domain image of the event or video around the event. For example, the event may trigger a camera. However, particle residence times in the beam are likely on the order of microseconds, making image capture difficult in this embodiment. However, wet dispersion samples may be passed through the beam much more slowly, particularly in the case of gas bubble analysis, or when any type of dispersed particle requires a significant surfactant concentration to remain stable in the dispersion. In such cases, a syringe driver, wet-to-waste sample presentation scheme may be performed slowly enough to avoid cavitation or foaming, potentially enabling the implementation of camera-on and image capture triggers.

[0101] Referring now to Figures 25A-25D, scattering data are shown for a series of decreasing aliquot concentrations (5.0 grams, 1.0 grams, 0.25 grams, 0.1 grams). As the aliquot concentration is reduced (by decreasing the mass of each aliquot while keeping the total amount of dispersant the same), a transition from a multiparticulate sample to a single-particle (or quasi-single-particle) sample is observed.

[0102] According to one embodiment, the camera is continuously active and a short running buffer of video is stored in memory. This helps to significantly ease the requirements on the time it takes for the camera to be powered on upon detection of an event identified from a measurement, as each detected event can be easily associated with an image frame of the current (past) event from the short video buffer. Furthermore, the problem of capturing potentially transient events in supersonic particle flows is solved, while also capturing the properties of multi-particle samples using a non-transient mode. A typical aliquot for laser diffraction analysis is 10 10 From 10 20Considering that the sample may contain particles, one particular advantage of this approach becomes clear: to avoid multiple scattering, it is simply not possible to record an image of the entire sample. On the other hand, the disclosed technique allows capturing images of all dispersed, transient objects.

[0103] Referring now to Figures 26 and 27, exemplary scattering data, including single particle resolution, are shown for small sample amounts. As can be seen by the small number of peaks in Figures 26A and 26B, the samples in these figures have much smaller sample amounts compared to the small sample amounts of the samples in Figures 27A-C.

[0104] As noted above, by definition, single particles must be monosized. However, as shown in Figure 28A, the applied mathematical reduction, optimized for a more representative / general characterization of multiparticle (ensemble) samples, results in analytical broadening of detected single-particle events, resulting in a non-zero width of the particle size distribution in the reported measurements. In Figure 28A, single-particle events are detected, and a single evaluated estimate can be derived from the reported distribution (e.g., mean, mode, dx50, etc.) for each event. Meanwhile, Figure 28B shows that the particle size bin width (i.e., the width of the particle size column / range each "bin" covers) is altered to accommodate some or all of the instrumental broadening of the reported single-particle PSD (particle size distribution).

[0105] At this point, single particles may be identified / classified as temporary subunits, which are then used for further analysis.

[0106] 29 illustrates a further embodiment of the present disclosure, in which the analysis is optimized to identify single particle events and / or reduce events to single particle size classes. With analysis optimized for identifying single particle events (i.e., as a transient sub-run), it may also be possible to switch analysis modes for each sub-run. For example, a "traditional" analysis may be applied to a "non-transient" sub-run (e.g., a multi-particle (ensemble) sample), while an "optimized" analysis may be applied to a "transient" sub-run (e.g., a single particle event). As will be appreciated by those skilled in the art, this switching between analysis modes may occur during a measurement depending on the sample being measured.

[0107] Single particle laser diffraction enables many processes and sample property calculations that were previously impossible. For example, Figures 30A and 30B illustrate how, by estimating the absolute particle number-particle size distribution, this estimate can be converted to an associated particle mass-particle number distribution, and thus a total sample mass estimate can be obtained.

[0108] Furthermore, because this technique provides a significantly more accurate measurement of sample concentration than "bulk" characterization methods like photometric calibration schemes, the validation of sample optical properties (e.g., using Beer-Lambert law combined with Mie scattering calculations) also significantly improves. In this regard, for unknown multiparticle samples, ultralow concentration measurements that generate single-particle data can be used to obtain highly accurate particle concentration values, which can then be used to validate or correct the complex refractive index prior to characterizing multiparticle samples using a combination of Mie scattering theory and Beer-Lambert law.

[0109] Because individual / single particles are detectable, local background estimates may be derived from the scattering of only the dispersion around each particle event, as shown in Figure 31. Traditional laser diffraction measurements typically measure the background before adding the dispersed phase. However, characterization over long time scales is challenging because instrument alignment and / or dispersion properties can drift over time. In contrast, embodiments of the present disclosure enable long-time scale characterization using laser diffraction, as the local background around each event can be estimated.

[0110] In other embodiments, an estimate of the background from a region (in time) of single scattering events may be used as an estimate of the background of a region (in time) of scattering measurements from a multiparticle sample. In this way, for multiparticle samples that inherently contain a fraction present as single particles, and / or where such particle fractions are intentionally added to the target multiparticle sample fraction, the background may be continuously estimated over time. In embodiments, an initial background may be recorded according to a "traditional" measurement, and the single particle background estimate may be used during subsequent dispersed phase measurements to infer the point in time when the previously recorded background is no longer an accurate estimate of the subsequent background. This may also be useful for monitoring changes / variations in the background to identify when measurements should be stopped.

[0111] Yet another embodiment may relate to sample titration. For example, when titrating a sample, changes in sample properties related to deliberate changes in dispersant or inclusion of admixtures in the dispersed phase and potential interactions of the admixtures with the primary particle size fraction surface may be monitored. Specific examples of this include, but are not limited to, PEGylation and / or Ostwald ripening. In such cases, it is important to monitor the background in a "live" or "real-time" manner, especially if the chemistry of the dispersant changes, thereby changing the background as the dispersant is titrated. In such cases, counting individual particle scattering events is not necessarily important, so a recirculating dispersion system may be used to titrate the dispersion, and the background may be continuously monitored throughout, but individual particles may be detected multiple times. In such cases, a population with the relative counts of each titrated result may be presented to the user (via the known technique of Mie scattering conversion from volumetric intensity-based measurements to numbers).

[0112] When the background from each scattering event is removed and the difference in optical contrast is large enough, polyspectroscopic scattering ellipsometry, single particle measurements, allow the detection and accurate characterization of particles of the same size but different masses.

[0113] Simultaneous collection of properties of electromagnetic radiation scattered at multiple detection angles has been identified as playing an important role in several types of measurements for material characterization, closely related to laser and X-ray diffraction. For example, intensity as a function of angle is not the only property that may be of interest in scattered (or unscattered) light. In this regard, characterization of electromagnetic radiation emitted from a test sample at multiple angles and simultaneously detected at all angles, such as, but not limited to, Mie scattering ellipsometry, diffraction spectroscopy, small-angle neutron scattering, and combinations of diffraction, spectroscopy, and imaging, is contemplated and disclosed herein. This may include, but is not limited to, physical quantities (in addition to intensity) such as discrete wavelengths or wavelength ranges, polarization states and quantities derived therefrom, such as D and tan(j), and, for example, as part of Mie scattering ellipsometry, the complex refractive index and data regarding the internal and external shape of the subsequently dispersed particle, and the phase shift within each angle, the phase difference or phase shift between angles, and / or the absolute phase difference at each detection angle relative to a reference path.

[0114] With regard to Mie scattering ellipsometry in particular, it is understood from the literature and commercially available solutions at the time of writing that no solution currently exists that can generate precise and accurate estimates of particle refractive index from samples where particle size, particle shape, or particle refractive index are polydisperse and / or multimodal. Mie scattering ellipsometry measurements may be used to record all Jones or Stokes vector quantities simultaneously or sequentially in time, and embodiments of the present disclosure are equally applicable. For example, transient and steady-state classes may be derived from ellipsometry data for any or all scattering angles of interest. For example, the angular scattering pattern of any sample may be identified by minima and maxima in light intensity, overall very low scattering intensity from an ensemble of particles with low scattering cross-sections, and / or scattering from a single particle. For single-particle samples with high scattering, traditional detector types such as CMOS may still be used. However, with newly available EMCCDs, single photon-recognizable or single photon-counting detectors at very low scattering cross sections of single particles may be used to recover signal to noise in measurements suitable for challenging applications, such as detecting refractive index differences between aqueous bioparticle dispersions, loaded and unloaded nano- and microparticle-based pharmaceuticals such as lipid loaded mRNA or adeno-associated viruses. In the single photon-recognizable case, the signal recorded is not an analog intensity level but a series of photon time arrival events; however, aspects of the embodiments disclosed herein may be applied to individual photon time-tagged data.

[0115] While specific embodiments have been described, the scope of the invention should be determined by reference to the appended claims.

Claims

1. 1. A method for investigating sample properties that change over time, comprising: illuminating a sample having particles with a light beam such that scattered light is produced by interaction of the light beam with the sample; obtaining measurements of the scattered light over a measurement period divided into a plurality of shorter sub-runs; Classifying each sub-run; and determining the sample particle characteristics of the sub-run. method.

2. The method of claim 1 , further comprising transforming each sub-run before classifying each sub-run.

3. The method of claim 1 , wherein determining the sample particle characteristics of the sub-runs is performed before classifying each sub-run.

4. 4. The method of claim 1, further comprising determining changes in sample particle properties over time.

5. The method of claim 1 , wherein the sample characteristics comprise particle size and / or particle concentration.

6. determining a particle size distribution and identifying a particle fraction for each subrun in the classification; The method of claim 5 , wherein the particle size is the average particle size of the particle fraction and / or the particle concentration is the concentration of the particle fraction.

7. classifying each sub-run includes determining a classification sample characteristic and comparing the classification sample characteristic to a threshold value for the same characteristic; 7. The method of claim 1, wherein the threshold is determined from statistics of the classification sample characteristics in at least a portion of the sub-runs.

8. Classifying each sub-lane is a) the classification sample characteristic is outside the control limits, said control limits being at least 2.5 standard deviations away from the mean; b) A run of more than m consecutive subruns of particle classification characteristics on the same side of the mean value. c) a run of consecutive subruns of n particle classification properties that all increase or decrease d) A run of successive subruns of o particle classification characteristics, alternating up and down. e) A run of consecutive subruns of p particle classification characteristics that deviate more than two standard deviations from the mean. f) a run of q consecutive sub-runs of particle classification characteristics that are more than one standard deviation away from the mean; and g) a run of consecutive subruns of r particle classification characteristics less than one standard deviation from the mean; The method of claim 1 , further comprising applying one or more classification rules selected from:

9. classifying each subrun includes classifying each subrun as transient or non-transient; The method of claim 1 , further comprising automatically classifying changes in dynamic particle properties into trending or steady states.

10. 10. The method of claim 9, including the specification of claim 6, further comprising automatically correlating a trend state in a first particle fraction with a complementary trend state in a second particle fraction.

11. A non-transitory machine-readable medium comprising instructions for configuring an apparatus having a processor to perform the method of any one of claims 1 to 10.

12. 1. An apparatus for investigating particle characteristics that change over time, comprising: a light source; a sample holder; a detector; and a processor; the sample holder is configured to receive a sample having particles; the light source is operable to illuminate the sample holder with a light beam so as to generate scattered light upon interaction of the light beam with the particles of the sample; the detector is configured to detect the scattered light over a measurement period; The processor: Dividing the measurement period into a plurality of shorter sub-runs; Classify each sub-run, configured to determine a sample particle characteristic of the sub-sample; Device.

13. configured to perform a diffraction analysis; 13. The apparatus of claim 12, comprising a plurality of detectors positioned to detect light scattered at a plurality of different scattering angles relative to the light beam incident on the sample.

14. Apparatus according to claim 12 or 13, configured to carry out the method according to any one of claims 1 to 10.

Images