Apparatus for characterizing particles and method for characterizing particles

Abstract

A device (10) for characterizing particles using nanoparticle tracking analysis (NTA) is provided. The device (10) comprises: a flow cell (101) for containing a sample comprising a plurality of particles suspended in a fluid; a pump (102) configured to provide a flow of the sample through the flow cell (101); a light source (103) configured to illuminate the sample; an imaging system (104) configured to collect light scattered or fluoresced by particles moving within the flow cell (101) and within a detection zone of the imaging system (102) and capture a video of the particles moving within the detection zone; and a computer (105) configured to process the video. The computer (105) is configured to determine an estimated flow rate of the sample through the flow cell (101). Determining the estimated flow rate includes performing one-dimensional particle tracking in a direction perpendicular to the expected flow direction of the sample.

Description

Technical Field

[0001] The present invention relates to an apparatus and method for characterizing particles using nanoparticle tracking analysis. Background Art

[0002] Nanoparticle tracking analysis (NTA) is a method for characterizing particles suspended in a fluid by tracking the movement of individual particles and determining the particle size based on their Brownian motion. Generally, the method includes irradiating a sample containing a plurality of particles suspended in a fluid, collecting the light scattered or fluoresced by the particles, and capturing a video of the particles as they move in the fluid. The fluid can be a liquid (such as water or some other liquid), or it can be a gas (such as air). The video is analyzed frame by frame to track the movement of the particles. The displacement of the particles caused by Brownian motion between the frames of the video is determined, and this particle displacement is used to determine the particle size using Fick's second law of diffusion and the Stokes-Einstein equation.

[0003] During NTA, it is desirable to provide a flow of the sample. This can improve the accuracy of the measurement because more of the sample is moved through the detection zone of the apparatus for performing the analysis, and the measurement is less likely to be dominated by anomalies. Very large or very small particles or contaminants will be swept out of the field of view of the video by the flow of the sample. However, providing flow presents problems in terms of precisely tracking the particles and determining the proportion of the particle displacement that is the result of the sample flow and part of the result of Brownian motion. These problems become more severe as the sample flow rate and concentration increase. As the sample flow rate or concentration increases, it may also be difficult to determine when the accuracy of particle tracking drops below an acceptable level.

[0004] Known NTA processes may include: performing two-dimensional particle tracking, for example, by identifying particles from the current frame of the video as the Euclidean nearest particles in subsequent frames of the video; estimating the sample flow rate based on the average distance traveled by the particles tracked by the two-dimensional particle tracking between subsequent frames of the video; correcting the drift of the particles based on the estimated flow rate; after correcting the drift, determining the distance traveled by the particles due to Brownian motion; and determining the particle size based on the distance traveled due to Brownian motion. Two-dimensional particle tracking may introduce unacceptable errors in identifying particles in subsequent frames of the video, which ultimately compromises the accuracy of the particle size measurement. Summary of the Invention

[0005] A first aspect of the present invention provides an apparatus for characterizing particles using nanoparticle tracking analysis (NTA). The apparatus includes: a flow cell for containing a sample, the sample including a plurality of particles suspended in a fluid; a pump configured to provide a flow of the sample through the flow cell; a light source configured to illuminate the sample; an imaging system configured to collect light scattered or fluoresced by particles moving within the flow cell and within a detection zone of the imaging system, and to capture a video of the particles moving within the detection zone; and a computer configured to process the video to determine an estimated flow rate of the sample through the flow cell. Determining the estimated flow rate of the sample through the flow cell includes performing one-dimensional particle tracking in a direction perpendicular to the expected flow direction of the sample.

[0006] As described herein, particle tracking is the process of identifying the same particle within subsequent frames of a video. For example, a given particle can be identified within the current frame of a video, and particle tracking can be used to identify the same given particle within subsequent frames of the video. A subsequent frame is a frame that appears after the current frame. In some embodiments, particle tracking will be "per frame", so the subsequent frame will always be the next frame of the current frame, but this is not required. For example, particles can be tracked every other frame or every third frame, etc.

[0007] It should be understood that the direction perpendicular to the expected flow direction of the sample is not limited to a direction that is exactly 90 degrees to the expected flow direction. Those skilled in the art will understand that in practice there will be a tolerance when selecting the direction in which to perform one-dimensional particle tracking.

[0008] One-dimensional particle tracking can include identifying particles from the current frame of a video within subsequent frames of the video. The term one-dimensional particle tracking is used herein to denote identifying particles from the current frame within subsequent frames of a video based on their position in one dimension (which can be considered as a position along a particular direction). Identifying particles within subsequent frames of a video can include identifying the nearest particle within the subsequent frame in the tracking direction (regardless of the proximity of the particle in any other direction, such as the expected flow direction of the sample). The tracking direction can be perpendicular to the expected flow direction of the sample.

[0009] After capturing the video, one-dimensional particle tracking can be performed on the stored video. Optionally, one-dimensional particle tracking can be performed while capturing the video.

[0010] Compared with other particle tracking techniques, performing one-dimensional particle tracking in a direction perpendicular to the expected flow direction of the sample can reduce the error in identifying particles from the current frame of the video in subsequent frames of the video, because there is no bulk flow in this direction, thus reducing the misidentification of a second particle that moves to a position in a subsequent frame very close to the position of the first particle in the previous frame, because the particles move less in the direction perpendicular to the flow. This improved identification can improve the estimation of the sample flow rate, which can be used to correct the particle drift caused by the flow. This, in turn, may improve the accuracy of determining the distance traveled by the particles due to Brownian motion, thereby improving the accuracy of determining the particle size using the Stokes-Einstein equation.

[0011] Identifying the nearest particle in a subsequent frame can include identifying the nearest particle within a one-dimensional tracking distance limit. The one-dimensional tracking distance limit can be determined from the video. Determining the one-dimensional tracking distance limit can include performing one-dimensional particle tracking in the tracking direction on a subset of the frames of the video without a tracking distance limit and determining the average distance that the particles move between consecutive frames of the video in the tracking direction. The average distance that the particles move can be used to determine the tracking distance limit. The average value can be the median. For example, the tracking distance limit can be twice the median or the median + / - 3 standard deviations.

[0012] Setting the tracking distance limit minimizes the number of potential nearest particles in subsequent frames. This minimizes the number of particles in subsequent frames, and for this purpose, in order to determine the nearest particle in a subsequent frame, it is necessary to determine the distance from the particles in the current frame. This advantageously improves the processing speed and minimizes the consumption of processing resources.

[0013] The estimated flow rate can be determined based on the average distance that the particles tracked by one-dimensional particle tracking move between video frames. The distance that the particles move between consecutive frames of the video can be measured two-dimensionally (for example, this can accommodate the case where the camera is not perfectly aligned with the flow cell).

[0014] The estimated flow rate can be determined based on the average distance that the particles tracked by one-dimensional particle tracking move between consecutive frames of all the frames of the video or between an appropriate subset of the frames of the video, where the appropriate subset of the frames of the video is a subset of the frames of the video that includes fewer than all the frames of the video. It can be assumed that the flow of the sample through the detection area of the imaging system is uniform, in which case, the estimated flow rate can be determined based on the average distance that the particles tracked by one-dimensional particle tracking move between consecutive frames of a true subset of the video frames.

[0015] The average value can be the median. For errors, the median may be more robust than other average values in identifying particles in subsequent frames of the video. As described above, performing one-dimensional particle tracking in a direction perpendicular to the expected flow direction of the sample can reduce (but not necessarily completely eliminate) errors in identifying particles in subsequent frames of the video.

[0016] In other embodiments, the average value can be the mean. In cases where the error in identifying particles in subsequent frames of the video is below an acceptable limit, using the mean can provide a more accurate estimate of the flow rate.

[0017] The computer can be configured to: use the estimated flow rate to determine the corrected position of the particle, where the estimated flow rate component of the particle movement is removed; perform two-dimensional particle tracking on the corrected position of the particle; and determine the residual flow rate based on the average distance that the particles tracked by the two-dimensional particle tracking of the corrected position move between subsequent frames of the video.

[0018] Performing two-dimensional particle tracking can include identifying the particles from the current frame in subsequent frames as the particles at the shortest Euclidean distance from the particle positions in the current frame.

[0019] The computer can be configured to determine the residual correction trajectory of each particle, including correcting the two-dimensional particle tracking of the corrected position of the particle to remove the residual flow rate.

[0020] The computer can be configured to determine the particle size based on the average distance that the particles move between subsequent frames of the video obtained from the residual correction trajectory.

[0021] The computer can be configured to determine the mean square displacement of the particle based on the average distance that the particle moves between subsequent frames of the video obtained from the residual correction trajectory. The computer can be configured to determine the diffusion coefficient of the particle based on the mean square displacement. The computer can be configured to determine the particle size using the Stokes-Einstein equation based on the diffusion coefficient. The diffusion coefficient of the particle can be obtained from the mean square displacement using Fick's law of diffusion.

[0022] Two-dimensional particle tracking of the corrected position can include identifying the particles from the current frame of the video in subsequent frames of the video. This can include identifying the nearest particles in the subsequent frames. The nearest particles can be selected from the particles within the two-dimensional tracking distance limit.

[0023] The two-dimensional tracking distance limit can be determined in response to the movement of the particles tracked by the one-dimensional tracking. For example, the two-dimensional tracking distance limit can be determined based on the median distance that the particles move when tracked by the one-dimensional tracking. Setting the two-dimensional tracking distance limit provides similar advantages to setting the one-dimensional tracking distance limit as described above.

[0024] The computer may be configured to generate a measurement warning if the estimated flow rate and / or the residual flow rate (where applicable) exceeds a predetermined threshold. An estimated flow rate or a residual flow rate that exceeds the predetermined threshold may indicate that the accuracy of the particle size measurement obtained using the device is lower than an acceptable level.

[0025] The computer may be configured to determine the particle concentration of a sample. The computer may be configured to generate a measurement warning if the particle concentration exceeds a predetermined threshold. A particle concentration that exceeds the predetermined threshold may indicate that the accuracy of the particle size measurement obtained using the device is lower than an acceptable level.

[0026] A second aspect of the present invention provides a method for determining an estimated flow rate of a sample during nanoparticle tracking analysis, comprising:

[0027] Providing a flow of the sample through a flow cell, the sample comprising a plurality of particles suspended in a fluid;

[0028] Illuminating the sample with a light source;

[0029] Collecting light scattered or fluoresced by particles moving within the flow cell and within a detection zone of an imaging system;

[0030] Capturing a video of particles moving within the detection zone; and

[0031] Processing the video to determine an estimated flow rate of the sample through the flow cell, wherein determining the estimated flow rate of the sample comprises performing one-dimensional particle tracking in a direction perpendicular to the expected flow direction of the sample.

[0032] Performing one-dimensional particle tracking may comprise identifying particles from a current frame of the video in subsequent frames of the video. Identifying particles in subsequent frames of the video may comprise identifying the nearest particle in the subsequent frame in the tracking direction (regardless of any separation in any other direction). The tracking direction may be perpendicular to the expected flow direction of the sample.

[0033] Identifying the nearest particle in a subsequent frame may comprise identifying the nearest particle within a one-dimensional tracking distance limit.

[0034] The estimated flow rate may be determined based on the average distance traveled by the particles tracked by the one-dimensional particle tracking between subsequent frames of the video. This distance may be measured two-dimensionally.

[0035] The estimated flow rate may be determined based on the average distance traveled by the particles tracked by the one-dimensional particle tracking between subsequent frames of all frames of the video or between a true subset of the frames of the video.

[0036] The average value may be the median. In other embodiments, the average value may be the mean.

[0037] The method may include: using an estimated flow rate to determine a corrected position of a particle, wherein an estimated flow rate component of the particle movement is removed; performing two-dimensional particle tracking on the corrected position of the particle; and determining a residual flow rate based on an average distance by which the particle tracked by the two-dimensional particle tracking of the corrected position moves between subsequent frames of the video.

[0038] Performing two-dimensional particle tracking may include identifying, in a subsequent frame, a particle from the current frame as the particle at the shortest Euclidean distance from the position of the particle in the current frame.

[0039] The method may include determining a residual corrected trajectory for each particle, including correcting the two-dimensional particle tracking of the corrected position of the particle to remove the residual flow rate.

[0040] The method may include determining a particle size based on an average distance by which a particle moves between subsequent frames of a video obtained from the residual corrected trajectory.

[0041] The method may include determining a mean squared displacement of a particle based on an average distance by which the particle moves between subsequent frames of a video obtained from the residual corrected trajectory, determining a diffusion coefficient of the particle based on the mean squared displacement, and determining a particle size using the Stokes-Einstein equation based on the diffusion coefficient.

[0042] Two-dimensional particle tracking of the corrected position may include identifying, in a subsequent frame of the video, a particle from the current frame of the video. This may include identifying the nearest particle in the subsequent frame within a two-dimensional tracking distance limit.

[0043] The two-dimensional tracking distance limit may be determined in response to movement of a particle tracked by one-dimensional tracking.

[0044] The method may include: generating a measurement warning if the estimated flow rate and / or the residual flow rate (where applicable) exceeds a predetermined threshold.

[0045] The method may include determining a particle concentration of a sample. The method may include: generating a measurement warning if the particle concentration exceeds a predetermined threshold.

[0046] It should be understood that any optional features, alternatives, or advantages applicable to the first aspect of the present invention are also applicable to the second aspect of the present invention (and vice versa).

[0047] Although embodiments of the present invention have been described herein with reference to NTA, it should be understood that the methods and techniques according to embodiments of the present invention are applicable to any suitable particle characterization means involving tracking individual particles.

[0048] Brief Description of the Drawings

[0049] Embodiments of the present invention will now be described by way of example only with reference to the drawings:

[0050] Figure 1 Shows the current and subsequent frames of a video and illustrates how two-dimensional particle tracking can be used in a known NTA process to determine the distance traveled by particles within a sample due to Brownian motion;

[0051] Figure 2a and 2b Shows the current and subsequent frames of a video and illustrates how two-dimensional tracking can be used in NTA to determine the distance traveled by particles in a sample due to Brownian motion when flow is introduced into the sample;

[0052] Figure 3a and 3b Shows the current and subsequent frames of a video and illustrates how errors can be introduced when using two-dimensional tracking in NTA and introducing flow into the sample;

[0053] Figure 4 Shows an apparatus for characterizing particles using nanoparticle tracking analysis according to an embodiment of the present invention;

[0054] Figure 5a and 5b Shows the current and subsequent frames of a video and illustrates how one-dimensional particle tracking can be performed in a direction perpendicular to the expected flow direction of the sample according to an embodiment of the present invention;

[0055] Figure 6 Shows the current and subsequent frames of a video and illustrates how to determine the corrected position of particles within a video frame, where the estimated flow velocity component of particle movement is removed;

[0056] Figure 7 Shows Figure 6 the subsequent frame of[] and illustrates how two-dimensional particle tracking can be performed on the corrected position of particles; and

[0057] Figure 8 Shows the residual corrected trajectory of the particles. Detailed Description

[0058] A typical NTA process involves irradiating a sample, collecting the light scattered or fluoresced by moving particles within the sample, and capturing a video of the moving particles. The video is then processed to identify the particles in each frame of the video, which appear as bright light spots. Each particle is then identified in each frame of the video, and the movement of each particle between video frames is tracked. Then, the distance traveled by each particle can be determined based on the number of pixels separating the particles between video frames and the pixel size known from the camera specifications used to capture the video.

[0059] Figure 1 Illustrates how two-dimensional particle tracking can be used in a known NTA process to determine the distance traveled by particles in a sample due to Brownian motion.Figure 1 shows the current frame 1a of the video and a subsequent frame 1b of the video. Particle 2 is shown in the current frame 1a and the subsequent frame 1b. The position of particle 2 in the current frame 1a is shown using a dashed line in the subsequent frame 1b. Particle 2 is identified in the subsequent frame 1b using two-dimensional particle tracking. Between frames 1a, 1b, particle 2 travels in two dimensions, i.e., in the x-direction and the y-direction defined by the axes in Figure 1 . In the subsequent frame 1b, particle 2 is identified as the Euclidean particle closest to the position of particle 2 in the current frame 1a.

[0060] The Euclidean distance “d” that particle 2 moves between frames 1a, 1b is considered the distance that particle 2 moves due to Brownian motion. This process is repeated over any suitable number of frames of the video to determine the total distance that particle 2 moves due to Brownian motion. Then, the total distance can be used in a known manner with the Stokes-Einstein equation to determine the size of particle 2. In practice, this process is performed for each of a plurality of particles within the sample to determine the size of the particles. For a polydisperse sample, its particle size distribution can be determined; while for a monodisperse sample, its average particle size, such as Dv50, can be determined.

[0061] When flow is introduced into the sample, the distance traveled by the particles within the sample will have a first component due to Brownian motion and a second component due to the sample flow. Thus, in order to reliably determine the distance that the particles travel due to Brownian motion, and thus to determine the size of the particles, the distance that the particles travel due to the sample flow must be taken into account. Figure 2a and 2b illustrate how two-dimensional tracking can be used in NTA to determine the distance that particles in a sample travel due to Brownian motion when flow is introduced into the sample. For example, in the Figure 2a and 2b example, the flow rate of the sample is relatively low.

[0062] Figure 2a shows the current frame 1a of the video and a subsequent frame 1b of the video. Particle 2 is shown in the current frame 1a and the subsequent frame 1b. The position of particle 2 in the current frame 1a is shown using a dashed line in the subsequent frame 1b. In the subsequent frame 1b, particle 2 is identified as the Euclidean particle within a tracking distance limit 3 that is closest to the position of particle 2 in the current frame 1a. The tracking distance limit 3 can be predetermined to maximize the efficiency of identifying particle 2 in the subsequent frame 1b. The Euclidean distance that particle 2 moves between frames 1a, 1b is taken as the sum of the Brownian motion component “d” of the distance that particle 2 moves and the flow component “drift”.

[0063] Figure 2bShows the corrected position of particle 2 in a subsequent frame 1b, where the flow component of the distance traveled by particle 2 is removed. The position of particle 2 in the current frame 1a is shown using a dashed line in the subsequent frame 1b. The Euclidean distance "d" that particle 2 moves from its position in the current frame 1a to its corrected position in the subsequent frame 1b is taken as the distance that particle 2 moves due to Brownian motion. Then, the size of particle 2 can be determined as described above.

[0064] The flow component of the distance traveled by particle 2 can be determined based on the flow rate of the sample and the time elapsed between the current frame 1a and the subsequent frame 1b. The time elapsed between the current frame 1a and the subsequent frame 1b will be known from the frame rate of the camera used to capture the video. The flow rate of the sample can be determined based on the average distance traveled by particles within the sample over a number of subsequent frames of the video (as determined by two-dimensional tracking of the particles) and the time elapsed on the subsequent frames.

[0065] Figure 3a and 3b Illustrates how errors can be introduced when two-dimensional tracking is used in NTA and flow is introduced into the sample. For example, in Figure 3a and 3b 's example, the flow rate of the sample is relatively high (i.e., the flow is high enough to cause particle tracking errors due to the flow).

[0066] Figure 3a Shows the current frame 1a of the video and the subsequent frame 1b of the video. The first particle 2 and the second particle 4 are shown in the current frame 1a and the subsequent frame 1b. The positions of particles 2 and 4 in the current frame 1a are shown using dashed lines in the subsequent frame 1b. In this example, within the tracking distance limit 3, the Euclidean particle closest to the position of the first particle 2 in the current frame 1a in the subsequent frame 1b is the second particle 4. Thus, if two-dimensional tracking is used, the incorrect particle is identified as the first particle 2 in the subsequent frame 1b. Then, as described above, this introduces an error in calculating the distance traveled by the first particle 2 due to Brownian motion. This error in the distance calculation ultimately introduces an error in the calculation of the size of the first particle 2, thereby introducing an error in the particle size distribution of the sample. Figure 3b Shows the incorrect correction of the position of the second particle 4 in the subsequent frame 1b after the second particle 4 has been incorrectly identified as the first particle 2, and the subsequent incorrect calculation of the distance traveled by the first particle 2.

[0067] Figure 4Shows an apparatus 10 for characterizing particles using nanoparticle tracking analysis (NTA) according to an embodiment of the present invention. The apparatus 10 includes: a flow cell 101 for accommodating a sample containing a plurality of particles suspended in a fluid; a pump 102 configured to provide a flow of the sample through the flow cell 101; a light source 103 configured to irradiate the sample; an imaging system 104 configured to collect light scattered or fluoresced by the particles moving within the flow cell 101 and within the detection area of the imaging system 104 and capture a video of the particles moving within the detection area; and a computer 105 configured to process the video. The computer 105 is configured to determine an estimated flow rate of the sample through the flow cell 101. Determining the estimated flow rate of the sample through the flow cell 101 includes performing one-dimensional particle tracking in a direction perpendicular to the expected flow direction of the sample.

[0068] The imaging system includes a microscope and a camera configured to capture a video through the microscope. For example, the microscope may include a magnification of 20 times, and the camera includes a charge-coupled device. The light source may be a laser. In other embodiments, any suitable alternative imaging system and / or light source may be used.

[0069] The apparatus further includes a glass chamber 106 and a metallized surface 107 disposed between the glass chamber 106 and the flow cell 101. The metallized surface is arranged to reflect light scattered or fluoresced from the particles back to the imaging system 104, thereby increasing particle contrast. The glass chamber may include a prism configured to refract light from the light source 103 to form a sheet of light substantially parallel to the metallized surface 107.

[0070] Figure 5a and 5b Shows how one-dimensional particle tracking is performed in a direction perpendicular to the expected flow direction of the sample according to an embodiment of the present invention. Figure 5a and 5b Show the current frame 1a of the video and a subsequent frame 1b of the video, respectively. A first particle 2 and a second particle 4 are shown in the current frame 1a and the subsequent frame 1b. The expected flow direction of the sample is indicated by an arrow labeled "F". As Figure 5a and 5b The axes in show, the expected flow of the sample is parallel to the x-axis. In Figure 5b The position of the first particle 2 relative to the second particle 4 in the current frame 1a is shown only in the dimension parallel to the y-axis.

[0071] In Figure 5bIn this case, the positions of particles 2 and 4 in the current frame 1a are shown by dashed lines in the subsequent frame 1b. Identifying particles 2 and 4 in the subsequent frame 1b includes identifying the nearest particle in the subsequent frame 1b in the tracking direction. The tracking direction is the direction parallel to the y-axis, i.e., the direction perpendicular to the sample flow direction. In some embodiments, a tracking distance limit may be used such that the first particle 2 is identified in the subsequent frame 1b as the particle closest in the direction parallel to the y-axis within the tracking distance limit 3 to the position of the first particle 2 in the current frame 1a. The tracking distance limit can be determined with reference to the expected maximum movement distance of the particles and is optional.

[0072] In the subsequent frame 1b, the second particle 4 is identified as the particle closest in the direction parallel to the y-axis within the tracking distance limit 3 to the position of the second particle 4 in the current frame 1a. As shown in FIG. 5, the correct particles 2 and 4 have been identified in the subsequent frame 1b.

[0073] According to an embodiment of the present invention, the estimated flow rate of the sample is determined based on the average distance traveled by the particles within the sample tracked by the one-dimensional particle tracking as described above with reference to FIG. 5 between subsequent frames of the video. After one-dimensional particle tracking, the displacement (two-dimensional) of each particle between subsequent frames of the video is measured. The median displacement of all particles and the time elapsed between video frames are used to estimate the flow rate of the sample. In other embodiments, different averages of the distance, such as the mean value, may be used.

[0074] After obtaining the estimated flow rate of the sample, the corrected positions of the particles within each frame of the video are determined, where the estimated flow rate component of the particle movement is removed. Figure 6 Illustrated how to determine the corrected positions of the particles within the video frame, where the estimated flow rate component of the particle movement is removed. Figure 6 Shows the current frame 1a of the video and the subsequent frame 1b of the video. The positions of three particles represented by a circle, a square, and a triangle respectively are shown in the current frame 1a. The positions of the three particles determined by the above one-dimensional tracking are shown in the subsequent frame 1b. The corrected positions of the particles in the subsequent frame 1b are shown using dashed lines. In Figure 6 this case, the expected flow direction of the sample is parallel to the x-axis. The corrected position of the particle is determined by determining the flow component of the distance the particle moves from the current frame 1a to the subsequent frame 1b and subtracting this component from the position of the particle in the x-direction in the subsequent frame 1b. The flow component of the particle movement distance is determined using the estimated flow rate of the sample and the time elapsed between the current frame 1a and the subsequent frame 1b.

[0075] After determining the corrected positions of the particles within each frame of the video, two-dimensional particle tracking is performed on the corrected positions of the particles. This is shown in Figure 7 this case. Figure 7 Shows Figure 6 the positions of the particles in the subsequent frame 1b. Figure 6The positions of the particles in the current frame 1a are Figure 7 shown using dashed lines in the subsequent frame 1b. In the subsequent frame 1b, these particles are identified as the Euclidean particles that are closest to the positions of the respective particles in the current frame 1a within the tracking distance limit 3. After correcting to remove the estimated flow velocity, the tracking limit 3 can be determined in response to the movement of the particles tracked by one-dimensional tracking.

[0076] During two-dimensional tracking, more than one particle can be identified within the tracking distance limit. For example, as Figure 7 shown, in the subsequent frame 1b within the tracking distance limit 3, two particles are identified from the particles represented by the square. The Euclidean distances d1, d2 are measured between the positions of the particles represented by the square in the current frame 1a and the two particles identified in the subsequent frame 1b. The distance d1 is less than the distance d2, so the particle at a distance d1 from the position of the particle represented by the square in the current frame 1a is identified as the particle represented by the square in the subsequent frame 1b. In Figure 7 the example of, this results in the correct particles being identified in the subsequent frame 1b, as shown.

[0077] After performing two-dimensional tracking, the residual flow velocity is determined based on the average distance that the particles tracked by two-dimensional particle tracking move between subsequent frames of the video. The average distance of the subsequent frames of the video and the elapsed time are used to determine the residual flow velocity. Then, the residual correction trajectory of each particle is determined by correcting the two-dimensional particle tracking to remove the residual flow velocity. Figure 8 Shows an example of the residual correction trajectory of the particles. This trajectory shows the movement of the particles through the sample, starting from the starting position in the first frame of the video, passing through the subsequent frames of the video, and finally reaching the ending position in the last frame of the video. Then, the size of each particle can be determined based on the average distance that the particles move between subsequent frames of the video, which is obtained from the residual correction trajectory using the above Stokes-Einstein equation.

[0078] Table 1 below shows the average movement components of the total distance that the particles move within the test sample. As already discussed, the average movement of the particles should represent the estimated flow velocity because Brownian motion is random and the average value of random movement is zero.

[0079] The first column ("Two-dimensional tracking only") contains the average movement components (or estimated flow velocity values) determined using known two-dimensional tracking, as described above with reference to Figures 2a to 3b . The second column ("1D / 2D tracking") contains: the estimated flow velocity determined using one-dimensional tracking (1D); and as described above with reference to FIGS. 5 to Figure 8The sum of the estimated flow velocity from one-dimensional tracking and the residual flow velocity determined using two-dimensional tracking (2D) with corrected particle positions. The test sample included 100 nm polystyrene latex particles, and the flow velocity was approximately -140 pixels per frame. This flow velocity was approximately 15 times the typical Brownian displacement of the particles.

[0080] Table 1: Drift Estimation for -140 Pixels / Frame Flow

[0081] 2D tracking only 1D / 2D tracking 13 -137 / -137 4 -139 / -140 9 -136 / -137

[0082] As shown in Table 1, the average moving component obtained using the technique according to an embodiment of the present invention is much more accurate than the average moving component obtained using known techniques.

Claims

1. An apparatus for characterizing particles using nanoparticle tracking analysis, comprising: a flow cell for containing a sample comprising a plurality of particles suspended in a fluid; a pump configured to provide flow of the sample through the flow cell; a light source configured to irradiate the sample; a video imaging system configured to collect light scattered or fluoresced by particles moving within the flow cell and within a detection zone of the imaging system and to capture video of the particles moving within the detection zone; and a computer configured to process the video to determine an estimated flow rate of the sample through the flow cell, wherein determining the estimated flow rate of the sample includes performing one-dimensional particle tracking in a direction perpendicular to the expected flow direction of the sample.

2. The apparatus according to claim 1, wherein the one-dimensional particle tracking includes identifying particles from a current frame of the video in subsequent frames of the video.

3. The apparatus according to claim 2, wherein Identifying the particles in the subsequent frames of the video includes identifying the nearest particle in the subsequent frame in the tracking direction.

4. The apparatus according to claim 3, wherein, Identifying the nearest particle in the subsequent frame includes identifying the nearest particle within a one-dimensional tracking distance limit.

5. The device according to any one of the preceding claims, wherein, The estimated flow rate is determined based on an average distance traveled by one or more particles tracked by the one-dimensional particle tracking between subsequent frames of the video.

6. The apparatus according to claim 5, wherein the average distance is a median distance.

7. The apparatus according to any one of the preceding claims, wherein the computer is configured to: use the estimated flow rate to determine a corrected position of the particles after removing an estimated flow rate component of the particles' movement; perform two-dimensional particle tracking on the corrected positions of the particles; and determine a residual flow rate based on an average distance traveled by the particles tracked by the two-dimensional particle tracking of the corrected positions between subsequent frames of the video.

8. The apparatus according to claim 7, wherein the computer is configured to determine a residual correction trajectory for each particle, including correcting the two-dimensional particle tracking of the corrected positions of the particles to remove the residual flow rate.

9. The apparatus according to claim 8, wherein the computer is configured to determine the particle size of each particle based on an average distance traveled by the particle between subsequent frames of the video obtained from the residual correction trajectory.

10. The apparatus according to claim 9, wherein the computer is configured to: determine the mean squared displacement of each particle based on an average distance traveled by the particle between subsequent frames of the video obtained from the residual correction trajectory, determine the diffusion coefficient of each particle based on the mean squared displacement, and use the Stokes-Einstein equation to determine the particle size of each particle based on the diffusion coefficient.

11. The device according to any one of claims 7 to 10, wherein, The two-dimensional particle tracking of the corrected positions includes identifying particles from a current frame of the video in subsequent frames of the video, including identifying the nearest particle in the subsequent frame within a two-dimensional tracking distance limit.

12. The apparatus according to claim 11, wherein, The two-dimensional tracking distance limit is determined in response to movement of the particles tracked by the one-dimensional particle tracking.

13. The apparatus according to any one of the preceding claims, wherein the computer is configured to generate a measurement warning if the estimated flow rate exceeds a predetermined threshold; and / or, wherein the computer is configured to determine the particle concentration of the sample and generate a measurement warning if the particle concentration exceeds a predetermined threshold.

14. The device according to any one of the preceding claims, wherein, Performing one-dimensional particle tracking on the stored video after or during the capture of the video.

15. A method for determining an estimated flow rate of a sample during nanoparticle tracking analysis, comprising: Providing a flow of the sample through a flow cell, the sample comprising a plurality of particles suspended in a fluid; Illuminating the sample with a light source; Collecting light scattered or fluoresced by the particles moving within the flow cell and within the detection zone of the imaging system; Capturing a video of the particles moving within the detection zone; and Processing the video to determine an estimated flow rate of the sample through the flow cell, including performing one-dimensional particle tracking in a direction perpendicular to the expected flow direction of the sample.

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