Multi-light-intensity scanning measurement method for nano-particle tracking analysis
By employing a multi-intensity scanning measurement method and combining particle signal characteristics to select an appropriate light intensity for nanoparticle tracking and analysis, the problem of particle size distribution curve deviation in traditional methods is solved, achieving high-precision detection across the entire particle size range and improving the accuracy and stability of the detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DANDONG BETTERSIZE INSTR LTD
- Filing Date
- 2026-02-12
- Publication Date
- 2026-05-26
AI Technical Summary
In traditional nanoparticle tracking analysis, when using a single light intensity for measurement, the particle size distribution curve has a large deviation, making it difficult to accurately detect small particles under high light intensity and avoid saturation of large particles under low light intensity.
The method employs a multi-intensity scanning measurement approach, which involves sequentially scanning the same sample with different light intensities. The measurement results are then evaluated and fused based on particle signal characteristics. An appropriate light intensity is selected for detection, and abnormal trajectories are eliminated, achieving high-precision detection across the entire particle size range.
This results in more realistic, continuous, and accurate measurement results across the entire particle size range, improving the sensitivity of small particle detection and the stability of large particle measurement, thereby enhancing the overall detection accuracy.
Smart Images
Figure CN122084498A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of nanoparticle tracking and analysis technology, specifically a multi-intensity scanning measurement method for nanoparticle tracking and analysis. Background Technology
[0002] Nanoparticle Tracking Analysis (NTA) is a single-particle characterization technique based on Brownian motion and light scattering principles, capable of simultaneously providing nanoparticle size distribution, concentration, and fluorescence information. Its core principle involves recording the Brownian motion trajectory of nanoparticles in a liquid using a camera and calculating the hydrodynamic diameter of the particles based on the Stokes-Einstein equations. When a sample contains a wide particle size distribution, the scattering efficiency of different particles varies. Small particles scatter very weak light, requiring higher laser intensities to be detected by the camera. Large particles scatter very strongly; if excessively high laser intensities are used, they may be overexposed, appearing larger and irregularly shaped than they actually are, making it difficult for the camera to accurately capture their center and edges. Therefore, large particles require lower laser intensities for accurate analysis. Consequently, the particle size distribution curves obtained from traditional fixed-intensity measurements deviate significantly from the actual situation. Summary of the Invention
[0003] The purpose of this invention is to provide a multi-intensity scanning measurement method for nanoparticle tracking and analysis. This method sequentially scans the same sample with different light intensities, and then combines the particle signal characteristics to evaluate and fuse the measurement results for each intensity. This achieves accurate detection of small particles under high light intensity, avoids saturation of large particles under low light intensity, and provides more realistic, continuous, and accurate measurement results across the entire particle size range. This addresses the problem of biased particle size distribution curves obtained when using a single light intensity for measurement.
[0004] A multi-intensity scanning measurement method for nanoparticle tracking and analysis specifically includes the following steps:
[0005] Step S1: Introduce the nanoparticle sample to be tested into the nanoparticle tracking analyzer and set multiple laser power levels (P1). <P2<⋯<P n The intensity of light is changed by controlling the output power of the laser.
[0006] Step S2: Fix the camera exposure, frame rate, and gain, and scan the same sample multiple times with different light intensities in sequence. Obtain particle scattering signal data and particle motion trajectory data at each light intensity level.
[0007] Step S3: Process the measurement data collected under each preset light intensity to generate the corresponding concentration distribution curve (CD1, CD2, ..., CD2). n );
[0008] Step S4: Use a peak identification algorithm to extract significant peaks;
[0009] Step S5: Eliminate abnormal trajectories based on particle physical characteristics criteria, and select the appropriate light intensity from multiple preset light intensities based on the number of effective trajectories and particle morphology characteristics within each particle size range.
[0010] The following criteria are used to select the appropriate light intensity for each particle size range:
[0011] For the peak position range of small particles, the maximum light intensity is selected where the scattered light signal from large particles received at this light intensity will not obscure the signal of small particles, and the number of effective trajectories does not show a nonlinear surge with the change of light intensity.
[0012] For the peak position range of medium particles, the highest light intensity before the number of effective trajectories does not show a nonlinear surge with light intensity and the average particle area does not show a nonlinear surge is selected.
[0013] For the peak position range of large particles, select the highest light intensity where the average roundness of the particles is maintained above the preset threshold.
[0014] Set criteria such as brightness threshold, area threshold, and roundness. When any particle trajectory meets any anomaly criterion, it will be removed from the measurement results dataset.
[0015] In the case of bimodal distribution, within the range corresponding to the peak position of large particles, the highest light intensity is selected as the adaptive light intensity, where the average area of the particles does not show a nonlinear surge and the average roundness remains above the preset threshold (to prevent morphological distortion caused by overexposure halo). Within the range corresponding to the peak position of small particles, the maximum light intensity is selected as the adaptive light intensity, where the scattered light signal from the large particles received at this light intensity will not cover the signal from the small particles and the number of effective trajectories does not show a nonlinear surge with the change of light intensity.
[0016] In the case of three peaks, within the interval corresponding to the large particle peak, the highest light intensity is selected as the adaptive light intensity when the average particle area does not show a nonlinear surge and the average roundness remains above a preset threshold (to prevent morphological distortion caused by overexposure halo). Within the interval corresponding to the small particle peak, the maximum light intensity at which the scattered light signal from the large particle received does not obscure the signal from the small particle, and the number of effective trajectories does not show a nonlinear surge with the change in light intensity, is selected as the adaptive light intensity for this interval. Within the interval corresponding to the medium particle peak, the highest light intensity before the number of effective trajectories changes nonlinearly with the change in light intensity and the average particle area does not show a nonlinear surge is selected as the adaptive light intensity for this interval. If two adjacent peaks meet the above criteria at the same light intensity, they are classified into a "light intensity compatible group" and share the same set of measurement data.
[0017] Step S6: Perform fusion of multi-intensity measurement results, directly splice components separated by particle size distribution range, and perform weighted smoothing fusion of components with adjacent particle size distribution ranges and overlapping signals.
[0018] The weighted smoothing fusion of components with adjacent particle size distribution ranges and overlapping signals is specifically as follows:
[0019] Establish a smooth transition window in the valley region between the two peaks. and define the relationship with particle size Weighting factors of linear complementary changes and The fusion concentration is calculated using the following formula. :
[0020] in, , ;
[0021] in, and The concentration data correspond to the higher and lower light intensities selected for the particle size range, respectively.
[0022] Step S7: Convert the fused "concentration distribution curve" into a "particle size distribution map" and output the particle size distribution result after fusion.
[0023] Beneficial effects:
[0024] 1. Light intensity adjustment directly changes the luminous intensity of the incident light source, regulating the absolute intensity of the scattered light from the particles at the source, thus achieving physical-level control of the scattered signal. In contrast, exposure time and gain adjustment only affect the signal reception and amplification stages, and cannot fundamentally improve the signal dynamic range and signal-to-noise ratio.
[0025] 2. By performing multi-level controllable scanning and adaptive optimization of light intensity, the limitations of the measurement range under traditional single light intensity conditions are overcome, enabling the system to adaptively select the appropriate light intensity in samples with different particle size distributions, and achieve high-precision detection across the entire particle size range.
[0026] 3. Under high light intensity conditions, it effectively enhances the scattering signal of small particles, improves the signal-to-noise ratio and detection sensitivity of small particle end signals, thereby significantly improving the detection accuracy of micro particle groups.
[0027] 4. Under low light intensity conditions, it can effectively suppress the strong scattering signal generated by large particles, ensuring the stability and reliability of measurement results at the large particle size end.
[0028] 5. By fusing and continuously completing multiple light intensity measurement results, the overall accuracy of small and large particle size measurement results was improved. Attached Figure Description
[0029] Figure 1 This is a flowchart of a multi-intensity scanning measurement method for nanoparticle tracking and analysis. Detailed Implementation
[0030] This embodiment uses the measurement of a broadly distributed nanoparticle sample comprising a small-diameter component (first component), a medium-diameter component (second component), and a large-diameter component (third component) as an example to illustrate the implementation process of the present invention in detail. The particle size of the first component is smaller than that of the second component, and the particle size of the second component is smaller than that of the third component.
[0031] Step S1: Introduce the nanoparticle sample to be tested into the nanoparticle tracking analyzer and set multiple laser power levels, for example, setting the output power range to 1%~100%. To cover a wide dynamic range while ensuring adjustment accuracy at the low power end, 20 logarithmically increasing laser power levels are set. ~ The growth rate of adjacent gears is approximately 1.27. Specific gear settings are shown in Table 1. The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
[0032] Table 1 Laser Power Levels
[0033]
[0034] The intensity of light can be changed by controlling the output power of the laser.
[0035] Step S2: Fix the camera exposure, frame rate, and gain, and control the laser to perform the above steps sequentially. ~ The power output was adjusted to perform 20 consecutive scan measurements on the same sample. At each power level, a particle scattering video of a specific duration (e.g., 30 seconds) was acquired to obtain particle scattering signal data and particle motion trajectory data.
[0036] Step S3: Process the 20 sets of measurement data to generate 20 corresponding concentration distribution curves (CD).
[0037] Step S4: Using a peak identification algorithm, identify the three main characteristic peaks corresponding to the first, second, and third components in all curves;
[0038] Step S5: Eliminate abnormal trajectories based on particle physical characteristics, and select the appropriate light intensity from multiple preset light intensities based on the number of effective trajectories and particle morphology characteristics within each particle size range.
[0039] 1. For the first component (small particle size): System analysis of the high-power segment (e.g.) ~ The measurement data is determined by monitoring the changing trend of the effective trajectory number and the interference of the large particle imaging signal. Specifically, the criterion is: the maximum light intensity at which the effective trajectory number does not exhibit a nonlinear surge with light intensity, and at which point the light spot signal imaged by the third component (large particle size component) on the camera does not physically obstruct the weak light spot signal of the first component, is selected as the adaptive measurement light intensity for the first component (denoted as ). This principle ensures that the concentration information of small particles is extracted to the maximum extent without introducing background noise or being overwhelmed by the halo of large particles.
[0040] 2. For the second component (medium particle size): System analysis of the medium power range (e.g.) The data is locked through dual feedback from monitoring the number of trajectories and the imaging area. Specifically, the criteria are: selecting the maximum light intensity level where the number of effective trajectories does not show a non-linear surge with light intensity, and the average pixel area of the particle imaging does not show a non-linear surge before, as the adaptive measurement light intensity for the second component (denoted as ). If the area growth rate is observed to cross the linear threshold at a certain power level, the system will automatically backtrack to the previous power level and lock it.
[0041] 3. For the third component (large particle size): System analysis of the low-power segment ( The data focuses on preventing morphological distortion caused by overexposure. The specific criteria are: selecting the maximum light intensity level where the average pixel area of the particle image does not show a non-linear surge, and the average circularity of the image spot remains above a preset threshold (e.g., not less than 0.7), as the adaptation measurement light intensity for the third component (denoted as ). ).
[0042] Step S6: Based on the distribution spacing between characteristic peaks and the degree of signal overlap, execute a differentiated fusion algorithm:
[0043] Step S6.1: For samples with a large particle size range and significant low concentration ranges or no signal "vacuum bands" between components, a confidence interval direct splicing strategy is adopted, which automatically identifies the particle size point where the minimum concentration value between two adjacent peaks is located as the cutting point.
[0044] Step S6.2: For components with similar particle size distributions and overlapping signals, establish a smooth transition window in the valley region between the two peaks. and define the relationship with particle size The weighting factors are linearly complementary, with the weighting factor for high light intensity set to [value missing]. The low light intensity weighting factor is set to ; Utilizing the aforementioned weighting factors for concentration data under high light intensity Concentration data under low light intensity Weighted superposition calculations are performed to eliminate concentration jumps caused by light intensity switching, ensuring that the fused particle size distribution curve has smooth continuity and physical morphology authenticity across the entire range.
[0045]
[0046] Step S7: Convert the fused "concentration distribution curve" into a "particle size distribution map" and output the particle size distribution result after fusion.
Claims
1. A multi-intensity scanning measurement method for tracking and analyzing nanoparticles, characterized in that, Includes the following steps: Step S1: Set multiple laser power levels ( (This refers to the measurement of multi-level changes in light intensity by controlling the output power of the laser.) Step S2: Fix the camera acquisition parameters, and scan and measure the same sample sequentially using the multiple light intensities to obtain the particle scattering signal and motion trajectory data at each light intensity level; Step S3: Process the measurement data and generate the concentration distribution curves corresponding to each light intensity. ); Step S4: Extract significant peaks using a peak identification algorithm; Step S5: Eliminate abnormal trajectories based on particle physical characteristics criteria, and select the appropriate light intensity suitable for the particle size range from multiple preset light intensities based on the number of effective trajectories and particle morphology characteristics within each particle size range. Step S6: Perform fusion of multi-intensity measurement results, directly splice components separated by particle size distribution range, and perform weighted smoothing fusion of components with adjacent particle size distribution ranges and overlapping signals. Step S7: Output the full particle size concentration distribution results after fusion.
2. The multi-intensity scanning measurement method for nanoparticle tracking and analysis according to claim 1, characterized in that, In step S2, the camera acquisition parameters include exposure time, frame rate, and gain, and remain fixed during multiple scan measurements.
3. The multi-intensity scanning measurement method for nanoparticle tracking and analysis according to claim 1, characterized in that, In step S4, the peak identification algorithm is used to identify significant peaks in the concentration distribution curve and to determine the component characteristics of the sample particle size distribution based on the number and position of the peaks.
4. The multi-intensity scanning measurement method for nanoparticle tracking and analysis according to claim 1, characterized in that, In step S5, the appropriate light intensity for each path interval is selected using the following criteria: For the small particle peak range, the maximum light intensity is selected where the scattered light signal from the large particles received at this light intensity will not obscure the signal from the small particles, and the number of effective trajectories does not show a nonlinear surge with the change of light intensity. For the peak position range of medium particles, the highest light intensity before the number of effective trajectories does not show a nonlinear surge with light intensity and the average particle area does not show a nonlinear surge is selected. For the peak position range of large particles, select the highest light intensity where the average roundness of the particles is maintained above the preset threshold.
5. The multi-intensity scanning measurement method for nanoparticle tracking and analysis according to claim 1, characterized in that, Step S5 includes both bimodal and trimodal cases; For the bimodal case: Within the range corresponding to the peak position of large particles, the highest light intensity that does not show a nonlinear surge in the average area of particles and whose average roundness remains above a preset threshold is selected as the adaptive light intensity for that range. Within the range corresponding to the peak position of small particles, the maximum light intensity at which the scattered light signal of large particles received at this light intensity will not obscure the signal of small particles and the number of effective trajectories will not show a nonlinear surge with the change of light intensity is selected as the adaptive light intensity for this range. For the three-peak situation: Within the range corresponding to the peak position of large particles, the highest light intensity that does not show a nonlinear surge in the average area of particles and whose average roundness remains above a preset threshold is selected as the adaptive light intensity for that range. Within the range corresponding to the peak position of small particles, the maximum light intensity at which the scattered light signal of large particles received at this light intensity will not obscure the signal of small particles and the number of effective trajectories will not show a nonlinear surge with the change of light intensity is selected as the adaptive light intensity for this range. Within the range corresponding to the peak position of medium particles, the highest light intensity before the number of effective trajectories changes with light intensity without a nonlinear surge and the average particle area does not show a nonlinear surge is selected as the appropriate light intensity for this range.
6. The multi-intensity scanning measurement method for nanoparticle tracking and analysis according to claim 5, characterized in that, If two adjacent peaks both meet the matching light intensity criterion under the same light intensity, then the two adjacent peaks are divided into the same light intensity compatible group and share the measurement data under that light intensity.
7. The multi-intensity scanning measurement method for nanoparticle tracking and analysis according to claim 1, characterized in that, In step S6, the weighted smoothing fusion of components with adjacent particle size distribution ranges and overlapping signals specifically involves: Establish a smooth transition window in the valley region between the two peaks. And define the variation with particle size Weighting factors of linear complementary changes and The fusion concentration is calculated using the following formula. : ; in, , ; in, and The concentration data correspond to the higher and lower light intensities selected for the particle size range, respectively.