CN116223313A - In-situ test method for electrolyte particles - Google Patents
In-situ test method for electrolyte particles Download PDFInfo
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- 239000002245 particle Substances 0.000 title claims abstract description 148
- 239000003792 electrolyte Substances 0.000 title claims abstract description 68
- 238000011065 in-situ storage Methods 0.000 title claims abstract description 22
- 238000010998 test method Methods 0.000 title claims abstract description 10
- 238000001514 detection method Methods 0.000 claims abstract description 40
- 238000000034 method Methods 0.000 claims abstract description 38
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- 238000004519 manufacturing process Methods 0.000 claims abstract description 14
- 238000012360 testing method Methods 0.000 claims abstract description 13
- 238000009826 distribution Methods 0.000 claims abstract description 9
- 230000003287 optical effect Effects 0.000 claims abstract description 9
- 238000003384 imaging method Methods 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims description 16
- 239000013618 particulate matter Substances 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 9
- 239000012535 impurity Substances 0.000 claims description 7
- 230000003628 erosive effect Effects 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 5
- 238000002347 injection Methods 0.000 claims description 5
- 239000007924 injection Substances 0.000 claims description 5
- 239000000835 fiber Substances 0.000 claims description 4
- 239000012530 fluid Substances 0.000 claims description 4
- 239000007788 liquid Substances 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000008034 disappearance Effects 0.000 claims description 3
- 239000006185 dispersion Substances 0.000 claims description 3
- 238000011049 filling Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 229910003002 lithium salt Inorganic materials 0.000 claims description 3
- 159000000002 lithium salts Chemical class 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 239000011859 microparticle Substances 0.000 claims description 2
- 238000004064 recycling Methods 0.000 abstract description 4
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 abstract description 2
- 229910052744 lithium Inorganic materials 0.000 abstract description 2
- 230000010365 information processing Effects 0.000 abstract 1
- 238000007689 inspection Methods 0.000 abstract 1
- 230000003749 cleanliness Effects 0.000 description 4
- 239000008187 granular material Substances 0.000 description 3
- KRHYYFGTRYWZRS-UHFFFAOYSA-N Fluorane Chemical compound F KRHYYFGTRYWZRS-UHFFFAOYSA-N 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 238000001035 drying Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008033 biological extinction Effects 0.000 description 1
- 239000013078 crystal Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 230000001678 irradiating effect Effects 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229920002120 photoresistant polymer Polymers 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
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- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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Abstract
The invention discloses an in-situ test method of electrolyte particles, belonging to the technical field of lithium batteries; the method comprises the following steps: connecting a sample detection Cell Flow Cell on an electrolyte production or use device, and arranging a fixed detection window; in the process that the electrolyte sample to be detected flows through a Flow Cell of a sample detection pool, a high-frequency imaging detector dynamically and continuously detects particulate matters in the electrolyte sample to be detected at a detection window to capture optical pixels and shoot microscopic images; the particle size distribution and morphology photo of the electrolyte particles are formed through image information processing, classifying and counting analysis of the particles, so that the production and use processes of the electrolyte are guided. The invention is used on an electrolyte production or use device in an in-situ connection mode, the electrolyte is not required to be extracted or dried independently for testing, the quick and accurate inspection is realized, the electrolyte after detection cannot be polluted and damaged by equipment, and the recycling can be realized.
Description
Technical Field
The invention relates to a detection method of electrolyte insoluble particles, in particular to an in-situ test method of electrolyte particles, and belongs to the technical field of lithium batteries.
Background
The quantity and the morphology of particles in the electrolyte are one of critical control indexes in the production and detection of the electrolyte, and directly relate to the impurity control level of the electrolyte, and are also important factors affecting the safety performance of the electrolyte. If the particle can be directly detected, reliable basis can be provided for the type selection of the filter element of the electrolyte, and the life cycle of the filter element can be monitored.
In the past, we have carried out the topography observation and the relative count measurement of granule in the liquid through cleanliness meter or photoresistance method, namely the test of concentration, and the hardware that cleanliness tester adopted is metallographic microscope, and the field of vision is little, and the granule number of seeing is extremely limited, as shown in figure 1, cleanliness tester can obtain the granule image, but can not carry out a large amount of normal position tests, and the operation is complicated, and sample preparation process also can produce trace hydrofluoric acid because of meeting the steam in the air, causes destruction and pollution to check out equipment and environment. In addition, the particulate matters tested by the cleanliness meter are required to intercept large particles in the solution through a filter membrane, and the large particles are subjected to integral imaging after being dried. The photoresistance method is used for detecting particles through an extinction method, only equivalent volume diameters are provided, the morphology information of the particles cannot be obtained, and the classification and the traceability of the particles cannot be carried out; the photoresist rule cannot directly observe the actual sample particles, and thus cannot identify whether it is a harmful substance.
Therefore, a faster in-situ detection method is needed in the electrolyte production or injection process, so as to solve the problems of electrolyte pollution and damage caused in the detection process and inaccurate detection result existing in the electrolyte particulate matter detection method.
Disclosure of Invention
The purpose of the invention is that: the problems existing in the prior art are overcome, the in-situ test method for the electrolyte particles is provided, the in-situ test method is connected to an electrolyte production or use device, the complex processes of independent extraction, drying and the like in the electrolyte detection process are avoided, the rapid and accurate detection is realized, the electrolyte after detection cannot be polluted and damaged by equipment, and the recycling can be realized.
In order to achieve the above purpose, the present invention adopts the following technical scheme: an in-situ test method of electrolyte particles comprises the following steps:
s1, arranging in-situ detection equipment:
connecting a sample detection Cell Flow Cell on an electrolyte production or use device, arranging a fixed detection window, controlling the electrolyte to Flow into the detection window by using a high-precision micro-Flow pump, and synchronously controlling the strobe frequency, the dispersion width and the shutter time of a camera of background light according to a set sampling rate;
s2, collecting a particulate matter image:
in the process that an electrolyte sample to be detected flows through a Flow Cell of a sample detection pool, a high-frequency imaging detector dynamically and continuously detects particulate matters in the electrolyte sample to be detected at a detection window to capture optical pixels, wherein the micro particulate matters are amplified through a high-precision lens, and the micro particulate matters are shot into Gao Qinggao-resolution microscopic images through a high-sensitivity area array camera;
s3, digitally analyzing the particle information and counting:
1) Preprocessing the microscopic image information shot in the step S2, wherein the preprocessing flow comprises matrix convolution processing, VL conversion, gaussian filtering, automatic white balance and automatic contrast, removing background noise and acquiring a high-quality microscopic image;
2) Based on discretization processing of a high-sensitivity area array camera on an image, obtaining each pixel point which only represents the color nearby the imaging surface, carrying out gray level identification extraction and boundary calculation on the preprocessed microscopic image by adopting a sub-pixel level edge algorithm, setting a gray level value in a scattering area as a threshold value, removing scattering pixel points through automatic threshold analysis, and extracting an image contour which is more close to real particles;
3) Accurately counting particles in a unit volume of fluid through a high-precision injection pump, performing front-back contrast dynamic analysis on a plurality of microscopic images, judging whether background particles in the extracted image contour are adhered in a liquid pool or not, eliminating repeatedly shot particles, and correcting the number of the particles;
4) Carrying out granularity and particle shape analysis according to the gray level and the outline of the extracted particles, calculating the granularity and particle shape parameters of the particles, and carrying out normalization treatment on the data;
5) Based on the particle classification of the particle size and the particle shape index combination parameters of the particles, identifying and analyzing the sources of the particles by using a method comprising pattern identification and model training;
s4, analyzing and processing the digitalized data to form a granularity particle shape report:
the granularity particle shape report comprises information of three dimensions of a statistics layer, a feature layer and an identity layer, wherein the statistics layer comprises a global particle overview and a portrait picture of single particles, and provides the size distribution of volume and quantity from the statistics angle; the characteristic layer comprises various particle sizes and particle shape indexes, and provides a graph, a histogram, a box-type graph and a scatter diagram for display and analysis; under the support of the characteristic parameters, the identity layer classifies and identifies the particles by combining the calculated physical parameters to form the particle size distribution and the morphology photo of the electrolyte particles.
In the step S1, the set strobe frequency is 10-50 Hz, the strobe time is 1-500 mu S, and the camera shutter time is 1-10 ms.
In the step S1, an optical surface utilizing blue parallel light is arranged in a sample detection Cell Flow Cell, matched clamping pieces are 50-500 mu m, an intermediate channel is 0.1-10 mm, and the size range of the test particles is 0.3-1000 mu m.
In the step S2, the micro particles are amplified by 0.75-9 times through a high-precision lens, and the resolution of the photographed microscopic image is 1200-1800 ten thousand.
In the step S2, the viscosity value range of the electrolyte is 0-500cp; the particulate matter includes insoluble lithium salt aggregates, fibers, metallic foreign matter, and crystalline impurities in the electrolyte.
In the step S3, the particle size of the particulate matter includes: 1) The shape information comprises a sphere shape, a strip shape, a semitransparent shape, an opaque shape and a sharp shape; 2) The particle size parameters comprise area, volume, convex hull perimeter, area equivalent diameter, volume equivalent diameter, perimeter equivalent diameter, legend ellipse major axis, legend ellipse minor axis, ferrett maximum diameter, ferrett minimum diameter, diameter geodesic length and thickness; 3) Particle shape parameters include ellipticity, aspect ratio, elongation, flatness, irregularity, compactness, spread, filling, wodel sphericity, roundness, solidity, convexity, average concavity, particle robustness, maximum concavity index, roughness.
The particle size shape calculation of the particulate matter comprises the following parameters:
particle size parameters: calculation of normalized parameter volume equivalent diameter x from volume V V ，
Calculating normalized parameter area equivalent diameter x from projection area A A ，/>
Calculation of surface equivalent diameter x from surface area S S ，/>
Calculation of circumference equivalent diameter x from circumference P P ，/>
Legend ellipse major axis x Lmax And Legend elliptical short axis x Lmin The method comprises the steps of carrying out a first treatment on the surface of the Maximum diameter x of Ferrett Fmax And Feret minimum diameter x Fmin The method comprises the steps of carrying out a first treatment on the surface of the Diameter geodesic length x LG And thickness x E ；
Particle shape parameters:
d imax is the maximum inscribed circle diameter d imin Is the minimum circumscribed circle diameter; />
A box ＝x Fmin .x LF ，x LF A feret diameter perpendicular to the minimum feret diameter; wobble sphericity ψ ->
Roundness C->
Solidity = a/a c ，A c Is the area of the grain boundary convex hull; convexity=p c /P，P c A length of convex shell that is the grain boundary; average concavity psi FP ，/>
Wherein->
For angle-average feret diameter, +.>
Particle robustness Ω 1 ，
Wherein omega 1 The amount of erosion necessary to completely eliminate the profile; maximum concavity index Ω 2 ，/>
Wherein omega 2 To be in contact with convex edge shape A c The amount of erosion necessary for the complete disappearance of the relevant contour residuals; ratio of concavity/robustness Ω 3 ，
Roughness is described as fractal dimension D F In the complex logarithmic graph, the boundary perimeter P (λ) is linear with the step size λ, the upper limit of the step size being λ=0.3 x Fmax The linear equation is log 10 P(λ)＝(1-DF)log 10 λ+log 10 b, wherein b is the intercept of the fractal dimension graph.
The beneficial effects of the invention are as follows:
1) According to the method, the electrolyte directly flows through the detection equipment, the electrolyte is not required to be extracted or dried independently for testing, the quick and accurate detection is realized, the electrolyte after detection cannot be polluted and damaged by the equipment, and the recycling can be realized.
2) According to the method, the dynamic image is adopted for carrying out granularity analysis, and the image analysis provides definite numerical distribution of particle shape parameters by quantifying various shape distribution values instead of qualitatively describing various shapes, so that the real particle size and morphology of a sample can be accurately reflected, and the concentration of particles can be counted, classified and traced, so that accurate particle control and pollutant inlet positioning can be carried out in different production links; and can monitor impurities and visible foreign matters in each link in the whole production process of the electrolyte.
Drawings
FIG. 1 is a chart showing the statistics of the amount of electrolyte particulates in an embodiment of the present invention;
FIG. 2 is an optical block diagram of the present invention;
FIG. 3 is a graph of morphology of electrolyte particulates taken in an embodiment of the present invention;
FIG. 4 is a graphical representation of morphology descriptors of several different particulate matters listed in an embodiment of the present invention;
FIG. 5 is a report of electrolyte particulates generated in an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following description in conjunction with the accompanying drawings and specific embodiments.
Examples: as shown in fig. 1-5, the present invention provides an in-situ test method for electrolyte particulates, comprising the steps of:
s1, arranging in-situ detection equipment:
as shown in the optical structure diagram of fig. 2, a sample detection Cell Flow Cell is connected to an electrolyte production or use device, and a fixed detection window is provided, and the Flow of electrolyte into the detection window is controlled by using a high-precision micro-Flow pump; synchronously controlling the stroboscopic frequency of the background light to be 10-50 Hz according to the set sampling rate, the stroboscopic time to be 1-500 mu s, the frequency dispersion width and the shutter time of the camera to be 1-10 ms;
wherein, the sample detection Cell Flow Cell is provided with an optical surface utilizing blue parallel light, the matched clamping piece is 50-500 mu m, the middle channel is 0.1-10 mm, and the size range of the tested particles is 0.3-1000 mu m.
S2, collecting a particulate matter image:
in the process that an electrolyte sample to be detected flows through a Flow Cell of a sample detection Cell with fixed thickness, continuously irradiating particles in the Cell by a high-frequency pulse short-wave light source at a detection window, dynamically and continuously detecting particle capturing optical pixels in the electrolyte sample to be detected, wherein tiny particles are amplified by 0.75-9 times through a high-precision telecentric lens, all tiny particles are photographed into Gao Qinggao-resolution microscopic images through a high-sensitivity area array camera and stored, and the resolution of the microscopic images is 1200-1800 ten thousand;
wherein the viscosity value of the electrolyte is in the range of 0-500cp; the particles comprise insoluble lithium salt aggregates, fibers, metallic foreign matters, crystals and other impurities in the electrolyte.
S3, digitally analyzing the particle information and counting:
1) Preprocessing the microscopic image information shot in the step S2, wherein the preprocessing flow comprises matrix convolution processing, VL conversion, gaussian filtering, automatic white balance and automatic contrast, and background noise is removed, so that a high-quality microscopic image is acquired.
2) Based on discretization processing of a high-sensitivity area array camera on an image, obtaining each pixel point which only represents the color nearby the imaging surface, carrying out gray level identification extraction and boundary calculation on the preprocessed microscopic image by adopting a sub-pixel level edge algorithm, setting a gray level value in a scattering area as a threshold value, removing scattering pixel points through automatic threshold analysis, and extracting an image contour which is more close to real particles;
according to the size or shape characteristics of different particles, the image gray levels are different, so that the threshold value is set differently, and the setting of the threshold value is required to meet the requirement of removing scattering pixel points, so that a clearer and more real particle image outline is obtained.
3) Accurately counting particles in a unit volume of fluid through a high-precision injection pump, performing front-back contrast dynamic analysis on a plurality of microscopic images, judging whether background particles in the extracted image contour are adhered in a liquid pool or not, eliminating repeatedly shot particles, and correcting the number of the particles;
as shown in fig. 1, in order to accurately count the particles in a unit volume of fluid based on a statistical chart of the number of electrolyte particles stored by camera shooting in combination with a high-precision injection pump, the number of samples is 4992, and the measurement range is 0.3-1000 μm.
4) Carrying out granularity and particle shape analysis according to the gray level and the outline of the extracted particles, calculating the granularity and particle shape parameters of the particles, and carrying out normalization treatment on the data;
as shown in fig. 3, the information of the particle size and shape of the particles obtained after the gray scale and the contour of the particles are processed on the basis of a computer, and the particle size and shape of the particles include:
(1) The shape information comprises a sphere shape, a strip shape, a semitransparent shape, an opaque shape and a sharp shape;
(2) The particle size parameters comprise area, volume, convex hull perimeter, area equivalent diameter, volume equivalent diameter, perimeter equivalent diameter, legend ellipse major axis, legend ellipse minor axis, ferrett maximum diameter, ferrett minimum diameter, diameter geodesic length and thickness;
(3) Particle shape parameters include ellipticity, aspect ratio, elongation, flatness, irregularity, compactness, spread, filling, wodel sphericity, roundness, solidity, convexity, average concavity, particle robustness, maximum concavity index, roughness.
As shown in fig. 4, there are morphology descriptors for several different particulates whose particle size and shape calculations include the following parameters:
particle size parameters: calculation of normalized parameter volume equivalent diameter x from volume V V ，
Calculating normalized parameter area equivalent diameter x from projection area A A ，/>
Calculation of surface equivalent diameter x from surface area S S ，/>
Calculation of circumference equivalent diameter x from circumference P P ，/>
Legend ellipse major axis x Lmax And Legend elliptical short axis x Lmin The method comprises the steps of carrying out a first treatment on the surface of the Maximum diameter x of Ferrett Fmax And Feret minimum diameter x Fmin The method comprises the steps of carrying out a first treatment on the surface of the Diameter geodesic length x LG And thickness x E ；
Particle shape parameters:
for particles that are not very long, +.>
For very elongated particles (such as fibers), and (2)>
For very elongated particles (inverse of curl),
d imax is the maximum inscribed circle diameter d imin Is the minimum diameter of the circumcircleThe method comprises the steps of carrying out a first treatment on the surface of the The overall morphology of the particles means the degree to which the particles (or their projection surfaces) are close to a circle,/>
Ratio of feret box area to projected area, +.>
A box ＝x Fmin .x LF ，x LF A feret diameter perpendicular to the minimum feret diameter; the degree of the wodel sphericity ψ,
roundness C->
Solidity = a/a c ，A c Is the area of the grain boundary convex hull; convexity=p c /P，P c A length of convex shell that is the grain boundary; average concavity psi FP ，/>
Wherein->
For angle-average feret diameter, +.>
Particle robustness Ω 1 ，/>
Wherein omega 1 The amount of erosion necessary to completely eliminate the profile; maximum concavity index Ω 2 ，/>
Wherein omega 2 To be matched with the convexEdge shape A c The amount of erosion necessary for the complete disappearance of the relevant contour residuals; ratio of concavity/robustness Ω 3 ，/>
Roughness is described as fractal dimension D F In the complex logarithmic graph, the boundary perimeter P (λ) is linear with the step size λ, the upper limit of the step size being λ=0.3 x Fmax The linear equation is log 10 P(λ)＝(1-DF)log 10 λ+log 10 b, wherein b is the intercept of the fractal dimension graph.
5) Based on the particle classification of the particle size and the particle shape index combination parameters of the particles, identifying and analyzing the sources of the particles by using a method comprising pattern identification and model training;
and analyzing the results of different index combinations according to the calculated particle size and particle shape index of the particles, wherein the analysis can classify the particles based on the existing model training method.
The classification method comprises the following steps: the calculated result is round particles when the roundness is more than 0.95, and 0.87 is square.
S4, analyzing and processing the digitalized data to form a granularity particle shape report:
the granularity particle shape report comprises information of three dimensions of a statistics layer, a feature layer and an identity layer, wherein the statistics layer comprises a global particle overview and a portrait picture of single particles, and provides the size distribution of volume and quantity from the statistics angle; the characteristic layer comprises various particle sizes and particle shape indexes, and provides a graph, a histogram, a box-type graph and a scatter diagram for display and analysis; under the support of the characteristic parameters, the identity layer classifies and identifies the particles by combining the calculated physical parameters to form the particle size distribution and the morphology photo of the electrolyte particles.
As shown in fig. 5, in order to finally count and process a large amount of particulate matter data to form a particle size and shape report, the particle size and shape data obtained by calculation are classified and counted, and the source of particulate matters is judged according to the total number and shape of the particulate matters reacted in the report, so as to guide the source of particulate matters impurities or defects of products in the production link, and help the production enterprises to improve the existing technological process.
According to the in-situ test method for the electrolyte particles, the test device is connected to the electrolyte production or use device in situ, so that complicated processes such as independent extraction and drying in the electrolyte detection process are avoided, quick and accurate detection is realized, the detected electrolyte cannot be polluted and damaged by equipment, recycling can be realized, and the impurity judgment in the electrolyte is more accurate.
The foregoing is merely illustrative of the present invention and not restrictive, and other modifications and equivalents thereof may occur to those skilled in the art without departing from the spirit and scope of the present invention.
Claims (7)
1. An in-situ test method for electrolyte particles is characterized in that: the method comprises the following steps:
s1, arranging in-situ detection equipment:
connecting a sample detection Cell Flow Cell on an electrolyte production or use device, arranging a fixed detection window, controlling the electrolyte to Flow into the detection window by using a high-precision micro-Flow pump, and synchronously controlling the strobe frequency, the dispersion width and the shutter time of a camera of background light according to a set sampling rate;
s2, collecting a particulate matter image:
in the process that an electrolyte sample to be detected flows through a Flow Cell of a sample detection pool, a high-frequency imaging detector dynamically and continuously detects particulate matters in the electrolyte sample to be detected at a detection window to capture optical pixels, wherein the micro particulate matters are amplified through a high-precision lens, and the micro particulate matters are shot into Gao Qinggao-resolution microscopic images through a high-sensitivity area array camera;
s3, digitally analyzing the particle information and counting:
1) Preprocessing the microscopic image information shot in the step S2, wherein the preprocessing flow comprises matrix convolution processing, VL conversion, gaussian filtering, automatic white balance and automatic contrast, removing background noise and acquiring a high-quality microscopic image;
2) Based on discretization processing of a high-sensitivity area array camera on an image, obtaining each pixel point which only represents the color nearby the imaging surface, carrying out gray level identification extraction and boundary calculation on the preprocessed microscopic image by adopting a sub-pixel level edge algorithm, setting a gray level value in a scattering area as a threshold value, removing scattering pixel points through automatic threshold analysis, and extracting an image contour which is more close to real particles;
3) Accurately counting particles in a unit volume of fluid through a high-precision injection pump, performing front-back contrast dynamic analysis on a plurality of microscopic images, judging whether background particles in the extracted image contour are adhered in a liquid pool or not, eliminating repeatedly shot particles, and correcting the number of the particles;
4) Carrying out granularity and particle shape analysis according to the gray level and the outline of the extracted particles, calculating the granularity and particle shape parameters of the particles, and carrying out normalization treatment on the data;
5) Based on the particle classification of the particle size and the particle shape index combination parameters of the particles, identifying and analyzing the sources of the particles by using a method comprising pattern identification and model training;
s4, analyzing and processing the digitalized data to form a granularity particle shape report:
the granularity particle shape report comprises information of three dimensions of a statistics layer, a feature layer and an identity layer, wherein the statistics layer comprises a global particle overview and a portrait picture of single particles, and provides the size distribution of volume and quantity from the statistics angle; the characteristic layer comprises various particle sizes and particle shape indexes, and provides a graph, a histogram, a box-type graph and a scatter diagram for display and analysis; under the support of the characteristic parameters, the identity layer classifies and identifies the particles by combining the calculated physical parameters to form the particle size distribution and the morphology photo of the electrolyte particles.
2. The method for in situ testing of electrolyte particulates according to claim 1, wherein: in the step S1, the set strobe frequency is 10-50 Hz, the strobe time is 1-500 mu S, and the camera shutter time is 1-10 ms.
3. The method for in situ testing of electrolyte particulates according to claim 1, wherein: in the step S1, an optical surface utilizing blue parallel light is arranged in a sample detection Cell Flow Cell, matched clamping pieces are 50-500 mu m, an intermediate channel is 0.1-10 mm, and the size range of the test particles is 0.3-1000 mu m.
4. The method for in situ testing of electrolyte particulates according to claim 1, wherein: in the step S2, the micro particles are amplified by 0.75-9 times through a high-precision lens, and the resolution of the photographed microscopic image is 1200-1800 ten thousand.
5. The method for in situ testing of electrolyte particulates according to claim 1, wherein: in the step S2, the viscosity value range of the electrolyte is 0-500cp; the particulate matter includes insoluble lithium salt aggregates, fibers, metallic foreign matter, and crystalline impurities in the electrolyte.
6. The method for in situ testing of electrolyte particulates according to claim 1, wherein: in the step S3, the particle size of the particulate matter includes: 1) The shape information comprises a sphere shape, a strip shape, a semitransparent shape, an opaque shape and a sharp shape; 2) The particle size parameters comprise area, volume, convex hull perimeter, area equivalent diameter, volume equivalent diameter, perimeter equivalent diameter, legend ellipse major axis, legend ellipse minor axis, ferrett maximum diameter, ferrett minimum diameter, diameter geodesic length and thickness; 3) Particle shape parameters include ellipticity, aspect ratio, elongation, flatness, irregularity, compactness, spread, filling, wodel sphericity, roundness, solidity, convexity, average concavity, particle robustness, maximum concavity index, roughness.
7. The method for in situ testing of electrolyte particulates according to claim 6, wherein: the particle size shape calculation of the particulate matter comprises the following parameters:
particle size parameters: calculation of normalized parameter volume equivalent diameter x from volume V V ，
Calculating normalized parameter area equivalent diameter x from projection area A A ，/>
Calculation of surface equivalent diameter x from surface area S S ，/>
Calculation of circumference equivalent diameter x from circumference P P ，/>
Legend ellipse major axis x Lmax And Legend elliptical short axis x Lmin The method comprises the steps of carrying out a first treatment on the surface of the Maximum diameter x of Ferrett Fmax And Feret minimum diameter x Fmin The method comprises the steps of carrying out a first treatment on the surface of the Diameter geodesic length x LG And thickness x E ；
Particle shape parameters:
d imax is the maximum inscribed circle diameter d imin Is the minimum circumscribed circle diameter; />
A box ＝x Fmin .x LF ，x LF Is perpendicular to the minimum Feret diameterA Rate diameter; wobble sphericity ψ ->
Roundness C->
Solidity = a/a c ，A c Is the area of the grain boundary convex hull; convexity=p c /P，P c A length of convex shell that is the grain boundary; average concavity psi FP ，/>
Wherein->
For angle-average feret diameter, +.>
Particle robustness Ω 1 ，
Wherein omega 1 The amount of erosion necessary to completely eliminate the profile; maximum concavity index Ω 2 ，/>
Wherein omega 2 To be in contact with convex edge shape A c The amount of erosion necessary for the complete disappearance of the relevant contour residuals; ratio of concavity/robustness Ω 3 ，
Roughness is described as fractal dimension D F In the complex logarithmic graph, the boundary perimeter P (λ) is linear with the step size λ, the upper limit of the step size being λ=0.3 x Fmax The linear equation is log 10 P(λ)＝(1-D F )log 10 λ+log 10 b, wherein b is the intercept of the fractal dimension graph。/>
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117237303A (en) * | 2023-09-19 | 2023-12-15 | 中南大学 | Non-contact rock-fill grain grading detection method and device based on machine vision |
| CN117388128A (en) * | 2023-09-12 | 2024-01-12 | 珠海醋酸纤维有限公司 | Method for directly testing particle impurities in spinning solution |
| CN117594151A (en) * | 2023-11-27 | 2024-02-23 | 北京航空航天大学 | Method for realizing qualitative and quantitative TEP (test equipment) by utilizing image recognition |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117388128A (en) * | 2023-09-12 | 2024-01-12 | 珠海醋酸纤维有限公司 | Method for directly testing particle impurities in spinning solution |
| CN117237303A (en) * | 2023-09-19 | 2023-12-15 | 中南大学 | Non-contact rock-fill grain grading detection method and device based on machine vision |
| CN117237303B (en) * | 2023-09-19 | 2024-05-03 | 中南大学 | Non-contact rock-fill grain grading detection method and device based on machine vision |
| CN117594151A (en) * | 2023-11-27 | 2024-02-23 | 北京航空航天大学 | Method for realizing qualitative and quantitative TEP (test equipment) by utilizing image recognition |
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