Viscoelastic microfluidic nanoparticle sorting modeling method and system

Abstract

The present application provides a kind of viscoelastic microfluidic nanoparticle sorting modeling method and system, including straight channel section particle trajectory prediction model and amplification section particle trajectory analysis method, the internal connection between sample liquid flow, sheath liquid flow and sheath liquid polyethylene oxide concentration and the inertial lift force, elastic force, viscous drag and virtual mass force that nanoparticle suffers is constructed by mathematical model, the motion law of nanoparticle in straight channel section is revealed based on kinetic equation, the lateral displacement of nanoparticle when reaching the end position of straight channel section is solved, the lateral equilibrium position of nanoparticle at the end of straight channel section is combined with the flow field simulation result in amplification section, and the motion trajectory of particle in amplification section can be obtained, and then the mechanism of viscoelastic microfluidic nanoparticle sorting is comprehensively revealed.

Description

Technical Field

[0001] The present invention relates to the technical field of viscoelastic microfluidic particle sorting, and in particular to a viscoelastic microfluidic nanoparticle sorting modeling method and system. Background Art

[0002] Particle sorting is widely used in research across multiple disciplines, including biology, chemistry, and medicine. Exosome sorting is a prominent application in the biomedical field. Exosomes are small extracellular vesicles with diameters between 30 and 200 nm that contain nucleic acids and proteins from their original cells and play a vital role in intercellular communication. Exosomes are currently receiving significant attention in the scientific community as potential diagnostic biomarkers and therapeutic vectors for various diseases, such as cancer, infectious diseases, and neurodegenerative disorders.

[0003] To better decipher the biological information carried by exosomes, high-purity isolation of exosomes from complex biological samples is necessary. Existing sorting methods can be categorized into two types: active manipulation and passive manipulation. Active manipulation involves the use of external driving forces, such as magnetic fields, electric fields, acoustic fields, and centrifugal motion, to separate exosomes. Because of their reliance on external forces, active methods often require large, expensive instruments and manual intervention, often facing challenges such as high instrument costs, bulky systems, and complex operations. Passive sorting techniques, instead of relying on external physical fields to drive exosomes, utilize the coupling between the microstructure, microfluidics, and exosomes within the microfluidic chip to separate exosomes. While passive techniques can overcome the disadvantages of active methods, most passive techniques typically suffer from low sorting resolution, making it difficult to guarantee separation purity.

[0004] Patent publication number CN116099581A discloses a microfluidic chip for pre-focused cell sorting. This chip introduces a Newtonian fluid and a viscoelastic fluid into different inlets. The polygonal flow channels within the chip create a stable viscoelastic-Newtonian interface by co-flowing the two solutions. The inertial and viscoelastic effects generated within the microfluidic channel induce inertial lift forces on the microchannel walls and viscoelastic lift forces within the viscoelastic solution, which act together on cell particles within the channel. Viscoelastic microfluidic exosome sorting technology uses a viscoelastic fluid medium to drive exosomes. Compared to currently mainstream passive methods, viscoelastic microfluidic technology enhances control over exosomes by introducing additional elastic forces, potentially improving exosome purity. However, current research on the sorting mechanism of viscoelastic microfluidic exosome sorting technology is still in-depth, often relying on extensive experimental trials to achieve the final sorting goal, and lacks theoretical guidance for the rapid design of viscoelastic microfluidic chips. Therefore, by constructing a theoretical model to reveal the viscoelastic microfluidic nanoparticle sorting mechanism, we can provide a theoretical basis for the optimization of viscoelastic microfluidic chips, and can quickly iterate the design according to the sorting requirements of different application scenarios, thereby realizing the flexible expansion of viscoelastic microfluidic technology.

[0005] Therefore, it is necessary to propose a new technical solution to improve the above technical problems. Summary of the Invention

[0006] In view of the defects in the prior art, the purpose of the present invention is to provide a viscoelastic microfluidic nanoparticle sorting modeling method and system.

[0007] According to the present invention, a viscoelastic microfluidic nanoparticle sorting modeling method is provided, which comprises the following steps:

[0008] Step S1: Use the total flow rate Q in the straight channel to construct the flow velocity distribution function u in the plane in the height direction x (y);

[0009] Step S2: Based on the particle transverse coordinate y p , particle diameter a p , the particle velocity u in the x direction px , the particle's velocity u in the y direction py , channel height h, channel width w, sheath fluid flow rate Q c , sample flow Q s , fluid density ρ f 、PEO molecular weight M W , PEO concentration of sheath fluid c c , PEO concentration of the sample c s , solvent viscosity η of PEO solution s , shear rate Avogadro's constant N A , Boltzmann constant k B , and temperature T, construct the inertial lift force F on the nanoparticles i , elastic force F e , viscous drag F d and virtual mass force F v Expressions of

[0010] Step S3: Based on F i , F e , F d and F v Construct a nanoparticle dynamics model to calculate the trajectory of the nanoparticles after they completely enter the sheath fluid;

[0011] Step S4: Calculate the streamlines in the amplified section using ANSYS Fluent, and obtain the motion trajectory of the nanoparticles in the amplified section based on the lateral equilibrium position of the nanoparticles at the end of the straight channel.

[0012] Preferably, the u in step S1 x (y) is a parabolic function, which passes the condition:

[0013] u x (0) = u xmax =2u xavg =2Q / wh,u x (w / 2)=0 to get u x (y), as shown in formula (1):

[0014]

[0015] Among them, u x (0) is the flow velocity at the wall of the straight channel section; u xmax is the maximum flow velocity in the straight channel section; u xavg is the average flow velocity in the straight channel section; y is the transverse coordinate value in the straight channel section.

[0016] Preferably, the F in step S2 i 、F e 、F d and F v It is expressed by formula (2):

[0017]

[0018] where e i and e j are unit vectors pointing from the main flow direction to the wall from the center of the channel, respectively.

[0019] Preferably, the kinetic model in step S3 is characterized by formula (3), and further organized into a differential equation form as shown in formula (4):

[0020]

[0021] Preferably, the step S4 determines the movement trajectory of the nanoparticles in the amplification section based on the lateral displacement of the nanoparticles at the end of the straight channel section, thereby determining from which outlet the nanoparticles will flow out.

[0022] The present invention also provides a viscoelastic microfluidic nanoparticle sorting modeling system, which includes the following modules:

[0023] Module M1: Use the total flow rate Q in the straight channel to construct the flow velocity distribution function u in the plane in the height direction x (y);

[0024] Module M2: Based on the particle transverse coordinate y p , particle diameter a p , the particle velocity u in the x direction px , the particle's velocity u in the y direction py , channel height h, channel width w, sheath fluid flow rate Q c , sample flow Q s , fluid density ρ f 、PEO molecular weight M W , PEO concentration of sheath fluid c c , PEO concentration of the sample c s , solvent viscosity η of PEO solution s , shear rate Avogadro's constant N A , Boltzmann constant k B , and temperature T, construct the inertial lift force F on the nanoparticles i , elastic force F e , viscous drag F d and virtual mass force F v Expressions of

[0025] Module M3: Based on F i , F e , F d and F v Construct a nanoparticle dynamics model to calculate the trajectory of the nanoparticles after they completely enter the sheath fluid;

[0026] Module M4: Use ANSYS Fluent to calculate the streamlines in the amplified section, and obtain the motion trajectory of the nanoparticles in the amplified section based on the lateral equilibrium position of the nanoparticles at the end of the straight channel.

[0027] Preferably, u x (y) is a parabolic function, and is subject to the condition:

[0028] u x (0) = u xmax = 2u xavg = 2Q / wh, u x (w / 2) = 0, u x (y) is given by equation (1):

[0029]

[0030] where u x (0) is the flow velocity at the wall position of the straight channel segment; u xmax is the maximum flow velocity in the straight channel segment; u xavg is the average flow velocity in the straight channel segment; and y is the transverse coordinate value in the straight channel segment.

[0031] Preferably, F i , F e , F d and F v in the module M2 are represented by equation (2):

[0032]

[0033] where e i and e j are unit vectors in the direction of the main flow and the wall surface direction, respectively.

[0034] Preferably, the dynamic model in the module M3 is characterized by equation (3), and is further arranged in the form of a differential equation as shown in equation (4):

[0035]

[0036]

[0037] Preferably, the module M4 determines the motion trajectory of the nanoparticle in the amplification segment according to the transverse displacement of the nanoparticle at the end of the straight channel segment, so as to determine which outlet the nanoparticle will flow out of.

[0038] Compared with the prior art, the present application has the following beneficial effects:

[0039] 1. The present application establishes the internal relationship between the sample liquid flow, the sheath liquid flow, the sheath liquid polyethylene oxide concentration and the various forces acting on the nanoparticle through a mathematical model, further calculates the motion trajectory of the size-differentiated nanoparticle in the microfluidic chip channel, and systematically and comprehensively reveals the mechanism of viscoelastic microfluidic nanoparticle sorting.

[0040] 2. This invention can provide a theoretical basis for the optimized design of viscoelastic microfluidic chips for exosome sorting, and enable the flexible expansion of viscoelastic microfluidic technology to other research directions and application fields;

[0041] 3. The viscoelastic microfluidic nanoparticle sorting modeling method of the present invention can provide a theoretical basis for viscoelastic microfluidic sorting of exosomes, and can be quickly iterated according to the sorting requirements of different application scenarios, greatly reducing the large amount of unnecessary investment caused by traditional empirical design, thereby achieving flexible expansion and wide application in other research fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0043] Figure 1 Schematic diagram of the movement of nanoparticles in a viscoelastic microfluidic chip channel according to an embodiment of the present invention;

[0044] Figure 2 Figure 1 is a motion trajectory diagram of four types of nanoparticles in a straight channel section in an embodiment of the present invention;

[0045] Figure 3 is a streamline diagram in the upper half of the enlarged section in an embodiment of the present invention;

[0046] Figure 4 Schematic diagram of the design of the viscoelastic microfluidic chip in an embodiment of the present invention;

[0047] Figure 5 This is a partial design diagram of the viscoelastic microfluidic chip at the beginning of the straight channel section in an embodiment of the present invention.

[0048] in:

[0049] First entrance 1 Amplification section 5

[0050] Second entrance 2 First exit 6

[0051] Circular branch road 3 Second exit 7

[0052] Straight channel section 4 DETAILED DESCRIPTION

[0053] The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0054] Example 1:

[0055] Reference Figure 1 and Figure 2 According to the present invention, a viscoelastic microfluidic nanoparticle sorting modeling method is provided, the method comprising the following steps:

[0056] Step S1: Use the total flow rate Q in the straight channel to construct the flow velocity distribution function u in the plane in the height direction x (y);u x (y) is a parabolic function, which passes the condition:

[0057] u x (0) = u xmax =2u xavg =2Q / wh,u x (w / 2)=0 to get u x (y), as shown in formula (1):

[0058]

[0059] Among them, u x (0) is the flow velocity at the wall of the straight channel section; u xmax is the maximum flow velocity in the straight channel section; u xavg is the average flow velocity in the straight channel section; y is the horizontal coordinate value in the straight channel section. This formula is the distribution of flow velocity along the y direction in the straight channel section. The definitions of the x and y directions refer to Figure 1 .

[0060] Step S2: Based on the particle transverse coordinate y p , particle diameter a p , the particle velocity u in the x direction px , the particle's velocity u in the y direction py , channel height h, channel width w, sheath fluid flow rate Q c , sample flow Q s , fluid density ρ f 、PEO molecular weight M W , PEO concentration of sheath fluid c c , PEO concentration of the sample c s , solvent viscosity η of PEO solution s , shear rate Avogadro's constant N A , Boltzmann constant k B , and temperature T, construct the inertial lift force F on the nanoparticles i , elastic force F e , viscous drag F d and virtual mass force F v The expression of Fi 、F e 、F d and F v It is expressed by formula (2):

[0061]

[0062] where e i and e j are the unit vectors pointing from the main flow direction to the wall direction and from the center of the channel, respectively. This formula is the expression of the inertial lift, elastic force, viscous drag and virtual mass force.

[0063] Step S3: Based on F i , F e , F d and F v A nanoparticle dynamics model is constructed to calculate the trajectory of the nanoparticles after they completely enter the sheath fluid. The dynamics model is represented by Equation (3) and further organized into a differential equation as shown in Equation (4):

[0064]

[0065] Based on the theoretical model proposed in the present invention, the motion trajectories of various nanoparticles in a microfluidic chip under any working condition can be quickly obtained using MATLAB software. Based on the motion trajectory of the particles, it is possible to intuitively judge whether the nanoparticles can be well sorted. It is no longer necessary to do many sets of experiments to see the general rules as in the traditional method, which is time-consuming and laborious. Formula (3) is obtained based on Newton's second law and is used to describe the dynamic process of the particles. The left side of the formula (π*ap^3*ρp) / 6 is the volume of the nanoparticle, and dup / dt is the derivative of the velocity with respect to time, that is, the acceleration. The right side is the resultant force exerted on the nanoparticle. Formula (4) is obtained by substituting the specific expressions of each force in Formula 2 into Formula 3 and sorting them out.

[0066] Step S4: ANSYS Fluent is used to calculate the streamlines in the amplification section, and the motion trajectory of the nanoparticles in the amplification section is obtained based on the lateral equilibrium position of the nanoparticles at the end of the straight channel. The motion trajectory of the nanoparticles in the amplification section is determined based on the lateral displacement of the nanoparticles at the end of the straight channel section, thereby determining which outlet the nanoparticles will flow out from.

[0067] The present invention also provides a viscoelastic microfluidic nanoparticle sorting modeling system, which can be realized by executing the process steps of the viscoelastic microfluidic nanoparticle sorting modeling method. That is, those skilled in the art can understand the viscoelastic microfluidic nanoparticle sorting modeling method as a preferred embodiment of the viscoelastic microfluidic nanoparticle sorting modeling system.

[0068] Example 2:

[0069] The present application also provides a viscoelastic microfluidic nanoparticle sorting modeling system, the system comprising the following modules:

[0070] Module M1: constructing a flow velocity distribution function u in the plane in the height direction with the total flow rate Q in the straight channel x (y); u x (y) is a parabolic function, passing through the conditions:

[0071] u x (0) = u xmax = 2u xavg = 2Q / wh, u x (w / 2) = 0, u x (y) is as shown in equation (1):

[0072]

[0073] Wherein, u x (0) is the flow rate at the position of the wall surface of the straight channel segment; u xmax is the maximum flow rate in the straight channel segment; u xavg is the average flow rate in the straight channel segment; y is the transverse coordinate value in the straight channel segment.

[0074] Module M2: constructing the expression of the inertial lift force F i , the elastic force F e , the viscous drag force F d and the virtual mass force F v experienced by the nanoparticle based on the particle transverse coordinate y p , the particle diameter a p , the particle velocity in the x direction u px , the particle velocity in the y direction u py , the channel height h, the channel width w, the sheath flow rate Q c , the sample flow rate Q s , the fluid density ρ f , the PEO molecular weight M W , the PEO concentration c c of the sheath fluid, the PEO concentration c s of the sample, the solvent viscosity η s of the PEO solution, the shear rate , the Avogadro constant N A , the Boltzmann constant k B , and the temperature T; F i , F e , F d and F v . i , F e , F d and F vIt is expressed by formula (2):

[0075]

[0076] where e i and e j are unit vectors pointing from the main flow direction to the wall from the center of the channel, respectively.

[0077] Module M3: Based on F i , F e , F d and F v A nanoparticle dynamics model is constructed to calculate the trajectory of the nanoparticles after they completely enter the sheath fluid. The dynamics model is represented by Equation (3) and further organized into a differential equation as shown in Equation (4):

[0078]

[0079] Module M4: Use ANSYS Fluent to calculate the streamlines in the amplification section and obtain the motion trajectory of the nanoparticles in the amplification section based on the lateral equilibrium position of the nanoparticles at the end of the straight channel; judge the motion trajectory of the nanoparticles in the amplification section based on the lateral displacement of the nanoparticles at the end of the straight channel section, and thus determine which outlet the nanoparticles will flow out from.

[0080] Example 3:

[0081] The present invention discloses a viscoelastic microfluidic nanoparticle sorting modeling method, including a straight channel section particle trajectory prediction model and an amplification section particle trajectory analysis method. The straight channel section particle trajectory prediction model is based on the inertial lift, elastic force, viscous drag and virtual mass force exerted on the nanoparticles in the straight channel section. By constructing a dynamic equation to reveal the motion law of the nanoparticles in the straight channel section, the lateral displacement of the nanoparticles when they reach the end position of the straight channel section is solved. Furthermore, the flow field distribution in the amplification section is calculated by ANSYS Fluent, and the motion trajectory of the particles in the amplification section can be obtained in combination with the lateral equilibrium position of the nanoparticles at the end of the straight channel section. Since the inertial lift and elastic force that play a dominant role in the movement of nanoparticles are related to the size of the nanoparticles, small-diameter and large-diameter nanoparticles will flow out from outlet one and outlet two respectively after passing through the straight channel section and the amplification section, thereby realizing the sorting of nanoparticles. The present invention constructs the intrinsic relationship between the sample liquid flow rate, sheath liquid flow rate, polyethylene oxide concentration in the sheath liquid and the various forces acting on the nanoparticles through a mathematical model, further calculates the motion trajectories of nanoparticles of different sizes in the microfluidic chip channel, and systematically and comprehensively reveals the mechanism of viscoelastic microfluidic nanoparticle sorting.

[0082] A viscoelastic microfluidic nanoparticle sorting modeling method, the specific steps are as follows:

[0083] Step one, using the total flow rate Q in the straight channel to construct the flow rate distribution function u in the height direction in the plane x (y);

[0084] Step two, based on the particle transverse coordinate y p , particle diameter a p , particle velocity in x direction u px , particle velocity in y direction u py , channel height h, channel width w, sheath flow rate Q c , sample flow rate Q s , fluid density ρ f , PEO molecular weight M W , PEO concentration c of the sheath fluid c , PEO concentration c of the sample s , solvent viscosity η of the PEO solution s , shear rate Avogadro's number N A , Boltzmann constant k B , and temperature T, construct the expression of the inertial lift force F experienced by the nanoparticle i , elastic force F e , viscous drag force F d , and virtual mass force F v ;

[0085] Step three, based on F i , F e , F d and F v , construct the nanoparticle dynamics model to calculate the motion trajectory of the nanoparticle after completely entering the sheath fluid;

[0086] Step four, use ANSYS Fluent to calculate the streamline in the enlarged section, and based on the transverse equilibrium position of the nanoparticle at the end of the straight channel, obtain the motion trajectory of the nanoparticle in the enlarged section;

[0087] Preferably, u x (y) is a parabolic function, which can be obtained by the conditions u x (0)=u xmax =2u xavg =2Q / wh, u x (w / 2)=0, u x (y) in step one is as shown in formula (1):

[0088]

[0089] wherein, u x(0) is the flow velocity at the wall of the straight channel section; u xmax is the maximum flow velocity in the straight channel section; u xavg is the average flow velocity in the straight channel section; y is the horizontal coordinate value in the straight channel section;

[0090] Preferably, F in step 4 i 、F e 、F d and F v It can be expressed by formula (2):

[0091]

[0092] where e i and e j are unit vectors pointing from the main flow direction to the wall from the center of the channel, respectively.

[0093] Preferably, the diameter of the sorting particles does not exceed 1 micron. When analyzing the motion trajectory of the nanoparticles in the straight channel section, the barrier effect of the sheath fluid-sample interface can be ignored, that is, it is assumed that all nanoparticles can penetrate the sheath fluid-sample interface. c With Q s The ratio of is usually not less than 5, which makes the width of the sample in the straight channel section very small. Therefore, it is assumed that the nanoparticles are always completely immersed in the sheath fluid during the entire movement process.

[0094] Preferably, the kinetic model in step 4 can be represented by formula (3), and can be further organized into a differential equation form as shown in formula (4):

[0095]

[0096] Preferably, the diameter of the sorted particles does not exceed 1 micron. The coupling between the particles and the fluid in the amplification section can be neglected, and the nanoparticles are assumed to move along streamlines in the amplification section. Therefore, a point at the entrance of the amplification section is selected, and the streamline passing through this point in the amplification section can be approximately regarded as the trajectory of the nanoparticle at that point.

[0097] Based on the flow simulation results in the semi-symmetrical region above the amplification section, two adjacent streamlines are obtained, flowing out of outlets 1 and 2, respectively. These streamlines are defined as streamline 1 and streamline 2. If the nanoparticle's position at the amplification section entrance is between streamline 1 and the outer wall of the channel, it will flow out of outlet 1. If the nanoparticle's position at the amplification section entrance is between streamline 2 and the channel centerline, it will flow out of outlet 2.

[0098] Preferably, F i , F e , F d and F vare all related to the diameter of the nanoparticles. Under the same conditions, nanoparticles of different sizes will show different motion trajectories in the channel of the viscoelastic microfluidic chip. Therefore, by adjusting the sheath fluid flow rate Q c , sample flow Q s , PEO concentration of sheath fluid c c and the PEO concentration of the sample c s Appropriate conditions can be used to separate nanoparticles of different sizes.

[0099] The present invention constructs a viscoelastic microfluidic chip based on a viscoelastic microfluidic nanoparticle sorting modeling method, comprising an inlet 1, an inlet 2, a circular bifurcation branch, a straight channel section, an amplifying section, an outlet 1 and an outlet 2;

[0100] The initial end of the circular bifurcated branch is connected to the end of the first inlet, the end of the circular bifurcated branch is connected to the straight channel section, the second inlet is arranged inside the circular bifurcated branch, the initial end of the amplified section is connected to the end of the straight channel section, the end of the amplified section is connected to the first outlet and the second outlet, and the first outlet is provided with two outlets, which are symmetrical with respect to the second outlet.

[0101] The channel height of the viscoelastic microfluidic chip is 50 μm, the width and length of the straight channel section are 20 μm and 30 mm respectively, and the particle solution in the first inlet is introduced into the straight channel section together with the sheath solution in the second inlet through a circular bifurcation branch with a width of 20 μm;

[0102] Preferably, the sheath liquid flowing in from the second inlet is prepared by dissolving powder of a high molecular weight polymer polyethylene oxide (PEO) in deionized water.

[0103] like Figure 1 As shown in the figure, all nanoparticles have the same initial lateral position at the beginning of the straight channel section under the action of the sheath fluid. However, since the inertial lift and elastic force that dominate the movement of nanoparticles are related to the size of the nanoparticles, small-diameter and large-diameter nanoparticles will flow out from outlet one and outlet two respectively after passing through the straight channel section and the amplification section, thereby realizing the sorting of nanoparticles.

[0104] like Figure 2 As shown in Q c =1.2mL / h, Q s =0.2mL / h, c s =0%, c c = 0.1%, the lateral coordinates of particles with diameters of 0.1μm, 0.2μm, 0.5μm and 1μm at the end of the straight channel are 6.81μm, 4.17μm, 0.35μm and 0μm respectively, with obvious lateral position differences.

[0105] like Figure 3 As shown in the figure, based on ANSYS Fluent, the streamline distribution in the amplified section can be calculated, and then the motion trajectory of the nanoparticles in the amplified section can be obtained in combination with the lateral position of the nanoparticles at the end of the straight channel.

[0106] like Figure 4 As shown, a viscoelastic microfluidic chip is constructed based on the viscoelastic microfluidic nanoparticle sorting modeling method, and the viscoelastic microfluidic chip consists of an inlet 1, an inlet 2, a circular bifurcation branch 3, a straight channel section 4, an amplifying section 5, an outlet 1 6 and an outlet 2 7.

[0107] like Figure 5 As shown, the width and length of the straight channel section 4 are 20 μm and 30 mm respectively. The particle solution in the inlet 1 is introduced into the straight channel section 4 together with the sheath solution in the inlet 2 2 through the circular bifurcation branch 3 with a width of 20 μm.

[0108] Working principle of the present invention:

[0109] The present invention proposes a viscoelastic microfluidic nanoparticle sorting modeling method, which includes two parts: a straight channel section particle trajectory prediction model and an amplification section particle trajectory analysis method. The straight channel section particle trajectory prediction model is based on the inertial lift, elastic force, viscous drag and virtual mass force experienced by the nanoparticles in the straight channel section. By constructing a kinetic equation to reveal the motion law of the particles in the straight channel section, the lateral displacement of the nanoparticles when they reach the end position of the straight channel section is solved. Furthermore, the flow field distribution in the amplification section is calculated by ANSYS Fluent, and the motion trajectory of the particles in the amplification section can be obtained in combination with the lateral equilibrium position of the nanoparticles at the end of the straight channel section. Since the inertial lift and elastic force that play a dominant role in the movement of nanoparticles are related to the size of the nanoparticles, small-diameter and large-diameter nanoparticles will flow out from outlet one and outlet two, respectively, after passing through the straight channel section and the amplification section, thereby achieving the sorting of nanoparticles.

[0110] Those skilled in the art may understand this embodiment as a more specific description of Embodiment 1 and Embodiment 2.

[0111] Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, and units provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same functions of the system and its various devices, modules, and units provided by the present invention in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; the devices, modules, and units for implementing various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0112] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. A viscoelastic microfluidic nanoparticle sorting modeling method, characterized in that: The method comprises the following steps: Step S1: Use the total flow rate Q in the straight channel to construct the flow velocity distribution function u in the plane in the height direction x (y); Step S2: Based on the particle transverse coordinate y p , particle diameter a p , the particle velocity u in the x direction px , the particle's velocity u in the y direction py , channel height h, channel width w, sheath fluid flow rate Q c , sample flow Q s , fluid density ρ f 、PEO molecular weight M W , PEO concentration of sheath fluid c c , PEO concentration of the sample c s , solvent viscosity η of PEO solution s , shear rate Avogadro's constant N A , Boltzmann constant k B , and temperature T, construct the inertial lift force F on the nanoparticles i , elastic force F e , viscous drag F d and virtual mass force F v Expressions of Step S3: Based on F i , F e , F d and F v Construct a nanoparticle dynamics model to calculate the trajectory of the nanoparticles after they completely enter the sheath fluid; Step S4: Calculate the streamlines in the amplified section using ANSYS Fluent, and obtain the motion trajectory of the nanoparticles in the amplified section based on the lateral equilibrium position of the nanoparticles at the end of the straight channel; u in step S1 x (y) is a parabolic function, through the condition u x (00=u xmax =2u xavg =2Q / wh,u x (w2)=0 to get u x (y), as shown in formula (1): Among them, u x (0) is the flow velocity at the wall of the straight channel section; u xmax is the maximum flow velocity in the straight channel section; u xavg is the average flow velocity in the straight channel section; y is the horizontal coordinate value in the straight channel section; The F in step S2 i 、F e 、F d and F v It is expressed by formula (2): where e i and e j are unit vectors pointing from the main flow direction to the wall from the center of the channel, respectively.

2. The viscoelastic microfluidic nanoparticle sorting modeling method according to claim 1, characterized in that: The kinetic model in step S3 is characterized by formula (3), and further organized into a differential equation form as shown in formula (4):

3. The viscoelastic microfluidic nanoparticle sorting modeling method according to claim 1, characterized in that: The step S4 judges the movement trajectory of the nanoparticles in the amplification section according to the lateral displacement of the nanoparticles at the end of the straight channel section, thereby determining from which outlet the nanoparticles will flow out.

4. A viscoelastic microfluidic nanoparticle sorting modeling system, characterized in that: The system includes the following modules: Module M1: Use the total flow rate Q in the straight channel to construct the flow velocity distribution function u in the plane in the height direction x (y); Module M2: Based on the particle transverse coordinate y p , particle diameter a p , the particle velocity u in the x direction px , the particle's velocity u in the y direction py , channel height h, channel width w, sheath fluid flow rate Q c , sample flow Q s , fluid density ρ f 、PEO molecular weight M W , PEO concentration of sheath fluid c c , PEO concentration of the sample c s , solvent viscosity η of PEO solution s , shear rate Avogadro's constant N A , Boltzmann constant k B , and temperature T, construct the inertial lift force F on the nanoparticles i , elastic force F e , viscous drag F d and virtual mass force F v Expressions of Module M3: Based on F i , F e , F d and F v Construct a nanoparticle dynamics model to calculate the trajectory of the nanoparticles after they completely enter the sheath fluid; Module M4: Calculate the streamlines in the amplified section using ANSYS Fluent, and obtain the trajectory of the nanoparticles in the amplified section based on the lateral equilibrium position of the nanoparticles at the end of the straight channel; u in the module M1 x (y) is a parabolic function, through the condition u x (0) = u xmax =2u xavg =2Q / wh,u x (w / 2)=0 to get u x (y), as shown in formula (1): Among them, u x (0) is the flow velocity at the wall of the straight channel section; u xmax is the maximum flow velocity in the straight channel section; u xavg is the average flow velocity in the straight channel section; y is the horizontal coordinate value in the straight channel section; The F in the module M2 i 、F e 、F d and F v It is expressed by formula (2): where e i and e j are unit vectors pointing from the main flow direction to the wall from the center of the channel, respectively.

5. The viscoelastic microfluidic nanoparticle sorting modeling system according to claim 4, characterized in that: The dynamic model in the module M3 is represented by formula (3) and further organized into the differential equation form shown in formula (4):

6. The viscoelastic microfluidic nanoparticle sorting modeling system according to claim 4, characterized in that: The module M4 determines the movement trajectory of the nanoparticles in the amplification section according to the lateral displacement of the nanoparticles at the end of the straight channel section, thereby determining from which outlet the nanoparticles will flow out.

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