Viscoelastic microfluidic nanoparticle sorting modeling method and system

Abstract

The invention provides a viscoelastic microfluidic nanoparticle sorting modeling method and system, and the method comprises a straight channel section particle trajectory prediction model and an amplification section particle trajectory analysis method. Internal relations among sample liquid flow, sheath liquid flow and polyethylene oxide concentration in sheath liquid and inertial lift force, elastic force, viscous drag force and virtual mass force borne by the nano-particles are constructed through a mathematical model, and the movement law of the nano-particles in a straight channel section is disclosed based on a kinetic equation. The transverse displacement of the nano-particles when reaching the tail end of the straight channel section is solved, and the movement track of the nano-particles in the amplification section can be obtained by combining the flow field simulation result in the amplification section and the transverse balance position of the nano-particles at the tail end of the straight channel section, so that the mechanism of sorting the viscoelastic micro-fluidic nano-particles is systematically and 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 in many disciplines such as biology, chemistry, and medicine. Among them, exosome sorting in the biomedical field is one of the typical applications. Exosomes are small extracellular vesicles with a diameter between 30nm and 200nm. They contain nucleic acids and proteins from their original cells and play a vital role in intercellular communication. At present, exosomes have received great attention in the scientific community as potential diagnostic biomarkers and therapeutic carriers for various diseases (such as cancer, infectious diseases, and neurodegenerative diseases).

[0003] In order to better interpret the biological information carried in exosomes, it is necessary to achieve high-purity separation of exosomes from complex biological samples. Existing sorting methods can be divided into two categories: active manipulation and passive manipulation. Active manipulation refers to the sorting of exosomes by forming external driving forces such as magnetic fields, electric fields, acoustic fields, and centrifugal motion. Due to the reliance on external forces, active methods often require the use of large and expensive instruments and the introduction of additional manual operations, and usually face problems such as high instrument costs, large system volume, and complex operations. Passive sorting technology does not require the use of forces formed by external physical fields to drive exosomes, but instead relies on the coupling between microstructures, microfluidics, and exosomes in microfluidic chips to achieve exosome sorting. Passive technology can avoid the disadvantages of active methods, but the sorting resolution of most passive technologies is usually low, and it is difficult to ensure sorting purity.

[0004] In the patent document with the publication number CN116099581A, a microfluidic chip for pre-focusing and sorting cells is disclosed. Newtonian fluid and viscoelastic fluid are introduced into different inlets of the chip. Through the polygonal flow channel shape set in the microfluidic chip, the two solutions are used to co-flow to produce a stable viscoelastic-Newtonian interface, and the inertial effect and viscoelastic effect generated in the flow channel microfluidic induce inertial lift on the microchannel wall and viscoelastic lift in the viscoelastic solution, which act together on the cell particles in the flow channel. The viscoelastic microfluidic exosome sorting technology uses a viscoelastic fluid medium to drive exosomes. Compared with the current mainstream passive technology, the viscoelastic microfluidic technology strengthens the control of exosomes by introducing additional elastic forces, which can improve the sorting purity of exosomes. However, the current research on the sorting mechanism of viscoelastic microfluidic exosome sorting technology is not in-depth, and usually relies on a large number of experimental attempts to achieve the final sorting purpose, and it is impossible to achieve the rapid design of viscoelastic microfluidic chips through theoretical guidance. 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 achieving 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 object of the present invention is to provide a viscoelastic microfluidic nanoparticle sorting modeling method and system.

[0007] According to a viscoelastic microfluidic nanoparticle sorting modeling method provided by the present invention, the method 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 in the y direction u 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 Expression 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, u in step S1 x (y) is a parabolic function, through 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, 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 and the channel center to the wall, 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 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.

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

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

[0024] Module M2: Based on the particle lateral coordinate y p , particle diameter a p , the particle velocity u in the x direction px , the particle's velocity in the y direction u 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 Expression 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: 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.

[0027] Preferably, the u in the module M1 x (y) is a parabolic function, through the condition:

[0028] u x (0) = u xmax =2u xavg =2Q / wh,u x (w / 2) = 0 to get u x (y), as shown in formula (1):

[0029]

[0030] 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.

[0031] Preferably, the F in the module M2 i 、F e 、F d and F v It is expressed by formula (2):

[0032]

[0033] where e i and e j are unit vectors pointing from the main flow direction and the channel center to the wall, respectively.

[0034] Preferably, the kinetic model in the module M3 is characterized by formula (3), and further organized into a differential equation form as shown in formula (4):

[0035]

[0036]

[0037] Preferably, 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.

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

[0039] 1. 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 trajectory of nanoparticles of different sizes in the microfluidic chip channel, and systematically and comprehensively reveals the mechanism of viscoelastic microfluidic nanoparticle sorting;

[0040] 2. The present invention can provide a theoretical basis for the optimal design of viscoelastic microfluidic chips for exosome sorting, and realize 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 from the detailed description of non-limiting embodiments made with reference to the following drawings:

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

[0044] Figure 2 Graph showing the motion trajectories 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 The viscoelastic microfluidic chip design structure diagram in the 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 Enlargement 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 is described in detail below in conjunction with specific embodiments. The following embodiments 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 of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0054] Embodiment 1:

[0055] Reference Figure 1 and Figure 2 According to a viscoelastic microfluidic nanoparticle sorting modeling method provided by the present invention, the method comprises 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, through 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 velocity in the straight channel section; y is the horizontal coordinate value in the straight channel section. This formula is the distribution of the 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 in the y direction u 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 from the center of the channel. This formula is the expression of 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 motion 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 form 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 very intuitive to judge whether the nanoparticles can be well sorted. It is no longer necessary to do many groups of experiments to see the general rules like the traditional method, which is time-consuming and laborious. Formula (3) is based on Newton's second law and is used to describe the dynamic process of 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 velocity with respect to time, that is, 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 it out.

[0066] Step S4: Calculate the streamlines in the amplification section using ANSYS Fluent, 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 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.

[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 implementation of the viscoelastic microfluidic nanoparticle sorting modeling system.

[0068] Embodiment 2:

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

[0070] Module M1: Use the total flow rate Q in the straight channel to construct the velocity distribution function u in the plane in the height direction x (y);u x (y) is a parabolic function, through the condition:

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

[0072]

[0073] 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.

[0074] Module M2: Based on the particle lateral coordinate y p , particle diameter a p , the particle velocity u in the x direction px , the particle's velocity in the y direction u 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 F 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 and the channel center to the wall, respectively.

[0077] Module M3: Based on F i , F e , F d and F v A nanoparticle dynamics model is constructed to calculate the motion 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 form 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, so as to determine from which outlet the nanoparticles will flow out.

[0080] Embodiment 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 of the nanoparticles in the straight channel section, and the motion law of the nanoparticles in the straight channel section is revealed by constructing a kinetic equation, so as to solve the lateral displacement of the nanoparticles when they reach the end position of the straight channel section. Further, 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 leading role in the movement of the nanoparticles are related to the size of the nanoparticles, the 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 the 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 movement trajectory 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 1: Use the total flow rate Q in the straight channel to construct the velocity distribution function u in the mid-plane in the height direction x (y);

[0084] Step 2: 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 in the y direction u 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 Expression of

[0085] Step 3: 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;

[0086] Step 4: 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;

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

[0088]

[0089] 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;

[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 and the channel center to the wall, respectively.

[0093] Preferably, the diameter of the sorted particles does not exceed 1 micron, and the barrier effect of the sheath fluid-sample interface can be ignored when analyzing the motion trajectory of the nanoparticles in the straight channel section, 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. 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 characterized 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 effect between the particles and the fluid in the amplification section can be ignored, and it is considered that the nanoparticles move along the streamlines in the amplification section. Therefore, a point at the entrance of the amplification section is selected, and the streamline passing through the point in the amplification section can be approximately regarded as the movement trajectory of the nanoparticles at the point.

[0097] Based on the fluid simulation results in the upper semi-symmetrical region of the amplification section, two adjacent streamlines can be obtained, which flow out from outlet 1 and outlet 2 respectively, and are defined as streamline 1 and streamline 2. If the position of the nanoparticle at the entrance of the amplification section is between streamline 1 and the outer wall of the channel, it will flow out from outlet 1. If the position of the nanoparticle at the entrance of the amplification section is between streamline 2 and the center line of the channel, it will flow out from 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 amplification section, an outlet 1 and an outlet 2;

[0100] The initial end of the circular forked branch is connected to the end of the inlet 1, the end of the circular forked branch is connected to the straight channel section, the inlet 2 is arranged inside the circular forked branch, the initial end of the enlarged section is connected to the end of the straight channel section, the end of the enlarged section is connected to the outlet 1 and the outlet 2, the outlet 1 is provided with two, and is symmetrical with respect to the outlet 2;

[0101] The channel height in 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 inlet 1 is introduced into the straight channel section together with the sheath solution in the inlet 2 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 will 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% condition, 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, 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 amplification 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, and 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 annular 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 of the nanoparticles in the straight channel section. By constructing a kinetic equation, the motion law of the particles in the straight channel section is revealed, thereby solving the lateral displacement of the nanoparticles when they reach the end position of the straight channel section. Further, 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.

[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 know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions 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 as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0112] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

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 in the y direction u 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 Expression 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.

2. The viscoelastic microfluidic nanoparticle sorting modeling method according to claim 1, characterized in that: u in step S1 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 transverse coordinate value in the straight channel section.

3. The viscoelastic microfluidic nanoparticle sorting modeling method according to claim 1, characterized in that: 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 and the channel center to the wall, respectively.

4. 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):

5. The viscoelastic microfluidic nanoparticle sorting modeling method according to claim 1, characterized in that: The step S4 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.

6. 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 velocity distribution function u in the plane in the height direction x (y); Module M2: Based on the particle lateral coordinate y p , particle diameter a p , the particle velocity u in the x direction px , the particle's velocity in the y direction u 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 Expression 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: 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.

7. The viscoelastic microfluidic nanoparticle sorting modeling system according to claim 6, characterized in that: The 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 transverse coordinate value in the straight channel section.

8. The viscoelastic microfluidic nanoparticle sorting modeling system according to claim 6, characterized in that: 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 and the channel center to the wall, respectively.

9. The viscoelastic microfluidic nanoparticle sorting modeling system according to claim 6, characterized in that: The dynamic model in the module M3 is characterized by equation (3) and further organized into a differential equation form as shown in equation (4):

10. The viscoelastic microfluidic nanoparticle sorting modeling system according to claim 6, 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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