Droplet robot system realized by electret induced polarization on droplet

Abstract

A droplet holder that polarizes droplets includes a top plate formed of a magnetically responsive material and movable in a defined sequence in response to movement of a regionalized electromagnetic field. The gripper further has electret tabs depending downwardly from the top plate. The electret sheets are separated from each other and are chargeable to the same polarity so as to trap droplets between them due to electret induced polarization (EPD) on the droplets. The gripper may be included in a multi-physical droplet robotic system for automated manipulation and movement of droplets. The system includes a programmable control matrix that generates a movable, regionalized electromagnetic field through coils to engage the magnetic material on the top plate and move the EPD gripper and captured polarizable droplets in a defined sequence along a specific path, as well as to implement other microfluidic operations according to the program of the control matrix.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority under 35 USC § 119(e) to U.S. Application No. 63 / 589,497, filed on October 11, 2023, and U.S. Application No. 63 / 590,701, filed on October 16, 2023, both of which are incorporated herein by reference in their entirety. Technical Field

[0003] The present invention relates to automated liquid manipulation, and more particularly, to automated droplet manipulation using electrostatic fields. Background Art

[0004] Robotic systems, with their high efficiency, high precision, and high reliability, have begun to drive the transformation of scientific research from labor-intensive empirical practices to automated standardized operations [1-3]. They have shown the potential to accelerate drug discovery [4-6], synthesize precision materials and structures [7-9], and promote medical diagnosis [10-12]. For these applications, automated liquid manipulation is one of the essential functions, because samples and reagents in liquid form are often involved in scientific research related to biology, chemistry, materials science, and medicine. Automated liquid manipulation can help improve efficiency with limited labor while minimizing human errors during the experimental process [13, 14]. Therefore, the demand for automated liquid manipulation platforms continues to grow, with the goal of completely transforming scientific research experiments from manual empirical processes to automated intelligent processes.

[0005] At present, the automation of liquid manipulation is mainly based on liquid handling workstations, which transfer liquids by controlling experimental consumables such as pipettes, centrifuge tubes, microplates, etc. [15-17]. These workstations are bulky, resource-intensive (requiring additional laboratory environment and resources), and have limited functions. The high cost of such systems, coupled with insufficient flexibility and limited scalability, limits their widespread application [18-20]. In addition, commercial systems are usually non-modular and not open source [19-21], further limiting their scalability and widespread application in laboratories. To overcome these challenges, droplet robotic systems have been proposed for manipulating discrete fluids [12, 21, 22]. Compared with bulky robotic arms, droplet robotic systems are miniaturized, economical, and flexible. They can handle extremely small amounts of liquid and can achieve spatial cross-manipulation to process multiple droplets in parallel [11, 23-26]. In addition, due to their small footprint and flexible operation, droplet robotic systems can be easily integrated into other laboratory automation systems [27-29]. However, they still face the challenge of compatibility with various fluid types and biochemical samples [28–30].

[0006] Currently common droplet automated manipulation technologies include air- and oil-based electrowetting on dielectrics (EWOD) technology, as well as magnetic, acoustic, and thermal systems. Figure 1A -D[30-34]. However, the hardware platforms based on these droplet manipulation technologies lack compatibility with different liquid types and biochemical samples carried in the droplets. For example, EWOD is not suitable for manipulating liquids with low dielectric constants and poor conductivity[35,36], and there is also a problem of protein adsorption, which poses a challenge to manipulating biological samples without biofouling[37-39]. In magnetic-based systems, the introduced magnetic nanoparticles may react with certain reagents[40,41] and also interfere with the absorbance of the droplets, resulting in inaccurate detection results. In addition, due to the high external energy, both surface acoustic waves and thermal methods may cause damage to the cells or proteins loaded in the droplets

[42] . Therefore, these technologies have low compatibility with various liquid types or biochemical samples, and require manual adjustment or replacement when changing the sample content, which not only affects the accuracy and repeatability of the results, but also requires additional labor to supervise and modify the platform.

[0007] Veley et al., On-chip manipulation of free droplets, Nature 426, 515–516 (2003). https: / / doi.org / 10.1038 / 426515a, describe a liquid-liquid microfluidic system for manipulating freely suspended microliter and nanoliter droplets of water or hydrocarbons that float on a denser perfluorinated oil and are driven by an alternating or constant electric field applied by an array of electrodes under the oil. There are also reports on the application of dielectrophoresis to separate particles in microfluidic channels to achieve droplet manipulation. However, this method requires special electrode design and high voltage input, so Joule heating and high field strength may hinder the biocompatibility of the system.

[0008] Another article by Dai et al., “Controllable High-Speed ​​Electrostatic Manipulation of Water Droplets on a Superhydrophobic Surface,” Advanced Materials, Vol. 31, Issue 43 (October 25, 2019) https: / / doi.org / 10.1002 / adma.201905449, discloses the use of electrostatic charging to achieve controllable high-speed “all-in-one” lossless droplet manipulation, that is, in-plane movement and stopping / fixation in arbitrary directions on superhydrophobic surfaces. In this article, droplets that have been pre-charged to a certain level are forced to move in the direction of the electric field or in the opposite direction of the electric field. However, if the force caused by the net charge of the droplet is expected to drive the movement, the charge density of the droplet needs to be maintained at a high level, which may require an additional charging process. In addition, due to the randomness of contact charging and charge dissipation, as well as the differences in the chargeable capacity of different liquids, the robustness and feasibility of actual manipulation based on net charge remain to be verified.

[0009] Another article by Li et al., “Photopyroelectric microfluidics”, Science Advances, Vol. 6, Issue 28 (September 16, 2020) https: / / www.science.org / doi / 10.1126 / sciadv.abc1693, describes the precise manipulation of various liquids using photothermoelectric microfluidics, where even illumination from a beam of light generates a unique wave-like dielectrophoretic force field that enables the desired non-destructive manipulation of droplets. In this article, a photothermal film composed of a graphene-polymer composite is used. The film senses the light stimulus and responds by generating localized and inhomogeneous heat generation. As a result, the thermoelectric crystals convert the heat into additional charges, forming a wave-like dielectrophoretic force distribution that can capture, distribute, and separate fluids. The process involves the conversion of light energy into heat energy and then back into electrical energy, and requires a specific matrix. In addition, the generated heat may have an additional impact on the activity of biological samples. Summary of the invention

[0010] According to the present invention, a droplet robot system based on the electret-induced polarization (EPD) mechanism on droplets is proposed. Instead of the traditional AC / DC power supply with fixed electrodes, EPD directly uses the intrinsic electrostatic charge carried by the electret to generate a non-uniform electrostatic field, polarizing and attracting the droplets. EPD can theoretically achieve full liquid actuation, and experiments have shown that it can actuate various conductive / non-conductive and inorganic / organic liquids. In addition, it is naturally independent of high-voltage AC / DC power supplies or external additives, ensuring its compatibility with biological samples (body fluids / living cells / proteins).

[0011] Based on the EPD mechanism, a multi-physics droplet robotic system is designed to automate the droplet manipulation process. The system consists of the following components: (1) a programmable control matrix that generates a localized electromagnetic field by exciting coils; (2) an EPD gripper, which is made of a mixture of electret and magnetically responsive materials, can be controlled by a programmable magnetic field and can induce a moving non-uniform electrostatic field that can attract the droplets below; and (3) a target droplet sample. By programming the control matrix, the coils can be powered in a defined sequence, allowing the EPD gripper to drive droplets in a specific path and achieve other microfluidic operations.

[0012] EPD exploits the polarization of dielectrics in a non-uniform electrostatic field, showing potential for manipulating non-aqueous or non-conductive liquids. Based on both simulation and experimental results, EPD has demonstrated its versatility for a variety of inorganic / organic liquids, while variations in droplet volume (nL-mL), conductivity, and relative dielectric constant (2.25-84.2) within common ranges do not hinder liquid manipulation. In addition, the external stimulus required for EPD actuation is a non-uniform electrostatic field, which avoids the effects of Joule heating on biological samples and thereby ensures compatibility with a variety of biological samples such as body fluids, proteins, and living cells.

[0013] In order to achieve automation, a multi-physics system combined with EPD was designed, which includes: a programmable control matrix that generates a localized electromagnetic field; an EPD gripper that can be controlled by a programmable magnetic field and senses a moving non-uniform electrostatic field; and a polarizable target droplet. Its advantages in the versatility of manipulating liquids, biocompatibility, and programmability make it possible to assist basic scientific research as an automated experimental platform.

[0014] Compared with existing droplet robotic systems, the proposed EPD-based droplet robotic system has significant advantages in terms of the types of liquids that can be operated and compatibility with biochemical samples and matrices. In addition, it also shows a comprehensive performance that is superior to other platforms in terms of programmability, manufacturing cost, power consumption, and compatibility with matrices, thus laying the foundation for an automated experimental platform in multidisciplinary fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] This patent or application file contains at least one drawing printed in color. Copies of this patent or patent application publication in color will be provided by the Office upon request and payment of the necessary fee.

[0016] The foregoing and other objects and advantages of the present invention will become more apparent when considered in conjunction with the following detailed description and the accompanying drawings, in which like numerals represent like elements throughout the various views, and in which:

[0017] Figure 1A This is a schematic diagram of the existing common automated droplet manipulation technology, namely electrowetting on dielectrics (EWOD). Figure 1B shows magnetic-based droplet actuation, Figure 1C shows acoustic-based droplet actuation, and Figure 1D Thermal-based droplet actuation is shown;

[0018] Figure 2A The working principle of the droplet robotic system based on the electret induced polarization (EPD) mechanism on the droplet is shown. Figure 2B This is an optical image of the EPD-based multi-physics droplet robotic system and its core components. Figure 2C shows superimposed sequential images that visualize the movement path of a droplet actuated by an EPD-based multi-physics droplet robotics system, and Figure 2D A bar chart comparison between the proposed EPD-based droplet robotic system and the existing automated droplet robotic system from six perspectives is shown;

[0019] Figure 3A A comparison graph showing the EPD and EWOD of water (inorganic) is shown. Figure 3B Showing a comparison of glycerol (alcohol), Figure 3C Showing a comparison of triacetin, Figure 3D Comparison of serum is shown, Figure 3E shows a comparison of saliva, and Figure 3F A comparison of urine is shown;

[0020] Figure 4A is an optical image of the electret-induced motion of a droplet floating on an oil surface and two potential hypotheses to explain the phenomenon, Figure 4B The actuation of a droplet by two electrets with opposite charge polarity is shown to investigate the leading hypothesis, Figure 4C shows a simulation of a droplet with a measured amount of positive charge attracted to both a positive electret and a negative electret, Figure 4D Simulation showing the average Maxwell surface stress tensor applied to droplets with positive, negative, and neutral charge densities, Figure 4E Figure 2 shows the comparison of the experimentally measured acceleration of EPD-based droplet actuation and the simulated Maxwell stress tensor applied to the droplet. Figure 4F The actuation path of the droplet under the EPD effect is shown. Figure 4G The relationship between the effective actuation distance of the droplet and the amount of electret charge is shown, and Figure 4H It is the phase diagram of the dynamic behavior of the droplet when adjusting the charge of the electret and the height difference between the electret and the droplet;

[0021] Figure 5A Shows a simulation generated by a positive electret (glass) and the average Maxwell stress tensor applied to a droplet with a measured amount of positive charge and the simulated average Maxwell stress tensor applied to the droplet by an electret with different amounts of charge, and Figure 5B Shows a simulation generated by a negative electret (PTFE) and the average Maxwell stress tensor exerted on a droplet with a measured amount of positive charge;

[0022] Fig. 6A The droplet is generated by a positive electret (glass) simulation Simulation of the actuation of , and the average Maxwell stress tensor exerted on a droplet with a measured amount of positive charge, and Figure 6B A simulation generated by negative electret (PTFE) and the average Maxwell stress tensor exerted on a droplet with a measured amount of positive charge;

[0023] Fig. 7A This is a schematic diagram of the versatility of EPD with various liquids. Figure 7B Demonstrating EPD-based droplet actuation for volumes ranging from nanoliters to milliliters, Figure 7C shows the simulated Maxwell stress tensor applied to droplets with different conductivities, Fig.7D shows the simulated Maxwell stress tensor applied to a droplet with different relative permittivity, Fig. 7E Optical image showing EPD-based droplet actuation, Figure 7F The simulated effective actuation distances for water, triacetin, and paraffin (asterisks) obtained by intersecting the calculated Maxwell stress tensor (solid line) with the minimum force required for effective actuation (dashed line) are shown, and Figure 7G shows a comparison between simulated and measured effective actuation distance (EAD);

[0024] Figure 8 is a schematic diagram of EPD-based actuation of multiple droplets;

[0025] Fig. 9 is a graph showing calculation of the force required for effective actuation;

[0026] Fig. 10Ais a schematic diagram of the compatibility of EPD with biological samples, Fig. 10B Optical images showing EPD-based actuation of body fluids, Fig. 10C A series of photographs showing EPD-based actuation of a fluorescent protein solution under a fluorescence microscope are shown. Fig. 10D EPD-based actuation of live A549 cells in culture is shown, where the percentage of live cells was analyzed and compared to a control in air, Fig.10E shows the changes in viable and non-viable cell concentrations after treatment and subsequent incubation on EPD / EWOD, Fig.10F is a schematic diagram of the compatibility of EPD with various matrices, Figure 10G is an optical image showing EPD-based droplet actuation on a hydrophobic surface, Fig. 10H is an optical image showing EPD-based actuation of a droplet floating on an oil surface and a graph showing the effect of a surfactant, and Fig.10I Optical images showing EPD-based actuation of droplets merging in an oil environment with a hydrophobic matrix;

[0027] Fig.11 is a photo of a droplet taken under a fluorescence microscope, showing the adsorption of proteins on the double-plate EWOD after actuation;

[0028] Fig. 12A shows the maximum actuation voltages for serum, saliva, and urine on oil-based EWOD without droplet fragmentation, and Fig. 12B Graph showing actuation of human serum, saliva, and urine on oil-based EWOD at an operating voltage of 65 V (the highest voltage that prevents complete fragmentation of the three body fluids);

[0029] Fig.13A Actuation of 0.05 mg / mL FITC-BSA on oil-based EWOD at an operating voltage of 100 V is shown, where droplet fragmentation occurs and prevents subsequent continuous actuation, and Fig. 13B shows a fluorescence microscopy image illustrating protein adsorption on oil-based EWOD after actuation of a droplet of 0.05 mg / mL FITC-BSA solution, indicated by a change in fluorescence intensity on the substrate;

[0030] Fig.14A is a bar graph showing the effects of EPD and electrowetting effect on cell activity, and Fig. 14B is a bar graph showing the effects of EPD and oil-based EWOD system settings on cell culture;

[0031] Fig.15A The right side shows a schematic diagram of EPD-based actuation of a 10% water-in-oil emulsion, while the right side is an optical image showing the interior of the emulsion, Fig. 15BA schematic diagram of EPD-based actuation of a 10% oil-in-water emulsion is shown on the right, while an optical image showing the interior of the emulsion is shown on the right, and Fig. 15C is an optical image showing EPD-based actuation of water-encapsulated particles (iron oxide magnetic nanoparticles);

[0032] Fig.16A is a SEM image of the surface morphology of the superhydrophobic surface, and Fig. 16B shows the measurement of contact angle of superhydrophobic surface;

[0033] Fig.17 EPD-based droplet actuation on a hydrophilic surface is shown, showing that the droplet exhibits a tendency to move with different contact angles on the advancing and receding sides;

[0034] Fig.18 The adjustment of the maximum actuation velocity by introducing a surface surfactant into the oil matrix is ​​shown;

[0035] Fig.19 shows a graph comparing the evaporation of a droplet on a hydrophobic substrate and an oil substrate;

[0036] Fig. 20A is a schematic diagram of the multi-physics droplet robot system of the present invention, Fig. 20B The general workflow of the proposed multi-physics droplet robotic system is shown. Fig. 20C is an optical image of a programmable control matrix made on a multi-layer PCB, Fig.20D is the operation diagram of the control matrix and the corresponding simulation results, Fig.20E is a schematic diagram of the EPD holder. Fig.20F is the simulated electric field strength of the EPD holder (bottom view), Figure 20G Schematic diagram of the force generated by the EPD gripper on the droplet in the X direction (left) and Y direction (right) at different positions. Fig. 20H are the simulated magnetic Maxwell stress tensor on the EPD holder and the electric Maxwell stress tensor on the target droplet;

[0037] Fig.21A shows a side view of the simulated potential distribution of the EPD holder, and Fig.21B A bottom view showing a simulated potential distribution;

[0038] Fig. 22 shows the locations of four stable equilibrium points generated by the EPD gripper according to simulation results;

[0039] Fig.23A is the simulation of the X component of the force generated by the sheet electret applied to the droplet at different positions, and Fig. 23B is the Y component of the force applied to the droplet at different positions, where the stable equilibrium points of the force are marked by arrows;

[0040] Fig.24 It is a comparison of the forces on droplet generation between slice-shaped electret and clamped electret;

[0041] Fig.25 The resolution and accuracy of EPD compared with EWOD and magnetic-based systems in experiments are shown;

[0042] Fig.26A shows the self-assembly of multiple droplets, which is one of the fundamental microfluidic functions that can be performed by an EPD-based droplet robotic system. Fig.26B shows the merging of two droplets, and Fig.26C shows mixing of merging droplets by circular motion;

[0043] Fig.27A is a lateral optical image of a floating water droplet at the oil-gas interface. Fig.27B is a schematic diagram of two floating water droplets at the oil-gas interface, showing that the distorted interface will lead to attractive forces. Fig.27C shows the simulated EPD forces applied to two droplets by the EPD gripper, indicating that both droplets are subject to EPD forces directed toward each other, and Fig.27D is a graph of the time required for two 20 μL droplets to merge, indicating that the presence of the EPD gripper can help promote droplet merging;

[0044] Fig.28A is a schematic diagram of different body fluids (serum, saliva, and urine), showing that their conductivity and protein concentrations vary significantly. Fig.28B Figure 2 shows the characteristics of serum, saliva, and urine actuation of an EPD-based multi-physics droplet robotic system. Fig.28C It is an optical image of the EPD-based droplet robotic system and auxiliary detection chip. Fig.28D is an optical image of a microchip device designed to be tested as a set with two calibrations and one actual biological sample. Fig.28E is a step-by-step workflow of an EPD-based droplet robotic system when performing lithium detection, and Fig.28F shows the comparison of lithium test results of serum, saliva and urine samples by the EPD-based system with reference values;

[0045] Fig.29 Actuation of human serum, saliva, and urine on EWOD is shown at operating voltages of 100 V and 150 V;

[0046] Fig.30 is a set of graphs of actuation standard curves for serum lithium, salivary lithium, and urine lithium established by measuring the absorbance of calibration samples at 540 nm and 630 nm;

[0047] Fig.31 It is a set of curve graphs of standard curves of serum lithium, salivary lithium and urine lithium established by in situ photography and RGB analysis;

[0048] Fig.32A Schematic diagram of the workflow for establishing an inflammatory cell-bacteria model and detecting the concentration of inflammatory mediators in situ on an EPD-based system. Fig.32B is a standard curve for human IL-1β concentration, and Fig.32C It is a curve diagram of the detected concentration of human IL-1β after the first, second, and third bacterial infection;

[0049] Fig.33 is a SEM image of the surface morphology of the microfluidic detection chip; and

[0050] Fig.34 Shows the types of fluids that can be operated and their compatibility with biological samples, and Figure 2D EWOD is shown on the right for comparison. DETAILED DESCRIPTION

[0051] Based on the observed attraction between various droplets and electrets, a droplet robotic system based on the electret-induced polarization (EPD) mechanism on droplets is proposed. Traditional liquid polarization is generated by AC / DC electric fields [39, 73,76]. However, EPD does not use a traditional AC / DC power supply with fixed electrodes, but directly uses the intrinsic electrostatic charge carried on the electret to generate a non-uniform electrostatic field to polarize and attract the droplets, such as Figure 2A as shown in .

[0052] The most basic physical principle of EPD can be attributed to polarization, which already exists and has been discussed in the literature on micro-object actuation [46, 83]:

[0053]

[0054] Where E represents the electric field strength, r represents the particle radius, is the dielectric constant of the surrounding medium, and CM is the Clausius–Mossotti factor related to the effective polarizability of the particle.

[0055] The traditional method of polarizing liquids is to apply a high-frequency, high-voltage AC / DC electric field in a microfluidic device [84-86, 60]. Although electrostatic charge-induced polarization and AC / DC-induced polarization are derived from the same polarization control principle, they need to be discussed separately. For AC / DC-induced polarization and electrostatic charge-induced polarization, the equivalent circuit models are different. The former model is connected to the power supply externally, while the latter model is an isolated system.

[0056] In the AC / DC electric field model [86, 87], an external power source connects the upper and lower electrodes and keeps the voltage U constant. In this closed circuit, both the conduction current and the displacement current exist and compete in a frequency-dependent manner. Therefore, the above equation should be expressed as [54, 69]:

[0057]

[0058] Where ω is the frequency of the applied electric field, ε and σ are the dielectric constant and conductivity, and the subscripts d and m represent the droplet and the surrounding medium, respectively. Therefore, the AC / DC-induced polarization is related to both the conductivity and dielectric constant of the manipulated droplet, and the frequency setting needs to be customized according to the electrical properties of the manipulated droplet [86, 60].

[0059] For the isolated system model of electrostatic charge, there is no conduction current. CM is therefore independent of conductivity and frequency and depends only on the dielectric constant:

[0060]

[0061] In addition to the expression of CM, E is also different for the two polarizations. In the AC / DC electric field model, the voltage U remains constant. The charge Q accumulated on the electrode ( , powered by a power supply) varies with the total system capacitance C, causing the electric field strength E to change [85, 87, 88]. For example, in some cases

[89] (e.g. Less than 100 mS / m and field frequencies less than about 15 kHz 10 ), the equivalent capacitance of the electric double layer at the electrode interface becomes very small. At this time, E in the surrounding medium tends to zero, resulting in an undesirable shielding effect [90, 91].

[0062] In contrast, in the equivalent model of electrostatic charge-induced polarization, the charge Q accumulated on the electret is constant, resulting in a stable E:

[0063]

[0064] where φ is the surface charge density of the electret and d is the distance from the electret.

[0065] Based on the above analysis, AC / DC and electrostatic charge induced polarization have different influencing parameters and limitations due to the differences in their equivalent circuit models.

[0066] Based on the newly proposed EPD mechanism, a multi-physics droplet robotic system was designed to automate the droplet manipulation process ( Figure 2B). The system includes: (1) a programmable control matrix that generates a localized electromagnetic field by exciting coils; (2) an EPD gripper made of a mixture of electret and magnetically responsive materials, which can be controlled by a programmable magnetic field and induces a moving non-uniform electrostatic field that can attract droplets below; and (3) a target droplet sample. By programming the control matrix, the EPD gripper can actuate droplets in specific paths, such as the shapes of the letters "H", "K", "U" ( Figure 2C ), and other microfluidic operations.

[0067] Compared with existing droplet robotic systems, the EPD-based droplet robotic system of the present invention shows significant advantages, especially in terms of the range of operable liquid types and compatibility with biological samples ( Figure 2D ). In principle, the EPD-based droplet robotic system can expand the range of operable liquids from conductive or aqueous liquids to dielectric liquids [35, 43], and has the potential to achieve actuation of all liquids. It has been experimentally verified that it can actuate various inorganic / organic liquids, including but not limited to water (inorganic), glycerol (alcohol) and triacetin (ester) ( Figure 2D , Figures 3A-3C ), with relative permittivity ranging from 2.25 to 84.2. In contrast, both air- and oil-based EWODs face challenges in actuating organic liquids with low permittivity (e.g., triacetin with a relative permittivity of 7.01) due to the limitations of the electrowetting mechanism. Figures 3A-3C , the position of the front edge of the droplet normalized by the distance between two adjacent coils / electrodes is defined as the relative movement distance. The results indicate that EWOD faces challenges in actuating organic liquids with low dielectric constants, even when an oil environment is introduced. In contrast, EPD can actuate them smoothly without changing any parameter settings, showing the versatility of EPD for various liquids

[0068] In addition, since high voltage AC / DC fields are not required in EPD, the adverse effects of Joule heating and high electric field strength on biological samples can be avoided [44-46]. Figure 2D , Figure 3D-3F ), as well as the actuation of protein and living cell solutions on EPD and EWOD, and EPD shows good compatibility with biological samples. Figure 3D-3FIn the figure, the front edge position of the droplet normalized by the distance between two adjacent coils / electrodes is defined as the relative movement distance. This result indicates that air-based EWOD faces challenges in actuating biological fluids. When the silicone oil environment was introduced into the EWOD, the actuation of the three body fluids was improved to varying degrees. However, due to the high viscosity resistance caused by the oil environment, droplet fragmentation occurred during actuation of the three body fluids, which hindered subsequent continuous actuation

[91] . In contrast, EPD can actuate them smoothly without changing any parameter settings, showing the compatibility of EPD with body fluids.

[0069] In addition, these experiments showed that EPD also exhibited overwhelming comprehensive performance in terms of compatibility with substrates and surrounding environments (air / oil / air-oil interface), actuation speed (up to 60 mm / s), operating voltage (5.5 V), and manufacturing cost (consumables less than $1, and control systems less than about $100). Figure 2D , Fig.34 ). These superior properties make the proposed EPD-based droplet robotic system have the potential to enhance liquid-based scientific experiments in multiple fields such as clinical, biological, and engineering sciences. Taking lithium monitoring in multiple body fluids as an example, the proposed EPD-based multi-physics droplet robotic system has the potential to assist biochemical experiments and promote the application of robots in basic science.

[0070] Figure 2A The working principle of EPD-based droplet actuation is shown. Figure 2B This is an optical image of an EPD-based multi-physics droplet robotic system and its core components. Figure 2C , superimposed sequential images (from video frames) are shown, which visualize the movement paths of droplets actuated by the EPD-based multi-physics droplet robotics system, including the shapes of “H”, “K”, and “U”, respectively. Figure 2C The scale bar in the figure is 10 mm. Figure 2D In the figure, the EPD-based droplet robot system proposed in the present invention is compared with the existing automated droplet robot system from the six perspectives shown on the left. In particular, the operable liquid types and compatibility with biological samples are shown, and the Figure 2D The EWOD on the right is compared. The evaluation is based on Fig.34 The EPD is described in detail in the table below. It specifically demonstrates the EPD’s broad range of fluid types and compatibility with biological samples, and is based on Figures 3A-3F Comparisons were made with air-based EWOD and oil-based EWOD.

[0071] The present invention is based on the newly observed attractive effects between various liquid droplets and intrinsically charged electrets ( Figure 4AWhen a water droplet is placed about 30 mm away from a negatively charged electret, the water droplet is attracted to the electret with a positive acceleration ( Figure 4B and 4C ), and once it reaches the position below the edge of the electret, the water droplet changes direction. The mechanism of droplet actuation caused by the electret with intrinsic charge can be explained by two potential hypotheses: 1. motion induced by the net charge of the droplet, and 2. motion induced by the polarization of the droplet ( Figure 4D ). In the first assumption, due to the electrification of contact with other solid surfaces / immiscible liquids, the droplets tend to acquire an electrostatic charge of a certain polarity (usually a positive charge for deionized water) [47, 48]. Therefore, the electrostatic field formed by the electret can exert an attractive / repulsive Coulomb force on the charged droplets [49, 50]. In the second assumption, since the electret generates an inhomogeneous electrostatic field, the droplets are polarized into dipoles. This is called the electret-induced polarization (EPD) of the droplets. Based on the difference in polarizability between the droplets and the surrounding medium, the polarized droplets will move toward / against the direction of the electric field maximum [51, 52]. Compared with the traditional "AC / DC electric field induced" polarization [51, 52], EPD utilizes the electrostatic charge carried by the electret, eliminates the effect of the conduction current, and therefore leads to different variables and application scenarios.

[0072] As shown in the figure, Figure 4A Optical images of the electret-induced actuation of a droplet floating on an oil surface, and two possible hypotheses to explain the phenomenon. Scale bar is 5 mm. Figure 4B Actuation of a droplet by two electrets with opposite charge polarity is shown to investigate the dominant hypothesis. Again, the scale bar is 5 mm. Similarly, Figure 4C The acceleration of the droplet during the period of attraction by the fixed electret is shown. It is a positive charge with a measured amount (2×10 -4 C / m 3 ) is attracted by both a positive electret and a negative electret. The arrows in the figure represent the Maxwell stress tensor applied to the droplet. In addition, Figure 4D Two potential hypotheses are presented to explain the observed attraction between the electret and the droplet (left), while the dominant hypothesis can be confirmed by actuating the droplet with two electrets of opposite charge polarity (right). Scale bar is 5 mm, and error bars are SD (n=3). Simulations of the averaged Maxwell surface stress tensor for droplets with positive, negative, and neutral charge densities quantify the effects induced by the net droplet charge (red arrow) and EPD (green arrow). The electret sheet is located at 0 mm, has a width of 6 mm, and a height of 0.2 mm. The direction of the Maxwell stress tensor is the same as that of the position coordinate. Figure 4EShown is a simulation of the average Maxwell surface stress tensor applied to droplets with positive, negative, and neutral charge densities, with the set value determined by the actual measured droplet charge

[47] . The results indicate that the force induced by the EPD effect (green arrow) is much larger than the force induced by the net charge of the droplet (red arrow). Figure 4F The relationship between the effective actuation distance of the droplet and the absolute charge of the electret is shown, including both experimental results (blue dots) and simulation results (yellow dots). Error bars, SD (n = 3).

[0073] Figure 4G Phase diagram showing the dynamic behavior of a droplet achieved by adjusting the charge of the electret and the height difference between the electret and the droplet. This includes (1) the droplet remaining stationary (green area), (2) normal lateral actuation, which is also the optimal working area (blue area), and (3) adsorption on the electret (yellow area). The blue and yellow lines represent the maximum and minimum height differences required for effective actuation, respectively. Error bars, SD (n = 3). Scale bar: 2 mm. Figure 4H The relationship between the maximum actuation velocity and the absolute charge of a 10 μL droplet at different height differences is shown. Error bars, SD (n = 3).

[0074] In practical situations, the forces induced by the net charge of the droplet and the droplet polarization may coexist and can be described as follows [53, 54]:

[0075]

[0076] where the first term is induced by the net charge of the droplet, while the second term is induced by the polarization, i.e., EPD. ρ is the volume charge density of the droplet, φ is the surface charge density of the electret, r is the radius of the droplet, and is assumed to be much smaller than the scale of the field inhomogeneities

[54] ; d is the distance between the electret and the droplet, and are the dielectric constants of the surrounding medium and the droplet, respectively. To verify which hypothesis is dominant in the droplet actuation process, two electrets with opposite charge polarities were used to actuate the same water droplet floating on the HFE oil surface ( Figure 4D ). According to the experimental results, Figure 5A and 5B The water droplet is attracted to both the positively and negatively charged electrets, which is consistent with the simulation results in . This result proves that the direction of the generated force F does not change with the polarity of φ (Eq. 1). Therefore, the force induced by EPD is much larger than the force induced by the net charge of the droplet, indicating the dominance of the EPD effect in the system of the present invention. The simulation results of applying the Maxwell stress tensor to droplets with positive, negative and neutral charge densities further support this conclusion: the EPD effect is dominant, while the force induced by the net charge of the droplet accounts for only about 8%.

[0077] To achieve well-controlled EPD-based droplet actuation, the lateral distance between the electret and the droplet should be smaller than the effective actuation distance, as measured and simulated ( Fig. 6A and 6B ). Similarly, the vertical distance between the electret and the droplet also needs to fit within an appropriate range ( Figure 4G ). If the height of the electret is too high or the charge density of the electret is too low, the EPD force will be smaller than the lateral resistance and the droplet will remain stationary. Conversely, excessive EPD force will cause the droplet to be absorbed, resulting in failed actuation. Under the optimal working condition ( Figure 4G In state 2, the droplet can be actuated horizontally, and the maximum actuation speed can be increased by increasing the charge of the electret or reducing the height difference between the electret and the droplet ( Figure 4H ).

[0078] The droplet actuated by EPD is subject to two forces. One is the driving force F generated by the EPD effect. EPD , and the other is the drag force f on the droplet as it passes through the oil layer. From equation 1 and Stokes' law, we have:

[0079] and

[0080] where r is the radius of the droplet and is assumed to be much smaller than the scale of the field inhomogeneities, η is the viscosity of the oil layer, and ν is the velocity of the droplet.

[0081] Therefore, when the droplet accelerates to its maximum speed, we should have:

[0082]

[0083] This results in:

[0084]

[0085] Therefore, we can have:

[0086]

[0087] Based on these empirical relationships, the optimal working area of ​​the system can be determined, including the lateral position, vertical position, charge and movement speed of the electret, laying the foundation for further realizing an automated droplet actuation system.

[0088] A water droplet acquires a positive charge upon contact with the HFE [ ( 47 ) ]. Therefore, if the first assumption based on the net charge dominates, i.e., the first term in Eq. 1 dominates, the droplet will be repelled by the positively charged electret and attracted by the negatively charged electret, since the polarity of the generated force varies with φ. In contrast, if the second assumption based on the polarization of the droplet dominates, the droplet will always move toward the electret, regardless of the polarity of φ, i.e., when ε d Greater than ε m When , the electric field strength is maximum. Figure 4B The results in show that, regardless of the electret polarity, the water droplet always moves toward the local electric field maximum (the edge of the electret), which is consistent with the case of hypothesis 2 and indicates that EPD is dominant.

[0089] The simulation results considering the actual charge of the droplet further support this inference. The results show that the positive charge (2×10 -4 C / m 3 ) on the droplet is always directed toward the electret, including the positive electret and the negative electret ( Figure 5A and 5B ). Therefore, the droplet should be attracted by both the positive electret and the negative electret, which is consistent with the experimental results. To quantify the effect of the net charge of the droplet in this system, the x-component of the Maxwell surface stress tensor ( ) applied to the droplet with positive, negative, and neutral charge densities was simulated and calculated. Figure 4D ). According to Equation 1, the force applied to the uncharged droplet (ρ=0) should be induced by EPD alone. Therefore, the part that varies with the droplet charge density ρ should be induced by the net charge of the droplet. Based on the simulation results, the force induced by the net charge of the droplet accounts for only about 8% of the EPD force, indicating that the EPD effect is dominant. The simulated Maxwell stress tensor is compared with the measured acceleration of the droplet when it is attracted by the electret ( Figure 4E and 4F ). The simulated stress and the experimentally measured acceleration both show similar trends, that is, they first increase and then decrease as the droplet approaches the electret, proving the consistency between the experiment and the simulation.

[0090] Based on the understanding of the EPD mechanism, this effect can be used to build an automated droplet actuation system. Therefore, it is necessary to further explore the effect of the electret charge and relative position on droplet actuation in order to find the optimal working area. First, the effective actuation distance is measured and simulated to find the lateral threshold of the comfort zone. The effective actuation distance is positively linearly correlated with the charge of the electret, which is basically consistent with the simulation results ( Figure 4G , Fig. 6A and 6B ). Therefore, in order to achieve effective actuation, the lateral distance between the electret and the droplet should be smaller than the effective actuation distance, e.g. Figure 4GAs shown in . Similarly, the vertical distance between the electret and the droplet is also adjusted and studied. With the change of the height and charge of the electret, three different droplet dynamic behavior scenarios can be observed ( Figure 4H ). In the first scenario, when the EPD force is relatively smaller than the lateral resistance, the droplet will remain stationary. As the charge of the electret increases or the height difference decreases, the droplet can be actuated horizontally toward the electret. In order to achieve the optimal working state, the height difference between the charge of the electret and the droplet should be within this range. As the charge continues to increase or the height difference continues to decrease, excessive EPD force will cause the droplet to be absorbed by the electret, resulting in failed actuation.

[0091] The maximum velocity of droplet actuation was also studied within the optimal working region. The maximum velocity has been shown to increase linearly with the charge, while the reduction of the height difference between the electret and the droplet can further increase the maximum droplet movement speed to 23 mm / s ( Figure 4H ). Based on the explored relationship, the optimal working area of ​​the system can be determined, including the lateral position, vertical position, charge carried and movement speed of the electret, laying the foundation for further realizing the automated droplet actuation system.

[0092] Compared with the limitations of EWOD on droplet volume (usually nL-μL, depending on the electrode size)

[55] and liquid type (usually only applicable to conductive and aqueous liquids)

[35] , EPD shows universal advantages for a wide range of liquids, including a wider range of droplet volume, liquid conductivity and liquid dielectric constant ( Figures 7A-7G ). Figure 7B The EPD effect is used to actuate droplets with volumes ranging from nanoliters to milliliters, covering a volume range of up to four orders of magnitude. As the droplet volume increases, the maximum speed of the droplet movement also increases from 22 mm / s (10 μL) to 60 mm / s (100 μL), which is comparable to other common droplet actuation technologies (Table).

[0093] The droplets actuated by EPD are subject to two forces: one is the driving force generated by the EPD effect, and the other is the drag force f on the droplets when they pass through the oil layer.

[0094] According to Equation 1 and Stokes' law:

[0095]

[0096] and

[0097]

[0098] where V is the volume of the droplet, r is the radius of the droplet and is assumed to be much smaller than the scale of the field inhomogeneities, η is the viscosity of the oil layer, and ν is the velocity of the droplet.

[0099] Therefore, the acceleration a of the droplet can be described as:

[0100]

[0101] where m is the mass of the droplet.

[0102] Therefore, when the droplet accelerates to its maximum speed,

[0103] a=0

[0104] This results in:

[0105]

[0106] therefore:

[0107]

[0108] In addition to the droplet volume, EPD can also actuate different numbers of droplets simultaneously, such as Figure 8 As shown in , a schematic diagram of the actuation of multiple droplets (i.e., 1 droplet and 7 droplets) based on EPD can be actuated by electret. In this schematic, when actuating multiple droplets, a surface surfactant is introduced into the oil matrix to prevent the droplets from merging. Therefore, EPD can also actuate multiple droplets simultaneously, such as Figure 8 as shown in .

[0109] Fig. 7A It is a schematic diagram of the universality of EPD and various liquids. Figure 7B EPD-based droplet actuation is shown for volumes ranging from nanoliters to milliliters. The maximum actuation velocity increases with droplet volume according to a two-thirds power relationship. Scale bar: 5 mm. Figure 7C Shows the simulated Maxwell stress tensor applied to a droplet with different conductivities, showing that the resulting EPD force on the droplet does not vary with conductivity. The electret sheet is located at 0 mm, has a width of 6 mm, and a height of 0.2 mm. The direction of the Maxwell stress tensor is the same as the position coordinate. Fig.7D The simulated Maxwell stress tensor (solid line) applied to a droplet with different relative dielectric constants is shown. Under NTP, the calculated Maxwell stress tensor for the liquid with the smallest relative dielectric constant of 1.8 (purple line) is still much larger than the minimum force required for effective actuation (dashed blue line), indicating that EPD has the potential to manipulate all types of liquids. Fig. 7E Optical images showing EPD-based droplet actuation for three common inorganic liquids and three common organic liquids, including alkanes, alcohols, and esters. The scale bar is 5 mm. Figure 7FThe simulated effective actuation distances for water, triacetin, and paraffin (asterisks) are shown in Figure 1, obtained by intersecting the calculated Maxwell stress tensor (solid line) and the minimum force required for effective actuation (dashed line). Figure 7G Comparison between simulated and measured effective actuation distances (EAD) is shown, showing no significant difference (one-sample t-test, P>0.05), demonstrating the validity of the simulation results. Error bars, SD (n=3).

[0110] The Maxwell stress tensor generated by EPD was simulated and calculated for liquids with different electrical conductivities ( Figure 7C ). The results show that the force exerted by the EPD on the droplet does not change with changes in conductivity, which is consistent with the mechanism discussed above that the EPD uses electrostatic charge to polarize the droplet without generating a conduction current. Therefore, for EPD-based droplet actuation, the range of liquids can expand the operable range of liquids from conductive liquids to dielectric liquids. For liquids with different relative dielectric constants, the simulation results show that the force generated by the EPD decreases as the dielectric constant decreases ( Fig.7D ). However, for liquids with a minimum relative dielectric constant of 1.8 (at room temperature and pressure

[56] ), the generated EPD force is still much larger than the minimum force required for effective actuation ( Fig.7D The blue dashed line in the figure is obtained based on the measured maximum effective actuation distance of the water droplet, as shown in Fig. 9 ), indicating that EPD has the potential to manipulate all types of liquids. Fig. 9 In the figure, the effective actuation distance (EAD) of a water droplet is first measured (black square). Then, the force required for effective actuation is determined (dashed line) based on the intersection of the measured EAD of water and the simulated Maxwell stress tensor applied to the water droplet.

[0111] In order to verify the versatility of EPD to operable liquid types, that is, its adaptability to a wide range of operable liquid types, three common inorganic liquids and three common organic liquids were selected in the experiment, with relative dielectric constants ranging from 2.25 to 84.2, including alkanes, alcohols and esters ( Fig. 7E ). The experimental results show that EPD can successfully actuate various inorganic / organic liquids without changing any parameter settings, because even for liquids with low dielectric constants, the generated EPD force is sufficient to overcome the movement resistance ( Fig.7D and 7F In contrast, EWOD is not sufficient to manipulate organic liquids with low dielectric constants, either in air or oil environments, due to the limitations of the electrowetting mechanism ( Figures 3A to 3C In addition, the effective actuation distances of different simulated droplets can be obtained by the intersection between the calculated Maxwell stress tensor and the minimum force required for effective actuation ( Figure 7FThe results show that the effective actuation distance of the droplet decreases with the dielectric constant of the droplet, and the calculated results match the experimental results with insignificant differences, as shown in Figure 7G Based on both simulation and experimental results, the EPD effect shows good versatility and a wider range of operable liquids, indicating the potential for all-liquid processing.

[0112] Considering that droplet robots are often used to carry biological samples

[69] , some biocompatibility evaluations of EPD-based droplet actuation have been performed. The evaluations covered a range of biological samples, including body fluids, proteins, and living cells ( Fig. 10A ). For body fluids, human serum, saliva, and urine were tested, and the EPD could successfully actuate different body fluids without any modification ( Fig. 10B ). However, when manipulating these fluids on a regular double-plate EWOD, droplet actuation becomes challenging and the movable distances of different fluids are different ( Figure 3A and 3B , Fig. 12A and 12B ). The different performances between EPD and EWOD when actuating various body fluids may be attributed to two reasons, one of which is that the conductivity of different body fluids varies greatly, affecting the working voltage setting and actuation robustness of EWOD [57, 58]. In contrast, EPD has been shown to be independent of liquid conductivity ( Figure 7C Another reason is that proteins in body fluids seem to adsorb significantly on EWOD substrates, especially on air-based EWOD ( Fig.11 and Fig.13A and 13B The motion of droplets on EWOD is hindered due to electrostatic interactions with the charged electrodes and hydrophobic interactions with the surface ( Fig.11 )[37–39]. In particular, Fig.11 A fluorescence microscope photo shows protein adsorption after actuating a 0.01% BSA solution droplet on a double-plate EWOD. Although the introduction of certain additives (e.g., Pluronics) can partially alleviate the protein adsorption problem in EWOD, it still cannot fundamentally solve the problem because additives may have potential cytotoxicity and the type and concentration of surfactants need to be customized according to the protein solution being operated

[83] . However, in contrast, when a droplet containing fluorescent protein was actuated in EPD, no significant fluorescence intensity changes were observed before and after the droplet movement, either on the droplet trajectory or inside the droplet (P>0.05) ( Fig. 10C ). This result indicates that EPD-based droplet actuation does not cause observable protein carryover, thereby minimizing the risk of biological contamination.

[0113] As mentioned earlier, Fig. 10AFigure 1 is a schematic diagram of the compatibility of EPD with biological samples (including body fluids, proteins, and living cells). Fig. 10B Optical images showing EPD-based actuation of body fluids including human serum, saliva, and urine are presented in . The scale bar is 3 mm. Fig. 10C A series of photos under a fluorescence microscope are shown, showing the actuation of the fluorescent protein solution based on EPD. There was no significant difference in the fluorescence intensity on the droplet track or inside the droplet before and after the droplet movement (paired t-test, P>0.05), showing that no protein residue was observed in the EPD-based droplet actuation. Error bars, SD (n=4). Fig. 10D Figure 2 shows EPD-based actuation of live A549 cells in culture, where the percentage of live cells was analyzed and compared to a control in air. Error bars, SD (n=3). Thus, live cells can also be actuated under the effect of EPD. Fig.10E Shown are the changes in viable and non-viable cell concentrations after 30-min treatment on EPD / EWOD and then 12-h incubation, demonstrating the cell-related biocompatibility of EPD. Error bars, SD (n=3). Fig.10F A schematic diagram showing the compatibility of EPD with various environments and matrices is shown in FIG. Figure 10G Optical images illustrating EPD-based actuation of droplets in air on a hydrophobic surface are shown. Scale bar is 3 mm. Fig. 10H Optical image showing EPD-based actuation of a droplet floating on an oil surface, where the maximum actuation velocity can be tuned by introducing a surface surfactant. Scale bar 3 mm. Fig.10I Optical image showing EPD-based actuation of droplets merging in an oil environment with a hydrophobic matrix. Scale bar: 3 mm.

[0114] In addition to body fluids and protein solutions, the effects of EPD on cell viability were also investigated ( Fig. 10D ). Compared with the control group in air, the percentage of live cells did not change significantly during the actuation process, demonstrating that EPD can maintain the viability of live cells during actuation. In addition to cell viability, the effect of EPD on cell proliferation ability was also verified. Cells treated with EPD showed significant proliferation after 12 hours of incubation, while the concentration of live cells increased by 16.6% ( Fig.10E ). In contrast, the number of viable cells treated with air-based EWOD decreased by 20.8% after 12 h of incubation. Similar negative effects on viable cells were also observed in oil-based EWOD ( Fig.14A and 14B ).

[0115] exist Fig.14AIn the figure, after being treated with EPD / oil-based EWOD for 30 minutes and then incubated for 12 hours, the effects of EPD and electrowetting effect on cell activity were indicated by changes in the percentage of live cells. The extent of the decrease in cell activity treated with oil-based EWOD was greater than that of the cell activity treated with EPD, indicating that the introduction of an oil environment still cannot completely avoid the effects of Joule heating and high electric field intensity on cell activity. Error bars, SD (n=3). Fig. 14B The effect of EPD and oil-based EWOD system settings on cell culture is indicated by the change in the percentage of viable cells after 24 h of culture on EPD / oil-based EWOD without power supply. The percentage of viable cells of cells cultured on oil-based EWOD (58.5%) was significantly lower than that of cells cultured on EPD (91.2%). This may be because the silicone oil environment on the double-plate EWOD limits the gas exchange between the droplets and the atmosphere, resulting in suffocation of the cells within the droplets. Error bars, SD (n = 3).

[0116] These negative effects of EWOD on living cells may be caused by Joule heat and high electric field strength in the electrowetting effect [44, 45], which are eliminated in EPD based on static charge. Therefore, compared with air- or oil-based EWOD, EPD shows better compatibility with living cells. This indicates that living cells can maintain good proliferation ability after EPD treatment compared with EWOD, further demonstrating the biocompatibility of EPD. In addition to biological samples, when the droplets contain other chemical samples, such as water-in-oil emulsions, oil-in-water emulsions, or water-in-nanoparticles ( Figures 15A-15C ), EPD-based droplet actuation is still feasible. Fig.15A An optical image of a 10% water-in-oil emulsion based on EPD actuation is shown, while the optical image on the right side of the figure shows the interior of the emulsion. Fig. 15B An optical image of a 10% oil-in-water emulsion based on EPD actuation is shown, while the optical image on the right side of the figure shows the interior of the emulsion. Fig. 15C Optical images based on EPD actuation of water-encapsulated particles (iron oxide magnetic nanoparticles, 30 nm) are shown.

[0117] The compatibility of EPD-based droplet actuation with various environments and substrates is also demonstrated ( Fig.10F As long as the dielectric constant of the droplet is different from that of the surrounding medium (Equation 1), a droplet in an air environment ( Figure 10G )、Oil-gas interface( Fig. 10H ) and oil ( Fig.10I ) can be actuated by EPD (Eq. 1). As for the substrate, unlike EWOD [84, 85], EPD has no requirements on the substrate material or thickness, but it still requires a hydrophobic surface ( Fig.16A and 16B) or oil matrix to reduce actuation resistance and droplet retention. Actuation on hydrophilic surfaces will be limited by increased drag ( Fig.17 ).exist Fig.17 In the paper, the actuation of EPD-based droplets on hydrophilic surfaces showed a tendency to move with different contact angles on the advancing and receding sides. The increased drag on the hydrophilic surface compared to the hydrophobic surface led to the failure of droplet actuation. The contact angle hysteresis of the droplet on the hydrophilic surface was significantly greater than that on the hydrophobic surface

[12] , and the contact area of ​​the droplet was also increased, resulting in a substantially larger relative friction force [14, 15]:

[0118]

[0119] Where w is the width of the bottom of the droplet perpendicular to the direction of motion, γ is the surface tension at the liquid-air interface, and are the advancing and receding contact angles of the liquid-solid interface. and In the case of 16 Compared to the resistance of the hydrophilic surface (in the case of and 12 Theoretically, it can be increased by more than 40 times.

[0120] When utilizing an oil matrix, the maximum actuation velocity can be further adjusted by introducing a surfactant ( Fig.18 The change in droplet shape from a spherical shape to an ellipsoid shape may be the main reason for the increase in velocity [60, 86]. Considering that the evaporation of droplets on the oil matrix is ​​only 30% of that on the hydrophobic matrix ( Fig.19 ), so subsequent quantitative experiments were performed by manipulating droplets floating on the oil-gas interface instead of the hydrophobic matrix.

[0121] Fig.19 The data for the curve graph in was obtained when a 15 µL water droplet was dropped on a superhydrophobic substrate with a contact angle of 163° and an HFE oil surface, which was placed on an open countertop. During the test period, the room temperature and humidity levels remained unchanged. The camera photographed the droplets from above every 10 minutes. ImageJ was used to analyze the size of the droplets and calculate the volume change of the droplets. The results show that within two hours at room temperature, the droplet evaporation on the oil substrate (red line) was only 30% of the droplet evaporation on the hydrophobic substrate (blue line).

[0122] In order to further utilize the EPD effect to realize automated liquid manipulation, the following Figures 20A-20H and Figure 2BThe multi-physics droplet robotic system shown in . As mentioned above, the system consists of three basic parts, 1) a printed circuit board (PCB) control matrix, which can be programmed to generate a local magnetic field by energizing specific coils; 2) an EPD gripper made of a magnetically responsive material and an electret composite material, which can be moved by the magnetic field generated by the control matrix and simultaneously induce a moving non-uniform electrostatic field to attract the droplets below; and 3) a polarizable target droplet. When the system is used to perform automated movement or actuation of droplets, commands need to be uploaded to the control board first to power the specified coils in the control matrix and generate a local electromagnetic field ( Fig. 20B ). Then, the nearby EPD gripper will be actuated by magnetic force and moved to the specified position. The displaced gripper then attracts the droplet via the EPD, causing the droplet to move with it. By repeating this process, multiple EPD grippers can work together to gradually actuate multiple droplets to the target position.

[0123] exist Fig. 20A , a schematic diagram of the proposed multi-physics droplet robotic system is shown, and Fig. 20B The general workflow of the proposed multi-physics droplet robotics system is shown in Figure . Fig. 20C is a photograph of a programmable control matrix fabricated on a multi-layer PCB, consisting of row switches, column switches, a matrix of electromagnetic coils, and signal / power sockets. Fig.20D is an operational schematic diagram of the control matrix and the corresponding simulation results, which shows the distribution of the magnetic field when the coil is energized at coordinates (j, i). Fig.20E Schematic diagram of the EPD holder, which consists of a magnetically responsive material and an electret composite. Fig.20F The simulated electric field intensity of the EPD holder is shown in (bottom view), where there are four local maxima of the electric field. Figure 20G The forces generated by the EPD gripper on the droplet in the X direction (left) and Y direction (right) at different positions are shown, with four force stable equilibrium points marked with arrows. The directions of the forces in the X and Y directions are consistent with the directions of the X and Y coordinates, respectively. Fig. 20H The simulated magnetic Maxwell stress tensor on the EPD holder and the electric Maxwell stress tensor on the target droplet are shown. The arrows indicate the direction of the current in the coil.

[0124] In the programmable control matrix, the electromagnetic coils are fabricated on a multilayer PCB and two integrated switches control the row and column selection ( Fig. 20C By activating the row and column switches corresponding to the specified coordinates, a DC current will flow through the specified coil and generate a local magnetic field ( Fig.20D). From the magnetic field simulation results, the generated perpendicular magnetic field intensity is concentrated within the range of the specified coil. Therefore, it can accurately drive the magnetically responsive EPD gripper located near the specified coordinates without affecting the EPD gripper at a longer distance, providing a basis for the collaborative cooperation of multiple grippers. Compared with using a robotic arm to actuate the EPD gripper, the method of using a PCB to generate a controllable magnetic field not only reduces the manufacturing cost of the system, but also provides the potential for spatial cross-manipulation to process multiple droplets in parallel.

[0125] For the EPD gripper, a composite material of magnetic response material and electret is used. The magnetic response part at the top can move the entire gripper to the specified coordinates under the magnetic force; while the electret part at the bottom can generate a non-uniform electrostatic field to polarize and attract the target droplet below ( Fig.20E ). Unlike the slice-shaped electret used in the above feature setup, the electret portion here is designed in a holder shape to increase the number of local maxima of the generated electric field ( Fig.20F ).exist Fig.21A A side view of the simulated potential distribution of the EPD holder is shown in FIG. Fig.21B The bottom view of the simulated potential distribution is shown. Considering that the droplet will move towards the local electric field maximum under the EPD effect (Eq. 1), such a gripper shape will enhance the stability of the droplet actuation by providing multiple attraction points. The simulated EPD force exerted on the droplet can further support this inference. Figure 20G In the figure, the arrows marking the switching positions of the force directions are the stable equilibrium points of the droplets, indicating that nearby droplets tend to be attracted and stabilized at this point. Therefore, nearby droplets also tend to be attracted and stabilized at any of these points ( Fig. 22 ). Fig.20F The four stable force balance points generated in both the X and Y directions at the same position of the local electric field maximum shown in the figure confirm that the droplet does tend to move toward the four points of the local electric field maximum. Compared with the sheet-like electret with only one balance point, the design of the gripper shape not only improves the stability of the droplet actuation, but also increases the force applied to the droplet and the effective actuation distance ( Fig.23A , Fig. 23B and Fig.24 ). Fig.23A is a simulation of the X component of the force generated by a sheet electret applied to a droplet at different positions. The stable equilibrium points of the force are marked with arrows. The direction of the force in the X direction is consistent with the direction of the X coordinate. Fig. 23B is the Y component of the force applied to the droplet at different positions, and the stable equilibrium points of the force are marked with arrows. The direction of the force in the Y direction is consistent with the direction of the Y coordinate.

[0126] Fig.24is a comparison of the forces of sheet-like and gripper-like electrets on droplet generation. The Y coordinate of the droplet is set to 0 mm, and the x-component of the force applied to the droplet is simulated and compared. The yellow dashed line represents the hypothetical minimum force required for effective actuation. Therefore, the intersection of the dashed line and the force calculated by the simulation (solid line) represents the effective actuation distance (yellow dot). It turns out that the effective actuation distance of the gripper-like is greater than that of the sheet-like electret, which means that the gripper-like electret can actuate the droplet over a longer distance, thereby enhancing the ability to self-assemble and capture / merge sub-droplets. Fig.24 The curves on the right side of the figure show the forces exerted on the droplet by the clamp-shaped and sheet-shaped electrets at the effective actuation distance, respectively. With the programmable control matrix and EPD clamp of the present invention, the operation of the entire system involves multi-physical field coupling. By converting electrical energy into a programmable magnetic field, the control matrix exerts a magnetic force on the EPD clamp, and the charge carried by the EPD clamp generates an electrostatic field to actuate or attract the target droplet. The magnetic Maxwell stress tensor and the electric Maxwell stress tensor acting in the entire system are shown as Fig. 20H In a practical scenario, additional actuation magnets can be used to amplify the electromagnetic field generated by the control matrix and balance the weight of the EPD gripper, as shown in Figure 2B as shown in .

[0127] Based on the above design principles, by programming the control matrix, the movement of specified droplets can be achieved, following different paths, such as the letters "H", "K" and "U" ( Figure 2C ). The actuation resolution is determined by the size of the coil, which is approximately 1.5 mm in this case, while the actuation accuracy is related to the movement of the actuation magnet and is slightly lower than that of EWOD but comparable to magnetic-based droplet actuation platforms

[12] ( Fig.25 ). In Figure 26, the resolution is defined as the actuation distance of one step, which depends on the coil / electrode / electromagnet size of the platform. In the experiment, the resolution achievable by the three systems is basically equivalent. Regarding accuracy, it is negatively correlated with the standard deviation of the actuation distance per step. The results indicate that EWOD has the highest accuracy, followed by EPD and magnetic-based systems. Error bars, SD (n= 9). Scale bar: 2 mm. Other basic microfluidic functions such as self-assembly, merging and mixing of multiple droplets can also be performed in the designed multi-physics droplet robotic system. Fig.26A The basic microfluidic functions that can be performed by the EPD-based droplet robotic system are shown in Figure 2 for the self-assembly of multiple droplets. Fig.26B for the merging of two droplets, and Fig.26C For mixing the merging droplets through cyclic motion. Although two droplets floating at the oil-air interface can gradually approach and eventually merge under capillary forces [77–81], the presence of the EPD gripper can further accelerate their approach and merging through attractive forces ( Figures 27A-27D).

[0128] Fig.27A is a lateral optical image of a floating water droplet at the oil-gas interface. Fig.27B is a schematic diagram of two floating water droplets at the oil-gas interface, showing that the distorted interface will lead to attractive forces. Fig.27C The simulated EPD forces applied by the EPD gripper to two droplets are shown, indicating that both droplets experience EPD forces directed toward each other. Fig.27D is a graph of the time required for two 20 μL droplets to merge, indicating that the presence of the EPD gripper can help promote droplet merging.

[0129] Taking advantage of the demonstrated versatility of the EPD for the types of operable liquids and its compatibility with biological samples, the automated EPD-based droplet robotic system of the present invention has the potential to impact liquid-based scientific experiments in multiple fields. First, as an example, the proposed droplet robotic system can be applied to automatically perform bioassays for lithium detection in different biological fluids and to establish an in vitro cell-bacteria model for dynamic monitoring. In the demonstrated application, six different solutions, living cells, living bacteria, and three body fluids were manipulated, fully demonstrating the impact and applicability of EPD in multidisciplinary scientific research that requires precise liquid manipulation. The detection of biomarkers / drugs in multiple biological fluids can be used to explore the metabolic relationships between various biological fluids, thereby facilitating non-invasive drug monitoring and precision medication [82-84]. Therefore, the droplet robotic system of the present invention can be applied to transform traditional labor-intensive bioassays into automated bioassays for multiple biological fluids. The detection of biomarkers in multiple biological fluids will accelerate the process of exploring the relationship between biomarker concentrations in different biological fluids, facilitating accurate diagnosis and appropriate medication

[61] . However, conductivity and protein concentration vary greatly between different biofluids (such as human saliva, blood, and urine) and fluctuate over a wide range due to individual differences [62-66]. Nevertheless, the EPD-based multi-physics droplet robotic system of the present invention can perform automated lithium drug detection in serum, saliva, and urine. Fig.28A is a schematic representation of different body fluids (serum, saliva, and urine), showing that their conductivity and protein concentrations vary greatly [62-66]. Fig.28B Characterization of the actuation of serum, saliva, and urine on an EPD-based multi-physics droplet robotic system is shown. The solid line shows the trajectory of the EPD gripper, while the dashed line shows the trajectory of the actuated biofluid. Fig.28C It is an optical image of the EPD-based droplet robotic system and auxiliary detection chip. Fig.28D is an optical image of a microchip device designed to be tested as a set with two calibrations and one actual biological sample detection. For visualization purposes, dyed droplets are used here instead of transparent droplets. Fig.28EFigure 3 is a step-by-step workflow of the EPD-based droplet robotic system when performing lithium detection, showing the tasks performed by each EPD gripper and its trajectory along with representative screenshots. Fig.28F Comparison of test lithium results with reference values ​​for serum, saliva, and urine samples is shown, where there is no statistically significant difference between the test values ​​and the reference values ​​(one-sample t-test, P>0.05), demonstrating the reliability of the bioassay results performed on the chip. Error bars, SD (n=3).

[0130] For traditional liquid handling technologies, such as EWOD, the difference in liquid conductivity between different biological fluids will affect the setting of the working voltage and the robustness of actuation [57, 58], and the proteins in them will be adsorbed on the surface due to hydrophobic interactions and electrostatic interactions, resulting in sample fixation or cross-contamination [37, 38]. In the experiment, most of the biological fluids could not move smoothly on the tested EWOD platform, and increasing the working voltage ( Fig.29 ) or introduce oil environment ( Figure 3D-3F and Fig.13A and 12B ) also varies significantly between different biofluids. Therefore, in practical applications, the actuation of various biofluids on EWOD requires diluting the biofluid

[93] , removing proteins

[97] , or introducing a certain amount of surfactant

[95] , and also requires modifying the voltage and frequency settings[99-101]. However, the common dual-plate structure of EWOD can be used to manipulate three biofluids (serum, saliva, urine) at 100 V and 150 V (maximum operating voltage), respectively ( Fig.29 ).exist Fig.29 , the relative movement distance in the Y axis is the relative position of the front edge of the droplet, and the final distance of one step of water movement is defined as 1. The results show that the tested EWOD faces challenges when actuating body fluids, and the performance improvement brought about by the increase in operating voltage also varies significantly between different biological fluids. Although the EWOD platform has made some targeted improvements to address system compatibility issues for some specific sample types [37, 39, 67], in order to process a large number of samples in parallel, the automated manipulation platform should still be adaptive to various samples and there is no need to adjust parameters with the sample. In contrast, according to the features of the present invention ( Figure 8 and 11 ), liquid conductivity and protein concentration do not have a significant effect on EPD-based droplet actuation. When three biofluids (serum, saliva, urine) were tested on the proposed EPD-based droplet robotic system, all tested samples could be moved smoothly, with essentially no difference in actuation performance, and without changing any parameter settings ( Fig.28B ).

[0131] The superiority of the EPD-based droplet robotic system of the present invention can be demonstrated in biochemical laboratory tasks by using the system to perform bioassays for lithium detection in various biological fluids. Detecting lithium concentrations in various body fluids can help establish the metabolic relationship between lithium drugs in the human body, thereby facilitating non-invasive drug monitoring and precision medication for patients with bipolar disorder [68, 69]. In order to achieve the detection of lithium concentrations in different biological fluids, a microfluidic detection chip was designed and powered by the EPD-based droplet robotic system ( Fig.28C The detection chip is designed to perform two calibrations and one sample detection in parallel for each test ( Fig.28D ). On the detection chip, three reagent loading areas are divided into loading mask, probe and buffer solution, and three working areas are designated for merging, mixing and reacting these reagents with calibration samples / test samples. The overall operation flow of the system is demonstrated with two in-situ calibrations and one sample detection as a group.

[0132] In actual applications, the detection content can be adjusted as needed. For example, if the standard calibration curve is known, the three samples can be directly detected as a group. As a demonstration, in the initial state (step 0), the two working areas of the in-situ calibration are preloaded with two calibration samples with known lithium concentrations, and the masking solution is also preloaded, as shown in Figure 28. After loading the biological sample to be tested, the three EPD holders can be programmed to work together to realize the steps of automated determination within the detection chip, including sample preparation, calibration 1, calibration 2, and sample detection. The tasks performed by each EPD holder and its trajectory are listed step by step with representative screenshots, as shown in Figure 28.

[0133] In the sample preparation stage (steps 0-2), after the actual biological sample to be tested is loaded onto the chip, EPD holders 1 and 2 transport the sample and masking solution to the sample area for mixing. This step is to prevent other ions in the actual biological sample from affecting the detection results. In the second stage of calibration 1 (steps 3-7), buffer and probe solutions are injected into the chip respectively (steps 3 and 5). At the same time, EPD holders 2 and 3 will capture the generated sub-droplets and transport them to the calibration area (A) to mix with the prepared calibration sample (steps 4 and 6). The lithium ions in the sample will bind to the probe after dilution, thereby quantitatively changing the absorbance curve (step 7). Similarly, in the third stage of calibration 2 and the fourth stage of sample detection, the generation, transportation and mixing processes of buffer and probe solutions are repeated respectively (steps 8 and 9). In particular, EPD holders 1 and 2 are responsible for transporting the tested biological sample and mixing it with the masking solution to shield other interfering ions in the biological sample. EPD holders 2 and 3 are responsible for capturing the injected sub-droplets of buffer and probe solutions and mixing them with the prepared calibration sample / test biological sample. The lithium ions in the sample will bind to the probe after dilution, thereby quantitatively changing the absorbance curve. In this way, two in-situ calibrations and one actual biological sample detection can be automatically performed, and the lithium concentration in the tested biological sample can be calculated based on the linear calibration curve. Linear standard curves for serum lithium, salivary lithium, and urine lithium can be established ( Fig.30 ). From this, the lithium concentration in the actual serum / saliva / urine sample tested can also be quantitatively calculated ( Fig.30 Red dots, error bars, SD (n=3). The lithium concentrations in serum, saliva, and urine of the tested humans showed no statistically significant differences from the reference values ​​(P>0.05, Fig.28F ), demonstrating the reliability of the bioassay results achieved by the EPD-based droplet robotic system. In addition to measuring absorbance at specific wavelengths, similar standard curves and detection results can be obtained through simple in situ photography and RGB analysis, providing feasibility for further improving the integration of the system ( Fig.31 ). In order to establish a standard curve, the RGB index of the reagent blank (0 μM) was subtracted from the measured RGB index of the other calibration samples. The RGB index of all calibration samples minus the background was then plotted, and the slope of the linear fit standard curve was calculated. A white LED shadowless lamp was used as the background, and photos of the mixed droplets were taken with a camera (PowerShot G7X MarkⅢ, Canon). The photographs taken were processed in Adobe Photoshop to obtain the values ​​of R and G through RGB analysis. RGB is defined as: Index = Ag / Ar, where Ar = -log(R / 255) and Ag = -log(G / 255). Therefore, the lithium concentration (red dot) in the actual sample tested can be calculated based on this value. Error bars, SD (n = 3).

[0134] In addition to the application examples of automating bioassays of various biofluids, EPD has also been validated for establishing a cell-bacteria model with dynamic monitoring. As a demonstration, an in vitro inflammatory cell-bacteria model was established and the generated inflammatory mediator IL-1β ( Fig.32A and 32B ). By using an EPD-based droplet robotic system to dynamically and repeatedly infect cells with bacteria, the results verified the non-monotonic relationship between the concentration of inflammatory mediators and repeated bacterial infection, which can help explore the generation of inflammatory mediators and the relationship between diseases and inflammatory mediators [99-101].

[0135] The application of EPD-based droplet robotic system in establishing inflammatory cell-bacteria model in vitro and in situ detection of human IL-1β inflammatory mediators Fig.32A The schematic diagram of the workflow for establishing an inflammatory cell-bacteria model and in situ detection of inflammatory mediator concentrations on an EPD-based system is shown in the figure. Scale bar: 50 μm. Fig.32B A standard curve of human IL-1β concentrations is shown. Fig.32C It is a bar graph of human IL-1β concentrations detected after the first, second, and third bacterial infection. The results show that a 10% bacterial infection can increase the concentration of IL-1β produced by 11 times within 12 hours. A second infection with the same concentration of bacteria can increase the IL-1β concentration by about 7 times in another 12 hours. At this time, the cell membrane begins to become transparent, but most cells remain intact. The subsequent third bacterial infection causes most cells to divide and die, while the concentration of inflammatory mediators is basically the same as the second infection. Error bars, SD (n = 3).

[0136] In the above two applications, droplets with volumes ranging from 5 μL to 1 mL can be manipulated, and six different solutions, living cells, living bacteria, and three body fluids can be manipulated simultaneously, making full use of the superior liquid manipulability and biocompatibility of EPD. In addition, the proposed system also exhibits the ability to perform spatial cross-manipulation, thus creating the potential for working with multiple robots in parallel. Compared with other workstations that repeat the same experimental steps in a linear time manner, the EPD-based multi-physics droplet robot system of the present invention exhibits higher flexibility. Therefore, based on the demonstrated applications, the proposed EPD-based droplet robot system shows a wide range of applicability and impact on scientific research, and has the potential for further application in multiple fields requiring precise liquid manipulation.

[0137] Disclosed is a droplet robot system based on the EPD mechanism, which solves the compatibility issues of existing droplet actuation platforms regarding liquid types and biochemical samples. The EPD mechanism of the present invention utilizes electret materials to generate a non-uniform electrostatic field to polarize and attract various droplets. Compared with traditional liquid polarization generated by AC / DC electric fields, EPD utilizes the intrinsic electrostatic charge carried by the electret. Therefore, EPD does not generate a conduction current and is different at the level of the equivalent circuit model, resulting in different variables and application scenarios. Therefore, the EPD mechanism supplements the existing liquid polarization principle from an electrostatic perspective.

[0138] Thanks to the novel EPD mechanism, we verified that EPD-based droplet actuation is effective. It has superior adaptability to liquid types and biochemical samples, while also achieving full automation based on a multi-physics control system. Compared with existing droplet actuation platforms, the proposed EPD-based droplet robotic system exhibits high versatility in operable liquid types (various inorganic / organic liquids with relative dielectric constants ranging from 2.25-84.2 and volumes ranging from 500 nL-1 mL), high compatibility with biochemical samples (a variety of body fluids, proteins, and living cells), high compatibility with substrates and environments (air / oil / air-oil interfaces), high actuation speed (up to 60 mm / s), low operating voltage (5.5 V), and low manufacturing cost (consumables less than $1, and control system less than about $100) ( Fig.34 ). Therefore, it shows significant advantages in terms of the types of liquids that can be operated and compatibility with biological samples. EPD utilizes the polarization of dielectrics in non-uniform electrostatic fields, thus showing the potential to manipulate non-aqueous or non-conductive liquids. Based on simulation and experimental results, it is demonstrated that EPD is universal for various inorganic / organic liquids, and changes in droplet volume (nL-mL), conductivity, and relative dielectric constant (2.25-84.2) within common ranges do not hinder the manipulation of liquids. In addition, the external stimulus required for EPD actuation is a non-uniform electrostatic field, which avoids the effect of Joule heating on biological samples and thereby ensures compatibility with various biological samples (such as body fluids, proteins, and living cells). In order to provide automation, a multi-physics system coupled to EPD is designed, which consists of: a programmable control matrix that generates a regionalized electromagnetic field; an EPD gripper that can be controlled by a programmable magnetic field and senses the movement of the non-uniform electrostatic field; and a polarizable target droplet. Its advantages in terms of versatility in manipulating liquids, biocompatibility, and programmability give it the potential to assist basic scientific research as an automated experimental platform, which can be used for automated lithium monitoring in a variety of biofluids.

[0139] To further enhance its adaptability for multidisciplinary applications in practice, the EPD-based droplet robotic system can be developed from both technical and application perspectives. From a technical perspective, the system can be miniaturized, modularized, and integrated to achieve nanoscale fluid manipulation, which is consistent with the trend toward smaller and more flexible systems in scientific research. The system is primarily designed to manipulate μL-scale (500 nL-1 mL) droplets. If the size is reduced while maintaining the original design, the charge density of the electret needs to be increased to ensure functionality ( Fig.25 ). In addition, it can be microfabricated into an electret material with controllable two-dimensional charge distribution. This method has the potential to further reduce the size of the system by directly programming the local electric field distribution to actuate droplets without physically moving the electret material.

[0140] In addition, in-situ image processing functions can be integrated into the system to form a "result analysis module". Temperature control functions can also be integrated into the system to form a "heating and cooling module". By integrating other auxiliary functions into the system to form specific modules, the steps required by different experiments can be completed in situ by simply activating and combining different modules in sequence. This modularity also ensures that the system is easy to integrate into other laboratory equipment and that miniature and portable systems can be developed for field research. Another promising area for technological development is the improvement of controllability and speed. At the actuation system level, the magnetic force generated by the programmable control matrix can be further enhanced by increasing the current (for example, replacing the switch IC with a chip with a higher current threshold) or modifying the coil design (for example, more layers of coils), improving system robustness and switching frequency. At the actuation material level, different electret materials can be used (such as CYTOP, which can provide up to 2 mC / m for a 15μm thick film) 2In terms of application progress, the proposed droplet robotic system provides a new way to achieve high-throughput and high-precision experiments, promoting future experimental efficiency, such as by incorporating machine learning and artificial intelligence (AI) into the system of the present invention. AI can be used to help analyze large amounts of data obtained from automated systems to discover hidden correlations between various parameters and results. The ability to learn from past experience will not only provide the potential to improve the design of various experimental steps, but also break through the bottleneck of scientific development, such as discovering unknown parameters that affect human diseases. In terms of application progress, the proposed droplet robotic system provides a new way to achieve high-throughput and high-precision experiments, significantly improving future experimental efficiency. In addition, the automated system also provides the possibility of remote experiments, especially for experiments involving dangerous operations and hazardous chemicals. By remotely uploading commands to a system located in the laboratory, technicians can perform experiments and obtain experimental data in a safe manner. This remote experiment method also lays the foundation for promoting communication and cooperation between laboratories and helps to share research results in real time.

[0141] The EPD-based droplet robotic system has the versatility to operate liquid types, compatibility with biochemical samples and matrices, and excellent comprehensive performance in terms of programmability, manufacturing cost, and power consumption. It has shown potential for application in automated material synthesis, medical diagnosis, drug development, etc., and is expected to profoundly influence countless scientific disciplines and promote the application of robotic systems in scientific research.

[0142] The following materials were used in the tests described in this article. HFE-7500 (Novec EngineeredFluid, 3M) was used as the oil matrix for EPD-based droplet actuation. A surfactant (Pico-Surf, Sphere Fluidics) of 0.05% was also added to HFE-7500 to adjust the shape and surface area of ​​the droplets floating on the HFE surface. For the hydrophobic matrix, a superhydrophobic coating (204A, Wateroff) was spin-coated onto a polyethylene terephthalate (PET) membrane sheet and the contact angle was 163° after drying for 24 hours. Unless otherwise stated, most experiments used an oil matrix. DI water, HCl (Aladdin), H2O2 (QuantaRed stabilized peroxide solution, Thermofisher), paraffin (Aladdin), glycerol (Sigma-Aldrich), triacetin (Aladdin), and hexadecane (Sigma-Aldrich) were actuated by EPD, respectively. Methylene blue (TCI), Oil Red O (C.1.26125, Aladdin), and Rhodamine B (Aladdin) were added to visualize the motion of transparent droplets in some optical image demonstrations.

[0143] The negatively charged electret sheet is made of PTFE and is charged by contacting copper, while the positively charged electret sheet is made of glass and is charged by contacting PTFE. By varying the amount of friction, the amount of charge acquired by the electret can be adjusted accordingly. To measure the amount of charge possessed by the electret, the electret is placed in a Faraday cup connected to a programmable electrometer (Model 6514, Keithley Instruments) in charge measurement mode.

[0144] In the characterization experiments, unless otherwise stated, a slice or a piece of electret (35×30×0.2 mm) was used to actuate a 20 μL droplet floating on an oil surface. In order to quantitatively characterize the EPD-based droplet actuation process, the motion of the droplet was analyzed with a software tracker to calculate its position, velocity, and acceleration. A dye was introduced to assist in tracking the target droplet. Specifically, in order to measure the effective actuation distance, the amount of charge possessed by the electret was measured at the beginning of each test, and then it was attached to a self-constructed slider to horizontally approach the droplet at a height of 10 mm above the droplet. When the droplet began to move after the electret moved to a certain distance, the horizontal distance between the two was defined as the effective actuation distance. In order to measure the minimum and maximum height differences between the electret and the droplet required for effective actuation, the amount of charge possessed by the electret was first measured, and then it was attached to a self-constructed slider to vertically approach the droplet. When the electret approached the droplet vertically, it repeatedly moved laterally within a range of about 10 mm from the droplet. When the droplet starts to move with the electret at a certain height, this height is defined as the maximum height difference required for effective actuation. When the electret continues to drop to a certain height, and the droplet is absorbed into the electret and makes contact, this height is defined as the minimum height difference required for effective actuation. In order to measure the maximum speed of droplet actuation, the charge-measured electret is attached to a self-constructed slider at 5, 8, and 11 mm above the droplet. The electret is accelerated laterally and actuates the droplet with it. The maximum speed of the droplet is analyzed by the tracker.

[0145] The EPD-based droplet actuation was simulated using a finite element analysis tool (COMSOL Multiphysics 5.4). Some simplifications were made during modeling, and the scene was simplified to two dimensions (2D). The surrounding air environment was modeled as a square much larger than the electret and the droplet, and all surface boundaries were grounded. The electrostatic interaction in the system was considered to be dominant, so other physical phenomena were ignored in the simulation. According to the measured experimental parameters, the electret was set to a rectangle of 6×2 mm with a surface charge density of -1.74×10 -5 C / m 2 , while in most cases the droplet was assumed to be circular with a radius of 3 mm, unless otherwise stated. To analyze the average Maxwell stress tensor applied to the droplet, the line average of the X component of the Maxwell stress tensor applied to the circular edge of the droplet was calculated. To show how the net charge carried by the droplet affects the attraction of both the positive electret and the negative electret, the droplet was additionally assumed to carry 2×10 -4 C / m 3 The space charge density of the electret is assumed to be ±1.74×10 -5 C / m 2To quantify the effect of droplet charge density, the droplets were set to carry 0 and ±2×10 -4 C / m 3 The space charge density of the droplet is calculated

[59] , and the average Maxwell stress tensor applied to the droplet at different positions is calculated. The electret is set to be located at 0 mm. The simulation settings are adjusted according to the experimental conditions. The electret is set to be a rectangle of 0.2×35 mm, with a surface charge density of -2×10 -5 C / m 2 , and the droplet is set to be a circle with a radius of 2.7 mm. In the simulation, the conductivity and relative dielectric constant of the droplet are adjusted as needed to demonstrate the versatility of the types of liquids that can be manipulated by EPD.

[0146] All human urine and saliva samples were obtained under the research project HREC No. EA230092 approved by the Human Research Ethics Committee of the University of Hong Kong. After collection, urine and saliva were centrifuged at 6000 rpm for 20 minutes, and the supernatant was frozen at −20°C. Human serum (H3667) was purchased from Sigma-Aldrich, which was heat-inactivated and derived from sterile-filtered AB plasma from American men. Human serum was frozen in aliquots at −20°C until use.

[0147] Fluorescent protein (FITC-labeled goat anti-rabbit IgG (H+L), Beyotime) droplets floating on the oil surface were actuated by EPD, and the whole process was observed and recorded by fluorescence microscopy. To analyze the fluorescence intensity of the droplets and their moving paths, the average grayscale values ​​of four different points in the droplets, moving paths, and background were calculated by ImageJ software. The average value in the background was then subtracted from the values ​​of the droplets and moving paths.

[0148] A549 cells obtained from American Type Culture Collection (CCL-185) were used for live cell actuation experiments. First, A549 cells were maintained in DMEM containing 10% fetal bovine serum ( 10099141, Gibco) and 1% penicillin-streptomycin (15070063, Gibco) (10569010, Gibco). The cells were then incubated at 37 ° C, 5% CO2 for 48 hours, and then harvested for actuation experiments. In order to demonstrate the cell viability during the actuation process, three droplets and culture medium containing A549 cells were actuated with EPD. 10 μL samples were extracted from each cell suspension, and evenly mixed with 10 μL 0.4% trypan blue dye (15250061, Gibco) after actuation for 1, 3, 5, 7 and 9 minutes, and counted with Countess II FL automatic cell counter (Invitrogen, Thermo Fisher Scientific company). At the same time, 3 control droplets were also dropped in the culture dish and exposed to air together with the experimental group. 10 μL samples were also extracted from each droplet at 1, 3, 5, 7 and 9 minutes, and mixed evenly with 10 μL trypan blue dye, and counted accordingly. The effect of EPD on cell proliferation was also tested. After the droplets containing A549 cells were treated with EPD for 30 minutes, the cells were continued to be cultured for another 12 hours. 10 μL samples were extracted from each droplet before and after each entire experiment and mixed evenly with 10 μL trypan blue dye, and repeated three times. The concentration of live and non-live cells for each test was then calculated by Countess II FL automated cell counter.

[0149] THP-1 cells from the American Type Culture Collection (TIB-202) were used in experiments to analyze the effects of the EPD system settings on cell culture. First, THP-1 cells were maintained in RPMI 1640 medium (11875093, Gibco) containing 10% fetal bovine serum (10099141, Gibco) and 1% penicillin-streptomycin (15070063, Gibco). The cells were then incubated at 37°C, 5% CO2 for 48 hours and harvested for experiments. Then, three 300 μL droplets containing THP-1 cells and medium were placed on the EPD system and incubated for 24 hours without power. After 24 hours, 10 μL was extracted from each cell suspension droplet and mixed evenly with 10 μL 0.4% trypan blue dye, and repeated three times. The concentration of viable and non-viable cells for each test was then counted by the Countess IIFL automated cell counter.

[0150] For the EWOD experiments, two sheets of ITO glass were used to form the upper and lower plates. The ITO electrode patterns on both the upper and lower plates were fabricated by photolithography using a negative photoresist (SU-8 2025, MicroChemicals) on a mask aligner (MA / BA6, SUSS) (58). A 50 nm layer of Teflon-AF was then coated on the upper plate by spin coating a solution (1 wt% in FC-40 from 3M Co., USA) at 1500 rpm for 60 seconds. A 3.5 μm thick poly(p-xylene)-C film was deposited on the lower plate using a low-pressure chemical vapor deposition (CVD) device (LH300, LaChi Enterprise), and then a Teflon-AF solution (1 wt% in FC-40 from 3M Co., USA) was spin coated on the polystyrene-C film at 1500 rpm for 60 seconds. The assembled plates were baked on a hot plate at 165°C for 15 minutes. The voltage amplitude used when applying ITO glass on the digital microfluidics platform ranged from 60 V to 150 V and the frequency was 1000 Hz.

[0151] Water, glycerol, triacetin, human serum, human saliva, and human urine were tested on the prepared EWODs, respectively. Specifically, a 1.8 μL droplet was used in the air-based EWOD, while a 1 μL droplet with 0.5 μL of decamethyltetrasiloxane (Aladdin) was used in the oil-based EWOD. To quantitatively analyze the actuation of these liquids, the leading edge of the droplet was tracked using a software tracker, and its real-time position ( Figure 5A-5B ). The average position of the leading edge of the droplet 5-5.5 seconds after activating the adjacent coil / electrode is Figure 2D The relative moving distance is defined as , while the distance between two adjacent coils / electrodes is normalized to 1. 0.05% BSA solution was tested on EWOD, and protein adsorption on EWOD was observed from fluorescence microscopy.

[0152] Live A549 cells in culture were tested on EWOD. Droplets cannot be actuated directly on the dual-plate EWOD due to protein adsorption. Therefore, the previously described EWOD device was simplified to a pair of ITO glass plate electrodes (50 × 50 × 1.0 mm) to investigate how the electric field applied between the upper and lower plates of the EWOD affects live cells. An insulating rubber tape with a thickness of 1 mm was sandwiched between the two ITO glass plate electrodes as a spacer. The coating process of the ITO glass was the same as described above. To analyze the effect of the electrowetting effect on cell proliferation, 300 μL droplets containing live A549 cells were added between the electrodes of the air-based EWOD, while on the oil-based EWOD, an additional 50 μL of decamethyltetrasiloxane was added to each droplet. 300 μL droplets containing live A549 cells were added between the electrodes, and a DC voltage of 150 V was applied between the two electrodes for 30 minutes. Afterwards, the droplets were repeatedly rinsed with a pipette to rinse off the cells attached to the substrate and to evenly distribute the cells within the droplets. The droplets were then extracted and incubated in adherent culture for another 12 hours. Before and after, 10 μL samples were extracted from the cultured cells and mixed with 10 μL trypan blue. The entire experiment was repeated three times. The concentrations of live and non-living cells for each test were then calculated by the Countess II FL automated cell counter. To analyze the effect of the oil-based EWOD system settings on cell culture, 300 μL droplets containing live THP-1 cells and 100 μL of decamethyltetrasiloxane were added between the oil-based EWOD electrodes and cultured for 24 hours without power. Before and after the entire experiment, 10 μL samples were extracted from each droplet and evenly mixed with 10 μL trypan blue dye, repeated 3 times. The concentrations of live and non-living cells for each test were then calculated by the Countess II FL automated cell counter.

[0153] Programmable control matrix ( Figures 21A-21B ) is built on a multi-layer PCB, which contains a 32×32 coil matrix, four row switches MAX14662 (Maxim Integrated), two column switches MC33996 (NXP Semiconductors), and a 1×10 pin header for power supply and communication with Arduino Uno. The coil consists of three turns of 1 mm wide wire and is stacked in three layers on the PCB, powered by 0.2A current. By uploading the code to the Arduino Uno through a computer, it can selectively turn on specific row switches and column switches to power the coil at the specified coordinates and generate a local electromagnetic field.

[0154] The EPD gripper is made of a mixture of electret (PTFE) and magnetically responsive material (NdFeB magnet, DH101, 1 / 32 inch thick and 1 / 10 inch in diameter, K&J Magnetics). The electret material is formed into the shape of the gripper by origami, while the magnetically responsive material adheres to the bottom of the gripper, providing the ability to be driven by a magnetic field. In the EPD-based droplet robotic system, the EPD gripper is suspended below the control matrix by magnetic force. In order to balance the deadweight of the gripper, an actuation magnet is also utilized and placed above the control matrix. It can enhance the local magnetic field generated by the coil and provide additional attraction to the EPD gripper. When the coil is turned on at different coordinates, the actuation magnet and the suspended EPD gripper can be actuated simultaneously by magnetic force. In order to minimize the friction when the EPD gripper moves, a smooth pad (PET and glass plate) is placed between the gripper and the control matrix.

[0155] The multi-physics droplet robotic system was simulated using a finite element analysis tool (COMSOL Multiphysics 5.4). Some simplifications were made during the modeling. The surrounding air environment was modeled as a cubic shape, which was much larger than the coil and the gripper, and all surface boundaries were set to ground. In order to simulate the magnetic field generated by the control matrix, the electromagnetic interaction in the system was considered to be dominant, so other physical phenomena were ignored in the simulation. The three-layer coil was also simplified to a single-layer coil, and the inlet of each row of coils was connected, while the outlet of each column of coils was also connected. The simulation results are used to qualitatively characterize the distribution of the magnetic field. In order to simulate the electrostatic interaction between the EPD gripper and the droplet, electrostatic field physics was used. The simulation used the same EPD gripper size as the experimental setup, and the surface charge density of the electret material was set to -1.6×10 -5 C / m 2 To calculate the force applied on the droplet, the X and Y components of the Maxwell stress tensor are integrated over the droplet surface. Both magnetic and electrostatic field physics are utilized in the simulation of the entire system. The actuation magnets are neglected during the simulation and the magnetic Maxwell stress tensor applied to the EPD gripper through the activation coil and the electric Maxwell stress tensor applied to the droplet through the EPD gripper are presented, qualitatively describing the overall multiphysics coupling in the entire system.

[0156] The microfluidic detection chip is 3D printed with photosensitive resin and has a height of 7 mm and a length and width of 50 mm. The chip wall is coated with a super hydrophobic layer (NeverWet) ( Fig.33 When used, the chip was glued to a culture dish with epoxy (Devcon) and filled with HFE to provide an oil matrix. The chip shown was designed to perform three tests in parallel and consisted of three working areas and three reagent loading areas ( Fig.28D ). For the probe and buffer loading areas, two microtubes (LDPE, Scientific Commodities) with 0.034" ID × 0.052" OD were inserted at the bottom of the chip, considering that these reagents needed to be loaded repeatedly, and the inlet of the microtubes was located below the HFE-air interface. The microtubes were connected to two syringes mounted on a pump (SPLab01, DKInfusetek). The preloaded probe solution and buffer solution were injected into the chip, and the generated sub-droplets of buffer / probe would rise to the HFE surface due to buoyancy and be collected by the EPD holder above.

[0157] For the calibration test, 5 μL of prepared biological samples with known lithium concentrations (LiCl solutions spiked with human serum at concentrations of 0 μM, 400 μM, 800 μM, 1200 μM, 1600 μM, 2000 μM; LiCl solutions spiked with human saliva at concentrations of 0 μM, 800 μM, 1600 μM, 2400 μM, 3200 μM, 4000 μM; LiCl solutions spiked with human urine at concentrations of 0 μM, 1600 μM, 3200 μM, 4800 μM, 6400 μM, 8000 μM) were mixed with 15 μL of masking solution (ab235613 lithium assay kit, Abcam) to form calibration samples. These concentrations were selected based on the clinical empirical range of lithium in various human body fluids

[71] . Due to the high lithium concentration in human saliva and urine, the prepared saliva and urine lithium samples were diluted two-fold and four-fold, respectively, before mixing with the masking solution. In each calibration process, the prepared calibration sample needs to be mixed with 130 μL buffer solution and 100 μL probe solution (ab235613 lithium detection kit, Abcam). For each actual biological sample detection process, the actual sample of human serum / saliva / urine is spiked with LiCl solution (ab235613 lithium detection kit, Abcam) with a concentration as a reference value. The prepared biological sample needs to be mixed with 15 μL masking solution (ab235613 lithium detection kit, Abcam) first, and then mixed with 130 μL buffer solution and 100 μL probe solution during the sample detection process. Among them, the buffer and probe solutions are loaded each time by pumps and microtubules connected to the buffer solution and probe loading areas, while the masking samples, calibration samples, and tested biological samples are directly pre-loaded into the chip before testing.

[0158] The capture of the generated sub-droplets, the transport of reagents to the designated working area, the merging and mixing of reagents and samples are all performed by the EPD-based droplet robotic system. Each test is repeated at least three times, and the actual process is not necessarily performed according to the combination of two calibration and one test sample. To obtain the test results, the mixed droplets can be extracted and transferred to a 96-well plate to measure the absorbance at 540 nm and 630 nm by a microplate reader (Spectramax iD5, Molecular Devices), or simply analyzed by in situ photography and RGB analysis (as shown in Figure 32). To obtain Fig.31 The lithium standard curve in the sample was prepared by subtracting the absorbance ratio (OD) of the reagent blank (0 μM) from the measured absorbance ratios of the other calibration samples. 540 / OD 630 ). The absorbance ratios of all calibration samples minus the background are then plotted and the slope of the standard curve is calculated. This standard curve can be used to calculate lithium concentration measurements for the biological samples being tested.

[0159] To demonstrate the step-by-step workflow, a set of tests consisting of two calibrations and one real biological sample test was performed as an example. For a clear demonstration, dyed droplets were used instead of transparent real samples / reagents. The operation of the system was photographed from the bottom up with a camera (PowerShot G7X Mark Ⅲ, Canon) Fig.28E ). The video was shot and edited in a segmented setting to show a detailed step-by-step workflow and provide clear operational details. In the demonstration, droplets representing masking, calibration samples, and test biological samples are directly preloaded inside the chip, while solutions representing buffer and probes are loaded through pumps and microtubules. Three EPD holders are programmed to work together to capture the generated daughter droplets, transport reagents to the designated working area, and automatically merge and mix the reagents with the sample. Specifically, in the sample preparation stage (steps 0-2), after the test biological sample is loaded into the chip, EPD holders 1 and 2 transport the sample and masking solution to the sample area for mixing. This step is to protect other ions in the biological sample from affecting the test results. In the second stage of calibration 1 (steps 3-7), the buffer solution and the probe solution are injected into the chip by pumps, respectively (steps 3 and 5). At the same time, EPD holders 2 and 3 capture and transport the generated daughter droplets to the calibration area (A) to mix with the prepared calibration sample (steps 4 and 6). The lithium ions in the sample will bind to the probe after dilution, thereby quantitatively changing the absorbance curve (step 7). Likewise, in the third stage of calibration 2 and the fourth stage of sample detection, the processes of generating, delivering and mixing the buffer solution and the probe solution are repeated, respectively (steps 8 and 9).

[0160] To prepare the original bacteria and cells for the experiment, Escherichia coli (E. coli; ATCC 25922) was cultured in Luria-Bertani (LB) medium at 37°C overnight. Then, the E. coli were collected and washed three times by centrifugation (3000 rpm, 5 min) with PBS. The obtained bacterial cells were resuspended and diluted in PBS to approximately 6 × 10 7 CFU / mL was used for the experiment.THP-1 cells were maintained in RPMI 1640 medium (11875093, Gibco) with 10% fetal bovine serum (10099141, Gibco) and 1% penicillin-streptomycin (15070063, Gibco).The cells were then incubated at 37°C, 5% CO2 for 48 hours.The contents were then transferred to a centrifuge tube and centrifuged at 300g for 4 minutes.The cell pellet was suspended in fresh RPMI1640 medium for the experiment.

[0161] To establish an in vitro cell-bacteria inflammation model for dynamic monitoring on the EPD system, 3×10 6 / mL THP-1 cells and 995 μl droplets containing 6×10 7 5 μl droplets of CFU / mL Escherichia coli. The three groups of cell droplets and bacterial droplets were fused and mixed using the EPD system and then incubated for 12 hours. Then, one of the mixed cell-bacteria droplets was observed under a microscope and marked as the "first round infection sample". The other two cell-bacteria droplets were introduced into new bacterial droplets, respectively, and then incubated for another 12 hours. After repeating the same process for the second and third droplets, the "second round infection sample" and "third round infection sample" were obtained, respectively. Immediately after sample collection, 333 μl of mixed antibody solution (containing 1.2 nM Eu-labeled anti-Hil 1β antibody and 12 nM ULight-labeled anti-Hil 1β antibody, TRF1220C, LANCE Ultra IL-1β (Human) Detection Kit, Revvity) was introduced into each sample to detect the concentration of human IL-1β produced in each sample. The mixed droplets were then incubated on the EPD system for 60 minutes.

[0162] After processing the samples on the EPD system and performing the bioassay steps, the final droplets were extracted and centrifuged at 3000 rpm for 5 minutes to read the measurements. The supernatant obtained was then transferred to a 96-well plate (60 μL per well) for TR-FRET measurements (Spectramax iD5, Molecular Devices) with an excitation wavelength of 350 nm and an emission wavelength of 665 nm. To obtain a human IL-1β standard curve, standard dilutions were prepared with fresh RPMI 1640 medium and reconstituted hIL1β (TRF1220C, LANCEUltra IL1β (Human) Assay Kit, Revvity). After 45 μL of the standard dilution was mixed with 15 μL of the prepared antibody solution and incubated for 60 minutes, the same TR-FRET measurements were performed and the results were fitted by a sigmoidal dose-response curve.

[0163] The comparison is based on the detailed information in the table, which is either based on experimental results (marked with the figure number where the relevant data is presented) or on references. For the perspectives of "1 / operating voltage" and "1 / manufacturing cost", the lower the operating voltage and manufacturing cost in the table, the higher the evaluation result. For the perspectives of "versatility with operable liquids", "compatibility with biological samples" and "compatibility with matrices and environments", the table contains multiple sub-indicators, and the evaluation results are calculated by averaging the evaluations of the corresponding sub-indicators.

[0164] The above description is only a specific embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification or replacement obvious to those skilled in the art should fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

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[0270] Although the present invention is described with respect to certain embodiments, it should be understood that various modifications thereof will be apparent to those skilled in the art after reading the specification. Therefore, it should be understood that the invention disclosed herein is intended to cover these modifications within the scope of the appended claims.

Claims

1. A droplet holder for a polarizable droplet, comprising: a top plate formed of a magnetically responsive material and capable of moving in a defined sequence in response to motion of the regionalized electromagnetic field; as well as electret sheets depending downwardly from the top plate, the electret sheets being capable of being charged with the same polarity and being spaced far enough apart to allow the droplet to be located between them; Therein, due to the electret induced polarization (EPD) on the droplet, the droplet is attracted by the electret sheet and moves with the top plate.

2. The droplet holder according to claim 1, wherein: The top plate has a square shape and has at least four electret sheets, one of which depends downwardly from each side of the square top plate, and The electret sheets suspended downward from opposite sides of the top plate have the same charge polarity.

3. The droplet holder according to claim 2, wherein: The electret sheet is made of PTFE and is negatively charged by being in contact with copper.

4. The droplet holder according to claim 2, wherein: The electret sheet is made of glass and is positively charged by being in contact with the PTFE.

5. A multi-physics droplet robotic system for automatically manipulating and moving droplets, comprising: The EPD holder according to claim 1; as well as A programmable control matrix generates a regionalized electromagnetic field through coils, and the regionalized electromagnetic field changes in a defined order according to the program of the control matrix so as to move the EPD gripper in a specific path, and the EPD gripper is capable of driving droplets in the specific path and realizing other microfluidic operations.

6. The multi-physics droplet robotic system according to claim 5, wherein: The other microfluidic operation includes one of: self-assembly, merging of two droplets, and mixing of the merged droplets by cyclic motion.

7. The multi-physics droplet robotic system according to claim 5, wherein: The programmable magnetic field can actuate the EPD gripper, creating a non-uniform electrostatic field that can attract the movement of droplets below.

8. The multi-physics droplet robotic system according to claim 5, wherein: The programmable control matrix is ​​manufactured on a multi-layer PCB, which consists of row switches, column switches, an electromagnetic coil matrix and signal / power sockets.

9. The multi-physics droplet robotic system of claim 5, further comprising additional actuation magnets on the top plate for amplifying the electromagnetic field generated by the control matrix to balance the weight of the EPD holder.

10. The method for operating the multi-physics droplet robot system according to claim 5, comprising the following steps: Uploading commands to the control matrix; Supply power to the designated coil; Generate a local magnetic field; The EPD gripper is actuated by magnetic force; Move the EPD gripper in a specific path; as well as Droplet actuation by EPD.

11. A method for monitoring lithium in multiple body fluids, comprising the following steps: A microfluidic detection chip is provided, wherein the microfluidic detection chip has three reagent loading areas, which are respectively divided into loading masking solution, probe solution and buffer solution, and three working areas for combining, mixing and reacting these reagents with calibration samples / test samples, respectively. Two in-situ calibrations and one sample test were used as a group; Two calibration samples with known lithium concentrations are preloaded in the two working areas for in-situ calibration; Preload masking solution; Providing three EPD holders of the EPD holder according to claim 1, wherein the three EPD holders are programmed to work together to implement automated analysis steps within the detection chip, including sample preparation, calibration 1, calibration 2, and sample detection; Each EPD gripper performs tasks and moves step by step according to their trajectory; In the sample preparation stage (step 0-2), after the actual biological sample to be tested is loaded onto the chip, the EPD holders 1 and 2 are used to transport the sample and masking solution to the sample area for mixing; In the second stage of calibration 1 (steps 3-7), the buffer solution and the probe solution are injected into the chip (steps 3 and 5), respectively. Allowing EPD holders 2 and 3 to capture the generated daughter droplets and transport them to the calibration area to mix with the prepared calibration samples (steps 4 and 6); The lithium ions in the sample are allowed to bind to the probe after dilution, thereby quantitatively changing the absorbance curve (step 7); The processes of generating, transporting and mixing the buffer solution and the probe solution are repeated in the third stage of calibration 2 and the fourth stage of sample detection, respectively (steps 8 and 9), thereby automatically performing two in-situ calibrations and one actual biological sample detection, and calculating the lithium concentration in the biological sample based on the linear calibration curve.

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