Microfluidic component having means for determining external influencing factors based on operating signals

Abstract

The inventive concept described herein relates to a microfluidic component (1000) comprising a membrane actuator (100) having a membrane element (101) and an actuator element (102) for deflecting the membrane element (101). The signal generating device (103) is configured to generate an electrical control signal (104) having a time-varying signal curve for controlling the membrane actuator (101), whereby the actuator element (102) actuates the membrane element (101). The signal processing device (105) is configured to detect, during operation of the microfluidic component 1000, an influence on the time signal curve of the control signal (104) caused by one or more external influencers, and to identify and / or classify at least one causal external influencer based on the influence on the time signal curve.

Description

[Technical Field]

[0001] The inventions described herein relate to devices in the field of microfluidics. For example, the microfluidic components of the present invention may be microvalves or micropumps that can be actuated by a microstructured membrane actuator. This disclosure proposes an innovative concept that allows a microfluidic component to monitor itself during operation by evaluating the temporal progression of a control signal. [Background technology]

[0002] Fluid microactuators, such as micropumps or microvalves, driven by membrane actuators, can transport liquids or gases or switch fluid pathways. In practice, there can be several disturbances or changes in fluid boundary conditions that can impair the intended function of these actuators. Since these actuator components do not have sensory properties, they cannot detect disturbance events.

[0003] For example, microactuators are often used in micro-dosage systems. One possible method for monitoring microdosage or detecting disturbance events is to use flow sensors, such as anemometers, which allow for the measurement of pressure differences or volume changes. In addition, flow sensors or pressure sensors can be used to detect disturbances. However, this has the disadvantage that separate components must be properly integrated into the micro-dosage system.

[0004] A further approach to detecting disturbances is to use a sensor system in the actuator membrane, i.e., a special sensor is directly incorporated into the membrane of the microactuator, allowing, for example, the determination of the membrane's voltage state. Alternatively, additional electrodes can be attached to a piezoelectric ceramic. However, this has the disadvantage of requiring additional contact, increasing overall manufacturing complexity, and preventing the determination of direct information regarding pneumatic or hydraulic pressure states. In addition, the sensor technology must be housed in the critical membrane actuator region, which is subject to high voltages and large mechanical stresses.

[0005] A further possible method for detecting disturbance events or fault conditions is to use two micropumps connected in series, each containing a piezoelectric element. The two piezoelectric micropumps are connected such that one is operating and the other is inactive. The effect of the fluid signal from the operating micropump is then detected by the sensor properties of the piezoelectric element of the second, switched-off micropump.

[0006] In this case, in addition to the microactuator, an additional sensor element is again required, here in the form of a second micropump. Furthermore, the fluid signal of the micropump is affected by the fluid conduit between the two micropumps and no longer represents the process within the pump chamber of the active micropump.

[0007] A further possible method for detecting disturbances is to use another electrode on the piezoelectric actuated membrane. When piezoelectric ceramics are used as pump drive elements, they must be charged with a high voltage in a very short time. This charging current I = C * dU / dt is significantly higher than the expected sensor effect. Therefore, there are attempts to implement a separate "sensor electrode" on the piezoelectric ceramic. Using another electrode as a sensor electrode offers the possibility of utilizing the sensor effect of the piezoelectric actuated membrane.

[0008] However, this also requires a second electrode and corresponding wiring. In addition, a portion of the piezoelectric ceramic is occupied by the second electrode, and this portion cannot be used in the actuator system. Furthermore, the interaction is detected only in the arrangement of the piezoelectric ceramic where a separate actuator system is located. [Prior art documents] [Non-patent literature]

[0009] [Non-Patent Document 1] https: / / www.sciencedirect.com / science / article / pii / S0924424710002311 [Non-Patent Document 2] https: / / iopscience.iop.org / article / 10.1088 / 0960-1317 / 4 / 4 / 004 [Non-Patent Document 3] "Simulation of microfluid systems", Zengerle, R., Richter, M., Journal of Micromechanics and Microengineering, 1994, 4(4), pp. 192-204, 004 [Overview of the Initiative] [Problems that the invention aims to solve]

[0010] Therefore, it is desirable to improve existing systems for detecting disturbance events or monitoring the state of microfluidic components so that additional sensor technology or electrodes are not required. In addition, it is desirable that the state of the microfluidic components can be monitored during normal operation, i.e., during normal control, even if they do not need to be controlled in a special way. [Means for solving the problem]

[0011] This objective is achieved by a microfluidic component and a method for operating such a microfluidic component, as defined in the independent claims.

[0012] The microfluidic component of the present invention comprises a membrane actuator having a membrane element and an actuator element for deflecting the membrane element. Furthermore, the microfluidic component of the present invention comprises a signal generating device configured to generate an electrical control signal having a time-varying signal curve for controlling the membrane actuator, which the actuator element uses when acting on the membrane element. In addition, the microfluidic component of the present invention comprises a signal processing device configured to determine the influence of one or more external influencing factors on the time signal curve of the control signal during the operation of the microfluidic component. In addition, the signal processing device is configured to identify and / or classify at least one causative external influencing factor based on this influence on the time signal curve.

[0013] Therefore, the normal control signal of a membrane actuator, or its temporal evolution in at least one deflection process of the membrane actuator, is observed. In this case, different external influencing factors have different effects on the temporal evolution of the control signal. That is, the control signal changes depending on how the membrane actuator interacts with its environment. These ambient influences cause the membrane actuator (e.g., its voltage state or position) to change in such a way that certain electrical parameters change, and then directly affect the current flow of the control signal. Since these interactions can change over time, the interaction process can be inferred through precise determination or measurement of the control signal.

[0014] For example, an external influence in the form of a pressure change beneath a piezoelectric membrane transducer generates a force that results in a current flow on the piezoelectric ceramic, purely caused by this pressure change, due to the direct piezoelectric effect. In the context of this disclosure, this current flow is sometimes referred to as the "sensor current." This sensor current overlaps with the current required to electrically charge a capacitive load. That is, the additional current flow caused by the external influence overlaps with the control signal (e.g., the charging current), causing a deviation in the temporal progression of the control signal.

[0015] Accurate measurement of the control signal (e.g., charging current) of a membrane actuator is necessary for the normal operation of the membrane actuator, but is sufficient to measure fluid and mechanical time-varying processes at or within the microfluidic component of the present invention (e.g., the pump chamber and / or microvalve of a micropump). This is highly advantageous to the concept of the present invention described herein, as it does not require any modifications to the components constituting the microfluidic component. Preferably, the corresponding measurement function is incorporated into the driver electronics of the microfluidic component, in combination with suitable means for acquiring and evaluating the data.

[0016] Therefore, by using the concepts of the present invention as described herein, information indicating how a membrane actuator interacts with its environment using hydraulic, pneumatic, mechanical, or piezoelectric properties can be extracted from ordinary control signals in a purely electrical manner, without the use of further devices, and in real time. Relevant fluid changes, such as valve degradation, back pressure fluctuations, actuator fatigue, bubble formation, and the like, can be detected from this information.

[0017] The state of a microfluidic component can be monitored based on the signal evaluation of the temporal progression of the control signal according to the present invention. That is, during operation, especially during normal operation (i.e., no special calibration mode is required), the microfluidic component of the present invention can monitor itself. The temporal progression provides information about how the microfluidic component interacts with its environment, i.e., which external influencing factors are currently affecting the microfluidic component. Depending on the type of external influencing factor, the temporal progression of the control signal is affected differently. Each individual influencing factor leaves its specific trace, so to speak, in the provision of the control signal over time. Through this, for example, based on the temporal signal curve affected accordingly, the type of currently dominant external influencing factor can be determined. For example, it can be determined whether there is a defect, and if so, the type of defect can also be determined.

[0018] The present invention further relates to a corresponding method for operating a microfluidic component comprising a membrane actuator including a membrane element and an actuator element for deflecting the membrane element. In this case, the method includes the step of generating an electrical control signal for controlling the membrane actuator, thereby causing the actuator element to actuate the membrane element, and the electrical control signal includes a time-varying signal curve. In addition, the method of the present invention includes the step of determining a time-varying signal curve of a control signal that is affected by one or more external influencing factors during the operation of the microfluidic component, and the step of identifying and / or classifying at least one external influencing factor based on the determined affected time-varying signal curve.

[0019] Further embodiments and advantageous aspects of the microfluidic components of the present invention and corresponding methods for operating the microfluidic components are described in their respective dependent claims.

[0020] Several embodiments are illustrated in the drawings and subsequently described in more detail. [Brief explanation of the drawing]

[0021] [Figure 1] This is a schematic diagram showing a microfluidic component with signal evaluation according to one embodiment of the present invention. [Figure 2] This is a schematic diagram showing different embodiments of the membrane actuator. [Figure 3] A further schematic diagram showing a microfluidic component with signal evaluation according to one embodiment of the present invention. [Figure 4] This is a schematic diagram showing a signal evaluation device according to one embodiment of the present invention. [Figure 5] This diagram shows an overview of the physical processes within a piezoelectric element. [Figure 6] This graph illustrates a mathematical function used to describe the voltage dependence of the capacitance of a micropump. [Figure 7A] This graph illustrates mathematical functions used to describe the time-dependent signal curves of current and voltage in the case of sinusoidal control in a micropump. [Figure 7B] This graph illustrates mathematical functions used to describe the time-dependent signal curves of current and voltage in the case of sinusoidal control of a capacitor. [Figure 8] This figure shows two Lissajous curves to visually represent the hysteresis effect in a micropump compared to a capacitor. [Figure 9A] This figure shows the Lissajous curve of a micropump that transports liquid using harmonic control and is exposed to bubbles as an external influencing factor. [Figure 9B] This figure shows the Lissajous curve of a micropump that transports liquid using harmonic control and is subject to closure as an external influencing factor. [Figure 10] This figure shows a so-called confusion matrix used to determine the system state or external influencing factors based on the frequency of correct predictions. [Figure 11] This figure shows the Lissajous curves of a micropump at different back pressures. [Figure 12]This figure shows three different time-dependent signal curves for control signals with different voltages, and the curves fitted to these signal curves. [Figure 13] This figure shows the signal curve of the amplitude of the current term Ip of a micropump during a suction stroke that depends on the applied voltage. [Figure 14] This figure shows the time decay constant of the micropump associated with the current term Ip in the suction stroke, which depends on the applied voltage. [Figure 15] This figure shows a comparison of the temporal decay constants of a micropump in the suction stroke and pressure stroke, each dependent on the applied voltage. [Figure 16A] This figure shows the signal curve of the amplitude of the current term ICE of a micropump during a suction stroke that depends on the applied voltage. [Figure 16B] This figure shows the time decay constant associated with the current term ICE of a micropump in a suction stroke that depends on the applied voltage. [Figure 17A] This figure shows the signal curve of the amplitude of the sum of two current terms IC+ICE of a micropump in a pressure stroke dependent on the applied voltage. [Figure 17B] This figure shows the time decay constant associated with the sum IC+ICE of the current terms in a micropump's pressure stroke, which depends on the applied voltage. [Figure 18A] This figure shows the signal curve of the amplitude of the sum of two current terms IC+ICE of a micropump in a pressure stroke dependent on the applied voltage at low voltage. [Figure 18B] This figure shows the time decay constant associated with the sum IC+ICE of the current terms in a micropump's suction stroke, which depends on the applied voltage at low voltages. [Figure 19] This is a block circuit diagram of the measurement structure used to perform the measurements shown in the following figures. [Figure 20] This figure shows the time signal curve of the amplitude of the current term Ip of an air-transporting micropump during suction and pressure strokes under normal conditions, i.e., without the influence of external factors. [Figure 21] Figure 20 is an enlarged view of the time signal curve during the suction stroke. [Figure 22] Figure 20 shows an enlarged view of the time signal process in the pressure stroke. [Figure 23] This figure shows the time signal curve of the amplitude of the current term Ip of a liquid transport micropump during suction and pressure strokes under normal conditions, i.e., without the influence of external factors. [Figure 24] Figure 23 is an enlarged view of the time signal curve during the suction stroke. [Figure 25] [Figure 25A] This is a magnified view of the overshoot of the time signal curve during the suction stroke as shown in Figure 24. [Figure 25B] This is a magnified view of the overshoot of the time signal curve during the suction stroke as shown in Figure 24. [Figure 25C] This is a magnified view of the overshoot of the time signal curve during the suction stroke as shown in Figure 24. [Figure 26] This figure shows an enlarged view of the time signal provision during the suction stroke as shown in Figure 23. [Figure 27A] This figure shows the temporal signal curve of the amplitude of the current term Ip of a liquid transport micropump during a suction stroke under the influence of external factors affecting bubble morphology. [Figure 27B] Figure 27A shows a detailed view of the time signal curve during the suction stroke. [Figure 27C] This figure shows the time signal curve of the amplitude of the current term Ip of the liquid transport micropump during the suction stroke, several pump strokes prior to the generation of bubbles in the pump chamber. [Figure 27D] This figure shows the time signal curve as shown in Figure 27A when air bubbles are present inside the pump chamber. [Figure 27E] This figure shows the time signal curve of the amplitude of the current term Ip of the liquid transport micropump during the suction stroke, several pump strokes after the bubbles have left the pump chamber. [Figure 28A]This figure shows the temporal signal curve of the amplitude of the current term Ip of a liquid transport micropump during suction and pressure strokes under the influence of external factors affecting bubble morphology. [Figure 28B] Figure 28A shows a detailed view of the time signal curve during pressure stroke. [Figure 29A] This figure shows the temporal signal curve of the amplitude of the current term Ip of a liquid transport micropump during suction and pressure strokes under the influence of an external influencing factor in the form of continuously decreasing back pressure. [Figure 29B] This figure shows the time progression of back pressure in the inlet and outlet reservoirs. [Figure 29C] Figure 29A shows a detailed view of the time signal curve during the suction stroke. [Figure 29D] Figure 29A shows a detailed view of the time signal curve during pressure stroke. [Figure 30A] This figure shows the temporal signal curve of the amplitude of the current term Ip of an air-transporting micropump during suction and pressure strokes under the influence of external factors in the form of continuously decreasing pressure. [Figure 30B] Figure 30A shows a detailed view of the time signal curve during the suction stroke. [Figure 30C] Figure 30B is an enlarged view of the time signal curve during the suction stroke. [Figure 30D] Figure 30A shows a detailed view of the time signal curve during the suction stroke. [Figure 30E] Figure 30D is an enlarged view of the time signal curve during the suction stroke. [Figure 31A] This figure shows the Lissajous curves of different micropumps during suction and pressure strokes without external influencing factors. [Figure 31B] This figure shows the Lissajous curves of different micropumps in suction and pressure strokes under the influence of external factors affecting the form of contact between membrane elements. [Figure 32]This is a schematic block circuit diagram illustrating the system structure according to one embodiment. [Figure 33] This is a schematic block diagram illustrating possible components of a signal evaluation device according to one embodiment. [Modes for carrying out the invention]

[0022] The embodiments are described in more detail below with reference to the drawings, and elements having the same or similar function are presented by the same reference number.

[0023] Method steps illustrated or described in the context of this disclosure may also be performed in an order other than that illustrated or described herein. In addition, method steps relating to certain features of the device are interchangeable with such features of the device, and vice versa.

[0024] In the context of this disclosure, where a fluid is mentioned, it should be understood as a liquid, a gas, or a mixture of a liquid and a gas.

[0025] While there are other examples, this disclosure particularly describes a membrane actuator having a membrane element and an actuator element. Where reference to pump chamber pressure is made in the context of this disclosure, this should be understood as the pressure applied to the membrane side of the membrane element in contact with the fluid. For example, such pressure can also occur in a valve containing such a membrane actuator.

[0026] Whereever references are made to variable pressure or variable pump chamber pressure in the context of this disclosure, this should be understood as time-varying pressure and placement-varying pressure. Unless otherwise indicated, the term “variable pressure” in the context of this disclosure should be understood as the time-resolved average value of time-varying pressure and placement-varying pressure.

[0027] In the context of the present disclosure, when reference is made to external influencing factors, this can be understood to be at least one of the following factors. · Environmental parameters (e.g., temperature, humidity), · Rheological properties of the fluid, · Disturbance variables (e.g., particles, air bubbles in the liquid), · Shape or dimensions of the membrane actuator, · Material parameters (e.g., Young's modulus, piezoelectric coefficient d31), · Operating parameters (e.g., control signal).

[0028] In the context of the present disclosure, when embodiments are described using examples of piezoelectrically actuated membrane actuators, the corresponding explanations are also applicable to electrostatically actuated membrane actuators, and vice versa.

[0029] In the context of the present disclosure, when embodiments are described using examples of micropumps, the corresponding explanations are also applicable to microvalves, and vice versa.

[0030] If the nomenclature used herein deviates, the following applies. · d = CE* · I d = I CE = I ce · τ h = τ p · I A = I a · τ A = τ a

[0031] First, purely schematically, FIG. 1 shows a cross-section of the microfluidic component 1000 of the present invention having a membrane actuator [0000636] 100. The membrane actuator 100 comprises a membrane element 101 and an actuator element 102. The actuator element 102 is used to deflect the membrane element 101.

[0032] In addition, the microfluidic component 1000 of the present invention comprises a signal generating device 103, which is configured to generate an electrical control signal 104 having a time-varying signal curve for controlling a membrane actuator 100, thereby causing the actuator element 102 to actuate the membrane element 101. In this non-limiting example, the signal generating device 103 generates an AC voltage signal U(t). Similarly, corresponding quantities derived therefrom, such as a corresponding AC current signal I(t), may be considered.

[0033] Furthermore, the microfluidic component 1000 of the present invention includes a signal processing device 105. This device is configured to determine the effect of one or more external influencing factors on the temporal signal curve of the control signal 104 during the operation of the microfluidic component 1000, and to identify and / or classify at least one causative external influencing factor based on this effect on the temporal signal curve.

[0034] Therefore, this external influencing factor has an effect on the temporal signal curve of the control signal 104. In this case, different external influencing factors cause different deviations in the temporal signal curve of the control signal 104. Some influencing factors cause characteristic deviations in the temporal signal curve that correspond to traces; that is, different external influencing factors leave different traces in the temporal signal curve of the control signal 104.

[0035] Several examples of external influencing factors and their detection or identification are given below. In addition, a specific explanation of how deviations in the temporal signal curve of the control signal 104 are determined is given below. It should be noted in advance that, at this point, the signal analysis of the control signal 104 can be performed by the present invention, taking into account different signal segments. In the context of this disclosure, these signal segments are also referred to as “current terms.” These individual current terms are based on different external influencing factors that can be identified and / or classified using the concepts of the present invention.

[0036] However, before going into detail, an overview of the physical processes in different membrane actuators 100, such as piezoelectric or electrostatic membrane actuators 100, is given to better understand the concept of signal analysis of the control signals 104 described herein. For this purpose, Figure 1 is again referred to.

[0037] Figure 1 shows that the actuator element 102 functionally interacts with the membrane element 101, i.e., the membrane element 101 can be deflected by the actuator element 102. This causes a volume displacement V below the membrane element 101. The actuator element 102 and the membrane element 101 are electrically controlled using a signal generating device 103. For example, a drive voltage U can be applied to the actuator element 102, while the member element 101 can be in contact with ground potential U0. In this case, a charge Q flows onto the actuator element 103.

[0038] The membrane element 101, when combined with the drive element 102, essentially possesses the characteristics of variable capacitance. In the case of a liquid, liquid pressure acts on the membrane element 101, or in the case of a gas, air pressure acts on the membrane element 101, which takes the form of pressure p. Furthermore, mechanical forces, such as those caused by the mechanical component 106, may act on the membrane element 101. In addition, surface forces may act on the membrane element 101, for example, in the form of a meniscus. A reference pressure p0, often atmospheric pressure, or a pressure set above the membrane element 101, acts above the actuator element 102.

[0039] The fluid below the membrane element 101, such as a gas or liquid, is pressurized at a pressure p. Pressure p is time-dependent and determined by the interaction between the membrane actuator 100 and the fluid. In a change of state, which is slower than the signal propagation within the membrane element 101 and in the fluid (determined by the speed of sound), the pressure or pressure distribution adapts so that the membrane element 101 is in a state of force equilibrium (action-reaction).

[0040] However, if the pressure p applied below the membrane element 101 differs from the reference pressure p0 above the membrane element 101, then air pressure (if the fluid is a gas) or liquid pressure (if the fluid is a liquid) is applied to the membrane element 101 via the membrane surface. Here, neither the fluid pressure p below the membrane element 101 nor the reference pressure p0 above the membrane element 101 needs to be constant and uniform.

[0041] Therefore, the membrane actuator 101 may have pneumatically or hydraulically driven interactions, that is, there may be pneumatic or hydraulic external influencing factors acting on the membrane actuator 100. As described above, when the mechanical component 106 applies a mechanical force to the membrane actuator 100, mechanical external influencing factors act on the membrane actuator 100 accordingly.

[0042] According to one embodiment of the present invention, the signal processing device 105 may be configured to identify and / or classify external influencing factors that affect the temporal signal curve, such as liquid pressure, air pressure, or mechanical force acting on at least one membrane side of the membrane element 101.

[0043] In some embodiments, the actuator element 102 may include a piezoelectric ceramic, which will be described in detail later. In this case, piezoelectric external influences may also act on the membrane actuator 100.

[0044] In this case, according to the present invention, the signal processing device 105 may be configured to identify and / or classify piezoelectric forces acting on at least one membrane side of the membrane element 101 as external influencing factors that affect the temporal signal curve.

[0045] In all cases, according to the present invention, time-dependent measurements of potentially overlapping pneumatic, hydraulic, piezoelectric, or mechanical interrelationships can be performed using time-accurate electrical measurements of the control signal 104 (e.g., a voltage signal U(t) or a current signal I(t), and the associated quantities thereof). That is, in the invention described herein, it is possible to detect different external influencing factors acting on the membrane actuator 100, such as pneumatic, hydraulic, piezoelectric, and / or mechanical external influencing factors, using signal analysis of the control signal 104. In addition, the detected influencing factors can be identified and / or classified.

[0046] Depending on the embodiment of the membrane actuator 100, the detectable or identifiable external influencing factors may differ slightly. Figure 2 shows a possible embodiment of the membrane actuator 100 of the present invention.

[0047] The left side of Figure 2 shows the piezoelectric membrane actuator 100. In this case, the actuator element 102 may include a piezoelectric ceramic or piezoelectric element fixed to the membrane element 101 using, for example, a suitable adhesive. As soon as the control signal 104 is applied to the piezoelectric ceramic, the piezoelectric ceramic deforms and acts on the membrane element 101.

[0048] For example, the piezoelectric membrane actuator 100 of the present invention can be used in a piezoelectric-driven micropump. That is, in this case, the microfluidic component 1000 of the present invention will include a piezoelectric-driven micropump. In this case, the piezoelectric membrane actuator 100 is used as a pump element, i.e., the raising and lowering of the membrane actuator 100 can perform pump strokes, i.e., suction strokes and pressure strokes. In this case, the piezoelectric-driven micropump may be configured as a three-chamber membrane pump with an active valve, or it may include a passive check valve.

[0049] Alternatively, for example, the piezoelectric membrane actuator 100 of the present invention may be used in a piezoelectric-driven microvalve. That is, in this case, the microfluidic component 100 of the present invention would comprise a piezoelectric-driven microvalve. In this case, the piezoelectric membrane actuator 100 is used as a means for operation, i.e., the microvalve can be opened and closed by raising and lowering the membrane actuator 100. The piezoelectric-driven microvalve may be configured as an NO valve (NO = normally open) or an NC valve (NC = normally closed).

[0050] The right side of Figure 2 illustrates an electrostatic membrane actuator 100. In this case, the membrane element 101 forms a movable electrode, and the actuator element 102 forms a counter electrode. In this case, the actuator element 102 and the membrane element 101 interact capacitively, which will be explained in more detail below. As soon as the control signal 104 is applied to the actuator element 101 (counter electrode), it attracts or pushes the membrane element 101 (electrode), generating an up-and-down motion of the membrane element 101.

[0051] For example, the electrostatic membrane actuator 100 of the present invention can be used in an electrostatically driven micropump. That is, in this case, the microfluidic component 1000 of the present invention will comprise an electrostatically driven micropump. In this case, the electrostatic membrane actuator 100 is used as a pump element, i.e., by raising and lowering the membrane actuator 100, pump strokes, which are suction strokes and pressure strokes, can be performed. Here, the electrostatically driven micropump may also be configured as a three-chamber membrane pump with an active valve, or it may comprise a passive check valve.

[0052] Alternatively, for example, the electrostatic membrane actuator 100 of the present invention may be used in an electrostatically driven microvalve. That is, in this case, the microfluidic component 1000 of the present invention would comprise an electrostatically driven microvalve. In this case, the electrostatic membrane actuator 100 is used as a means for operation, i.e., the microvalve can be opened or closed by raising or lowering the membrane actuator 100, respectively. The electrostatically driven microvalve may be configured as an NO valve (NO = normally open) or an NC valve (NC = normally closed).

[0053] As initially stated, the concept of the present invention provides measurement and / or signal evaluation of a control signal 104, enabling the determination of one or more external influencing factors acting on the membrane actuator 100 or microfluidic component 1000. This will be described in more detail below using exemplary embodiments of the piezoelectric or electrostatic driven membrane actuator 100 of the present invention.

[0054] 1. Piezoelectric driven membrane actuator 1.1 Piezoelectric-driven micropump The piezoelectric-driven micropump 1000 is typically controlled by a periodic control signal 104, such as a sinusoidal AC voltage or AC current signal. In this case, the membrane actuator 100 alternately moves upward (suction stroke) and downward (pressure stroke).

[0055] The control signal 104 is not irrelevant to how the membrane actuator 100 interacts with its environment. Each external influencing factor acting on the membrane actuator 100 (e.g., hydraulic, pneumatic, piezoelectric, mechanical factors) changes the mechanical properties of the membrane actuator 100 (e.g., its voltage state or position), which in turn changes the electrical parameters of the membrane actuator 100. Consequently, this directly affects the current flow. Since the external influencing factors change over time, the interaction process, or which external influencing factor is dominant, can be inferred using the signal analysis of the control signal 104 according to this invention.

[0056] Therefore, for example, a pressure change beneath the piezoelectric membrane actuator 100 causes a force to act, which in turn causes a current flow in the piezoelectric membrane actuator 100 due to the direct piezoelectric effect caused solely by the pressure change. In this disclosure, this current flow is also referred to as the “sensor current.” This sensor current overlaps with the current required to electrically charge a capacitive load.

[0057] In the case of the piezoelectric-driven micropump 1000, this sensor current is precisely determined using the signal analysis of the present invention, and other current components (in particular, the charge current I of the capacitance of the piezoelectric element) are also considered. c ) can be distinguished from this. Through this, for example, changes in pump chamber pressure can be measured indirectly with respect to time. The temporal change in pump chamber pressure is determined, among other things, by the fluid process in the fluid chamber. However, if the interrelationships regarding the reasons for the temporal change in pump chamber pressure are known, the process in the pump chamber can be measured by the signal analysis of the sensor current according to the present invention. However, even if the interrelationships are not fully known in detail, the fluid or mechanical processes in the pump chamber can be inferred, for example, by using so-called "machine learning".

[0058] At this point, it is clearly emphasized again that the concept of the present invention requires no additional sensor technology or additional electrodes. In addition, the membrane actuator 100 does not need to be controlled in a special way, for example, in a special calibration mode. It is sufficient to control it in the usual way using the control signal 104; that is, the concept of the present invention can be performed during the normal operation of the microfluidic component 1000. The time-dependent fluidic and mechanical processes in the microfluidic component 1000 can be measured using only precise measurement or signal analysis of the control signal 104 (e.g., charging current I(t)) on the membrane actuator 100, which is necessary anyway to operate the membrane actuator 100. This is very advantageous because the concept of the present invention described herein does not require any modification of the microfluidic component 1000 itself. For example, in relation to suitable means for data acquisition and data evaluation, it is sufficient to simply incorporate the signal analysis of the present invention into the driver electronics of the microfluidic component 1000.

[0059] As mentioned earlier, piezoelectric micropumps are typically controlled by periodic signals; therefore, the concept of the present invention, in addition to this, enables continuous time-resolved measurement of the suction and pressure strokes during the operation of the micropump. When changes in sensor current are measured in comparison to previous signal curves, changes in operation can be immediately inferred. In other words, the micropump continuously and persistently self-monitors itself during operation, which represents a significant improvement over the prior art.

[0060] 1.2 Piezoelectric Microvalve Similarly, in the case of the piezoelectric-driven microvalve 1000, there is a correlation between the air pressure and fluid pressure and the intrinsic voltage of the membrane actuator 100. These time-dependent pressure changes can be measured using a sensor current (in the same way as in the piezoelectric-driven micropump), allowing for the extraction of fluid information from the valve chamber or the movement of the valve. In addition, there is an embodiment in which the drive membrane element 101 (which may be reinforced in the center with a rod-shaped bulge) mechanically contacts the valve seat when the valve closes. This mechanical contact and the interruption of mechanical movement at high drive voltages result in a time-dependent current flow that can be detected as a sensor current according to the interruption back pressure in the micropump described earlier, through the piezoelectric effect.

[0061] In this case, it should be noted that if the valve is not attenuated (e.g., in the case of a gas valve), this contact occurs within a very short time in the case of a hardened seal of the valve, which is associated with a corresponding high stress and therefore results in a large but short transient signal of the sensor current. If the valve movement is more strongly attenuated (e.g., in the case of a liquid), the stress peak becomes smaller, and the sensor current signal is also smaller and more dispersed over time. This interrelationship can be derived directly or trained using machine learning methods.

[0062] In principle, the concept of the present invention can be used not only to detect valve closure but also to detect what medium (e.g., air or liquid) is present in the valve chamber. In addition, a time allocation of closure time in relation to the operating cycle can be used to describe its changes, and changes in environmental parameters such as a decrease in actuator bias, swelling of the soft valve seat, or pressure above the membrane element, or temperature can be detected. Furthermore, overdriving the membrane actuator 100 when the valve only operates to this detectable contact point, and thus avoiding excessive mechanical stress on the piezoelectric membrane actuator 100, can be avoided.

[0063] The following Table 1 outlines the external influencing factors that may act on an active piezoelectric microvalve and that can be identified and / or classified using the concepts of the present invention.

[0064] [Table 1]

[0065] 2. Electrostatic membrane actuator 2.1 Electrostatic-driven micropump In the case of the electrostatically driven membrane actuator 100 (Figure 2 - right side), compared to piezoelectric drive, there is no electromechanical coupling in the sense that the pressure change within the actuator element 102 itself causes an additional current flow. However, there is an analog dependency of the current flow on the membrane actuator 100, which depends on the mechanical and fluid responses.

[0066] First of all, this capacitance C depends on the mechanical position of the membrane actuator 100. For example, let's assume that the membrane element 101 is relaxed and flat at the start of the suction stroke and includes an initial capacitance C0. If a control signal (e.g., voltage U) is applied quickly, for example with a time constant τ = R·C0, C0 = 100pF = 1e-10F, and R = 10kΩ, then τ = 1μs. During this short time period, it is assumed that the membrane actuator 100 has not yet moved due to the inertia and friction of the materials involved (membrane, fluid).

[0067] Next, when the membrane 101 moves toward the counter electrode (actuator element 102) due to the interaction between the electrostatic attraction at the applied voltage U0 and the pump chamber pressure p, the capacitance increases. As a result, an additional current flow is induced on the counter electrode (actuator element 102), which is purely correlated with the movement of the membrane element 101, and then also depends on the pump chamber pressure p.

[0068] The interaction between the pump chamber pressure p (i.e., after the application of the control signal) and the membrane element 101 of the electrostatically driven micropump during its basic movement and suction stroke is as follows: Firstly, the restoring hook force acts in the opposite direction to the deflection force, and the deflection force of the membrane element 101 increases nonlinearly due to the change in capacitance. When the membrane element is deflected by about 1 / 3 in the direction of the counter electrode 102, the membrane element 102 "snaps," causing it to move very quickly in the direction of the counter electrode 102.

[0069] The movement of this membrane stops when the membrane element 102 comes into contact with the counter electrode 102. First, there is contact at the center of the membrane, and then the membrane element 101 nests with the counter electrode 102. After contact and nesting, the increase in capacitance slows down. The counter electrode 102 generates a strong inherent counter force until the membrane element 101 eventually reaches a state of force equilibrium and stops.

[0070] Throughout this movement of the membrane element 101, the capacitance C(t) at the applied voltage U0 increases, causing a corresponding current flow. If this current flow is measured in a time-resolved manner by the signal processing device 105 of the present invention, the time-resolved position of the membrane element 101 can be directly inferred (if the relationship between position and capacitance is known).

[0071] In contrast to piezoelectric drive, electrostatic drive is dominated by this "sensor current," so the signal generating device 103 and the signal processing device 105 face considerably lower requirements.

[0072] Furthermore, there is a difference between whether the pump chamber contains air or liquid. In the first case, since air has a significantly smaller mass than water, the membrane elements can move faster. This makes it possible to detect whether the electrostatically driven micropump contains air or liquid; that is, the state of aggregation of the fluid placed within the microfluidic component (micropump) 1000 can be detected.

[0073] During the attraction stroke, the control signal 104, for example, the voltage, is turned off and short-circuited, i.e., U=0V. The charge dissipates with a very short (electrical) time constant, and the voltage is no longer applied after this time. If there is a rapid voltage change, this time constant τ D0 The capacitance C at the start of the pressure stroke is along with the charge resistance R. D0 It depends on, that is, τ D0 =R·C D0 This is within the range of microseconds. Within this short time period, the liquid is still unable to flow through the valve.

[0074] However, as the membrane element 101 moves, the capacitance changes, but there is no longer any current flow when the membrane element 101 moves in a pressure stroke (due to U=0V), and therefore the movement can no longer be measured by the sensor current.

[0075] Alternatively, instead of completely turning off the voltage in the pressure stroke, it would be possible to reduce it to a small value U1, for example, U1 = 0.01 U0 or even lower. This would make almost the entire stroke available, but there would still be available charge after the membrane actuator 100 is turned off, as well as residual current flow due to the movement of the membrane actuator 100.

[0076] The membrane element 101 is released from the counter electrode 102 and returns to its initial state. During this movement, the capacitance decreases, and a reverse current flow occurs.

[0077] 2.2 Electrostatically driven micro valve Similar to the piezoelectric microvalve described above, the movement of the membrane actuator 100 and its contact with the valve seat can also be detected when the electrostatically driven microvalve is closed. See section 1.2 for a more detailed explanation.

[0078] In summary, it should be noted that the embodiments may provide a piezoelectric membrane actuator 100 or an electrostatic membrane actuator 100. The piezoelectric membrane actuator 100 may be used in a piezoelectric microfluidic component 1000, such as a piezoelectric microfluidic pump or a piezoelectric microfluidic valve. The electrostatic membrane actuator 1000 may be used in an electrostatic microfluidic component 1000, such as an electrostatic microfluidic pump or an electrostatic microfluidic valve.

[0079] In all these embodiments, for example, the signal processing device 104 may be configured to determine the type of fluid used in the microfluidic component 1000 based on the temporal signal curve of the control signal 104. Alternatively, or in addition, the signal processing device 105 may be configured to perform a distinction of the fluid's coagulation state based on the temporal signal curve of the control signal 104, i.e., to distinguish whether the fluid is a gas or a liquid. Depending on the type of fluid or the fluid's coagulation state, the mechanical properties of the membrane actuator 100 may change, i.e., the type of fluid and / or its coagulation state may be described as external influencing factors that can be identified and / or classified using the concepts of the present invention.

[0080] In an embodiment in which the microfluidic component 1000 comprises a microfluidic pump having a pump chamber, at least one membrane side of the membrane element 101 is in contact with the fluid placed in the pump chamber, and a variable pump chamber pressure is generated in the pump chamber by acting on the membrane actuator 100. In such an embodiment, the signal processing device 105 may be configured to determine the variable pump chamber pressure based on the temporal signal curve of the control signal 104 and to use this to identify and / or classify external influencing factors. That is, the pump chamber pressure causes a signal portion that results in a temporal deviation of the control signal 104. The pump chamber pressure then undergoes a characteristic change due to the presence of the external influencing factor. Thus, a deviation in the pump chamber pressure causes a change in the temporal signal curve of the control signal 104, and by extension, the external influencing factor causing it can be determined. In summary, this means that an external influencing factor changes the pump chamber pressure, and then the pump chamber pressure causes a change in the temporal signal curve of the control signal 104, and by extension, the external influencing factor can be determined. This means that the external influencing factor can be determined based on a deviation in the pump chamber pressure. Next, this will be explained in detail based on specific examples.

[0081] In an embodiment in which the microfluidic component 1000 includes a microfluidic valve that enables opening and closing of a fluid path, the signal processing device 105 may be configured to identify and / or classify external influencing factors based on the temporal signal curve of the control signal 104 and to use these to determine the time-varying operating parameters of the microfluidic valve.

[0082] At this point, it should be noted that the external influencing factors are those that cause deviations in the temporal signal curve of the control signal 104.

[0083] As a mere example, the first three rows of Table 1 are explained for this purpose.

[0084] Line 1: A change in the valve state, such as contact or release of the sealing element, causes a characteristic change in the temporal signal curve of the control signal 104. This change in the temporal signal curve makes it possible to determine the corresponding operating parameter, such as whether the valve is open or closed.

[0085] Second line: Depending on the viscosity of the fluid used, the damping behavior changes when the valve is opened or closed, resulting in a characteristic change in the temporal signal curve of the control signal 104. This change in the temporal signal curve makes it possible to determine the corresponding operating parameters, such as the presence of bubbles or condensate, spontaneous aspiration, or false aspiration.

[0086] Third line: For example, when opening a valve, an additional force may act on the actuator (so-called sticking), or the mechanical movement of the valve may be hindered by an additional force, for example, if a particle prevents the valve from closing completely. This results in a temporally distinct charge and discharge curve, i.e., a characteristic change in the temporal signal curve of the control signal 104. This change in the temporal signal curve makes it possible to determine the corresponding operating parameter, for example, whether the particle is blocking or preventing the valve seat from opening due to sticking.

[0087] 3. Measurement of sensor current Next, the concept of the present invention will be explained using the example of a piezoelectric actuated membrane actuator 100. However, referring to the above description, the examples described herein are also applicable to an electrostatic actuated membrane actuator 100.

[0088] Firstly, measuring the current flow to or from the piezoelectric ceramic (actuator element) 102 without affecting the charging and discharging currents is a significant challenge. Furthermore, the measurement circuit (signal processing device) 105 must possess high signal quality to accurately measure weak currents. The entire circuit concept must be achievable with the highest possible space and cost efficiency.

[0089] Figure 3 shows a schematic diagram of an embodiment that includes a signal processing device 105 configured to determine the temporal progression of the control signal 104.

[0090] Figure 3 shows the piezoelectric (or electrostatic) driven membrane actuator 100 described above. In the case of the piezoelectric driven membrane actuator 100, the actuator element 102 may be a piezoelectric ceramic. The actuator element 102 may be configured to be arranged on a carrier substrate 107. In some embodiments, the membrane element 101 may be configured as the carrier substrate on which the actuator element 102 is arranged. For example, the carrier substrate 107, or the membrane element 101 and the actuator element 102 may be connected by adhesive bonding techniques.

[0091] The control electrode is located on the top side of the actuator element 102, and the ground electrode, which is electrically short-circuited by point contact with the carrier substrate 107, or the membrane element 101, is located on the bottom side.

[0092] The actuator element 102 is connected to a signal generating device (control electronic circuit) 103. The signal generating device 103 generates a control signal 104. The carrier substrate 107, or the membrane element 101, is connected to a signal processing device (measurement circuit) 105, which is configured to detect the combined charge current 108 dissipating from the carrier substrate 107, or the membrane element 101.

[0093] In this embodiment, the signal generation device 103 includes a voltage source that generates the control signal 104 in the form of an AC voltage signal. Alternatively, the signal generation device 103 may include a current source that generates the control signal 104 in the form of an AC signal.

[0094] Supply voltage V of voltage source 103 inThe output of the voltage source 103 is connected to the control electrode of the piezoelectric ceramic 102. The ground output of the voltage source 103 is connected to the ground of the measurement circuit 105. The ground electrode of the piezoelectric ceramic 102 (or the conductive surface of the carrier substrate 107) is connected to the inverting input of the operational amplifier 109. The non-inverting input is connected to the ground potential of the signal generating device 103. The circuit illustrated here generates a virtual ground potential at the inverting input of the operational amplifier 109. The control signal 104, i.e., the current flow I flowing into the piezoelectric ceramic 102, can be determined by the measuring resistor 110. This current flow I consists of at least two signal components, namely the charging current I C and sensor current I S Including I C +I S The formula =I=U / R is applied.

[0095] By using the voltage-controlled operational amplifier 109, the entire current flow I to or from the capacitive element (membrane actuator) 100 can be measured without loading the current source (signal generation device) 103 through the measurement circuit (signal processing device) 105. As a result, the terminal voltage Uk or V at the output of the measurement circuit 105 can be measured. out For this, we get Uk = -R*I.

[0096] The sensor current I shown in Figure 3 is illustrated. S and charging current I C For high-resolution measurements, correspondingly high amplification is used. However, in the case of large currents, especially when using a rectangular signal, this causes overdrive of the measurement circuit 105. For this reason, two proposed solutions (active and passive) to ensure the quality and reliability of the measurement circuit (signal processing device) 105 are presented below.

[0097] In an active solution, the measurement circuit 105 can be extended by a digital potentiometer. Thus, depending on the application and the control signal 104, the amplification of the measurement circuit 105 can be ideally adjusted. In addition, variable amplification is used to amplify the characteristic signal portion depending on the application. When using the corresponding high-speed logic circuit, a large charging current I C and a small sensor current I S can be measured.

[0098] Alternatively, FIG. 4 shows a passive solution that may use a fixed resistor and diode network. The maximum current is limited via two zener diodes 111, 112. The leakage current via the zener diodes 111, 112 is reduced by additional low-leakage current diodes 113, 114, resulting in improved signal quality. The maximum value of the terminal voltage Uk is determined by the zener voltage UZ and the forward voltages of the low-leak diodes UF,D and the zener diodes UF,ZD, and Uk < UZ + UF,ZD + UF,D = 4.3V + 0.7V + 0.5V = 5.5V. Among other things, the advantages of the passive solution are that it is very compact, highly reliable, simple, and easy to integrate into an ASIC.

[0099] Furthermore, the signal processing device 105 may include a measurement resistor 110 connected between the inverting input and the output of the operational amplifier 109. Then, the voltage of the control signal 104 drops across both ends of this measurement resistor 110, and the signal processing device 105 processes this.

[0100] The control signal 104 has different temporal signal curves depending on whether external influencing factors are acting on the microfluidic element 1000. In addition, the temporal signal curve of the control signal 104 varies depending on the dominant external influencing factor. That is, when there are no external influencing factors, the control signal 104 has a first temporal signal curve. However, when there are one or more external influencing factors, the control signal 104 is affected by them, and the affected control signal 104 includes a different second temporal signal curve.

[0101] In an embodiment of the present invention, the signal processing device 105 is configured to distinguish, accordingly, a first temporal signal curve of the control signal 104 corresponding to the operation of the membrane actuator 100 that is not affected by external influences from a different second temporal signal curve of the control signal 104 corresponding to the operation of the membrane actuator 100 under the influence of at least one external influencing factor.

[0102] 4. Qualitative consideration of external influencing factors and corresponding interrelationships of piezoelectric membrane actuators Next, in the section on sensor current I S However, a model is developed to represent the fluid interactions of the piezoelectric-driven micropump 1000. This model is simplified as follows, for example. Ignoring hysteresis • Ignoring piezoelectric creep • Assumptions for linear valve characteristic curves • Separation and superposition of fluid and electrical models • Linearization of changes in bubble state • Description of elasticity by simplifying Kirchhoff plate theory

[0103] These simplifications mean that this model cannot accurately represent the entire reality in the sense of a “white-box” model (see Section 7.1 “White Box”). However, important interrelationships are explained, which greatly simplifies the invention. In some cases of disturbance detection, “machine learning” using neural networks may be used (see Section 7.2 “Black Box”), and the models described thereafter can significantly simplify machine learning (see Section 7.3 “Gray Box”). By using the models described thereafter, machine learning training may also be completely omitted in other cases.

[0104] 4.1 Electromechanical coupling of membrane actuators The following section is also exemplarily described here based on the piezoelectrically actuated membrane actuator 100, and it is clear that all descriptions are also applicable to the electrostatically actuated membrane actuator 100.

[0105] First, the electromechanical coupling (at small signals) can be described by the following linear approximation.

[0106] [Number]

[0107] Q: Charge on the piezoelectric ceramic, U: Voltage at the piezoelectric ceramic, C* E : Coupling coefficient, p: Pressure applied to the membrane actuator. The coupling coefficient C* E describes the change in charge when the pressure difference changes. C el represents the capacitance of the piezoelectric ceramic.

[0108] That is, the charge Q flowing through the membrane actuator 100 also depends on the time-varying control signal 104 (voltage U) and further on the time-varying pressure p (Figure 1) under the membrane element 101. The charge Q includes the first term C el ·(U - U0), which is linked to the potential difference (U - U0) of the capacitance C el and the time-varying voltage U. In addition, the charge Q includes the second term

[0109] [Number]

[0110] which includes the piezoelectric coupling coefficient

[0111] [Number]

[0112] It is also linked to a time-varying and / or position-varying pressure p, or the pressure difference (p-p0) between the bottom and top sides of the membrane element 101. Equation [1a] describes the direct piezoelectric effect of the membrane transducer, or membrane actuator 100.

[0113] The corresponding "twin equation" can be formulated as follows: The piezoelectric membrane transducer 100 undergoes a volumetric displacement V when there is a change in pressure difference (p-p0) or potential difference (U-U0) (or both). This volumetric displacement V can be calculated as follows:

[0114]

number

[0115] Coefficient C p This represents the "fluid capacity" of the membrane transducer, that is, how much the displacement volume changes when the pressure difference between the top and bottom of the membrane changes. Coupling coefficient C E * indicates how much the displacement volume changes when the potential difference (U-U0) changes.

[0116] Coupling coefficient C from equation [1b] E * represents the coupling coefficient C according to equation [1a]. E *It may be emphasized that it is identical to this.

[0117] Under the model requirements of Kirchhoff plate theory, the coefficient C of the circular piezoelectric membrane (e.g., PZT ceramic) and the circular carrier membrane (e.g., metal or silicone) p (Fluid capacity) and C E An analytical solution for calculating the coupling coefficient has been found (Source: https: / / www.sciencedirect.com / science / article / pii / S0924424710002311). Therefore, the fluid capacity C p This was analytically determined, and this is the radius R of the piezoelectric ceramic. p , thickness T of piezoelectric ceramicp , the radius R of the carrier membrane M , carrier membrane thickness T M It depends on the elastic modulus and Poisson's number of the two materials (piezoelectric ceramic and carrier substrate). p and C E The exact expression of the (long) analytical equation for * is found in the appendix of the paper by M. Herz, the lead author of the aforementioned publication.

[0118] Coupling coefficient C E * is calculated analytically (using the same derivation formula), (C p It depends on the parameters described above (similarly to the above).

[0119] Coupling coefficient C E *In addition to that, the piezoelectric charge constant d 31 It is important to note that this is proportional to the coefficient of change, which is usually indicated in the datasheets of piezoelectric ceramic manufacturers.

[0120] C E To illustrate this, the stroke volume ΔV of a micropump without back pressure (p=p0) driven between voltage levels of a negative voltage U- (e.g., U-=-40V, the membrane is deflected "upward", i.e., away from the bottom of the pump chamber) and a positive voltage U+ (e.g., U+=100V, the membrane is deflected downward) is calculated from Equation 1b as follows:

[0121]

number

[0122] This is coefficient C E *Helps illustrate the meaning of the coefficient C. E * represents the coefficient d 31 It is directly proportional to and is always a negative value because it is negative for physical reasons (when an electric field is applied to the piezoelectric ceramic in the polarization direction (z direction) by applying a voltage, the piezoelectric ceramic contracts laterally). This is why the stroke volume in equation [1c] is positive.

[0123] The two coupling equations are symmetric and are illustrated below.

[0124]

Number

[0125] This assumes that the electrical effect and the piezoelectric effect are linearly added. The current flowing on the piezoelectric membrane actuator 100 is the result of the time differentiation of [Equation 1].

[0126]

Number

[0127] In this time differentiation, it is considered (by the chain rule) that the capacitance C el and the coupling coefficient C E * are not constants but are time-dependent (due to the large-signal behavior of the piezoelectric ceramic).

[0128] In the case of microfluidic components 100 such as micropumps and microvalves, the control of the piezoelectric ceramic regularly occurs at a high voltage (or high electric field strength, the electric field strength E in the piezoelectric ceramic is related to the thickness T of the piezoelectric ceramic p where E=(U-U0) / T p corresponding), so the capacitance C el and the coupling coefficient C e * have voltage dependence. During control, since the voltage changes with time, the values of C el and C E * also change according to the large-signal behavior of the piezoelectric material. Here too, it should be noted that C E * is proportional to the piezoelectric charge constant d 31 that C E * increases at a large signal (at a high voltage), and C E * is about 1.5 times larger (depending on the piezoelectric material) at a high electric field strength than at a low electric field strength.

[0129] Assuming that the ground potential U0 is constant and the atmospheric pressure p0 is constant (within a small time interval), the situation can be simplified as follows:

[0130]

number

[0131] In the example of the present invention, current I represents the control signal 104. Since all four terms on the right-hand side of [Equation 2] are time-dependent, there are a total of four signal parts, and in the context of this disclosure, "sensor current" I U , I C , I ce , I p It can also be called sensor current I U , I C , I ce , I p When these are added together, the total current I, i.e., the control signal 104, is the four signal parts I U , I C , I ce , I p Includes.

[0132]

number

[0133] Each of these adductors is linked to the temporal change of a particular physical quantity. After a sudden rectangular disturbance, the electromechanical system loses equilibrium and then returns to equilibrium with a typical time constant. More precisely, the next physical subsystem loses equilibrium and then attempts to return to equilibrium. 1) Free charge on metallization of piezoelectric ceramic (I u section) 2) After the voltage changes, the domain size changes, and the goal is to achieve a new equilibrium state (I c and I ce (section) 3) The pump chamber pressure aims to reach a new equilibrium state which is essentially caused by the flow of the medium through the valve (I p section).

[0134] 4.2. Compensation process after a sudden voltage change is applied Following a rapid voltage change, there are two essential effects (see Figure 5). 1) Electrons flow through the capacitance C of the piezoelectric ceramic 102, and by this a. The electron is the time constant τ A =R*C el The flow is on the piezoelectric ceramic 102, b. Atoms "stretch infinitely fast," and this stretch is then transmitted at the speed of sound. i. Capacitance changes very quickly (this large-signal capacitance is greater than the capacitance at low voltages. This is why more charge flows to the piezoelectric ceramic 102). ii. Coefficient d 31 This also increases (behavior during high signal conditions).

[0135] 1) All changes under τ A =R* Cel The current will decay within a certain time, preventing any further current flow from occurring on the piezoelectric ceramic 102. 2) Increase in favorably positioned magnetic domains (Weiss regions) due to domain growth. These are slowed down by defects and grain boundaries. These changes occur on a significantly slow time scale τ. D This occurs. Due to domain growth, the polarized volume increases favorably. This effect, also known as piezoelectric creep, accounts for about a few percent of the total effect. This alters the following: a. Here again, capacitance, and b. coefficient d 31 It also becomes slightly larger.

[0136] From this consideration, the capacitance C and the coefficient d 31 (Piezoelectric lateral or transverse effect or d 31 It should be noted that the effect (where a mechanical force acts laterally relative to the applied field) initially changes very quickly with voltage U, and then changes more slowly with domain growth.

[0137] Figure 6 shows an example of the measurement of the voltage-dependent capacitance C(U) of the piezoelectric ceramic 102. The capacitance in the large-signal behavior (typical voltage range of a micropump or a microvalve) was measured as a function of the applied voltage. The capacitance has voltage dependence, that is

[0138] [Number]

[0139] it can be seen that it is. Since the voltage has time dependence, the term

[0140] [Number]

[0141] also has time dependence. This is the proof that the term

[0142] [Number]

[0143] is different from zero. In addition, a slight hysteresis of the capacitance can be determined.

[0144] In the case of a sudden application of a voltage change, each of the above physical subsystems tries to reach its respective state of equilibrium independently, which takes different times, that is, each physical subsystem decays as follows with a specific time constant τ. 1) The change of voltage dU / dt with time occurs exponentially with the time constant τA = R * C el 2) The change of pressure dp / dt with time occurs for reasons related to the fluid. Therefore, the associated current term or sensor current I p is, so to speak, the typical stroke time τ h and can be used to "look into" the pump chamber with a microscope. 3) The change of capacitance dC​el / dt is related to the piezoelectric effect. The ε of the piezoelectric ceramic 102 r and the capacitance are large for other reasons as well, but especially because there are a very large number of dipoles in the piezoelectric ceramic 102 after polarization occurs. The electrons and atoms in the piezoelectric structure can follow the perturbation at "infinite speed", but the propagation of the magnetic domain (Weiss region) occurs on a rather slow time scale τ C This so-called "piezoelectric creep" is known to reach time scales in the range of seconds. 4) Change in the coupling constant dC E * / dt is also related to the piezoelectric effect. This effect is (at least partially) similar to 3). a. The electrons and atoms in the piezoelectric ceramic 102 can follow the perturbation at "infinite speed", but the propagation of the magnetic domain (Weiss region) occurs on a slow time scale τ d This occurs at τ C which is very similar to (or the same as) τ. It is known from "piezoelectric creep" that this time scale can reach within the range of seconds. b. The coupling constant C E * is proportional to the piezoelectric coefficient d 31 which depends on the large signal, that is, in the case of a high voltage amplitude, the coefficient d 31 is about 1.5 times larger than in the case of a small voltage, and thus can affect the differentiation of C E *.

[0145] From the above description, the compensation process can then be described by [Equation 4] as follows.

[0146]

Number

[0147] 1) Compensation process of free charges:

[0148]

Number

[0149] 2) Pump chamber pressure compensation process (without squeeze diaphragm attenuation, with linearized valve characteristic curve)

[0150]

number

[0151] Fluid time constant τ h The total fluid flow resistance R fluidic (Definition of fluid resistance R: Δp = R * Q) and fluid capacity C fluidic (Definition of fluid capacity C: ΔV = C * Δp) is the product. Flow resistance R fluidic The fluid capacity C can be approximated as the sum of the flow resistance of the pump chamber and the flow resistance of the open and close valves, but the fluid capacity C fluidic The fluid capacity C of the drive membrane p and the (optional) fluid volume (C) of the bubbles gas The sum of these is formed by the flow resistance and fluid capacity, and these flow resistances and fluid capacities are not constant but change in value during the pumping process. For example, the flow resistance of an open flap valve is considerably smaller at the start of the pumping process when the valve is open than at the end of the pumping process when the valve is closed. Nevertheless, this compensation process can be approximated by [Equation 6].

[0152] Therefore, the transient current curve I measured by the characteristics of the "inherent sensor technology" invention of the microfluidic component 1000 described herein p This provides direct insights into fluid processes in microfluidic components, such as micropumps.

[0153] 3) Domain compensation process a) Coupling coefficients

[0154]

number

[0155] Regarding

[0156]

number

[0157] b) Capacitance C el Regarding

[0158]

number

[0159] Capacitance C el and coupling coefficients

[0160]

number

[0161] The compensation process has the same physical cause, namely domain growth. Therefore, these two compensation processes occur with approximately the same time constant. τ d =τ C =τ piezo [Formula 9]

[0162] Here, amplitude I C0 and I d0 These can differ. The result is as follows: a) Coupling coefficients

[0163]

number

[0164] Regarding

[0165]

number

[0166] b) Capacitance C of piezoelectric ceramics el Regarding

[0167]

number

[0168] Therefore, the final result of the approach for the total current I is as follows:

[0169]

number

[0170] Therefore, the result is a system with seven coefficients, namely four current amplitudes and three time constants. The electrical time constants are fast, while the two fluid time constants are slower. By fitting the coefficients, the electromechanical coupling can be fully incorporated during pump operation.

[0171] In interpreting sensor currents, it is essential that these physical compensation processes after a disturbance (e.g., voltage application) attempt to achieve equilibrium independently. This allows us to consider the current flow as a superposition of these effects according to [Equation 12] and "fit it." Thus, information regarding capacitance charging, processes within the piezoelectric ceramic, and fluid interactions can be determined through a single measurement process.

[0172] 4.3 Electromechanical coupling and compensation processes in control using harmonic AC voltage signals When a sinusoidal AC voltage signal is applied to a piezoelectric membrane actuator 100, such as a piezoelectric-driven micromembrane pump 1000, there is an overlap between the time constant, the described material effects, and the fluid-mechanical system response. This leads to distortion of the current signal.

[0173] Figure 7A shows the current and voltage curves for a sinusoidal controlled micromembrane pump. For comparison, Figure 7B shows the time-resolved current curve for capacitor control.

[0174] To visually illustrate the hysteresis effect, it is appropriate to plot the output signal (in this case, the measured current) against the input signal (AC voltage). The resulting figure (Lissajous curve) is illustrated in Figure 8 and provides information about the ratio of the output signal to the input signal. In an ideal capacitor, there is a 90° phase offset between voltage and current, resulting in a circular Lissajous curve 181. External influencing factors acting on the microfluidic component 1000 or membrane actuator 100 distort the current signal (Figure 7a), resulting in a Lissajous curve 182 (Figure 8) that deviates from the circular shape 181.

[0175] When there are different time constants and overlapping effects, the initial value problem yields a nonlinear differential equation that cannot be solved in a simple way. Methods from the field of machine learning (see Section 7, and also Figures 32 and 33) can be used to describe different system states (e.g., bubbles in a liquid agent, closure of fluid pathways) from the recorded current curve. With the help of function approximations from the field of classification, patterns can be reliably derived.

[0176] Figures 9A and 9B exemplify the Lissajous curves of a micropump dispensing a single unit of liquid using harmonic control, while being subjected to the disturbances of "bubbles" (Figure 9A) and "clotting" (Figure 9B). Both figures show repeating patterns.

[0177] By extracting significant features, the amount of data can be reduced to a minimum without losing important information. When this reduced data is provided to a classification algorithm, any measurement data not yet examined can be mapped to each state with a certain probability.

[0178] The confusion matrix shown in Figure 10 indicates the frequency of correct predictions for data not yet examined. A higher value on the main diagonal indicates a much better prediction of the state. The first row and first column each describe a normal state without external influencing factors. The second row and second column each describe a system state with obstruction. The second row and third column each describe a system state where the bubble is in front of the pump chamber (bubble_up). The fourth row and fourth column each describe a system state where the bubble is located inside the pump chamber (bubble_pump). The fifth row and fifth column each describe a system state where the bubble is located behind the pump chamber (bubble_down).

[0179] In addition to classifying states, machine learning tools also offer the possibility of regression on system size (e.g., inverse pressure).

[0180] Therefore, Figure 11 illustrates the voltage-current curves, or Lissajous curves, of a micropump at different reverse pressures. Here again, there are repeating patterns that can be appropriately extracted and classified.

[0181] 5. Experiments for examining the compensation process Subsequent experiments conducted by the inventors can be used to further explain the compensation process described above. These experiments were designed with the following parameters to avoid as many interference effects as possible. • Structural size is 5 x 5 mm 2 A silicon micropump (5x5 high-flow pump) is used, and this micropump has a pump chamber with a height of 20 μm. This is because the pressure drop within the pump chamber does not play a dominant role compared to the pressure drop at the microvalve. Therefore, a homogeneous pump chamber pressure p can be assumed as an acceptable approximation. • The micropump circulates between U=0V and U=U+V, and a stroke is considered where the voltage ends at U=0V. Through this, (when the voltage becomes zero)

[0182]

number

[0183] This section can be ignored. The starting voltage increases in steps from U=1V to U=100V. • The transient current data I(t) is measured. The measurement data is then fitted using an exponential function.

[0184] All measurements in this section use rectangular control, meaning the voltage change across the piezoelectric ceramic occurs abruptly at the start. The current flowing through the piezoelectric ceramic is measured, and the obtained current measurements are fitted using an exponential function.

[0185] Figure 12 shows the raw measurement data, i.e., transient measurement points for the current flowing through the piezoelectric ceramic. The plotted dots represent individual measurements, with the upper measurement 171 determined to be at a voltage of U=10V, the middle measurement 171 at a voltage of U=60V, and the lower measurement 173 at a voltage of U=120V.

[0186] When each measurement data is fitted with a simple exponential function, a significant deviation occurs in the measurement results. This can be seen from the solid curves 1711, 1721, and 1731. However, when a linear superposition of two exponential functions is fitted, it matches the measurement data very well, as can be seen from the dotted curves 1712, 1722, and 1732. These dotted curves coincide with the individual measurement points and are almost identical. This indicates that, according to the explanation above, two compensation processes are overlapping. Specifically, for each suction stroke and each pressure stroke, I p0 , τ h , I c0 +I d0 , τ piezo The following four fitting parameters can be collected from this measurement series.

[0187] 5.1 Sensor Current: Measurement and Interpretation of Amplitude The following describes the dependence of these four fitting parameters on the drive voltage for the suction stroke and pressure stroke.

[0188] Figure 13 shows the current amplitude I related to voltage during the suction stroke. p0 This shows a dependence on the current amplitude I. This is linked to the corresponding time constant, which will be explained later with reference to Figure 14. However, initially, the current amplitude I p0 The (y-axis) can be seen as a function of the voltage (x-axis) in Figure 13.

[0189] Notes. current amplitude I p0 From the pump chamber pressure compensation process (without squeeze diaphragm attenuation and with a linearized valve characteristic curve), the following equation is obtained according to [Equation 6].

[0190]

number

[0191] Here, the pump chamber pressure is as follows:

[0192]

number

[0193] There are others, but in particular, equation [13a] was published in 1994 (with different nomenclature) (https: / / iopscience.iop.org / article / 10.1088 / 0960-1317 / 4 / 4 / 004).

[0194] Equation [13a] is important because it describes (in an approximated linearized model) the time behavior of the pump chamber pressure when the pressure first rises after a (exponential) voltage change, and then decreases due to the flow through the microvalves (through current resistors REV and RAV, respectively).

[0195] Equation [13a] is a particular solution to a general differential equation with a disturbance element for voltage U(t).

[0196] This differential equation can be illustrated as follows:

[0197]

number

[0198] The parameters used are as follows: • p1 and p2: Used to calculate the pressure at the inlet (p1) and the back pressure at the outlet (p2) applied to the micropump. · C fluidic : Membrane capacity C p , (optional) bubble volume C gas , capacity of the inlet valve (C EV ) and the capacity of the outlet valve (C AV The fluid capacity as the sum of the last two fluid capacities (C). EV and C AV ) is usually very small and can be ignored in most cases. · R EV and R AV These represent the fluid resistances of two passive check valves. In reality, these are not constants and depend on pressure (especially when the pressure difference between the valves is very small). However, in the analytical solution of the differential equation, they are assumed to be constant. In particular, through this approximation step, the solution p(t) of equation [13b] approximates the real curve only in the sense of the gray-box model. For a certain stroke (e.g., a pressure stroke), the outlet valve opens and the inlet valve closes. Fluid conductance 1 / R AV is 1 / R EV It is significantly larger. Therefore, 1 / R EV The term can be ignored in the solution for p(t) of the pressure stroke (and vice versa for the suction stroke).

[0199] Equation [13a] describes a particular solution to the differential equation [13b] with the following initial conditions: 1) The initial condition is p(0)=0. 2) The disturbance element is the charge resistance R el capacitance C of piezoelectric ceramic across el The charge is as follows:

[0200]

number

[0201] In the case of control via a piezoelectric amplifier, this rise time τ a This can be within the range of a few microseconds, which is due to (among other reasons, but especially due to the higher electrical internal resistance of the battery) in autonomous battery-operated micropump control electronics. a This is within the range of =0.5ms. That is, a positive voltage is applied to the piezoelectric ceramic, which describes the pressure stroke (the membrane moves toward the bottom of the pump chamber, and the stroke volume is displaced through the discharge valve). 3) In the solution to equation [13a], the pressure and back pressure were set to zero, or to the reference pressure of atmospheric pressure p1=p2=p0. 4) The flow resistance of the inlet valve (which is closed during the pressure stroke) is ignored. The differential equation [13b] shows that when the sign of the pressure difference across the valve changes, the parameter 1 / R AV and 1 / R EV Please note that because the variables change rapidly, only a partial solution may be possible.

[0202] Further solutions to these differential equations can be determined using general primary pressure p1 and inverse pressure p2 as follows:

[0203] There is an exponential voltage rise (same as equation [13a]), where the solution for primary pressure p1 and reverse pressure p2 is as follows:

[0204]

number

[0205] The solution using sinusoidal voltage excitation is as follows:

[0206]

number

[0207] If the initial condition is p(0)=0, then the following applies:

[0208]

number

[0209] The voltage U(t) can be designed by the developer of the micropump system using a control signal and has a significant impact on the system's mechanical and fluid response. If the voltage change U(t) is fast enough to excite the natural vibrations of the valve (or even the membrane transducer), they may oscillate. If the occurrence of the voltage change is very slow (fluid time constant τ), h (much slower than), the fluid flows through the valve at a slow membrane velocity so quickly that significant pressure can no longer be generated within the pump chamber. This is especially true for harmonic excitation with a small angular frequency ω.

[0210] These time-dependent equations [13a], [13e], and [13g] are differentiated with respect to time, and C E *When multiplied, the "sensor current" I p This can be directly obtained, which, according to the present invention, provides an opportunity to electrically detect fluid or mechanical effects "connected" to the pump chamber pressure using data science methods and "machine learning".

[0211] In summary, with respect to the above equation [13a], there are other factors, but in particular the pump chamber pressure, and the time constant τ a It increases with time constant τ h Note that it decreases. Since the pressure amplitude p(t) is proportional to the voltage amplitude U-U0, the time derivative dp / dt is proportional to U-U0.

[0212] Sensor current I p0 Since it is proportional to dp / dt, we would assume that the amplitude increases linearly with the voltage. However, this increase is nonlinear. The nonlinear part can be explained as follows:

[0213] amplitude I p0 is the coupling coefficient

[0214]

number

[0215] This is proportional to the coefficient d of the piezoelectric ceramic. 31 It is proportional to d. Figure 13 clearly shows the nonlinear behavior of the voltage amplitude characteristic curve. This is because d 31 The nonlinear rise of is a clear indicator of what can be measured in this measurement. So far, the increase in the rise of the characteristic curve between very low voltages (small signal) and large voltages (large signal) corresponds to a coefficient of approximately 1.5, so the coefficient 1.5 corresponds to the coefficient d between the small signal and the large signal. 31 This was assumed as a practical value for the increase of [the variable].

[0216] The coefficient that correlates with the pressure amplitude in the pump chamber is amplitude I p0 It can be extracted from, i.e., the coefficient d 31 The measure of the large signal behavior can also be determined (through the variation in voltage amplitude). Therefore, the coefficient d 31 The large signal behavior can also be identified in the inventive concept disclosed herein.

[0217] 5.2 Sensor Current: Measurement and Interpretation of Time Constant Figure 14 shows the curve of the time constant τh during the suction stroke while the pressure in the pump chamber decreases. The time constant (y-axis) is plotted as a function of the applied voltage (x-axis).

[0218] For small voltage values, the noise is still too large, resulting in a time constant τ hIt is not possible to determine this precisely. According to the experiments described herein, the “high-flow pump” has a typical stroke time τ significantly less than 1 millisecond for air as the medium (the pump is designed in this way). h It has the following characteristics. In the case of a gas, this pump exhibits an increase in transport speed above 1 kHz, so the fluid time constant is less than 1 millisecond; i.e., an exponential fit with a time constant of less than 1 millisecond can be interpreted as the sensor current.

[0219] time constant τ h This increases with increasing voltage amplitude. This is not physically contradictory, as at higher voltages the pump membrane moves closer to the bottom of the pump chamber, increasing flow resistance and slowing down the outflow process.

[0220] According to the experiments described herein, the pressure drop at the pump chamber slit is still greater than the pressure drop at the microvalve (despite the pump chamber height being 20 μm). Therefore, as the voltage increases, the actuator moves closer to the pump chamber, increasing the slit resistance, and τ h It increases.

[0221] For comparison, Figure 15 shows the time constant with respect to the pressure stroke. Specifically, curve 181 corresponds to the curve showing the temporal evolution of the time constant of the suction stroke, which was previously explained with reference to Figure 14. Curve 182 shows the temporal evolution of the time constant with respect to the pressure stroke.

[0222] First of all, from Figure 15, it can be directly inferred that the time constant for the suction stroke (curve 181) is slightly larger than that for the pressure stroke, that is, the pressure drop for the suction stroke is longer than that for the pressure stroke. With respect to the pump chamber pressure from [Equation 13], this can be explained as follows.

[0223]

number

[0224] time constant τ h Regarding this, the result is as follows: τ h =R fluidic (C M +C gas ) [Formula 14]

[0225] Fluid capacity C of the drive membrane p and the fluid capacity of air C gas Since the sum of these values ​​is within the pump chamber, the typical stroke time is proportional to the fluid volume. In general, fluid volume describes the change in volume as a function of the change in pressure. In a driving membrane, the fluid volume is essentially constant in the context of Kirchhoff plate theory, but is always larger at lower absolute pressures (suction stroke) than at higher absolute pressures (pressure stroke) due to the phase change equation (isothermal, adiabatic, or polytropic depending on the rate of phase change).

[0226] However, the fluid capacity of air C gas This is not constant, but corresponds to the tangent line of the isothermal or adiabatic equation of state, and is proportional to the ineffective volume. • Pressure stroke: The remaining invalid volume after compression • Suction stroke: Remaining ineffective volume after expansion

[0227] In other words, the fluid capacity C of the bubble gas The stroke duration is greater in the suction stroke than in the pressure stroke, and therefore, the typical stroke duration in the suction stroke is greater than the pressure stroke when pumping a compressible medium such as air.

[0228] Incidentally, this also means that 50 / 50 is not the optimal duty factor when pumping compressible media such as air, and that the suction stroke may require more time than the pressure stroke.

[0229] 5.3 Extraction of flow resistance within microfluidic components (in the case of microfluidic valves or within the pump chamber of a micropump) The sensor current, which depends on the pressure beneath the membrane element 101, can be expressed as follows (consider [Equation 23]):

[0230]

number

[0231] Equation [15a] can be derived via the piezoelectric coupling coefficient k, where the square of k² is a measure of the ratio of the mechanical energy output to the electrical energy input.

[0232]

number

[0233] The following relation applies.

[0234]

number

[0235] Rearranging the expression, k 2 Solving for this, we get the following:

[0236]

number

[0237] Therefore, the sensor current I as a function of k p The following results are obtained.

[0238]

number

[0239] Therefore, equation [15a] and all subsequent equations can be expressed without k, and from equation [15a], the time constant τa Sensor current I after it has decreased p The following results are obtained.

[0240]

number

[0241] 5.3.1 Extraction of flow resistance in the case of an air pump If the microfluidic component 1000 is an air pump, i.e., a micropump configured to transport air, then the fluid resistance R fluidic This can be calculated as follows:

[0242]

number

[0243] In addition, the flow resistance R total This can be calculated from the amplitude as follows:

[0244]

number

[0245]

number

[0246] In other words, the flow resistance R fluidic These can be calculated independently.

[0247] 5.3.2 Extraction of further fluid parameters for the case of an air pump In addition, term dI p,0 / dU can be measured in small signals.

[0248]

number

[0249]

number

[0250] In addition, the time constant τ h is the current amplitude I p It can be calculated as follows:

[0251]

number

[0252] 5.4 Consideration of the fitted time constant: 1) Exponential function of pump chamber pressure change dp / dt 2) The exponential function with respect to the change in the coupling constant dd / dt.

[0253] The coupling constant d can change for different physical reasons. For one thing, the (static) amplitude of the coupling constant d can change at large signals; empirically, the amplitude increases by about 50% between small and large signals. This increase can be due, on the one hand, to domain growth (this effect exhibits a gradual relaxation), and on the other hand, to the nonlinearity of the atomic "interatomic force curves" (this effect does not exhibit a gradual relaxation).

[0254] coupling constant

[0255]

number

[0256] The temporal changes in this time constant are also linked to the "pulsation" of the domains. This time constant is defined by the obstacles encountered during domain growth propagation or contraction (as in the experiments above). This pulsation is purely a material property of the ceramic.

[0257] The piezoelectric creep mentioned earlier when applying a step function has the same cause. These time constants are slow, ranging from a few milliseconds to several seconds.

[0258] At small voltages, the entire exponential function appears to vanish. This can be explained, among other things, by the fact that pulsating domain growth hardly occurs at small signals. Furthermore, the coefficients...

[0259]

number

[0260] This is multiplied by the pump pressure p, which is also very small at low voltages.

[0261] 5.5 Amplitude Extraction and Discussion In addition to the time constant mentioned above, there are two signal components, namely the sensor current I p0 and I d0 The amplitude of this is also noteworthy.

[0262] I p The analytical formula for (t) is as follows:

[0263]

number

[0264] The following applies at t=0.

[0265]

number

[0266]

number

[0267] 5.6 Extraction and Consideration of Piezoelectric-Specific Fitting Parameters 5.6.1 Domain growth U*dC / dt + p*dd / dt, amplitude, and time constant for suction stroke and pressure stroke. Two signal parts or current term Ice and I C The compensation process (see [Equation 12]) is linked to the piezoelectric effect, or the piezoelectric material used. In this case, there are two parts: a) Coupling coefficients

[0268]

number

[0269] Regarding

[0270]

number

[0271] b) Capacitance C el Regarding

[0272]

number

[0273] 5.6.2 Consideration of Suction Stroke During the suction stroke, voltage U is turned off. This has the following effect: Since U=0, term I c It is zero. • The magnetic domain (Weiss region) shrinks, i.e., by coefficient d 31 It became smaller,

[0274]

number

[0275] It becomes negative. • In a suction stroke, p-p0 is less than zero, so this term is also negative. • Overall, I ce In the calculation, two negative coefficients are multiplied, i.e., I ce This is positive during the suction stroke.

[0276] The corresponding measurement sequences for the amplitude and time constant of the suction stroke are shown in Figures 16A and 16B. Figure 16A shows the piezoelectric-induced current term I as a function of voltage U (x-axis). ce Figure 16B shows the temporal change of the amplitude (y-axis). Figure 16B shows the piezoelectric time constant τ as a function of voltage U (x-axis). piezo This shows the time progression of the (y-axis).

[0277] Figure 16A shows the current term.

[0278]

number

[0279] This indicates that the value is positive. Since the pressure amplitude is correlated with the voltage amplitude, the amplitude increases with voltage. Coefficient d 31 ,or

[0280]

number

[0281] Due to the large signal effect, domain contraction is greater at high voltages than at low voltages. Time constant τ piezo Please note that this falls within a range of approximately 2 milliseconds.

[0282] 5.6.3 Consideration of Pressure Stroke The corresponding series of measurements for the amplitude and time constant of the pressure stroke are shown in Figures 17A and 17B. Figure 17A shows the current term I caused by piezoelectricity as a function of voltage U (x axis). ce The temporal evolution of the amplitude (y-axis) is also shown. Figure 17B then shows the piezoelectric time constant τ as a function of voltage U (x-axis). piezo This shows the time progression of the (y-axis).

[0283] In pressure stroke, item I ce =p-p0 is positive. The magnetic domain (Weiss region) expands.

[0284]

number

[0285] Therefore, the coefficient d 31 ,therefore

[0286]

number

[0287] It also increases, so it is positive. Current term or signal part I ce Therefore, it is true.

[0288]

number

[0289] The second piezoelectric-induced signal portion or current term I C The capacitance term (=capacitance term) is also positive because (U-U0) is positive, and the change in capacitance is also positive, and this is U c That is also a reason why it is correct.

[0290]

number

[0291] The amplitude measured in the pressure stroke is positive, but slightly smaller than that of the suction stroke. The time constant τ in the pressure stroke. piezo However, this is slightly smaller than the time constant in the suction stroke; in other words, domain growth works faster than domain contraction.

[0292] 5.6.4 Considerations at Low Voltage Figures 18A and 18B show corresponding series of amplitude and time constant measurements at low voltages. Figure 18a shows the current term I caused by piezoelectricity as a function of voltage U (x-axis) at low voltages in the pressure stroke. ceFigure 18B shows the temporal change of the amplitude (y-axis). Figure 18B shows the piezoelectric time constant τ as a function of the voltage U (x-axis) at low voltage during the suction stroke. piezo This shows the time progression of the (y-axis).

[0293] As can be seen here, at very small voltage amplitudes, there is no domain growth or hysteresis in either the pressure stroke or the suction stroke.

[0294] 5.7 Further Experiments In addition to the experiments described in Section 5, further experiments may be undertaken to verify and optimize the concept of the present invention.

[0295] For example, exposed piezoelectric ceramics can be measured. This is related to the pump chamber pressure term I. p The signal portion or current term I is completely lost, and therefore the signal portion or current term I is lost. c and I ce This would be the simplest way to find out if only the parameter I remains without the term dp / dt. ce0 , I c0 , τ piezo To investigate this, a piezoelectric membrane actuator 100 without a valve tip or valve flap can be examined. Then, dp / dt will always be approximately zero.

[0296] Alternatively, or in addition to the above, bonded and biased piezoelectric ceramics may be measured and compared to bare piezoelectric elements to determine how much the parameters change due to the bias.

[0297] Further experiments or measurements may be attempted, such as the following: 1) Start with U+ = 1 volt and increment the voltage U+ in small steps (therefore, ensure that the small signal is determined). 2) Consider the pressure stroke, i.e., from 0V to U+. This, in addition to that, results in the term U*dC / dt, i.e., amplitude I c0 Amplitude I ce0In addition to this, the time constant τ should be added, otherwise the other parameters will hardly change. Alternatively or in addition to this, piezo Instead, the individual time constant τ c and τ d Fitting is performed using these parameters τ, among others. c and τ d It can be confirmed that they are equal. 3) Different series resistors (or resistors) R110 that selectively lengthen the electrical time constant may be used to verify that they are not related. 4) In addition to the changes in reverse pressure (Section 6.3 and Figures 30A to 30E), the pressure may also be varied, for example, from -15 kPa to 0 kPa.

[0298] 6. Determination of external influencing factors based on temporal signal curves using examples of electrostatic and piezoelectric membrane actuators. In summary, the above explanation of the theory indicates that the control signal 104 (e.g., AC signal I(t)) contains a temporal signal curve that can be affected by external influencing factors. That is, the temporal signal curve deviates from the temporal signal curve in the absence of external influencing factors when those factors are present. Different influencing factors can cause different deviations in the temporal signal curve, corresponding to detectable characteristic traces in the temporal signal curve.

[0299] In the context of this disclosure, the temporal signal curve affected by external influencing factors is also referred to as the signal curve affected by the control signal 104. Therefore, the control signal 104 itself can also be referred to as the affected control signal 104.

[0300] The control signal 104 may include several signal portions or current terms, depending on whether the microfluidic component 1000 or the membrane actuator 100 is piezoelectrically driven or electrostatically driven.

[0301] 6.1 Electrostatic Membrane Actuator In the case of the electrostatic driven membrane actuator 100, for example, the sensor current may include two different current terms linked to the capacitance. The first signal portion or current term I U This describes the free charge on the actuator element 102. The second signal part or current term I C As mentioned above, this depends on the domain size. Second current term I C This is linked to the temporal change in capacitance between the membrane element 101 and the actuator element 102, caused by the movement of the membrane element 101. Therefore, in the electrostatic driven membrane actuator 100, the control signal 104 includes the following two signal parts or current terms.

[0302]

number

[0303] According to embodiments of the present invention, the signal processing device 105 processes at least two different signal portions I of the affected control signal 104. U , I C They are configured to distinguish each other from one another. • First signal portion I U This is linked to the time change in voltage when charging or discharging the capacitance between the membrane element 101 and the actuator element 102. • Second signal portion I C This is linked to the temporal change in capacitance between the membrane element 101 and the actuator element 102, caused by the movement of the membrane element 101.

[0304] In other words, the membrane actuator 100 may be an electrostatic driven membrane actuator (see Figure 2 - right side) in which the membrane element 101 forms a movable electrode and the actuator element 102 forms a counter electrode. The actuator element 102 and the membrane element 101 cooperate capacitively, and the control signal 104 causes a flow of charge on the counter electrode 102, thereby causing the membrane element 101 to move relative to the counter electrode 102. During this movement of the membrane element 101 relative to the counter electrode 102, there is a change in capacitance, and this capacitance affects the signal portion or current term I that consequently influences the temporal progression of the control signal 104. C Links to.

[0305] Therefore, the signal portion or current term I of the control signal 104 U and I C These have different causes, namely, individual signal parts or current term I U and I C There are different external influencing factors that can have different degrees of influence and therefore different degrees of influence on the temporal progression of the entire control signal 104. That is, individual signal parts or current term I U and I C This correlates with different external influencing factors that can have different effects on the temporal progression of the control signal 104.

[0306] Accordingly, according to embodiments of the present invention, the signal processing device 105 of the present invention is configured to perform a signal analysis of the temporal signal curve of the control signal 104 in order to determine and / or classify at least one causal external influencing factor, and individual signal portions I of the control signal 104 U , I C The individual signal portion or current term I is determined. U and I C This correlates with different external influencing factors that have different effects on the temporal progression of the control signal 104. Therefore, the signal processing device 105 is configured to assign a specific signal portion or current term IU, IC to a specific external influencing factor and use this to identify and / or classify each external influencing factor.

[0307] For example, in the signal processing device 105, as the membrane element 101 moves toward the actuator element 102, the capacitance between the membrane element 101 and the actuator element 102 changes over time, thereby affecting the signal portion I C Since the signal portion or current term I changes, C It may be configured to use this to determine membrane deflection or the temporal position of change of the membrane element 101.

[0308] That being said, the temporal position of the membrane element 101 also depends on the pressure acting on the membrane element 101 (see Figure 1). Therefore, according to an embodiment of the present invention, the signal processing device 105 processes the signal portion I C Based on this, the pressure acting on the membrane element 101 is determined and identified as an external influencing factor. That is, the pressure acting on the membrane element 101 is determined based on the signal analysis of the temporal transition of the control signal 104, in particular the signal portion or current term I C It can be determined based on the following.

[0309] Throughout the movement of the membrane element 101, the capacitance C el (t) increases with the applied voltage U0, causing a corresponding current flow. When this current flow is measured in a time-resolved manner using the signal processing device 105, the time-resolved position of the membrane element 104 can therefore be directly inferred (if there is a known relationship between position and capacitance).

[0310] In other words, the change in capacitance (= second signal part I) c Assuming the relationship between () and membrane deflection is known, deviations from the known signal curve can be interpreted as external influencing factors in the form of additional pressure.

[0311] In response to this, according to an embodiment of the present invention, the signal processing device 105 processes a second signal portion I that is free from external bending factors. C First, the temporal signal curve is known. The signal processing device 105 then processes the second signal portion IC In contrast to the known temporal progression of the second signal portion I caused by pressure C The system is configured to determine deviations in the temporal signal curve, determine the pressure acting on the membrane element 101 based on these deviations, and identify this as an external influencing factor.

[0312] 6.2 Piezoelectric Membrane Actuator The piezoelectric membrane actuator 100 has already been described in the theoretical section. Here, the actuator element 101 includes at least one piezoelectric element, for example, in the form of a bonded piezoelectric ceramic, which is functionally connected to the membrane element 101.

[0313] For example, when a control signal 104 is applied to the membrane actuator 100 or actuator element 102 in the form of an AC voltage signal, the actuator element 102 (e.g., piezoelectric ceramic) is deformed due to the inverse piezoelectric effect. As a result of this deformation of the actuator element 102, the membrane element 101 bulges upward or downward accordingly, i.e., the membrane element 101 is actuated or deflected through it. This allows a valve to open or close, or a micropump to perform a suction or pressure stroke.

[0314] Next, when an external influencing factor acts on the piezoelectric membrane actuator 100, for example, through particles, bubbles, or contact at the valve seat, this causes a force acting on the membrane element 101, which then applies a corresponding reaction force or inverse pressure (action = reaction). Through this, the piezoelectric actuator element 101 generates a corresponding current or voltage signal caused by the direct piezoelectric effect. This signal generated by the direct piezoelectric effect overlaps with the control signal 104 that causes the inverse piezoelectric effect. This overlapping signal caused by the external influencing factor then changes the temporal signal curve of the control signal 104. That is, the temporal transition of the control signal 104 is affected by the external influencing factor in such a way that there exists an affected control signal 104 in the sense of this disclosure.

[0315] In response to this, according to an embodiment of the present invention, the membrane actuator 100 is a piezoelectric-driven membrane actuator in which the actuator element 102 includes at least one piezoelectric element, and the control signal 104 causes deformation of the piezoelectric element by using the inverse piezoelectric effect, thereby causing the piezoelectric element to apply an operating force to the membrane element 101. The temporal signal curve of the control signal 104 is affected by the signal originating from the piezoelectric element, which is generated due to the reaction force of the membrane element 101 based on the direct piezoelectric effect.

[0316] For example, a pressure change beneath the piezoelectric membrane actuator 100 triggers a force, resulting in a current flow to the piezoelectric ceramic that is caused solely by the pressure change, due to the direct piezoelectric effect. In the context of this disclosure, this current flow is also referred to as the "sensor current."

[0317] In the piezoelectric membrane actuator 100, the sensor current may include the following four different signal parts or current terms (see Equation 4):

[0318]

number

[0319] Therefore, when compared to the electrostatic driven membrane actuator 100, the piezoelectric driven membrane actuator 100 has two further signal parts or current term I ce , I p Two signal parts or current terms I are added. U , I C This is essentially caused by capacitance and occurs in electrostatic and piezoelectric driven membrane actuators 100, whereas the two signal parts or current term I ce , I p This is essentially due to the piezoelectric effect and therefore occurs only in the piezoelectric membrane actuator 100. In the piezoelectric membrane actuator 100, the capacitance current term I C This relies at least partially on the piezoelectric effect, because the change in capacitance is caused by the large-signal effect of the piezoelectric ceramic (actuator element 102).

[0320] Here, this is again a separate part or current term I U , I C , I ce , I p However, this is the case when the control signal 104 is correlated with different external influencing factors that have different effects on its temporal progression.

[0321] Accordingly, according to embodiments of the present invention, the signal processing device 105 is configured to perform a signal analysis of the temporal signal curve of the control signal 104 in order to determine and / or classify at least one causal external influencing factor, and of the individual signal portions I of the control signal 104 U , I C , I ce , I p The individual signal portion I is determined. U , I C , I ce , I pThis correlates with different external influencing factors that have different effects on the temporal progression of the control signal 104. The signal processing device 105 is configured to assign specific signal portions to specific external influencing factors and to use them to identify and / or classify each external influencing factor.

[0322] According to one embodiment of the present invention, the signal processing device 105 therefore processes the four different signal portions or current term I of the affected control signal 104. U , I C , I ce , I p It may be configured to distinguish at least • First signal portion I U Essentially, this is linked to the time change in voltage when charging or discharging the capacitance between the membrane element 101 and the piezoelectric actuator element 102. • Second signal portion I P Essentially, this is linked to the temporal change in pressure acting on the membrane element 101, and the piezoelectric coupling coefficient.

[0323]

number

[0324] It is also proportional to, • Third signal portion I C Essentially, this is linked to the temporal change in capacitance caused by the large-signal effect of the piezoelectric actuator element 102. • Fourth signal portion I ce Essentially, this is the change in the piezoelectric coefficient d due to the large signal effect in the piezoelectric actuator element 102. 31 This is linked to the temporal change of the pressure acting on the membrane element 101, which is also proportional to the temporal change of the pressure acting on the membrane element 101.

[0325] As described above in the theoretical section, these individual signal parts I U , I C , I ce , I pEach of these aims to achieve equilibrium through an individual temporal compensation process, and in each temporal compensation process, each signal part is an individual temporal amplitude curve I U0 , I p0 , I c0 , I ce0 (See, for example, Figure 13) Furthermore, a separate time constant τ is used in which a time compensation process is performed. a , τ h , τ d =τ c =τ piezo (For example, see Figure 14.)

[0326] Accordingly, according to an embodiment of the present invention, the signal processing device 105 processes individual signal portions I U , I C , I ce , I p From one or more of these, each amplitude curve I U0 , I p0 , I c0 , I ce0 , and / or their respective time constants τ a , τ h , τ d =τ c =τ piezo It is configured to determine external impacts based on this.

[0327] 6.3 Evaluation and interpretation of measurements for determining external influencing factors The following sections describe the determination of external influencing factors using a specific series of measurements performed with the piezoelectric membrane actuator 100. From the above description, it is clear that these results are also valid for the electrostatic membrane actuator 100.

[0328] Figure 19 shows a schematic of the measurement station being used. It includes the microfluidic component 1000 of the present invention in the form of a micromembrane pump. The micromembrane pump 1000 includes the membrane actuator (not illustrated) 100 of the present invention, which has a membrane element 101 and an actuator element 102.

[0329] In the inlet reservoir 201, air and water may be selected as the medium or fluid to be transported. In a clockwise direction, two bubble detectors 202 and one pressure sensor 203 are configured to be positioned in front of (upstream of) the micropump 1000. Behind (downstream of) the micropump 1000, there are also two bubble detectors 202 and one pressure sensor 203. Through the illustrated conduit 204, the micropump 1000 transports the fluid into the outlet reservoir 205. Further sensors, such as a further pressure sensor 206 and a pressure controller 207 for monitoring and adjusting normalized atmospheric pressure, are configured to be positioned between the two reservoirs 201 and 205.

[0330] The entire logic is schematically illustrated in the center of Figure 19. This includes a signal generation device 103 and a signal processing device 105. The signal generation device 103 may include a function generator configured to generate different input functions (such as a step function, Dirac function, sine wave signal, or square signal) as control signals 104. Furthermore, this logic includes an oscilloscope 208 to determine the (affected) control signals 104. In addition, this logic may include a piezoelectric controller 209 for controlling and monitoring piezoelectric actuator elements, and a deflection sensor 210 for monitoring the deflection of membrane elements.

[0331] Using the hardware exemplified in the outline, the following can be accomplished: • Measurement of ambient pressure, temperature, and humidity. • Changes between water and air as the transport medium, • Injection of bubbles to simulate external influencing factors (disturbance factors) • Bubble detection using a bubble sensor, • Detection dependent on the membrane stroke time of the micropump. • Reverse pressure sensor for water and air (switchable) • (Optional) Measurement of conveyor speed in the inlet and outlet paths using wind speed and flow sensors. • (Optional) Measurement of conveyor speed using a differential pressure sensor.

[0332] This allows the following to be controlled: • Micropumps using any control or voltage signal (e.g., square or sinusoidal voltage), and / or • A switching valve for switching between water and air operation, or for injecting bubbles of different sizes.

[0333] In addition, primary pressure or reverse pressure changes are also possible.

[0334] Using the measurement stations exemplified in the outline, the following time-resolved measurements can be performed. • Sensor current for suction and pressure stroke by sensor current electronic circuit, • Actuator stroke for suction and pressure strokes, • Bubbles using a bubble sensor • (Optional) Flow rate.

[0335] The following series of measurements were performed, which will be explained in more detail next with reference to the drawings. • "Normal state" with air, 10Hz, control signal: rectangular, • "Normal state" with water, 10Hz, control signal: rectangular, • Water with bubbles, 10Hz, control signal: rectangular, • Water with reverse pressure, 10Hz, control signal: rectangular, • Water with bias pressure, 10Hz, control signal: rectangular, and • Contact at the bottom of the pump chamber; control signal: sinusoidal voltage, trajectory

[0336] The temporal progression of the control signal 104 was measured and is shown in the following figure. This illustrates how the temporal progression of the control signal 104 changes in the presence of an external influencing factor (compared to the "normal state" without the external influencing factor). As explained in the theoretical section above, the control signal 104 has up to four different signal parts or current terms I U , I C , I ce , I p It is often assumed to include the current term I p We will consider this, but this is proportional to the pressure below the membrane element (or pump chamber pressure), and furthermore, the piezoelectric coefficient

[0337]

number

[0338] It is also proportional to this current term I p This is also referred to as the sensor current in the following diagrams. Signal portion I p The following explanation regarding this will be covered by other signal parts or current term I U , I C , I ce The same could apply to this as well.

[0339] In addition, the measured control signals capture fluid effects that cannot be derived from a simplified model of the overlapping exponential compensation process.

[0340] For example, the inlet value may be excited to oscillate at its mechanical natural frequency, thereby causing pressure oscillations within the pump chamber. These pressure oscillations overlap with the damping pressure, resulting in term I p The vibrations are captured and then overlap with the damping process.

[0341] "Fluid resonance" is another example. It has long been known that the inertia of the liquid in the inlet and outlet pipelines can be coupled to a micropump under certain configurations. (See quote below.) "Simulation of microfluid systems", Zengerle, R., Richter, M., Journal of Micromechanics and Microengineering, 1994, 4(4), pp. 192-204, 004

[0342] When a micropump is operating, not only the liquid in the pump chamber but also the liquid column behind the open valve must move, because liquid cannot be compressed. This inertia of the liquid tube in the inlet and outlet is defined as "fluid inertance" L, which is perfectly analogous to electrical inductance. fluidic This can be described using a rough model. This fluid inertance is the fluid capacity C of the drive membrane. p Along with, fluid resonance frequency

[0343]

number

[0344] This represents a system that can oscillate. The vibrations possible through it result in fluctuations in the pump chamber pressure, which were demonstrated both theoretically and experimentally in the aforementioned paper published in 1994.

[0345] This fluid coupling can be reduced by directly adding pressure smoothing elements before and after the pump, as already described in 1994. However, these additional elements are advantageous in only a few applications, and fluid resonance often does not interfere with the pump operation.

[0346] All of these effects overlap with the "normal" exponential compensation process described earlier, and therefore provide additional information about the valve flap state and even the surrounding area outside the micropump.

[0347] Figure 20 shows the "normal condition" with air, where the pump frequency is 10 Hz and the control signal is rectangular. Here, a total of 10 measurement curves are recorded and superimposed. The measurement curves look almost identical, characterizing the normal condition, i.e., the absence of external influencing factors.

[0348] Air was used as the pumping medium, and the micropump 1000 operated without back pressure. Time constant τ A <<1ms, and frequency f=10Hz(τ A =τ a The rectangular voltage of ) was used as the control signal 104.

[0349] Figure 20 shows the suction stroke on a time scale, or between 0 ms and approximately 10 ms. This is followed by a time domain where the suction stroke is already completely completed (between approximately 10 ms and approximately 15 ms). The pressure stroke begins at 50 ms. The time domain in which the pressure stroke is performed extends to approximately 65 ms on the time scale. This is followed by a time domain up to 100 ms where the pressure stroke is already completely completed. Then, the next suction stroke (not illustrated here) begins at 100 ms. In this normal state, the expected exponential compensation (τ h In addition to fluid information (including related information), valve flap vibration may be observed. Since air has a density 100 times lower than water and a viscosity 50 times lower than water, the valve flap can vibrate. Furthermore, the air in the piping before and after the pump has a low mass and high fluid capacity such that fluid resonance cannot be observed when the air is pumped.

[0350] Figure 21 is a detailed diagram of the temporal signal curve during the suction stroke under normal conditions, i.e., without external influencing factors (using air as the pump medium). Initially, the signal curve includes a sharp rise resulting from the rectangular control signal 104, along with the capacitance of the piezoelectric charge. This sharp rise is cut off in the signal curve due to the Zener diodes 111 and 112 in Figure 4. The signal curve decays exponentially, i.e., the pump chamber pressure decreases exponentially. At the end of the decay process, there is a vibration resulting from the vibration of the inlet flap. In the range starting at approximately 10 ms, the suction stroke is completely finished, and the valve vibration decays. The decay rate of this vibration can also provide information about the geometric shape of the valve flap and contact ridge.

[0351] Figure 22 is a detailed diagram of the time signal curve during a pressure stroke under normal conditions, i.e., without external influencing factors (using air as the pump medium). The signal curve, here again, includes a sharp (negative) rise resulting from the rectangular control signal 104, along with the capacitance of the piezoelectric discharge. This sharp rise is cut off in the signal curve, caused by the Zener diodes 111 and 112 in Figure 4. What follows are artifacts occurring only during the pressure stroke, which can be ignored for the time being. Subsequently, the signal curve also decays exponentially, i.e., the pump chamber pressure increases exponentially. Towards the end of the decay process, there is a vibration caused by the vibration of the outlet flap. In the range starting at approximately 56 ms, the pressure stroke is completely finished, and the valve vibration decays.

[0352] Figure 23 shows the "normal condition" using bubble-free water, a pump frequency of 10 Hz, and a rectangular control signal. A total of 90 measurement curves were recorded and superimposed. The measurement curves appear almost identical, characterizing the normal condition, i.e., the absence of external influencing factors.

[0353] Water was used as the pumping medium, and the micropump 1000 operated without back pressure. Time constant τ AA rectangular voltage with frequency f=10Hz was used as the control signal 104.

[0354] Figure 23 shows the suction stroke on a timescale from 0 ms to approximately 10 ms. This is followed by the time domain where the pressure stroke is already fully completed (between approximately 10 ms and 50 ms). The pressure stroke begins at 50 ms. The time domain in which the pressure stroke is performed extends to approximately 57 ms on the timescale. This is followed by the time domain up to 100 ms, where the pressure stroke is already fully completed. The next suction stroke begins at 100 ms (not shown here).

[0355] Figure 24 is a detailed diagram of the temporal signal curve during the suction stroke under normal conditions, i.e., without external influencing factors (water is the pump medium). Firstly, the signal curve includes a sharp rise resulting from the rectangular control signal 104, along with the capacitance of the piezoelectric charge. This sharp rise is cut off in the signal curve, caused by the Zener diodes 111 and 112 in Figure 4. The signal curve decays exponentially, meaning the pump chamber pressure also decreases exponentially, resulting in small oscillations in the region of approximately 1.5 ms, which will be explained in more detail later. Towards the end of the decay process, there are almost no oscillations in the inlet flap due to the pump medium being water. Water has a viscosity 50 times higher than air, which significantly dampens these oscillations. In the region starting at approximately 6 ms, the suction stroke is completely finished, and the valve oscillations have decayed.

[0356] Figures 25A, 25B, and 25C are detailed diagrams of the overshoot previously described in the exponential decay process, with Figure 25A corresponding to Figure 24 described earlier, Figure 25B being a magnified view of the overshoot, and Figure 25C being a further magnified view.

[0357] As mentioned earlier, 90 curves were superimposed here. The original documents submitted for this application were originally color-coded, including the color coding of individual measurement curves. This color coding represents the recording time of each individual measurement curve, i.e., the time each measurement curve was recorded (relative to other measurement curves). According to this color coding, the measurement time progresses from yellow tones, through green tones, blue tones, and then purple tones. That is, measurement curves with yellow tones were recorded first, measurement curves with green tones were recorded afterward, measurement curves with blue tones were recorded afterward, and measurement curves with purple tones were recorded last. In addition, this color coding was used in all the figures described in this section.

[0358] For example, Figures 25B and 25C show that the measurement curve furthest from the actual exponential function (purple), as illustrated in Figure 25, was recorded first (yellowish hue). As the measurement time increases, the individual measurement curves approximate the exponential function curve, i.e., the gradient of the color code shifts inward in the direction of the exponential function in Figure 5 as the measurement time increases. This overshoot is presumed to be due to fluid resonance and / or degassing / or cavitation.

[0359] Figure 26 is a detailed diagram of the temporal signal curve during a pressure stroke under normal conditions, i.e., without external influencing factors (water is the pump medium). The signal curve, here again, includes a sharp (negative) rise resulting from the rectangular control signal 104, along with the capacitance of the piezoelectric discharge. This sharp rise is cut off in the signal curve by the Zener diodes 111 and 112 in Figure 4. What follows, again, are artifacts occurring only during the pressure stroke, which can be ignored for the time being. The signal curve then decays exponentially again; that is, the pump chamber pressure increases exponentially until it equalizes with the external pressure (e.g., atmospheric pressure). Towards the end of the decay process, there are only very small vibrations resulting from the vibration of the outlet flap. In the region starting at approximately 54 ms, the pressure stroke is here again completely finished, and the valve vibrations decay.

[0360] Figures 27A and 27B then show the case where a disturbance variable for the bubble morphology is added. This bubble corresponds to an external influencing factor that changes the temporal progression of the control signal 104. Figures 27A and 27B show the detection of bubbles according to the present invention during the suction stroke.

[0361] The micropump operated at a pump frequency of 10 Hz, and a rectangular voltage was used as the control signal. The pump medium was water, and bubbles were introduced into the system. In Figures 27A and 27B, 30 measurement curves are superimposed, with 20 suction strokes recorded before the bubbles entered the pump chamber, 3 suction strokes recorded while the bubbles were positioned in the pump chamber, and 7 further suction strokes recorded as the bubbles left the pump chamber. The color coding described above was used again.

[0362] It can be seen that the temporal signal curve changes significantly as the bubbles approach the pump chamber. This leads to a significant deviation compared to the exponential decay process under normal conditions (Figure 24). Here, (Figure 27B) significant overshoot can be observed in the region of approximately 4 ms to 5 ms, and further in the region of approximately 8 ms and 11 ms. At this point, it should be noted that since colored drawings cannot be used in this patent application, and therefore color coding cannot be used, all time representations shown herein are purely illustrative for describing the temporal signal curve.

[0363] This overshoot occurs as a result of the water column coming into contact with the pump, which is altered by bubbles. This changes the wavelength of the fluid resonance.

[0364] As soon as the bubble enters the pump chamber, the temporal signal curve changes significantly. In the approximately 4ms region, a very distinct spike is observed, i.e., a very clear deviation from the exponential decay function under normal conditions (Figure 24). In this regard, see Figure 27b, which is illustrated with the label "First sensor current measurement after bubble entry".

[0365] In addition, an increase in the valve's natural vibration is observed in subsequent second and third sensor current measurements while the air bubbles are still located within the pump chamber. Because the low-viscosity air is temporarily placed within the pump chamber, the valve flap can then vibrate, which is reflected in the measured signal.

[0366] When the pump chamber contains air, surface forces act on the pump membrane and valve flap. For example, these surface forces may prevent the valve from moving freely. Also, depending on where the meniscus is positioned, the drive membrane may also be subjected to asymmetrical stress. All of this affects the pressure inside the pump chamber. Since it is impossible to predict exactly how precisely the bubbles will move within the pump chamber, the signal behavior appears chaotic.

[0367] As soon as the bubbles leave the pump chamber and move away, the time-dependent signal curve again approximates the exponential decay function under normal conditions (Figure 24). Here, the further away the bubbles move, the more strongly the signal curve approximates the exponential decay function.

[0368] Next, Figures 27C, 27D, and 27E show, in addition, situations, or signal curves, where the lead time for the arrival and departure of the bubble is long. Firstly, Figure 27C shows the temporal signal curve of the control signal during the 10 suction strokes before the bubble enters. Next, Figure 27D shows the signal curves described previously with reference to Figures 27A and 27B, immediately before the bubble enters, during the bubble's passage, and immediately after the bubble leaves the pump chamber. Finally, Figure 27E shows the signal curve during the 10 suction strokes as the bubble moves further away.

[0369] As is clearly visible in Figures 27A to 27E, the formation of air bubbles or gas bubbles within the liquid-transporting microfluidic component significantly alters the temporal signal curve of the control signal. These are external influencing factors that can be detected according to the concepts of the present invention as described herein. Each external influencing factor, like the bubbles described exemplary, results in a characteristic deviation in the temporal signal curve; that is, each external influencing factor leaves a distinct trace in the temporal evolution of the control signal, which can be detected using the concepts of the present invention as described herein.

[0370] Figures 28A and 28B, purely for the sake of completeness, show that such characteristic traces can be determined not only in the suction stroke, as just explained with reference to Figures 27A to 27E, but also in the pressure stroke, even though the traces in the pressure stroke may not be significantly clearly distinguishable.

[0371] In summary, these experiments demonstrate that the entry of bubbles into the pump chamber can be detected very clearly. This is highly practical for many micropump applications, for example, the size of the bubbles can be fairly easily estimated using the number of pump strokes indicating bubble entry and the stroke volume of the micropump. In the case of this invention, the stroke volume was approximately 6 μl and the number of pump cycles was 3, so the volume of the bubbles was approximately 6 μl * 3 = 18 μl. Above all, very small bubbles are medically harmless, but large bubbles must be avoided at all costs, so estimating the amount of bubbles is useful in drug administration.

[0372] In addition, air bubbles pose a significant disturbance to administration accuracy. When the size of the air bubbles is known, disturbances to administration accuracy can be detected and compensated for by appropriate means. For example, when an 18 μl air bubble (medically harmless when it reaches the patient in the infusion) is detected and quantified, the administration does not need to be canceled with an error message (depending on the medical application) but can continue. Then, with the control of the micropump, three additional strokes can be performed, thereby ensuring that the pre-planned amount of drug reaches the patient. The number and size of the air bubbles can then be recorded in the error protocol.

[0373] Figures 29A to 29D illustrate further cases of detectable external influencing factors, in this case being the back pressure of a liquid-transporting micropump. For example, such a micropump may be used in a drug delivery system, where catheter occlusion may occur, which has a significant impact on the patient as the drug can no longer be properly administered. For example, such catheter occlusion can generate the aforementioned back pressure corresponding to the occlusion pressure of the micropump (the occlusion pressure being the maximum pressure that the micropump can generate using liquid as the pumping medium). Figure 29A first outlines the effect of back pressure on the temporal signal curves of the suction stroke 291 and pressure stroke 292.

[0374] The measurement curve illustrated in Figure 29B shows the applied back pressure. An initial starting pressure of 120 kPa was used, and the pressure continuously decreased to 0 kPa over approximately 10 seconds. Measurement curve 293 shows the pressure at the inlet reservoir 201 (Figure 19), measurement curve 294 shows the pressure at the outlet reservoir 205 (Figure 19), measurement curve 295 shows the pressure at the pump inlet, and measurement curve 296 shows the pressure at the pump outlet.

[0375] Next, Figure 29C shows an enlarged cross-section of Figure 29A, where the pressure curve of the suction stroke is illustrated. Several measurement curves recorded during the controlled pressure drop (Figure 39B) are superimposed here again. Color coding is used again, namely the curve in yellow tones which was recorded first (pressure: 120 kPa), and the curves in blue and purple tones which were recorded towards the end (pressure: 0 kPa).

[0376] The measurement curve at an applied starting pressure of 120 kPa roughly corresponds to the exponential decay process under normal conditions (Figure 24). Here, there is high back pressure. The inlet and outlet valves are closed, and there is no fluid resonance.

[0377] As illustrated by arrow 299, as the pressure decrease increases, the measurement curve moves upward, forming an increasingly pronounced overshoot 300. At such moderate back pressures, the valve begins to open, and fluid resonance becomes noticeable, which can be seen in the form of a continuously increasing overshoot 300.

[0378] The overshoot 300 is most clearly visible at atmospheric pressure, i.e., at the end of the measurement cycle at 0 kPa. Therefore, if there is no more back pressure, the valve can open without obstruction, fluid resonance occurs, which is then expressed as the aforementioned significant overshoot 300.

[0379] Figure 29D illustrates the measurement curve in the pressure stroke. Here, as the pressure decreases, as illustrated by arrow 299, the overshoot 300 becomes increasingly clear. The reason for this is the same as that described above for the suction stroke.

[0380] As is clearly visible in Figures 29A and 29B, different back pressures in the liquid-transporting microfluidic component result in significant changes in the temporal signal curve of the control signal. These are external influencing factors that can be detected according to the concepts of the present invention as described herein. Each external influencing factor, like the back pressures described exemplary herein, results in a characteristic deviation in the temporal signal curve; that is, each external influencing factor leaves a distinct trace in the temporal evolution of the control signal, which can be detected using the concepts of the present invention as described herein.

[0381] Pressure changes in both the inlet and outlet pipelines can be detected by suitable means for signal evaluation. This, in turn, has significant practical relevance. For example, a pressure sensor is installed in the drug delivery system at the outlet pathway to detect catheter occlusion. This additional component can be replaced by the “self-sensing system” of the present invention of the microfluidic component 1000 described herein.

[0382] Furthermore, slow pressure changes can also be detected. Closure of patient access is often done gradually, causing a slow pressure rise in the outlet duct. In the future, pressure sensors should be used to better determine the wearing time of wearable pump systems. Unable to predict this, patch pumps are often used for only fairly short periods to avoid this risk. Prediction would allow for longer wearing times, which would have significant economic advantages. Prediction of slow pressure rise according to the present invention by the “self-sensing system” of the micropump can be performed, for example, by storing the measured signals at larger intervals and comparing them with respect to changes, as shown in Figure 29C, etc.

[0383] Figures 30A to 30E show the pressure generated in further attempted cases that can be detected using the concepts of the present invention described herein. For example, the same thing can happen when a patient presses the medication bag so that the pressure at the pump inlet becomes higher. This can also have serious consequences for the patient in terms of the dosage of medication. Normally, this process is avoided by a safety valve, but this can also fail if a malfunction occurs.

[0384] Figure 30A, again, outlines the effects of different pressures on the temporal signal curves of the suction stroke 301 and pressure stroke 302. Measurements were performed on an air-transporting micropump driven by a sinusoidal control signal. The pump frequency was 10 Hz. The pressure was continuously controlled, varying from an initial pressure of several kPa down to a final pressure of 0 kPa.

[0385] It can be seen that there is a large vibration in the suction stroke 301. In this case, the color coding from yellow to green and blue to purple was used again. Therefore, it can be seen that the measurement curve has a higher amplitude as the pressure increases.

[0386] However, there is almost no vibration in the pressure stroke 302. This will be explained in more detail below. First, however, referring to Figure 30B, an enlarged view of the temporal signal transition in the suction stroke is illustrated.

[0387] Figure 30B shows that the measurement curve recorded at the high preliminary pressure at the start of the measurement has a large amplitude (yellow). As the pressure decreases, the amplitude decreases accordingly (blue, green). The amplitude decreases further until the temporal signal curve approximates the signal curve under normal conditions (purple).

[0388] Figure 30C shows a section of Figure 30B. At this point, the suction stroke is long complete. However, a very clearly distinguishable oscillation is clearly visible in the time signal curve. Here, reference numeral 303 indicates a large amplitude (yellow) recorded at the high pressure at the start of the measurement. Reference numeral 304 indicates a small signal amplitude (purple) recorded at the end of the measurement at a nearly inconspicuous pressure.

[0389] Since the damping is lower in the air pump than in the water pump, there are spontaneous resonant valve vibrations that cause pressure changes that may subsequently appear in the time signal curve. The valve vibrations are excited by the pressure because the inlet valve is open (free flow). Therefore, in Figure 30C, we can see that the measurement curve has a larger amplitude at higher pressures (yellow) than at lower pressures (purple).

[0390] Figure 30D shows the transition from the suction stroke to the pressure stroke. This shows the last milliseconds of the suction stroke and the first 3 milliseconds of the pressure stroke. This clearly demonstrates the different behavior between the suction stroke and the pressure stroke, namely, significantly less oscillation in the pressure stroke than in the suction stroke.

[0391] Figure 30E is an enlarged cross-sectional view of Figure 30D, illustrating the exponential decay process. In the pressure stroke, overpressure is generated within the pump chamber, so despite the pressure, the inlet valve is closed and cannot oscillate. Therefore, large oscillations over the entire duration are not observed here. However, in the pressure stroke, there are also changes in the valve oscillations occurring in the first few milliseconds. Here again, the color scheme progressing from yellow through green to purple is used. Furthermore, it is again observed that the amplitude of the signal curve is larger at higher pressures and decreases as the pressure decreases, increasingly approximating the exponential decay behavior under normal conditions in the pump stroke.

[0392] Therefore, as can be seen from Figures 30A to 30E, different pressures cause significant changes in the temporal signal curve of the control signal of the microfluidic component. These are external influencing factors that can be detected according to the concepts of the present invention as described herein. Each external influencing detector, as with the pressures described here, results in a characteristic deviation in the temporal signal curve; that is, each external influencing factor leaves a distinct trace in the temporal evolution of the control signal, which can be detected using the concepts of the present invention as described herein.

[0393] Figures 31A and 31B show Lissajous curves (current versus voltage) at different control voltages between U=20V and U=120V, which allow for the detection of contact of the membrane element 101 at the counter electrode during the suction stroke or at the bottom of the pump chamber during the pressure stroke. For example, this can be performed for quality measurement in the context of end-of-line testing. Alternatively, this can also be performed during operation, for example over several months or years, to determine the degradation of the membrane actuator 100.

[0394] Figures 31A and 31B show the sensor currents of two different membrane actuators. Figure 31A shows an actuator in which the drive element can vibrate freely. Figure 31B shows an actuator in which the drive element is in contact with the bottom of the pump chamber. The sensor current increases through the associated effects of this contact and the force acting on the drive element.

[0395] The sensor current is plotted against the voltage applied to the drive element, not against time. The temporal progression of the signal progresses counterclockwise. The negative amplitude of the suction stroke is the same for all measurements, at -40V. The positive amplitude increases in 5V increments from 20V to 120V.

[0396] Figure 31A shows the case where the membrane element 101 does not contact the pump chamber bottom during the suction stroke (positive voltage) and pressure stroke (negative voltage), and can vibrate freely. Figure 31B shows the case where the membrane element 101 contacts the bottom of the pump chamber when deflected during the suction stroke and is released from the bottom of the pump chamber during the pressure stroke. In the suction stroke, a clearly distinguishable bend (see arrow 321) is observed, which increases with increasing drive voltage. In the pressure stroke, the bend is also observed along the path of the Lissajous curve (see arrow 322), which emphasizes the release of the membrane element 101, although the bend is smaller.

[0397] 6.4 Further non-limiting examples of detectable external influencing factors The above explanation, with reference to Figures 20 to 31B, shows that each influencing factor can be identified based on the temporal signal curve of the control signal affected by the external influencing factor. The following section will show various ways in which influencing quantities act on and can affect the sensor current, based on specific examples.

[0398] As shown in the above description with reference to Figures 20 to 31B, each external influencing factor leaves a characteristic trace in the temporal signal curve of the control signal. The above description refers to the signal portion or current term I pThis was done using the example of linking the time change of pressure acting on the membrane element 101, and in addition, the piezoelectric coupling coefficient.

[0399]

number

[0400] The signal portion or current term I is proportional to p An explanation was given regarding the following: In addition to the above non-limiting examples of detectable or identifiable external influencing factors, the signal portion or current term I p Based on this, a series of further external influencing factors can be identified.

[0401] In response to this, according to an embodiment of the present invention, the signal processing device 105 processes the signal portion or current term I p Based on this, it is configured to check that the fluid state of microfluidic components such as micropumps operates without disturbance. Disturbances can occur in various forms. Therefore, the signal processing device 105 checks for single part or current term I p It may be configured to determine at least one of the following external influencing factors based on this. ○ Changes in back pressure (for example, if the back pressure changes, the sensor current will change significantly within that time domain until the catheter is closed as described above), ○ Preliminary pressure L, ○ Closure of the exit channel of catheters, etc. ○ For example, the presence of air bubbles in the pump chamber of a micropump (air bubbles can generate occlusion pressure in the microvalve due to capillary force, and this occlusion pressure can be identified by the sensor current), ○ Size of bubbles passing through the pump, ○ The arrival of air bubbles into the pump chamber via the inlet pipeline. ○ Bubbles moving away from the pump chamber via the outlet pipeline, ○ Changes in the resistance of the pump chamber, ○ (Rapid) change in stroke volume due to particle capture, ○ Changes in ambient parameters such as pressure changes above the membrane element, pressure changes at the valve inlet, pressure changes at the valve outlet, and temperature changes. ○ Valve state detection, i.e., whether the valve is open or closed. ○ Valve defects such as valve damage, ○ (Rapid) deterioration of valve sealing characteristics due to particles (for example, if hard particles block a check valve (e.g., an inlet valve), this can be detected using a sensor current in the pressure stroke, as the discharge of the stroke volume will no longer occur except through the outlet valve), ○ Settlement or aggregation of solid matter, e.g., precipitation, (slow) deterioration of valve sealing properties due to protein denaturation (if the valve becomes immobile due to protein denaturation or oil precipitation, this can be detected by the sensor current as the actuator element 102 is pressed against the closed valve in the corresponding stroke), ○ Occurrence of capillary adhesion when the meniscus blocks the valve, ○ Occurrence of van der Waals adhesion when corresponding molecules are deposited between the valve seat and the supporting web. ○ Swelling or change in elastic properties of the sealing element, ○ Changes in adhesive bonding between actuator elements and membrane elements, ○ The inertia of the surrounding liquid is coupled to the micropump ("fluid resonance"), so the micropump (in the case of an incompressible liquid as the pump medium) can perceive whether the connected fluid conduit is long or short, soft or hard. ○ For example, a change in the viscosity of the medium that allows checking whether the entire drug has been rinsed away and replaced with saline solution during the rinsing process.

[0402] Other signal portion or current term I C , I ce , I U These also affect the temporal progression of the control signal, namely these current terms I C , I ce , I UFurthermore, depending on which external influencing factor is currently dominant, it also affects the control signal. Therefore, according to the present invention, these signal portions or current terms I C , I ce , I U This can be used to identify several external influencing factors based on a signal analysis of the temporal evolution of the corresponding affected control signals.

[0403] Therefore, according to embodiments of the present invention, the signal processing device 105 is configured to verify that the piezoelectric hysteresis process remains unchanged. In some cases, one or more parameters τ c , I ce or I c The changes in the piezoelectric ceramic provide instructions regarding the mechanical and / or electrical changes. Therefore, the signal processing device 105 processes the signal portion or current term I C and I ce It may be configured to determine at least one of the following external influencing factors based on at least one of the following: ○ Mechanical fatigue of piezoelectric elements, such as failure or the growth of so-called subcritical cracks during very long periods of continuous operation. ○ Electrical fatigue of piezoelectric elements, such as loss of polarization.

[0404] Therefore, according to embodiments of the present invention, the signal processing device 105 is configured to verify that the charging process is performed properly. Accordingly, the signal processing device 105 verifies the signal portion or current term I U It may be configured to determine at least one of the following external influencing factors based on this. • Breakage of electrical contacts (for example, breakage of bond wires), • Short circuit of piezoelectric element (for example, due to condensation of water droplets under high humidity conditions), • Damage to the piezoelectric element (for example, when only a portion of the piezoelectric ceramic is electrically in contact, capacitance and therefore charging current decrease).

[0405] When the measurement and evaluation of the present invention are performed in real time, it becomes possible to verify the state of numerous microfluidic components 1000, such as micropumps or microvalves. Clearly, in electrostatically driven microfluidic components 1000, or membrane actuators 100, the signal portion or current term I U and I C It can be used for the same purpose. Furthermore, the above-mentioned exemplary amplitude I p , I U , I C , I ce In addition, alternatively or in addition to the respective damping constant or time constant τ a , τ h , τ P , τ d =τ c =τ piezo These may be used to detect the aforementioned conditions or external influencing factors based on them, i.e., to identify and / or classify them.

[0406] These fluid dynamics verifications are very convenient. • In the event of a serious disturbance, an alarm may be triggered and administration may be discontinued. • In the event of a temporary disturbance (e.g., air bubbles), the system controller may, after detection, estimate whether and to what extent the bubbles will affect the dosage, which may need to be readjusted. This disturbance may be related to the fluid specifications of each dosing task of the microfluidic component 1000 (e.g., a micropump), and a suitable response may follow. In a fluid network having several microfluidic components, such as micropumps (e.g., n micropumps), each micropump may report its status. Failure of an individual micropump can be compensated for by other micropumps, provided there is adequate redundancy.

[0407] The effects of these time-dependent mechanical, pneumatic, or hydraulic external influencing factors or disturbances on the membrane actuator 100 affect the sensor current, much like a "fingerprint."

[0408] For example, fluid resonance of the oscillating liquid column in the supply and discharge lines can lead to pressure deviations, which may then be affected by bubbles being transported in the inlet tube toward the microfluidic component 1000. These resonances are also affected by the elasticity, diameter, and length of the tubing used. Furthermore, if bubbles are present within the microfluidic component 1000, such as in the pump chamber of a micropump, the sensor current will change more rapidly because air has a viscosity 50 times lower than water. When bubbles pass through the microfluidic component 1000, the sensor current can be used to observe transport in the outlet tube in a similar manner. If bubbles remain partially in the pump chamber, this can also be detected.

[0409] Alternatively, or in addition, using the concepts of the present invention as described herein, the following temporal effects may be detected, i.e., identified and / or classified as external influencing factors:

[0410] Therefore, for example, it is possible to determine the adhesive strength and even the service life of the adhesive layer. The mechanical bias of the membrane actuator 100 affects the sensor current. The position of the actuator is very important not only immediately after assembly but throughout the entire lifecycle of the actuator 100. Loss of bias, i.e., a decrease in mechanical stress in the assembly (i.e., the assembly of the actuator element 102 and the membrane element 101), and the resulting descent of the membrane element 101, changes the properties of the microfluidic component 1000 and therefore should be detected during operation. This loss of bias can occur due to a malfunction in the adhesive connection (polymer chains break due to very high stress in the adhesive layer over time, and the bias decreases if the adhesive does not cure sufficiently). Creep of the adhesive reduces the tension and therefore affects the capacitance of the sensor current.

[0411] In addition to checking the bias, the complete wettability of the interface can also be verified. Trapped bubbles can alter the voltage state of the ceramic (i.e., the piezoelectric ceramic actuator element 102) and thus can be observed in the sensor current. Sudden or creep delamination of the assembly during operation can cause changes in the sensor current and thus be detected early.

[0412] Not only the adhesive layer, but also the ceramic (i.e., the piezoelectric ceramic actuator element 102) itself is subject to temporal changes. In particular, prolonged operation or extreme control voltages can cause depolarization of the piezoelectric ceramic 102. This can lead to loss of stroke and, in extreme cases, failure of the microfluidic component 1000. Early detection allows for adaptation of the voltage signal, for example, to perform repolarization or to avoid further depolarization. Such repolarization can be smoothed by extremely short voltage surges that do not cause mechanical deflection and therefore do not affect transport behavior.

[0413] In addition, the isolation of the actuator element 102 from the membrane element 101 is advantageous for several applications. For example, the use of a multilayer actuator would cause a short circuit in the membrane element 101 if it were not isolated. However, in many medical applications, it is also necessary to isolate the actuator element 101 from the fluid path. This isolation is then necessarily required for operation and its insulating properties should not change throughout the entire lifespan of the microfluidic component 1000. A slow increase in leakage current can be observed in the sensor current and therefore can be detected early.

[0414] These monitoring possibilities may also be applied to microvalves.

[0415] 7. Methods for identifying and / or classifying external influencing factors Based on the concepts of the present invention as described herein, the following compensation processes can be measured and distinguished in the microfluidic component 1000: • Charging of the capacitance of the piezoelectric membrane actuator 102, • Loss processes in piezoelectric ceramics exemplified by the expansion (contraction) of magnetic domains (Weiss regions), and • Changes in the pressure inside the pump chamber.

[0416] this is, In real time, Without affecting the control signal, • Without additional sensor elements, With each suction stroke, • For each pressure stroke, • By measuring the time dependence of the charging current only It is possible.

[0417] To implement the concept of the present invention, it is advantageous to provide one or more of the following components. • Measurement circuit (as part of signal processing device 105) for accurate time-resolved measurement of charge current, • Real-time data acquisition means for time-dependent charge current (as part of signal processing device 105), For example, means for evaluating recorded data in real time (as part of the signal processing device 105) to determine superimposed amplitudes and / or time constants, which may be used to achieve data reduction, and • Means for properly storing the data created by this evaluation.

[0418] In order to implement the concept of the present invention, there are, in principle, other methods, but in particular, two different methods with differing concepts for data storage and signal evaluation.

[0419] 7.1 White Box Model In a so-called white-box model, the connection between the measured signal (an affected control signal 104 including one or more associated signal segments or current terms) and the causative physical disturbance (external influencing factor) is known and can be unambiguously extracted from the affected control signal including each signal segment or current term. In the evaluation of the data, the desired explanation for the system has already been obtained, i.e., external influencing factors that affect the temporal signal curve can be detected and identified.

[0420] In the context of this disclosure, the phrase “identify” can be understood to mean that the type of external influencing factor or its physical background may be clearly indicated, for example, that a bubble is located inside the pump chamber, that the valve is undergoing coagulation, that the valve is blocked, that there is a certain back pressure, that there is a certain pressure, etc.

[0421] With respect to such a white-box model, according to an embodiment of the present invention, the signal processing device 105 includes a memory device that stores how a particular external influencing factor affects the temporal signal curve of the control signal 104. In this case, the signal processing device 105 is configured to identify the causative external influencing factor based on the influencing factor information stored in the memory device, based on the temporal signal curve of the affected control signal 104.

[0422] 7.2 Black Box Model In so-called black-box models, the relationship between the measured signal (the affected control signal 104, which includes one or more associated signal segments or current terms) and the physical disturbance (external influencing factor) is initially unknown and cannot be directly extracted from the affected control signal 104. In such cases, machine learning methods may be used, and a neural network may be utilized. In this case, the membrane actuator 100 can be trained by generating an external influencing factor (e.g., a disturbance) and capturing the system response in a statistically significant manner. As a result of this training, a weighted connection of the neural network is obtained. After this training, the membrane actuator 100 only needs these weights to capture each causative external influencing factor with a relatively high probability and classify accordingly.

[0423] In the context of this disclosure, the term “classify” may be understood as meaning that weights or probabilities are determined, and based on these, a description can be made of the probability that external influencing factors, determined based on the temporal signal curve of the affected control signal, can be assigned to a particular class (e.g., valve is blocked, valve is coagulating, bubble, pressure, back pressure, etc.). For example, such classification may be performed using the aforementioned neural network.

[0424] With respect to such a black-box model, according to embodiments of the present invention, the signal processing device 105 includes a neural network pre-trained by generating different external influencing factors and determining the respective effects of each on the temporal progression of the control signal 104. The neural network is configured to classify at least one causative external influencing factor based on the temporal signal curve of the affected control signal 104, based on pre-created training data.

[0425] As described in detail herein, the membrane actuator 100 is fluidically coupled to the system, and its interrelationships can be very complex, as they can overlap on one hand and influence each other on the other. According to the present invention, the sensor current describes an integral across all influencing variables, which also presupposes the use of “machine learning” and “neural networks.” Another indicator of this is that these influences can be detected purely through observation of the measurement curve of the sensor current, even if the direct physical relationship is not apparent.

[0426] 7.3 Gray Box Model Between the black-box and white-box models described above, there is a distinct step in the relationship between the measured signal (an affected control signal 104 including one or more associated signal segments or current terms) and the physical disturbance (external influencing factor) that can only be approximated by a semi-empirical model. This intermediate position may be called a gray-box model accordingly. In this case, training can be significantly simplified and implemented with an efficient optimization algorithm. Here, disturbance detection is performed with higher reliability than in the case of a pure neural network. 1) The "step response" of the piezoelectric-driven microfluidic membrane actuator 100 to rapid voltage changes was analyzed. The calculations in sections 4 and 5 can be considered a physical model, but this is subject to some simplification (e.g., the behavior of large signals, hysteresis or piezoelectric creep, and the phenomenon of fluid resonance are not considered). Therefore, this physical model cannot accurately represent reality. However, this model describes some fundamental characteristics and dependencies of the sensor current on the process of the membrane actuator 100. Therefore, this concept can be considered a gray-box description. 2) Another analog example of the gray-box description is the sensor current response to harmonic voltage control, as described in Section 4.3.

[0427] By using the corresponding physical model, one or more related signal parts or current terms I U , I C , I ce , I p The temporal signal curve of the affected control signal 104, including the signal curve, can be determined by fitting the model to the measurement results (see Section 5).

[0428] With respect to such a gray-box model, according to an embodiment of the present invention, the signal processing device 105 has a mathematical model having associated amplitude curves and / or associated time constants for each signal portion, for each individual signal portion I U , I C , I ce , I p The device includes a memory device that stores information for each of one or more of the following. In this case, the signal processing device may be configured to fit a mathematical model to the temporal signal curve of the affected control signal 104 and to identify the causal external influencing factors with the help of the fitted mathematical model.

[0429] Optionally, a neural network may be used to optimize the fitting. In this case, external influencing factors can be classified based on the fitting performed using a pre-trained neural network.

[0430] 7.4 Feasible Implementation and System Integration Figure 32 is a schematic block diagram of a possible hardware and software implementation of the concept of the invention described herein. In essence, it shows a self-sufficient system representing the microfluidic component 1000 of the present invention.

[0431] The microfluidic component 1000 comprises a membrane actuator 100 that can be driven piezoelectrically or electrostatically. Furthermore, the microfluidic component 1000 includes a signal generating device 103 configured to control the membrane actuator 100 by a control signal 104. External influencing factors 116 may act on the membrane actuator 100, which may have a characteristic effect on the temporal progression of the control signal 104 in the sense of individual traces.

[0432] The signal processing device 105 measures a control signal 104 that may include an affected temporal signal curve after the presence of an external influencing factor 116. The signal processing device 105 is further configured to determine the effect of the control signal 104 on the temporal signal curve and, based on that, identify and / or classify at least one external influencing factor 116 that caused it.

[0433] As described in Sections 4 and 5, the affected control signal 104 is composed of several (up to four) different signal parts I, which are also referred to in the context of this disclosure as the current term 117, depending on the embodiment of the actuator element 102. U , I C , I ce , I p It may include the following. The signal processing device 105 processes individual current terms I U , I C , I ce , Ip Based on this, external influencing factors 116 can be identified and / or classified.

[0434] The membrane actuator 100 can be controlled by a microcontroller. Optionally, the microcontroller may be configured with a signal generating device 103, as illustrated here in Figure 32. The charge and sensor current, i.e., the control signal 104, can be preprocessed by a signal processing device 105 (current term measurement circuit) and sampled using the microcontroller. Through this, the temporal signal curve of the control signal 104 can be discretized.

[0435] The current term data 117' thus discretized can be transferred to a high-performance PC 118 via a serial interface. If a neural network is used to classify external influencing factors 116, the neural network can be trained on the PC 118. For example, the neural network can be explicitly optimized for a microcontroller and transferred to the microcontroller after training. Optionally, the system state can also be detected using an external sensor system 119 and transferred to the PC 118.

[0436] Therefore, the microfluidic component 1000 of the present invention can detect its system state and react accordingly. For example, the control signal 104 can be adapted or modified to enable tracking or post-control of the operating point (see edge 121).

[0437] Optionally, a user interface 122 is provided, which can be used to manually control the operating point. Alternatively, or in addition, the user can obtain information via the user interface 122, such as information about identified and / or classified external influencing factors. If the signal processing device 105 determines that the operating point has deviated from the normal state due to, for example, a detected external influencing factor 116, the detected deviation from the normal state can be indicated to the user via light and / or acoustic signals using the user interface 122. For this purpose, for example, an alarm may be triggered.

[0438] Figure 33 shows a schematic block diagram of the signal processing device 105, or individual possible components of the microcontroller, when a neural network is used.

[0439] Figure 33 purely schematically shows an analog measurement circuit comprising an operational amplifier (OPA) 109, a measuring resistor 110, and Zener diodes 111 and 112, as previously described with reference to Figures 3 and 4. Also as described therein, the ground electrode of the microfluidic component 1000 and the ground electrode of the analog circuit can be interconnected. This generates a virtual ground potential so that the analog circuit does not load the signal processing device 103, and therefore the control signal 104 is not distorted.

[0440] The control signal 104, amplified using the OPA 109, can be transferred to the data processing module 123. Here, the data can be discretized, for example, using an ADC. Optionally, the signal from the signal generator (T=1 / f) can be input to the data preprocessing module 123 to determine the duration to be considered (1 pump cycle: 1 suction stroke, 1 pressure stroke). Optionally, the sampling rate can also be adjusted. For this purpose, a connection to a microcontroller may be provided.

[0441] The input vector 124, containing the discretized raw data 123, can then be transferred to the AI ​​module 125 (AI: Artificial Intelligence). The AI ​​module 125 may include a neural network. The output vector 126 of the AI ​​module 125 can then be transferred to the analog-to-digital converter (ADC) 127. The analog measurement values ​​from the analog measurement circuit can also be transferred to the ADC 127, which are highlighted using the edge 128. The signal binarized by the ADC 127 is then transferred to a predefined interface 129 (UART, USB, I 2 It can be transferred to C, SPI, ...).

[0442] Therefore, possible control of the microfluidic component 1000 could be attempted using a corresponding microcontroller equipped with an ADC (analog-to-digital converter), a serial interface coupled to a signal processing device 105 (measurement circuit), and a driver (e.g., a pump driver) with means for generating high voltage.

[0443] Concepts that can be intended for data evaluation may include one or more of the following components: • Stage classification using time-series classification (e.g., anomaly detection using an autoencoder). • Error response / control: Adapt / adjust the charging current to the current state (e.g., by adjusting the PWM signal of the micropump driver) to maintain a constant flow rate. • Direct integration of algorithms into the pump controller (ASIC) • Development of a "disturbance-variable IC" (small size, high energy efficiency) using FPGAs, for example, to evaluate data from sensor current electronic devices and provide the necessary information / closed-loop control for a pump driver. If the microfluidic component 1000 includes a pump and a valve, the pump may also react to an error condition in the valve.

[0444] The concepts of the present invention as described herein make it possible to provide a self-efficient microfluidic component 1000 comprising a membrane actuator 100 (e.g., piezoelectric or electrostatic driven) that detects the system state itself (e.g., using a neural network) and responds thereto in a problem-solving manner or notifies the user accordingly.

[0445] 8. Supplementary explanation regarding the above explanation The above description assumes prior knowledge, or basic theoretical knowledge, of pump chamber pressure, time behavior after voltage changes, and the usage boundary conditions of a micropump as an example of the microfluidic component 1000 of the present invention. For completeness, the presupposed foundational knowledge is explained below.

[0446] 8.1 Pressure in the pump chamber The fluid pressure beneath the membrane element 101 is a scalar quantity, and in principle, it can take on different values ​​at each point in the pump chamber. Therefore, the pressure may also differ at each point and at each time point.

[0447] Next, if the height of the pump chamber is very large and the pressure does not drop significantly even if there is flow within the pump chamber, this can be called a homogeneous pump chamber pressure p (early micropumps from the 1990s were manufactured in this way). In this case, the pump chamber pressure p is the same size at any point in time and at any point in time. The theoretical considerations of this disclosure assume such a case.

[0448] However, in modern micropumps, the pump chamber is configured to be very flat in order to reduce the ineffective volume and thus increase the compression ratio. The pressure drop within the pump chamber is no longer negligible, and in many cases, even greater than the pressure drop at the valve. Therefore, the assumption that the pressure in the pump chamber is homogeneous is not a good approximation, and this is why theoretical considerations only represent a reality with some uncertainty (gray box model).

[0449] In reality, when performing suction and pressure strokes, the faster the fluid inflow or outflow at a given time, the greater the pressure drop. Nevertheless, there is a summation effect of forces on the membrane element 101, and a time-dependent charge transfer dQ / dt=I is caused by the direct piezoelectric effect. p This causes the charge transfer, which correlates with the integral of the arrangement-dependent pressure at each time point t.

[0450] 8.2 Time behavior after voltage change Assuming infinitely fast voltage changes (which don't actually exist), several effects would occur, such as the following: 1) After applying a voltage infinitely fast (for example, within 1 μs when the voltage is generated by a frequency generator and piezoelectric amplifier that can also supply a corresponding current), the electric field acts within the piezoelectric ceramic (piezoelectric actuator element 102), and coefficient d 31 This causes the piezoelectric ceramic to shrink, and this shrinkage applies a bending torque to the membrane element 101 through the adhesive layer (between the membrane element 101 and the actuator element 102). These mechanical signals propagate at the speed of sound. Assuming the speed of sound in liquid is 1500 m / s and the lateral expansion of the micropump is limited to 0.015 mm, this signal takes t = 0.015 / 1500 = 10 μs to propagate throughout the membrane element 101 (therefore, this is faster the smaller the micropump). 2) Assuming that an incompressible liquid is placed beneath the membrane element 101 and the effect of a mechanical force on the bending actuator 102 appears after 10 μs, a pressure change occurs in the fluid beneath the membrane element 101 within the same time range (assuming the speed of sound is still 1500 m / s). Since the fluid does not move within the valve in this short time range, the incompressible liquid generates a reaction force (action = reaction). Since the liquid has not yet flowed in this short time range, there is no pressure drop in the pump chamber, and it can even be said to be a homogeneous pump chamber pressure. This maximum pressure is the occlusion pressure or "stall pressure". Due to the fairly slow time constant of the valve and the flow in the pump chamber, the pressure in the fluid begins to change, i.e., the fluid pressure becomes variable (due to the flow) and decreases over time. Instead of water, compressible air or partially compressible air (volume V gas If the air bubble is inside the pump chamber, this bubble will also be compressed (or expanded in the suction stroke) within a very short time after the voltage is turned on, i.e., before the medium can move through the valve, according to its equation of state, and the piezoelectric membrane transducer will lose force according to the fluid volume and will no longer be able to generate occlusion pressure. 3) The piezoelectric membrane interconnect (i.e., membrane element 101 + adhesive + actuator element 102) has a mechanical resonant frequency of several tens of kHz, depending on the design and whether there is liquid or air beneath the membrane element 101. The membrane element 101 is excited at its resonant frequency and overshoot (when the voltage change is faster than the resonant frequency), which can be observed in the form of increased volume, greater mechanical stress, and greater transient stroke. These greater strokes can be observed, for example, in a gas micropump when the pump is controlled by a rigid rectangular signal. In principle, this behavior can also be detected and investigated by the concepts of the present invention described herein. However, in practice, this phenomenon will not occur because the voltage level increase is usually configured not to excite these resonant frequencies. 4) The microvalves within the pump chamber are also moving elements and have a resonant frequency. This is on the order of kHz and can drop to less than 1 kHz (depending on the design) when the effective vibrating mass increases, for example due to the presence of liquid. Depending on the implementation of the control electronics, the microvalves may be excited to vibrate in the case of rapid voltage changes, which has already been observed experimentally. These vibrations of the valves lead to pressure vibrations within the pump chamber. These pressure vibrations are caused by the sensor current or current term I p These may have already been demonstrated in experiments using [specific technology / method]. These are particularly pronounced when there is air in the valve flap (e.g., Figure 21), but they can also be observed in water (Figure 25). In the case of vibrations of natural frequencies, the surrounding medium must also be moved, so the effective mass of the vibrating system increases, and therefore the observed natural frequencies are higher in water vibrations (Figure 25) than in air vibrations (Figure 21). Due to the lower viscosity, the damping of vibrations is significantly smaller in air than in water, so the vibration amplitude of the valve is significantly larger in air than in water. It must be emphasized that vibrations never occur in any valve design. If the damping is sufficiently large (e.g., if the height of the pump chamber is very low), the asymptotic boundary case can be reached even when there is a rectangular excitation in air, and there are no vibrations.

[0451] 8.3 Monitoring of operating microfluidic components based on sensor current The usage boundary conditions for the microfluidic component 1000 are known and typically do not change. One example of such a case is drug administration using a micropump, which is mounted near the body along with a drug reservoir and administers the drug to the patient via a catheter. In these cases, the measured current I (i.e., control signal 104) can be determined in time-resolved terms for each suction stroke and each pressure stroke, i.e., I(t). Thus, the suction stroke I of the pump suction (t) and the subsequent pressure stroke I pressure The time-current curve for (t) is obtained. These currents I suction (t) or Ipressure (t) is again the signal portion or current term I mentioned above. U , I C , I ce and I P It consists of.

[0452] In a steady state, when the micropump is operating without disturbance, the time-current curve of pump cycle n+1 is very similar to that of the previous pump cycle n. If a disturbance in pump operation occurs in pump cycle n+1 (e.g., valve failure, entry of large air bubbles, tubing detachment, tubing bending, obstruction of patient access, valve blockage by particles), at least one current term I U , I C , I ce , I P The temporal signal curve of changes, and therefore the temporal signal curve of the control signal 104 also changes according to disturbances in the suction stroke, pressure stroke, or both strokes. That is, the signal processing device 104 processes the current signal (control signal 104) I in cycle n+1, for example. suction (t) n+1 Current signal I for cycle n suction (t) n And, or any previous current signal (e.g., current signal I from cycle n-1) suction (t) n-1 By comparing it with the previous value, changes can be detected.

[0453] According to embodiments of the present invention, the signal processing device 105 may therefore be configured to compare the temporal signal curve of the control signal 104 for the operating cycle (n+1) of the microfluidic component 1000 with the temporal signal curve of the temporally preceding operating cycle (n) and to detect a deviation between the two signal curves.

[0454] In the event of a detected deviation, for example, the signal processing device 105 may be configured to indicate the detected deviation by optical and / or acoustic signals. Often, it is sufficient to indicate that a disturbance has occurred in the administration process. If the microdosing is related to safety (e.g., life-sustaining drugs), an alarm may be triggered and corresponding measures may be initiated (e.g., modification of the administration system). However, if it is not critical, it may be indicated that the administration module is not functioning correctly, and the pump may indicate this optically or acoustically and report it to the control center.

[0455] Preferably, control signal 104 and suction stroke I suction (t) and / or pressure stroke I pressue (t) can be captured in real time. Therefore, for example, if a micropump is pumping at a pump frequency of f=10Hz, the control signal I suction (t) or I pressue (t) can be captured every 100ms. It is possible to store this raw data completely, but it is not necessary. Comparing the raw data is very intensive in terms of memory and computational capacity. For example, the following possibilities exist for reducing the data: • The time range of interest for the measured current I(t) is known, and measurements are performed only within this range at a suitable sampling rate. In an example where a micropump is pumped at 10 Hz and controlled by electronics that generate voltage within 1 millisecond, the suction and pressure strokes (depending on the flow resistance of the valve and pump chamber and the viscosity of the fluid) are performed in a few milliseconds (see, for example, Figure 23). All current terms I pThe aforementioned potentially occurring disturbances affecting (t) cause significant changes only within this time window. Therefore, it is reasonable to sample a time range up to approximately 5 ms after the start of the suction or pressure stroke, and then no longer capture measurements. The remaining 45 ms to the next stroke time window can be used for evaluating the raw data and storing the appropriately processed data. According to embodiments of the present invention, the signal processing device 105 may be configured to use a first time portion of the operating cycle (suction stroke or pressure stroke) of the microfluidic component 1000 to determine the temporal signal curve of the control signal 104, and to use the remaining second time portion of the operating cycle up to the start of the subsequent operating cycle to store and / or evaluate the determined signal curve. • The amount of data can be further reduced by appropriately fitting the measurements to the time-dependent signal curve and saving only the respective fitting parameters. A model for this fit is already available for the gray-box model. In the example above, the total current I(t) is divided into two parts, each of which decays exponentially. U (t), I C (t), I ce (t), I P The signal was divided into the sum of (t). The amplitude and time constant of these partial currents were obtained as fitting parameters. According to embodiments of the present invention, for example, only these fitting parameters may be stored for each suction stroke and each pressure stroke. The large amount of raw data is then reduced to a very small number of values. That is, the signal processing device 105 processes the determined signal curve into at least one signal portion I of the control signal I(t). U , I C , I ce , I p It can be configured to store the temporal amplitude curve and associated time constant in the form of parameters, respectively. Therefore, as explained above, as long as the amount of data is reduced, the correspondingly reduced values ​​can then be compared very easily. Furthermore, the signal processing device 105 can evaluate the trend evolution of these values ​​over many pump cycles and use this information to assess the state of the micropump.

[0456] In many cases, it is not necessary to measure each individual pump cycle. For a 10Hz pump, this measurement (and comparison regarding undisturbed operation) can be performed at any longer interval, for example, once per second (i.e., every 10 strokes) or once per hour. This saves significant memory and computing power, and therefore energy, especially in battery-powered applications or long-term implants. Accordingly, according to embodiments of the present invention, the signal processing device 105 may be configured to capture the temporal signal curve of the control signal 104 between two operating cycles (e.g., the first and tenth pump strokes), with a number of further operating cycles between these two operating cycles during which the signal processing device 105 does not capture data. In addition, it would be theoretically possible to attempt to detect signs of fatigue and deterioration. In such cases, parameter comparisons can be performed by comparing parameters that have been stored over long periods (weeks, months, or even years) (for example, in high-quality, long-life industrial applications such as long-term implantation, or lubrication of rapidly rotating bearings or micro-cooling of servers). ○ For example, if there are micro-fractures in the piezoelectric ceramic during operation over a very long period of time: • The rigidity of the membrane may decrease, which can reduce the stroke of the micropump (this is due to the current term I p (to change), • Piezoelectric properties (d 31 The capacitance may change, and the coefficient d 31 teeth

[0457]

number

[0458] Since it is directly proportional to the current term I ce and I C It will have an effect. ○ Alternatively, if the adhesive strength between the piezoelectric ceramic (actuator element 102) and the membrane element 101 decreases, this adhesive layer will have strong shear stress, and therefore, if the polymer chains of the adhesive crack over time, this will reduce the bias of the piezoelectric membrane transducer, lower the compression ratio, and consequently the current term I p It could have an impact.

[0459] Therefore, in summary, the concept of the present invention described herein provides a membrane actuator 100 for a micropump or microvalve, the membrane actuator 100 in contact with a fluid (gas or liquid) on one side to which a time-varying electrical control signal 104 U(t) is applied, where U(t) or a quantity I(t) derived therefrom is determined in an electrically precise manner by a measuring circuit 105 during the operation of the membrane actuator, and the time-varying effect on the electrical control signal 104 I(t) is captured through hydraulic, pneumatic, piezoelectric, or mechanical processes acting on the membrane actuator 100, and it should be noted that these different processes can be detected and distinguished by evaluation of the control signal 104 I(t).

[0460] The embodiments described above merely provide examples of the principles of the innovative concepts described herein. Modifications and changes to the arrangements and details described herein will be obvious to those skilled in the art. Accordingly, the concepts described herein are intended to be limited only to the scope of the claims set forth below, and not to be limited by any specific details presented herein based on the description and embodiments.

[0461] Some or all of the method steps may be performed by (or using) a hardware device such as a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, some or more of the most important method steps may be performed by such a device.

[0462] Depending on specific implementation requirements, embodiments of the present invention may be implemented in hardware or software. The implementation may involve using a digital storage medium, such as a floppy disk, DVD, Blu-ray disk, CD, ROM, PROM, EPROM, EEPROM, or FLASH memory, a hard disk, or any other magnetic or optical memory having electronically readable control signals stored thereon, which may or may not work with a computer system programmable to perform each of these methods. This is why the digital storage medium may be computer-readable.

[0463] Accordingly, some embodiments of the present invention include a data carrier containing electronically readable control signals that can cooperate with a computer system programmable to perform any of the methods described herein.

[0464] Generally, embodiments of the present invention may be implemented as a computer program product having program code, the program code being effective in performing one of the methods when the computer program product runs on a computer.

[0465] Program code can also be stored, for example, on a machine-readable carrier.

[0466] Other embodiments include a computer program for performing any of the methods described herein, the computer program being stored on a machine-readable carrier. In other words, one embodiment of the method of the present invention is a computer program having program code for performing any of the methods described herein when the computer program is running on a computer.

[0467] Accordingly, a further embodiment of the method of the present invention is a data carrier (or digital storage medium or computer-readable medium) on which a computer program for performing any of the methods described herein is recorded. The data carrier, digital storage medium, or computer-readable medium is typically tangible or non-volatile.

[0468] A further embodiment of the method of the present invention is a data stream or sequence of signals representing a computer program for performing any of the methods described herein. The data stream or sequence of signals may be configured to be transmitted, for example, over a data communication link, such as the Internet.

[0469] A further embodiment includes a processing unit, such as a computer or a programmable logical device, configured or adapted to perform any of the methods described herein.

[0470] A further embodiment includes a computer on which a computer program for performing any of the methods described herein is installed.

[0471] A further embodiment of the present invention includes a device or system configured to transmit a computer program to a receiver for performing at least one of the methods described herein. The transmission may be, for example, electronic or optical. The receiver may be, for example, a computer, a mobile device, a memory device, or a similar device. The device or system may include, for example, a file server for transmitting the computer program to the receiver.

[0472] In some embodiments, a programmable logic device (e.g., a field-programmable gate array, FPGA) may be used to perform some or all of the functions of the methods described herein. In some embodiments, a field-programmable gate array may work in conjunction with a microprocessor to perform any of the methods described herein. Generally, these methods are performed in some embodiments by any hardware device. The hardware device may be any general-purpose hardware, such as a computer processor (CPU), or method-specific hardware, such as an ASIC. [Explanation of symbols]

[0473] 100 Membrane Actuators 101 Membrane elements 102 Actuator element 103 Signal Generation Devices 104 Electrical control signals 105 Signal Processing Devices 106 Mechanical Components 107 Carrier substrate 108 Combined charge flow 109 Voltage-controlled operational amplifier 110 Measuring resistor 111, 112 Zener diodes 113, 114 Low leakage current diodes 116 External influence factors 117 Current term 117' Current term data 118 PC 119 External Sensor System 121 Edge 122 User Interface 123 Discretized raw data 124 Input Vectors 125 AI Modules 126 Output Vector 127 Analog-to-Digital Converter (ADC) 128 Edge 129 Predefined Interfaces 171 measurement values 1711, 1721, 1731 curve 1712, 1722, 1732 curves 181 Circular Lissajous curve 182 Lissajous curve 201 Inlet side reservoir 202 Bubble detector 203 Pressure Sensor 204 Pipeline 205 Outlet side reservoir 206 Pressure Sensor 207 Pressure Controller 208 Oscilloscope 291 Suction Strokes 292 pressure stroke 293 Measurement curve 294 Measurement curve 295 Measurement curve 296 Measurement curve 300 Overshoot 301 Suction Stroke 302 Pressure stroke 1000 Microfluidic Components

Claims

1. A microfluidic component (1000), A membrane actuator (100) having a membrane element (101) and an actuator element (102) for deflecting the membrane element (101), A signal generating device configured to generate an electrical control signal (104) having a time-varying signal curve for controlling the membrane actuator (101), wherein the actuator element (102) operates the membrane element (101), and the signal generating device (103) The microfluidic component (1000) includes a signal processing device (105) configured to determine the influence of one or more external influencing factors on the temporal signal curve of the control signal (104) during its operation. Based on the influence on the temporal signal curve, identify and / or classify at least one causal external influencing factor. The microfluidic component (1000) is at least one of a microfluidic pump or a microfluidic valve. The membrane actuator (100) is a piezoelectric driven membrane actuator in which the actuator element (102) includes at least one piezoelectric element. The control signal (104) causes deformation of the piezoelectric element by using the inverse piezoelectric effect, thereby causing the piezoelectric element to apply an operating force on the membrane element (101). The temporal signal curve of the control signal (104) is affected by the signal originating from the piezoelectric element, which is generated due to the reaction force of the membrane element (101) based on the direct piezoelectric effect. The signal processing device (105) is configured to perform a signal analysis of the temporal signal curve of the control signal (104) in order to determine and / or classify the at least one causative external influencing factor. Individual signal portions (IU, IC, Ice, Ip) of the control signal (104) are determined, and these individual signal portions (IU, IC, Ice, Ip) correlate with a plurality of different external influencing factors that have different effects on the temporal signal curve of the control signal (104). The signal processing device (105) is a microfluidic component (1000) configured to assign specific signal portions to specific external influencing factors, and to use these to identify and / or classify each of the said external influencing factors.

2. The signal processing device (105) is The microfluidic component (1000) according to claim 1, configured to distinguish a first temporal signal curve of the control signal (104) corresponding to the operation of the membrane actuator (100) without external influence from a different second temporal signal curve of the control signal (104) corresponding to the operation of the membrane actuator (100) with at least one dominant external influencing factor.

3. The signal processing device (105) is The microfluidic component (1000) according to claim 1, configured to identify and / or classify liquid pressure, air pressure, or mechanical force acting on at least one membrane side of the membrane element (101) as external influencing factors that cause an effect on the temporal signal curve.

4. The signal processing device (105) processes at least four different signal portions (I Ｕ , I Ｃ , I ｃｅ , I ｐ ) are configured to distinguish each other, First signal portion (I Ｕ ) is linked to the temporal change in voltage when charging or discharging the capacitance between the membrane element (101) and the piezoelectric element (102), Second signal portion (I ｐ ) is linked to the temporal change in the pressure acting on the membrane element (101), Third signal portion (I Ｃ ) is linked to the temporal change in capacitance caused by the large signal effect of the piezoelectric element (102), The fourth signal portion (I ｃｅ ) is the piezoelectric coefficient d that changes due to the large-signal effect of the piezoelectric element (102) ３１ The microfluidic component (1000) according to claim 1, which links to the temporal change of

5. The individual signal portion (I Ｕ , I Ｃ , I ｃｅ , I ｐ Each of these aims to achieve equilibrium through an individual time compensation process, and in each of the said time compensation processes, each signal portion is an individual time amplitude curve (I Ｕ０ , I ｐ０ , I ｃ０ , I ｃｅ０ ) and the individual time constants (τ) on which the aforementioned time compensation process is carried out. ａ , τ ｈ , τ ｄ =τ ｃ =τ ｐｉｅｚｏ ) including, The signal processing device (105) processes the individual signal portion (I Ｕ , I Ｃ , I ｃｅ , I ｐ From one or more of the above, each of the aforementioned time amplitude curves (I Ｕ０ , I ｐ０ , I ｃ０ , I ｃｅ０ ) and / or the respective time constants (τ ａ , τ ｈ , τ ｄ =τ ｃ =τ ｐｉｅｚｏ The microfluidic component (1000) according to claim 1, configured to determine the external influencing factors based on ).

6. The signal processing device (105) processes the second signal portion (I ｐ Based on the following external influencing factors, ○ Changes in reverse pressure, ○ Preliminary pressure L, ○ Closure of the exit channel of catheters, etc. ○ Presence of air bubbles in the pump chamber, ○ Size of bubbles inside the pump, ○ The arrival of bubbles into the pump chamber via the inlet pipeline, ○ Bubbles moving away from the pump chamber via the outlet pipeline, ○ Change in the resistance of the pump chamber, ○ Changes in stroke volume due to particle capture, ○ Changes in ambient parameters such as pressure changes above the membrane element, pressure changes at the valve inlet, pressure changes at the valve outlet, and temperature changes. ○ Valve state detection, that is, whether the valve is open or closed, ○ Valve defects such as valve damage, ○ Deterioration of valve sealing characteristics caused by particles, ○ Sedimentation or aggregation of solid matter, for example, precipitation, deterioration of valve sealing properties due to protein denaturation, ○ Occurrence of capillary adhesion when the meniscus blocks the valve, ○ Occurrence of van der Waals adhesion when corresponding molecules are deposited between the valve seat and the supporting web. ○ Swelling or change in elastic properties of the sealing element, ○ Changes in adhesive bonding between the actuator element and the membrane element, ○ The inertia of the surrounding liquid is coupled to the micropump, so the micropump (in the case of an incompressible liquid as the pump medium) can recognize whether the connected fluid conduit is long or short, soft or hard. ○ For example, the change in the viscosity of the medium makes it possible to check whether the entire drug has been rinsed off and replaced with physiological saline during the rinsing process. A microfluidic component (1000) according to claim 1, configured to determine at least one of the following.

7. The signal processing device (105) processes the third and / or fourth signal portion (I Ｃ , I ｃｅ Based on the following external influencing factors, ○ Mechanical fatigue of the piezoelectric element (102), such as failure or the growth of so-called subcritical cracks during very long continuous operation. ○ Electrical fatigue of the piezoelectric element (102), such as loss of polarization. A microfluidic component (1000) according to claim 1, configured to determine at least one of the following.

8. The signal processing device (105) processes the first signal portion (I Ｕ Based on the following external influencing factors, - Tears in electrical contacts, - Short circuit of the piezoelectric element (102), - Damage to the piezoelectric element (102), for example, when only a portion of the piezoelectric ceramic is in electrical contact, the capacitance and therefore the charging current decreases. A microfluidic component (1000) according to claim 1, configured to determine at least one of the following.

9. The signal processing device (105) has a mathematical model having the associated amplitude curve and / or associated time constant of each of the signal portions, which is the individual signal portion (I Ｕ , I Ｃ , I ｃｅ , I ｐ ) comprises a storage device that stores information for each of the one or more of the above, The microfluidic component (1000) according to claim 1, wherein the signal processing device (105) is configured to fit the mathematical model to the temporal signal curve of the affected control signal (104) and to identify each of the causative external influencing factors with the help of the fitted mathematical model.

10. The signal processing device (105) includes a memory device that stores how a specific external influencing factor affects the temporal signal curve of the control signal (104), The microfluidic component (1000) according to claim 1, wherein the signal processing device (105) is configured to identify each of the external influencing factors that cause the signal based on the temporal signal curve of the affected control signal (104) and the influencing factor information stored in the storage device.

11. The signal processing device (105) includes a pre-trained neural network that generates different external influencing factors and determines the respective effects on the temporal progression of the control signal (104). The microfluidic component (1000) according to claim 1, wherein the neural network is configured to classify at least one causal external influencing factor based on the temporal signal curve of the affected control signal (104) and based on pre-created training data.

12. The microfluidic pump has a pump chamber, at least one membrane side of the membrane element (101) is in contact with a fluid disposed in the pump chamber, and a variable pump chamber pressure is generated in the pump chamber by operating the membrane actuator (100). The microfluidic component (1000) according to claim 1, wherein the signal processing device (105) is configured to determine the variable pump chamber pressure based on the temporal signal curve of the control signal (104), and to use this to identify and / or classify the external influencing factors.

13. The microfluidic valve enables the opening and / or closing of the fluid path, The microfluidic component (1000) according to claim 1, wherein the signal processing device (105) is configured to identify and / or classify the external influencing factors and / or determine the time-varying operating parameters of the microfluidic valve based on the time signal curve of the control signal (104).

14. The signal processing device (105) determines the type of fluid used within the microfluidic component (1000) based on the temporal signal curve of the control signal (104), and distinguishes between the condensation state of the fluid between gas and liquid. The microfluidic component (1000) according to claim 1, configured to identify and / or classify the determined fluid and / or its determined aggregation state as the external influencing factor.

15. The microfluidic component (1000) according to claim 1, wherein the signal processing device (105) is configured to compare the temporal signal curve of the control signal (104) of the operating cycle (n+1) of the microfluidic component (1000) with the temporal signal curve of the temporally preceding operating cycle (n), and to detect a deviation between the signal curves.

16. The signal processing device (105) is configured to indicate the detected deviation using light and / or acoustic signals, according to claim 15, for the microfluidic component (1000).

17. The signal processing device (105) operates during the operating cycle of the microfluidic component (1000). The first time portion of the operation cycle is used to determine the time signal curve of the control signal (104), The microfluidic component (1000) according to claim 1, configured to store and / or evaluate the determined signal curve using the remaining second time portion of the same operating cycle until the start of a subsequent operating cycle.

18. The signal processing device (105) processes the determined signal curve into at least one signal portion (I) of the control signal (104). Ｕ , I Ｃ , I ｃｅ , I ｐ The microfluidic component (1000) according to claim 17, which is configured to store the temporal amplitude curve and associated time constant of the microfluidic component (1000) in the form of parameters, respectively.

19. The microfluidic component (1000) according to claim 17, wherein the signal processing device (105) is configured to determine the temporal trend evolution of the temporal signal curve over a number of operating cycles, to use this trend evolution to determine the external influencing factors, and / or to evaluate the state of the microfluidic component (1000).

20. The microfluidic component (1000) according to claim 17, wherein the signal processing device (105) is configured to capture the temporal signal curve of the control signal (104) between two operating cycles, and there are a number of further operating cycles between these two operating cycles, during which the signal processing device (105) does not capture data.

21. The microfluidic component (1000) according to claim 1, wherein the microfluidic component is configured to detect the external influencing factors purely based on the signal analysis of the temporal signal curve of the electrical control signal (104) without using an additional sensor system within the membrane element (101) and / or the actuator element (102).

22. The signal processing device (105) includes an operational amplifier (109), the inverting input of the operational amplifier (109) is connected to the signal output of the membrane actuator (100), and the non-inverting input of the operational amplifier (109) is connected to the same ground potential as the signal generation device (103), thereby generating a virtual ground potential at the inverting input of the operational amplifier (109). The signal processing device (105) further comprises a measuring resistor (110) connected between the inverting input and the output of the operational amplifier (109), The control signal (104) that processes the signal processing device (105) is transmitted between the terminals of the measuring resistor (110) as described in claim 1 of the microfluidic component (1000).

23. A method for operating a microfluidic component (1000) having a membrane actuator (100) comprising a membrane element (101) and an actuator element (102) for deflecting the membrane element (101), wherein the microfluidic component (1000) is at least one of a microfluidic pump or a microfluidic valve, and the method is A step of generating an electrical control signal (104) for controlling the membrane actuator (100), wherein the actuator element (102) acts on the membrane element (101), and the electrical control signal (104) includes a time-varying signal curve, The steps include determining the temporal signal curve of the electrical control signal (104) that is affected by one or more external influencing factors during the operation of the microfluidic component (1000), The steps include identifying and / or classifying at least one external influencing factor based on the affected temporal signal curve determined above, The membrane actuator (100) is a piezoelectric driven membrane actuator in which the actuator element (102) includes at least one piezoelectric element. The control signal (104) causes deformation of the piezoelectric element by using the inverse piezoelectric effect, thereby causing the piezoelectric element to apply an operating force on the membrane element (101). The temporal signal curve of the control signal (104) is affected by the signal originating from the piezoelectric element, which is generated due to the reaction force of the membrane element (101) based on the direct piezoelectric effect. The method further includes performing a signal analysis of the temporal signal curve of the control signal (104) in order to determine and / or classify the at least one causative external influencing factor. Individual signal portions (IU, IC, Ice, Ip) of the control signal (104) are determined, and these individual signal portions (IU, IC, Ice, Ip) correlate with a plurality of different external influencing factors that have different effects on the temporal signal curve of the control signal (104). The method for operating a microfluidic component (1000) further includes assigning a specific signal portion to a specific external influencing factor and using this to identify and / or classify each of the said external influencing factors.

24. A computer-readable digital storage medium in which program code is stored, wherein the program code is program code for executing the method described in claim 23 when the program is executed on a computer.

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