A Droplet Routing Method for Digital Microfluidic Chips Based on an Improved A* Algorithm

Abstract

This invention discloses an experimental droplet routing method for digital microfluidic chips based on an improved A* algorithm. The improvement to the A* algorithm includes: incorporating the number of electrode wear cycles into the heuristic function of the A* algorithm; adding a coefficient related to the number of wear cycles, which can be modified according to the chip wear level; reducing the coefficient when chip wear is minor, prioritizing routing distance, and calculating the shortest distance as the target for route calculation; increasing the coefficient when chip wear is severe, aiming to calculate the route through areas of less worn chip and avoiding areas of severe wear; and adjusting the path after obtaining the droplet routing path, moving the repetitive wear areas at the start and end points to the middle of the path to reduce repeated wear on electrodes in the start and end areas. This invention, by improving the A* algorithm, allows droplet routing to consider electrode wear factors simultaneously, further improving the chip wear uniformity and extending the chip's lifespan.

Description

Technical Field

[0001] This invention relates to droplet routing in digital microfluidic chip experiments, and particularly to a method for droplet routing in digital microfluidic chip experiments based on an improved A* algorithm. Background Technology

[0002] Microfluidics is a rapidly developing scientific discipline that integrates microelectrodes, reaction chambers, microvalve, and microsensors onto a single chip, processing nanoliters or microliters of liquid at the micrometer scale—a process known as a laboratory-on-a-chip. Digital microfluidic chips typically consist of two layers. The upper layer is usually made of glass and is entirely connected to the ground electrode, providing a potential reference plane for the lower layer. The surfaces of both layers are coated with a hydrophobic material to facilitate droplet movement between them. When a voltage is applied to the electrodes, the droplet is drawn towards the electrode with the applied voltage due to the electric field.

[0003] Common failures encountered during biochemical experiments using chips include hydrophobic layer damage, dielectric layer breakdown, and parasitic leakage of capacitors, often caused by overuse of electrodes. These failures lead to decreased chip reliability and shortened lifespan. Therefore, when generating high-level synthetic results from biochemical experiments, the wear uniformity of the chip electrodes must be fully considered.

[0004] Existing high-level synthesis methods apply the A* algorithm to droplet routing path search, effectively calculating droplet routing paths while satisfying all constraints. However, the routing cost evaluation method used is Euclidean distance. Applying Euclidean distance to the A* algorithm results in a droplet routing path that is the shortest Euclidean distance from the starting point to the ending point, without considering chip wear leveling factors during the routing process. Therefore, this method has certain shortcomings.

[0005] In digital microfluidic biochips, adjacent steps in the droplet routing path may overlap. In such cases, the overlapping areas are reused multiple times. However, in actual biochemical experiments, the start and end points of the droplet routing often correspond to the previous and next experimental operations, meaning these points themselves experience significant wear. When using the improved A* algorithm to calculate the droplet routing path, if there are overlapping parts, these overlaps always occur one step before the end point. This means the overlapping portion of the routing path will inevitably fall within the end point region of the droplet route, which is clearly detrimental to the even wear of the microelectrodes. Therefore, adjustments to the calculated routing path are necessary. Summary of the Invention

[0006] To address the problems existing in the prior art, the purpose of this invention is to provide an experimental droplet routing method for digital microfluidic chips based on an improved A* algorithm.

[0007] To achieve the above objectives, this invention provides an experimental droplet routing method for digital microfluidic chips based on an improved A* algorithm, comprising:

[0008] S1 models the chip environment as a gridded space and establishes open lists, closed lists, and obstacle lists for path search;

[0009] S2, Modify the heuristic function of the A* algorithm by adding the number of electrode wears to the heuristic function and adding a coefficient related to the number of wears;

[0010] S3. After obtaining the droplet routing path, the path is adjusted to move the repeated wear areas at the start and end points to the middle of the path, so as to reduce the repeated wear of the electrodes in the start and end areas of the path.

[0011] In S1, the open list refers to the set of nodes to be searched in the search area, the closed list refers to the set of nodes that have been searched in the search area, and the obstacle list refers to the list containing the coordinates of all obstacle nodes. The routing path is not allowed to pass through these obstacles.

[0012] In S2, the heuristic function of the A* algorithm is modified, and the modified heuristic function is as follows:

[0013]

[0014]

[0015]

[0016] In the formula, F(n+1) represents the routing cost of the (n+1)th node, which consists of G(n+1) and H(n+1). G(n+1) represents the routing cost from the originating node to the (n+1)th node, and it consists of two parts: F(n) represents the routing cost from the originating node to the nth node; the latter term represents the routing cost of the droplet from the nth node to the (n+1)th node, which is defined as the sum of the number of wear cycles of the microelectrodes within the area occupied by the droplet at the (n+1)th node, where 'a' is a user-defined coefficient. H(n+1) represents the estimated routing cost from the (n+1)th node to the destination node, which is evaluated using the Euclidean distance between the (n+1)th node and the destination node.

[0017] The above three equations improve the A* algorithm by incorporating the wear count of the microelectrode as a parameter into the G value. When the wear count of the microelectrode of the node to be searched is too high, the routing cost F calculated by the heuristic function also increases accordingly, thus preventing the droplet routing path from passing through this severely worn microelectrode. The H value in the heuristic function still uses Euclidean distance, guiding the routing path to aim for the shortest distance when the microelectrode is not worn. In the equation, 'a' is a user-defined coefficient representing the weight of microelectrode wear in the droplet routing path calculation. In practical use, this coefficient can be set as a variable; as the chip wear increases, the value of 'a' increases, making the A* algorithm more careful to avoid areas with severe chip wear when the chip is severely worn.

[0018] S3 includes the following steps:

[0019] Step 1: The routing path needs to conform to fluid constraints;

[0020] Step 2: Determine if the routing path needs to be adjusted;

[0021] Step 3: Calculate the routing cost of the path points traversed by the route;

[0022] Step 4: Find the path point with the lowest routing cost in the routing path and move the overlapping area to this point;

[0023] In step 1, the routing path meets the fluid constraint condition that the location is more than one row of electrodes away from the nearest droplet.

[0024] In step 2, when the droplet routing path length is not an integer multiple of the droplet width, the routing paths will overlap in the destination area, so the routing path needs to be adjusted. When the droplet routing path length is exactly an integer multiple of the droplet width, the routing paths do not overlap, so no adjustment is needed.

[0025] In step 3, the routing cost of a path point is defined as the sum of the original wear count of the electrode and the wear count generated by the routing when the droplet passes through it.

[0026] Software simulation experiments have verified that the method has better test results in wear uniformity index compared with existing methods in CEP, master mixing and continuous dilution experiments. Attached Figure Description

[0027] Figure 1 This diagram illustrates the number of wear cycles of the chip electrodes.

[0028] Figure 2 This is a diagram showing the experimental droplet routing path for existing methods.

[0029] Figure 3This is a diagram showing the experimental droplet routing path of the present invention.

[0030] Figure 4 This is the unadjusted experimental droplet routing path diagram.

[0031] Figure 5 This is the adjusted experimental droplet routing path diagram.

[0032] Figure 6 This is a flowchart of the droplet routing method described in this paper. Detailed Implementation

[0033] The present invention will be further described below with reference to the embodiments and accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] like Figure 1 As shown, a 2×2 experimental droplet is located in the center of a 12×12 sample chip. The numbers on the grid represent the number of wear cycles on the chip electrodes. The blue area indicates the starting point of the droplet path, and the green area indicates the ending point. In the central region of the chip, microelectrodes with less wear are represented by light orange, while microelectrodes with more severe wear are represented by dark orange.

[0035] like Figure 2 As shown, the routing path obtained by the A* algorithm used in the existing method is the light blue area in the figure. It can be seen from the figure that the A* algorithm obtains the shortest path, but this path passes through the electrode with severe wear in the center of the chip. It passes through 8 microelectrodes with a wear count of 30, the routing path distance is 2, and the sum of the wear counts passed by the path is 240. This makes the electrode wear in this area more severe, and a certain electrode may be damaged due to overuse, leading to the end of the experiment.

[0036] like Figure 3 As shown, the routing path calculated by this invention passes through 21 electrodes with a wear count of 10, the routing path distance is 5, and the sum of the wear counts traversed by the routing path is 210. Comparing the two methods, it can be seen that although the routing path distance of this invention is longer than that of the existing method, it bypasses the areas of severe wear on the microelectrodes, thereby improving the wear uniformity of the microelectrodes, delaying the timing of damage to the most severely worn microelectrodes on the chip, and thus maximizing the chip lifespan.

[0037] like Figure 4 The diagram shows a route that meets fluid constraints and requires adjustment. The blue area represents the path's starting point, and the green area represents the path's ending point. The overlapping portion between the path's midpoint and ending point is shown in orange.

[0038] like Figure 5 As shown, the routing path is adjusted to move the overlapping parts of the path to the point with the least wear in the middle of the path. The orange area in the figure represents the overlapping part.

[0039] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for experimental droplet routing in digital microfluidic chips based on an improved A* algorithm, characterized in that, Includes the following steps: S1 models the chip environment as a gridded space and establishes open lists, closed lists, and obstacle lists for path search; S2, Modify the heuristic function of the A* algorithm by adding the number of electrode wears to the heuristic function and adding a coefficient related to the number of wears; S3. After obtaining the droplet routing path, the path is adjusted to move the repeated wear areas at the start and end points to the middle of the path, so as to reduce the repeated wear of the electrodes in the start and end areas of the path.

2. The experimental droplet routing method for digital microfluidic chips based on the improved A* algorithm according to claim 1, characterized in that, In S1, the open list refers to the set of nodes to be searched in the search area, the closed list refers to the set of nodes that have been searched in the search area, and the obstacle list refers to the list containing the coordinates of all obstacle nodes. The routing path is not allowed to pass through these obstacles.

3. The experimental droplet routing method for digital microfluidic chips based on the improved A* algorithm according to claim 1, characterized in that, In S2, the heuristic function of the A* algorithm is modified, and the modified heuristic function is as follows: In the formula, F(n+1) represents the routing cost of the (n+1)th node, which consists of G(n+1) and H(n+1). G(n+1) represents the routing cost from the originating node to the (n+1)th node, and it consists of two parts: F(n) represents the routing cost from the originating node to the nth node; the latter term represents the routing cost of the droplet from the nth node to the (n+1)th node, which is defined as the sum of the number of wear cycles of the microelectrodes within the area occupied by the droplet at the (n+1)th node, where 'a' is a user-defined coefficient. H(n+1) represents the estimated routing cost from the (n+1)th node to the destination node, which is evaluated using the Euclidean distance between the (n+1)th node and the destination node. The above three equations improve the A* algorithm by incorporating the wear count of the microelectrode as a parameter into the G value. When the wear count of the microelectrode of the node to be searched is too high, the routing cost F calculated by the heuristic function also increases accordingly, thus preventing the droplet routing path from passing through this severely worn microelectrode. The H value in the heuristic function still uses Euclidean distance, guiding the routing path to aim for the shortest distance when the microelectrode is not worn. In the equation, 'a' is a user-defined coefficient representing the weight of microelectrode wear in the droplet routing path calculation. In practical use, this coefficient can be set as a variable; as the chip wear increases, the value of 'a' increases, making the A* algorithm more careful to avoid areas with severe chip wear when the chip is severely worn.

4. The experimental droplet routing method for digital microfluidic chips based on the improved A* algorithm according to claim 1, characterized in that, S3 includes the following steps: Step 1: The routing path needs to conform to fluid constraints; Step 2: Determine if the routing path needs to be adjusted; Step 3: Calculate the routing cost of the path points traversed by the route; Step 4: Find the path point with the lowest routing cost in the routing path and move the overlapping area to this point.

5. The experimental droplet routing method for digital microfluidic chips based on the improved A* algorithm according to claim 4, characterized in that, In step 1, the routing path meets the fluid constraint condition that the location is more than one row of electrodes away from the nearest droplet.

6. The experimental droplet routing method for digital microfluidic chips based on the improved A* algorithm according to claim 4, characterized in that, In step 2, when the droplet routing path length is not an integer multiple of the droplet width, the routing paths will overlap in the destination area, so the routing path needs to be adjusted. When the droplet routing path length is exactly an integer multiple of the droplet width, the routing paths do not overlap, so no adjustment is needed.

7. The experimental droplet routing method for digital microfluidic chips based on the improved A* algorithm according to claim 4, characterized in that, In step 3, the routing cost of a path point is defined as the sum of the original wear count of the electrode and the wear count generated by the routing when the droplet passes through it.

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