Artificial surface plasmon structure sensor, multi-mode sensing clamping jaw and application of multi-mode sensing clamping jaw

Abstract

The invention discloses an artificial surface plasmon structure sensor, a multi-mode sensing clamping jaw and application of the artificial surface plasmon structure sensor and the multi-mode sensing clamping jaw, and relates to the technical field of intelligent laboratory automation. The artificial surface plasmon substrate comprises a periodic slot unit, and a gradual transition section and a coplanar waveguide transmission end which are sequentially arranged at the two ends of the periodic slot unit from near to far, the coplanar waveguide transmission end is connected with coaxial cables, and the coaxial cables on the two sides are connected with a signal input end and a signal output end of external SDR equipment respectively; the SDR device inputs an excitation signal to the sensor and collects a response signal of the sensor. The device has the grabbing force control function and the substance recognition function, dangerous liquid centrifuge tubes can be safely grabbed and recognized under the completely unmanned intervention condition, the requirement for manual contact with harmful substances is greatly reduced, and the safety and the automation level of an intelligent laboratory in dangerous operation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to an artificial surface plasmon structure sensor, a multi-modal sensing gripper and its application, belonging to the technical field of intelligent laboratory automation. BACKGROUND

[0002] In the field of intelligent laboratory automation, precise material operation and state sensing are the core technical links to realize high-throughput and unmanned experiments, especially in experimental environments involving strong corrosive, toxic or volatile hazardous substances. It is of great significance to replace manual operation with robots to ensure personnel safety and health. In traditional laboratories, centrifuge tube grabbing, pipetting and dispensing operations are performed manually. When contacting acids (such as sulfuric acid and hydrochloric acid), organic solvents (such as ethyl acetate and isopropanol) or other hazardous chemicals, it is extremely easy to cause exposure risks due to liquid splashing, gas evaporation or container breakage, causing health hazards to experimental personnel.

[0003] Currently, most automated grabbing solutions still lack integrated sensing capabilities for grabbing force and material composition. Traditional grippers cannot sense grabbing force in real time, which can cause unstable centrifuge tube grabbing or even breakage, increasing the risk of hazardous material leakage. Material identification often relies on independent external devices (such as spectrometers), which cannot complete composition judgment simultaneously during grabbing actions, and cannot meet the durability requirements of sensors in corrosive environments.

[0004] Therefore, it is urgent to develop an integrated intelligent gripper that can withstand corrosive environments and has grabbing force control and material identification functions, so that it can safely and reliably complete the grabbing, identification and processing of hazardous liquids without human intervention, minimizing the need for human contact with hazardous materials, and improving the safety, automation level and sustainability of intelligent laboratories in dangerous operations. SUMMARY

[0005] The present application aims to overcome the shortcomings of the prior art and provide an artificial surface plasmon structure sensor, a multi-modal sensing gripper and its application, which has grabbing force control and material identification functions, can safely and reliably complete the grabbing, identification and processing of hazardous liquid centrifuge tubes without human intervention, minimizes the need for human contact with hazardous materials, and improves the safety, automation level and sustainability of intelligent laboratories in dangerous operations.

[0006] To achieve the above-mentioned purpose, the present application is realized by adopting the following technical solutions: In a first aspect, the present application provides an artificial surface plasmon structure sensor, comprising an artificial surface plasmon substrate, the artificial surface plasmon substrate comprising a periodic groove unit and a gradually changing transition section and a coplanar waveguide transmission end arranged in turn at both ends of the periodic groove unit, the coplanar waveguide transmission end is connected with a coaxial cable, and the coaxial cable on both sides is connected with a signal input end and a signal output end of an external software defined radio device respectively, and the external software defined radio device inputs an excitation signal to the artificial surface plasmon structure sensor and collects a response signal thereof.

[0007] Further, both ends of the coplanar waveguide transmission end are connected with the coaxial cable through an IPEX1 connector seat.

[0008] In a second aspect, the present application also provides a multi-modal sensing gripper, comprising an electric gripper and two grippers arranged opposite to the output end of the electric gripper, the grippers are divided into sensing grippers and positioning grippers, the gripping contact surface of the sensing gripper is integrated with the artificial surface plasmon structure sensor according to any one of the first aspect by embedding, and the surface of the artificial surface plasmon structure sensor is attached with an elastic silicone converter.

[0009] Further, the sensing gripper is provided with a guide hole and a wire groove, the wire groove extends from one side of the guide hole to the end of the sensing gripper, and the coaxial cable on one side of the artificial surface plasmon structure sensor passes through the guide hole and is connected with the signal input end of the external software defined radio device, and the coaxial cable on the other side is arranged in the wire groove and extends to the end of the sensing gripper and is connected with the signal output end of the external software defined radio device.

[0010] Further, when the gripping force is in the range of 0-15N, the thickness of the elastic silicone converter is 2mm, and the hardness is A30.

[0011] Further, the gripping contact surface of the positioning gripper is provided with a U-shaped positioning groove, and the U-shaped positioning groove is matched with the outer diameter of the centrifugal tube.

[0012] In a third aspect, the present application also provides an application of the multi-modal sensing gripper according to any one of the second aspect, comprising gripping force control of a centrifugal tube and identification of reagent components inside the centrifugal tube.

[0013] Further, the process of the gripping force control of the centrifugal tube comprises: the external software defined radio device inputs an excitation signal to the artificial surface plasmon structure sensor and collects a response signal thereof; The centrifuge tube is gripped by the multi-modal perception gripper, and when the centrifuge tube contacts the elastic silica gel converter, the dielectric environment of the artificial surface plasmon structure sensor surface is changed by the elastic deformation of the elastic silica gel converter, so that the response signal produces a phase difference change; According to the phase difference-pressure relationship curve, the pressure value between the centrifuge tube and the multi-modal perception gripper is obtained through the phase difference change; The gripping force of the multi-modal perception gripper is controlled by the pressure value between the centrifuge tube and the multi-modal perception gripper; The method for obtaining the phase difference-pressure relationship curve comprises: A known pressure is applied to the elastic silica gel converter of the multi-modal perception gripper; The external software-defined radio device inputs an excitation signal to the artificial surface plasmon structure sensor and collects the response signal, and the corresponding phase difference change is obtained from the response signal; The phase difference-pressure relationship curve is fitted by synthesizing multiple sets of pressure-phase difference data.

[0014] Further, the process of identifying the reagent component in the centrifuge tube comprises: The external software-defined radio device inputs an excitation signal to the artificial surface plasmon structure sensor and collects the response signal; The amplitude curve is extracted from the response signal; The amplitude curve is input into the pre-trained K-nearest neighbor classification algorithm model, and the pre-test reagent component is output by performing frequency domain analysis on the K-nearest neighbor classification algorithm model.

[0015] Further, the pre-training method of the K-nearest neighbor classification algorithm model comprises: S1, obtaining training set data and test set data, the training set data and the test set data are composed of multiple pairs of known reagent components and corresponding amplitude curves; S2, the amplitude curve in the training set data is input, the K-nearest neighbor classification algorithm model is trained, the network parameters are optimized in the training process, until a preset training number is reached, and a trained K-nearest neighbor classification algorithm model is obtained; S3, the amplitude curve in the test set data is input, the K-nearest neighbor classification algorithm model is tested, a test result is obtained, the test result is matched with the known reagent component in the test set data, and a prediction success rate is calculated, when the prediction success rate is higher than a preset value, a pre-trained K-nearest neighbor classification algorithm model is obtained, otherwise, steps S2-S3 are repeated until the prediction success rate is higher than the preset value.

[0016] Compared with the prior art, the present application has the following beneficial effects: The application has the functions of grabbing force control and substance recognition, the normal distance change between the elastic silica gel converter and the centrifugal tube can be converted into the elastic deformation of the elastic silica gel converter through the elastic silica gel converter, the elastic deformation changes the dielectric environment of the surface plasmon structure sensor surface, and then causes the electromagnetic wave propagation phase shift, so that the indirect measurement of the grabbing pressure is realized through the phase difference of the sensor response signal; and the different dielectric constants of different reagent components cause the characteristics on the amplitude curve to be different, so that the reagent component recognition is realized, so that the grabbing, recognition and processing of the dangerous liquid centrifugal tube are safely and reliably completed without human intervention, the need for manual contact with harmful substances is minimized, and the safety, automation level and sustainability of the intelligent laboratory in dangerous operation are improved; The SSPP sensor of the application shows stable high-sensitivity sensing performance in the 4.7-4.9 GHz frequency band, the dispersion characteristics can be flexibly customized according to the dielectric properties and mechanical environment of the measured object in actual application, the overall layout of the multi-modal sensing jaw is compact and reasonable, and the functions of sensing, wiring and installation are integrated in a limited space. The structure not only provides reliable mechanical protection and environmental isolation for the surface plasmon structure sensor, but also guarantees the integrity of high-frequency signal transmission through the integrated cable management design, and is suitable for high-throughput experimental scenes that require high-frequency sampling, strong interference shielding and complex motion trajectories. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of a surface plasmon structure sensor in an embodiment of the application; Figure 2 It is a structural schematic diagram of a surface plasmon substrate of a surface plasmon structure sensor in an embodiment of the application; Figure 3 It is a structural schematic diagram of a multi-modal sensing jaw in an embodiment of the application; Figure 4 It is a structural schematic diagram of a sensing jaw (before installation of a surface plasmon structure sensor) in a multi-modal sensing jaw in an embodiment of the application, wherein a is a non-contact surface structure schematic diagram of the sensing jaw, b is a side view structure schematic diagram of the sensing jaw, and c is a contact surface structure schematic diagram of the sensing jaw; Figure 5 It is a structural schematic diagram of a positioning jaw in a multi-modal sensing jaw in an embodiment of the application, wherein a is a side view structure schematic diagram of the positioning jaw, and b is a contact surface structure schematic diagram of the positioning jaw; Figure 6 It is a structural schematic diagram of a sensing jaw (after installation of a surface plasmon structure sensor) in a multi-modal sensing jaw in an embodiment of the application; Figure 7This is a schematic diagram showing the electromagnetic properties of an artificial surface plasmon structure sensor characterized by S-parameters and dispersion relation in one embodiment of the present invention. (a) is a schematic diagram of the S11 parameter curve of the artificial surface plasmon structure sensor under different frequency signals, (b) is a schematic diagram of the normalized propagation constant curve of the artificial surface plasmon structure sensor under different periodic unit lengths p, (c) is a schematic diagram of the normalized propagation constant curve of the artificial surface plasmon structure sensor under different periodic unit heights h, and (d) is a schematic diagram of the normalized propagation constant curve of the artificial surface plasmon structure sensor under different SSPP substrate thicknesses t. Figure 8 This is a schematic diagram of the test system in Embodiment 3 of the present invention; Figure 9 This is a schematic diagram illustrating the signal transmission between an artificial surface plasmon structure sensor and an external software-defined wireless device in one embodiment of the present invention. Figure 10 In one embodiment of the present invention, an elastic silicone transducer with different hardness and thickness conditions is used to measure the phase difference ( The diagrams illustrate the effects of different forces on the elastic silicone transducer, where (a) shows the phase difference change of an A20 hardness and 1.5mm thickness elastic silicone transducer under different forces, (b) shows the phase difference change of an A30 hardness and 1.5mm thickness elastic silicone transducer under different forces, (c) shows the phase difference change of an A50 hardness and 1.5mm thickness elastic silicone transducer under different forces, (d) shows the phase difference change of an A20 hardness and 2mm thickness elastic silicone transducer under different forces, (e) shows the phase difference change of an A30 hardness and 2mm thickness elastic silicone transducer under different forces, and (f) shows the phase difference change of an A50 hardness and 2mm thickness elastic silicone transducer under different forces. Figure 11 In one embodiment of the present invention, the mechanical sensing performance of the multimodal sensing gripper during the gripping of empty centrifuge tubes and water-filled centrifuge tubes is described, where 'a' represents the phase difference in the empty centrifuge tube state. ) A schematic diagram showing the response characteristics over time and their relationship with the grasping force, where b is the phase difference at the bottom of the water-filled centrifuge tube ( A schematic diagram illustrating the response characteristics over time and their relationship with gripping force; Figure 12 This is a schematic diagram of the amplitude curves for 10 typical centrifuge tube states (empty tube, water, acetic acid (AcOH), ethyl acetate (EtOAc), sulfuric acid (H2SO4), ethanol (EtOH), isopropanol (IPA), glycerol (Gly), 783 (screen cleaning solution), and hydrochloric acid (HCl)) in one embodiment of the present invention. Figure 13 This is a schematic diagram of the reagent component identification structure based on the amplitude curves of 10 typical centrifuge tube states (empty tube, water, acetic acid (AcOH), ethyl acetate (EtOAc), sulfuric acid (H2SO4), ethanol (EtOH), isopropanol (IPA), glycerol (Gly), 783 (screen cleaning solution), and hydrochloric acid (HCl)) using a K-nearest neighbor classification algorithm model in one embodiment of the present invention. In the figure: 1-Electric gripper, 2-Sensing gripper, 3-Positioning gripper, 4-Artificial surface plasmon structure sensor, 401-Periodic groove unit, 402-Gradual transition section, 403-Coplanar waveguide transmission end, 404-Coaxial cable, 5-Centrifuge tube, 6-Guide hole, 7-Mounting hole, 8-Wire groove, 9-U-shaped positioning groove, 10-IPEX1 generation connector, 11-Elastic silicone converter. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0019] Example 1

[0020] like Figure 1 As shown, this embodiment of the invention provides an artificial surface plasmon structure sensor, including an artificial surface plasmon (SSPP) substrate. The SSPP substrate is made of PCB circuit board, and its material is F4R. The overall dimensions are 48mm in length, 14mm in width, and 0.8mm in thickness.

[0021] Combination Figure 2 The SSPP substrate includes periodic slot units 401 located in the center, with gradual transition sections 402 for impedance matching and coplanar waveguide (CPW) transmission terminals 403 for signal feeding arranged sequentially from near to far on both sides. IPEX1 connectors 10 are soldered to the CPW transmission terminals, achieving low-loss transmission and stable mechanical connection of RF signals. The IPEX1 connectors 10 maintain impedance matching with the sensor circuit, operating in the 1-6 GHz frequency band, effectively exciting the SSPP substrate and acquiring changes in its surface electromagnetic field distribution. The CPW transmission terminals are connected to a coaxial cable 404 via the IPEX1 connectors 10. The coaxial cable 404 is connected to the signal input and signal output terminals of an external software-defined radio (SDR) device.

[0022] Combination Figure 9The signal output end of the SDR device inputs a signal to the SSPP substrate, the signal is converted into a sensor signal after being processed by the SSPP substrate, and the sensor signal is output to the signal input end of the SDR device and received by the SDR device, so that the excitation and response collection of the sensor signal are realized.

[0023] The electromagnetic characteristics of the artificial surface plasmonic structure sensor provided in the embodiment are characterized by S parameters and dispersion relations.

[0024] As shown in a of 7, in a wide frequency range of 1-8 GHz, especially in the 5 GHz and 6 GHz frequency bands, the reflection loss is less than -10 dB, indicating that the structure has good impedance matching characteristics and energy transmission efficiency, effectively suppresses signal reflection, and improves detection sensitivity.

[0025] The dispersion behavior of the sensor can be cooperatively regulated by multiple structural parameters such as the period unit length p, the period unit height h (as shown in Figure 2 a) and the SSPP substrate thickness t, so as to adapt to the needs of different sensing scenarios. As shown in b of Figure 7 , when the period unit length p gradually increases from 2 mm to 1.2 mm, the dispersion curve moves significantly to the low frequency region and is all below the light cone line, indicating that the structure has strong slow wave effect and flexible frequency band adaptation ability, while maintaining significant separation from the free space light mode, effectively suppressing electromagnetic wave radiation loss and enhancing field confinement ability.

[0026] Then, the thickness t of the SSPP substrate is accurately regulated to realize systematic optimization of the dispersion characteristics and working performance of the SSPP sensor. As shown in c of Figure 7 , as the thickness t increases from 0.4 mm to 1.2 mm, the normalized propagation constant βp / π presents a regular evolution in the whole interval, and the overall dispersion curve moves to the low frequency region, but the west-east range is small, indicating that the thickening of the substrate can fine-tune the dispersion effect. As shown in d of Figure 7 , as the period unit height h gradually increases from a lower size, the dispersion curve of the structure as a whole shifts to the low frequency region, indicating that its slow wave effect is enhanced, and the electromagnetic energy is more concentrated on the surface of the structure, which is beneficial to improve the near-field sensing sensitivity. When the period unit height h is 3 mm, the dispersion curve appears above the light cone, indicating that there is no subwavelength effect at this time.

[0027] Through multi-parameter cooperative optimization, the SSPP sensor exhibits stable high-sensitivity sensing performance in the 4.7-4.9 GHz frequency band, and its dispersion characteristics can be flexibly customized according to the dielectric properties and mechanical environment of the measured object in actual application, providing a reliable sensing mechanism for liquid composition identification, grabbing force monitoring and multi-physical quantity synchronous sensing in centrifuge tubes under complex experimental conditions.

[0028] Embodiment 2

[0029] As Figure 3 shown, the embodiment of the application provides a multi-modal sensing gripper, which includes an electric gripper 1 and two grippers. In this embodiment, the total length of the multi-modal sensing gripper is 89 mm, the width is 15 mm, and the height is 11 mm. The whole is made by 3D printing. A mounting hole 7 with a diameter of 4 mm is arranged at one end of the gripper. The output end of the electric gripper 1 is also provided with a matching mounting hole 7. The rigid connection of the gripper and the electric gripper 1 can be achieved by screwing the corresponding mounting hole 7, thereby ensuring the structural stability and repeated positioning accuracy in the overall operation.

[0030] The gripper is divided into a sensing gripper 2 and a positioning gripper 3. The artificial surface plasmon structure sensor 4 of embodiment 1 is embedded at the gripping contact surface of the sensing gripper 2. The profile of the sensing gripper 2 matches the shape of the artificial surface plasmon structure sensor 4, and the embedding flatness of the sensor is ensured by precise machining.

[0031] In combination Figure 6 with the above, an elastic silicone converter 11 is attached to the surface of the artificial surface plasmon structure sensor 4. When the centrifugal tube 5 contacts the elastic silicone converter 11, the elastic silicone converter 11 converts the normal distance between the centrifugal tube 5 and itself into the elastic deformation of the silicone body. This elastic deformation changes the dielectric environment of the SSPP substrate surface, thereby causing the electromagnetic wave propagation phase shift.

[0032] As Figure 4 shown, the sensing gripper 2 is provided with a wire groove 8 and a guide hole 6. The wire groove 8 and the guide hole 6 are used to regularize the coaxial cable 404 and prevent it from being squeezed and disturbed in mechanical movement. The diameters of the wire groove 8 and the guide hole 6 need to be matched with the outer diameter of the coaxial cable 404. In this embodiment, the diameters of the wire groove 8 and the guide hole 6 are both 4 mm. The wire groove 8 extends from one side of the guide hole 6 to the end of the sensing gripper 2. One side of the artificial surface plasmon structure sensor 4 is connected to the signal input end of the external software-defined radio device through the coaxial cable 404 passing through the guide hole 6. The other side of the artificial surface plasmon structure sensor 4 is connected to the signal output end of the external software-defined radio device through the coaxial cable 404 arranged in the wire groove 8 and extending to the end of the sensing gripper 2.

[0033] As Figure 5 shown, the gripping contact surface of the positioning gripper 3 is provided with a U-shaped positioning groove 9. The U-shaped positioning groove 9 is matched with the outer diameter of the centrifugal tube 5. In this embodiment, the opening width of the U-shaped positioning groove 9 is 14.47 mm, which can meet the stable positioning needs of various specifications of the centrifugal tube 5.

[0034] In the initial stage of clamping, the centrifugal tube 5 is clamped in the U-shaped positioning groove 9, which can effectively avoid its deflection and shaking.

[0035] The multimodal sensing gripper in this embodiment has a compact and reasonable overall layout, integrating sensing, wiring, and installation functions within a limited space. This structure not only provides reliable mechanical protection and environmental isolation for the artificial surface plasmon structure sensor 4, but also ensures the integrity of high-frequency signal transmission through an integrated cable management design, making it suitable for high-throughput experimental scenarios requiring high-frequency sampling, strong interference shielding, and complex motion trajectories.

[0036] The following experiment investigates the effect of the Shore hardness (A20, A30, A50) and thickness (1.5mm, 2mm) of the elastic silicone transducer 11 on the sensor phase difference. The coupling influence law of the relationship between force sensing and grasping force provides a key design basis for high-precision force sensing.

[0037] result Figure 10 As shown, under different combinations of hardness and thickness, the phase difference changes significantly with increasing applied pressure, but their response characteristics differ significantly: at the same thickness, silicone transducers with lower hardness (e.g., A20) exhibit higher phase sensitivity in the low force range (0~5N), but their response tends to saturate, while transducers with higher hardness (e.g., A50) exhibit a wider linear response range and stronger overload resistance in the high force range (5~15N); at the same hardness, thinner (1.5mm) transducers have higher initial sensitivity, but their effective range is smaller, while thicker (2mm) transducers can significantly extend the force sensing range and improve linearity.

[0038] Linear and polynomial fitting can be used to establish different parameter combinations for different conditions. – The force mapping model shows that the combination of A30 hardness and 2mm thickness exhibits optimal comprehensive performance in the 0-15N range, with both good sensitivity and wide linear range. The results indicate that by matching the force detection range and accuracy requirements of the target application, the hardness and thickness parameters of the silicone converter can be selected to achieve high-precision and high-reliability calculation of the gripping force. This provides a key technical foundation for the widespread application of the sensing gripper described in this invention in precision operation, gripping of fragile objects, and automated experiments.

[0039] Example 3

[0040] Based on Example 2, this example provides an application of a multimodal sensing gripper, which includes gripping centrifuge tubes and identifying internal reagent components, specifically: The process of gripping force control includes: An external software-defined wireless device inputs an excitation signal to the artificial surface plasmon structure sensor 4 and acquires the sensor's response signal.

[0041] The electrically driven gripper 1 drives the gripper pair to grab the centrifugal tube. When the centrifugal tube 5 contacts the elastic silica gel converter 11, the normal distance change between the centrifugal tube 5 and the elastic silica gel converter 11 can be converted into the elastic deformation of the elastic silica gel converter 11, the elastic deformation changes the dielectric environment of the surface of the artificial surface plasmon structure sensor 4, and further causes the phase shift of electromagnetic wave propagation, resulting in the phase difference change of the output signal of the artificial surface plasmon structure sensor 4.

[0042] According to the phase difference change of the sensor response signal, combined with the pre-calibrated phase difference-pressure relationship curve, the pressure value between the centrifugal tube 5 and the multi-modal sensing gripper can be obtained.

[0043] Finally, the driving force of the electrically driven gripper 1 is adjusted in real time by using the measured pressure value between the centrifugal tube 5 and the multi-modal sensing gripper, realizing real-time control of the grabbing force of the centrifugal tube 5.

[0044] In the embodiment, the calibration method of the above-mentioned phase difference-pressure relationship curve is to apply a known pressure to the elastic silica gel converter 11, collect the response signal of the sensor, obtain the corresponding phase difference change from the response signal, and fit multiple groups of pressure-phase difference to obtain the phase-pressure relationship curve.

[0045] The process of identifying the reagent components in the centrifugal tube 5 includes: The external software defines the radio device to input a 4.7-4.9 GHz sweep excitation signal to the artificial surface plasmon structure sensor 4 and collects the response signal of the sensor (the principle is that the dielectric properties of different reagents are different (dielectric constant, conductivity, etc.), so that the amplitude and resonance frequency of the sweep curve are different).

[0046] The amplitude curve is extracted from the response signal, and the amplitude curve is used as the input of the pre-trained K nearest neighbor classification algorithm (KNN algorithm), and the output of the frequency domain analysis algorithm is obtained to obtain the pre-test reagent component.

[0047] In the embodiment, the pre-training method of the KNN algorithm includes: S1, obtaining training set data and test set data, the training set data and the test set data are composed of multiple pairs of known reagent components and corresponding amplitude curves (because the dielectric constants of different reagent components are different, the characteristics (such as mean, standard deviation, peak-to-peak value, main frequency position of frequency spectrum, high and low frequency energy distribution, morphological oscillation characteristics and specific frequency point amplitude) on the amplitude curve are also different).

[0048] S2, the amplitude curve in the training set data is used as the input, the KNN algorithm is trained, and the network parameters are optimized in the training process until a preset training number is reached, in the embodiment, each reagent component is repeatedly trained for 40 groups, and the trained KNN algorithm is obtained.

[0049] S3. Using the amplitude curve in the test set data as input, test the KNN algorithm and obtain the test results. Match the test results with the corresponding known reagent components in the test set data. When the prediction success rate is higher than the preset value, the pre-trained KNN algorithm is obtained. Otherwise, repeat steps S2 to S3 until the prediction success rate is higher than the preset value.

[0050] The performance of the multimodal sensing gripper provided in this embodiment is tested below. The test system, as shown in Figure 8, consists of a sensing and measurement unit, a control unit, and a signal processing unit, forming a closed-loop sensing and operating system. The sensing and measurement unit is the multimodal sensing gripper provided in this embodiment, responsible for real-time acquisition of dielectric and mechanical signals during the gripping process. The control unit is based on a microcontroller and receives instructions from the microcontroller through a relay module. It controls the 24V DC power supply to drive the electric gripper to complete gripping, lifting, and placement actions, and transmits and receives radio frequency signals using an SDR.

[0051] First, the mechanical sensing performance of the multimodal sensing gripper in grasping empty and water-filled centrifuge tubes was verified through three consecutive repeatable tests. The experimental results clearly showed the phase difference in the two states. The response characteristics that change over time and their mapping relationship with gripping force, such as Figure 11 As shown, during the grasping of an empty centrifuge tube (left side), the phase difference variation range is small (0.00~0.35 rad), corresponding to a low grasping force (0~3 N). The response curve is smooth and has good repeatability, indicating that the empty centrifuge tube has little impact on the phase of the SSPP sensor. During the grasping of a water-filled centrifuge tube (right side), the phase difference variation range increases significantly (0.00~0.8 rad), corresponding to a grasping force range of 0~6.94 N. This is because the high dielectric constant of water enhances the field-matter interaction, and the increased liquid mass increases the mechanical load. The system responds by... The force mapping model calculates the pressure value in real time, demonstrating a higher response amplitude and consistent repeatability. In both states, the phase difference rises rapidly when the gripping action occurs, remains stable during the holding phase, and quickly returns to zero after release, with no significant hysteresis in the dynamic response. This result proves that the sensing gripper described in this invention can effectively distinguish objects with different loads and dielectric properties, and achieves high-precision, high-repeatability gripping force monitoring through a unified mechanical sensing mechanism, providing reliable technical support for automated operations involving multiple material states in intelligent laboratories.

[0052] Then, 10 typical centrifuge tube states were identified, including empty tube (Empty), water (Water), acetic acid (AcOH), ethyl acetate (EtOAc), sulfuric acid (H2SO4), ethanol (EtOH), isopropanol (IPA), glycerol (Gly), 783 (washing water) and hydrochloric acid (HCl).

[0053] The amplitude curve extracted from the sensor response signal is shown in Figure 12 The amplitude response of each centrifuge tube state in the 4.8-5.2 GHz frequency band shows significant differences. The empty tube has the lowest amplitude response and flat curve in the entire frequency band, while different reagents show unique resonance frequency and amplitude characteristics due to their different dielectric properties: water and glycerol with high dielectric constant liquids induce obvious resonance peaks near 4.9 GHz, and polar liquids such as sulfuric acid and acetic acid show wide amplitude response in the 5.0-5.1 GHz range; all test states have distinguishable frequency spectrum fingerprints in the 4.8-5.2 GHz operating frequency band, by extracting the amplitude of multiple characteristic frequency points, the overall curve shape and resonance characteristics. In order to improve the sweep efficiency, the first trough to peak position, i.e. the characteristic region of 4.78-4.94 GHz, is selected for sweep operation.

[0054] Finally, based on the amplitude curves of the 10 centrifuge tube states obtained by sweep test, K nearest neighbor classification algorithm model is used to identify empty tube (Empty), water (Water), acetic acid (AcOH), ethyl acetate (EtOAc), sulfuric acid (H2SO4), ethanol (EtOH), isopropanol (IPA), glycerol (Gly), 783 (washing water) and hydrochloric acid (HCl).

[0055] By extracting multi-dimensional features including resonance frequency amplitude, specific frequency point response, curve steepness, frequency band width, etc. to construct feature vectors, and using pre-trained KNN algorithm model for reagent composition prediction. The results are shown in Figure 13 As shown in the figure, 10 states show good clustering in the feature space, with clear category boundaries and classification accuracy of more than 95%. Among them, the empty tube is easy to distinguish due to its weakest dielectric response, water and glycerol with high dielectric constant liquids are clustered in a specific area due to their significant resonance peaks, strong polar liquids such as sulfuric acid and hydrochloric acid form independent categories due to their unique loss characteristics, and ethanol, isopropanol and ethyl acetate are effectively separated according to their differences in dielectric relaxation frequency.

[0056] The KNN algorithm fully excavates the dielectric information captured by the SSPP structure sensor by virtue of its characteristics of not requiring complex model assumptions and strong adaptability to multi-modal data, and proves the effectiveness of multi-dimensional features in complex liquid identification.

[0057] The above is only the preferred embodiment of the present application, and it should be noted that those skilled in the art can make several improvements and modifications without departing from the technical principles of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. An artificial surface plasmon structure sensor, characterized by, The artificial surface plasmon substrate includes a periodic groove unit and a gradual transition section and a coplanar waveguide transmission end arranged in sequence from near to far at both ends of the periodic groove unit, the coplanar waveguide transmission end is connected with a coaxial cable, and the coaxial cable on both sides is connected with a signal input end and a signal output end of an external software defined radio device respectively, and an excitation signal is input to the artificial surface plasmon structure sensor and the response signal is collected through the external software defined radio device.

2. The artificial surface plasmon polariton structure sensor according to claim 1, wherein, Both ends of the coplanar waveguide transmission end are connected with the coaxial cable through an IPEX1 generation connection seat.

3. A multi-modal sensing gripper, comprising: The electrically operated clamping jaw and the two clamping jaws arranged opposite to the output end of the electrically operated clamping jaw, the clamping jaws are respectively a sensing clamping jaw and a positioning clamping jaw, the sensing clamping jaw is integrated with the artificial surface plasmon structure sensor in the embedded mode at the gripping contact surface, and the artificial surface plasmon structure sensor is attached with an elastic silica gel converter on the surface.

4. The multi-modal sensing jaw of claim 3, wherein, The sensing clamping jaw is provided with a guide hole and a wire groove, the wire groove extends from one side of the guide hole to the end of the sensing clamping jaw, and one side coaxial cable of the artificial surface plasmon structure sensor passes through the guide hole and is connected with the signal input end of the external software defined radio device, and the other side coaxial cable is arranged in the wire groove and extends to the end of the sensing clamping jaw and is connected with the signal output end of the external software defined radio device.

5. The multi-modal sensing jaw of claim 3, wherein, When the gripping force is in the range of 0-15N, the thickness of the elastic silica gel converter is 2mm, and the hardness is A30.

6. The multi-modal sensing jaw of claim 3, wherein, The gripping contact surface of the positioning clamping jaw is provided with a U-shaped positioning groove, and the U-shaped positioning groove is matched with the outer diameter of the centrifugal tube.

7. Use of a multi-modal sensing gripper according to any one of claims 3-6, characterized in that, The gripping force control and the reagent component recognition in the centrifugal tube are included.

8. Use of a multi-modal sensing jaw according to claim 7, characterized in that, The process of the gripping force control of the centrifugal tube includes: The external software defined radio device inputs an excitation signal to the artificial surface plasmon structure sensor and collects the response signal; The multi-modal perception clamping jaw is driven to grip the centrifugal tube, when the centrifugal tube contacts with the elastic silica gel converter, the dielectric environment of the surface of the artificial surface plasmon structure sensor is changed by the elastic deformation of the elastic silica gel converter, so that the phase difference of the response signal changes; According to the phase difference-pressure relationship curve, the pressure value between the centrifugal tube and the multi-modal perception clamping jaw is obtained through the phase difference change; The gripping force of the multi-modal perception clamping jaw is controlled by using the pressure value between the centrifugal tube and the multi-modal perception clamping jaw. The method for obtaining the phase difference-pressure relationship curve includes: A known pressure is applied to the elastic silica gel converter of the multi-modal perception clamping jaw; The external software defined radio device inputs an excitation signal to the artificial surface plasmon structure sensor and collects the response signal, and the corresponding phase difference change is obtained from the response signal; A phase difference-pressure relationship curve is fitted by comprehensively fitting a plurality of groups of pressure-phase difference data.

9. Use of a multi-modal sensing jaw according to claim 7, characterized in that, The process of the reagent component recognition in the centrifugal tube includes: The external software defined radio device inputs an excitation signal to the artificial surface plasmon structure sensor and collects the response signal; An amplitude curve is obtained by extracting the response signal. The amplitude curve is taken as an input of a pre-trained K nearest neighbor classification algorithm model, and a pretest reagent component is obtained through frequency domain analysis output of the K nearest neighbor classification algorithm model.

10. Use of a multi-modal sensing jaw according to claim 9, characterized in that, The pre-training method of the K nearest neighbor classification algorithm model comprises the following steps: S1, obtaining training set data and test set data, the training set data and the test set data are both composed of multiple pairs of known reagent components and corresponding amplitude curves; S2, taking the amplitude curve in the training set data as an input, training the K nearest neighbor classification algorithm model, optimizing the network parameters in the training process until a preset training number is reached, and obtaining a trained K nearest neighbor classification algorithm model; S3, taking the amplitude curve in the test set data as an input, testing the K nearest neighbor classification algorithm model, obtaining a test result, matching the test result with the corresponding known reagent component in the test set data, and calculating a prediction success rate, when the prediction success rate is higher than a preset value, obtaining a pre-trained K nearest neighbor classification algorithm model, otherwise, repeating steps S2-S3 until the prediction success rate is higher than the preset value.

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