Preparation method of PVDF piezoelectric catalytic material with hollow tubular structure

Abstract

The invention discloses a preparation method of a PVDF (Polyvinylidene Fluoride) piezoelectric catalytic material with a hollow tubular structure, and particularly relates to the field of preparation of piezoelectric catalytic materials.The preparation method comprises the steps that drying, dispersing, shearing, degassing and viscosity adjusting treatment is performed on PVDF particle raw material data, forming preparation dispersion liquid data is constructed, and a basic material system is provided for the subsequent disturbance forming stage; a periodic disturbance field is applied on the basis of preliminary mold filling data, chain segment secondary orientation is dynamically induced, and dynamic disturbance maintaining data is generated and serves as an intermediate state formed by an inverse fatigue structure. Pressure-controlled and speed-controlled flow treatment and periodic disturbance field induction treatment are applied according to molding preparation dispersion liquid data, chain segment arrangement and local stress distribution are dynamically and synchronously regulated and controlled, and dynamic curing freezing and preparation path closed-loop optimization are combined. And stable construction of the inverse fatigue effect of the hollow tubular PVDF piezoelectric catalytic material in a periodic water pressure disturbance environment is realized.

Description

Technical Field

[0001] This invention relates to the field of piezoelectric catalytic material preparation technology, and more specifically, to a method for preparing PVDF piezoelectric catalytic material with a hollow tubular structure. Background Technology

[0002] As the application of piezoelectric catalytic materials in environmental pollution control and green energy conversion continues to expand, traditional sheet-like PVDF piezoelectric catalysts have exposed serious local stress concentration problems under periodic fluid impact or water pressure pulsation environment, leading to microcrack initiation, surface peeling and continuous decay of piezoelectric performance, which in turn accelerates material structure fatigue and significantly reduces catalytic activity, restricting its long-term stable application in dynamic liquid phase environment.

[0003] Especially under continuous high-frequency pulsating loading, traditional materials cannot coordinate external deformation and internal piezoelectric response modes, resulting in uncontrollable deformation energy conversion efficiency and easily amplifying fatigue failure effects.

[0004] Existing fabrication technologies mainly focus on morphological optimization and lack a systematic fabrication path modeling mechanism for regulating structural mechanical response and functional synergy, which is not conducive to fundamentally solving the problem of fatigue degradation.

[0005] Therefore, it is urgent to establish a novel method for preparing PVDF piezoelectric catalytic materials that can achieve the reverse fatigue effect under periodic water pressure disturbance, based on a hollow tubular structure as the core, combined with the flexibility and stress dispersion mechanism of PVDF, and through mathematical model derivation of the preparation path and control of structural parameters. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for preparing PVDF piezoelectric catalytic materials with hollow tubular structures. By applying controlled pressure and speed flow treatment and periodic disturbance field induction treatment based on the data of the molding preparation dispersion, the chain segment arrangement and local stress distribution are dynamically and synchronously regulated. Combined with dynamic solidification freezing and closed-loop optimization of the preparation path, the hollow tubular PVDF piezoelectric catalytic materials are stably constructed under periodic water pressure disturbance environment to reverse fatigue effect, thereby solving the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure, comprising:

[0008] By applying drying, dispersion, shearing, degassing and viscosity adjustment treatments to PVDF particle raw material data, a molding preparation dispersion data is constructed, providing a basic material system for the subsequent perturbation molding stage;

[0009] By applying a periodic perturbation field based on the initial filling data, the secondary orientation of the chain segments is dynamically induced, generating dynamic perturbation retention data as an intermediate state for the formation of the inverse fatigue structure.

[0010] Through dynamic curing and cooling control, the chain segment arrangement in the data is frozen by freezing dynamic disturbances, forming data that has been stabilized after stress release.

[0011] By applying perturbation activation processing and performing piezoelectric function testing, inverse fatigue initial state activation data and function verification pass data are extracted to generate primary fatigue test data for performance characteristic evaluation.

[0012] By extracting dynamic deformation features and correcting path deviations, adaptive closed-loop optimization of the preparation process is achieved, and the final optimized preparation path data is output.

[0013] In a preferred embodiment, PVDF raw material data is collected, including particle size distribution, initial moisture content parameter, and melt index parameter. The PVDF raw material data is used to characterize the basic state of the PVDF piezoelectric catalyst raw material.

[0014] Temperature-controlled drying is applied to the PVDF raw material data, and the drying curve parameters are solved during the temperature-controlled drying operation to generate dry PVDF particle data. The dry PVDF particle data is introduced into the solvent system and shear stirring is performed to form homogeneous dispersion data.

[0015] Shear pretreatment is applied to the homogeneous dispersion data to identify the initial orientation direction of the chain segments and generate primary orientation dispersion data of the chain segments. The primary orientation dispersion data of the chain segments is then introduced into a vacuum settling step to remove internal air bubbles and form degassed homogeneous dispersion data.

[0016] Viscosity adjustment is applied to the degassed homogenized dispersion data, the solvent ratio is adjusted, and the target viscosity dispersion data is output. The storage temperature and time of the target viscosity dispersion data are controlled, and the molding preparation dispersion data is output.

[0017] In a preferred embodiment, pressure and speed control flow processing is performed based on the molding preparation dispersion data, the flow trajectory is guided along the axial direction of the hollow tube mold, and the material is continuously piled up and filled according to the mold cavity space shape to generate preliminary mold filling data.

[0018] The initial filling data is subjected to filling uniformity detection processing, and the filling rate and filling pressure parameters in the initial filling data are statistically analyzed to generate filling detection data.

[0019] In a preferred embodiment, it is determined whether the filling detection data meets the preset filling uniformity standard. If it does, periodic disturbance processing is applied. If it does not, the injection flow rate parameter corresponding to the initial filling data is corrected and the pressure and speed control flow processing step is returned to be re-executed.

[0020] The initial molding data is subjected to a periodic perturbation field loading process, and the perturbation frequency and amplitude parameters are adjusted to output dynamic perturbation molding data. The dynamic perturbation molding data is then subjected to chain segment secondary orientation detection processing to extract the chain segment arrangement direction features in the dynamic perturbation molding data and generate chain segment orientation detection data.

[0021] Determine whether the chain segment orientation detection data meets the set chain segment orientation standard. If it does, apply dynamic solidification processing. If it does not, correct the periodic perturbation field parameters and return to the periodic perturbation field loading process.

[0022] The dynamic perturbation shaping data that conforms to the chain segment orientation standard is subjected to a perturbation persistence process to maintain the duration of the perturbation field and generate dynamic perturbation persistence data.

[0023] In a preferred embodiment, the dynamic disturbance holding data is subjected to dynamic curing processing, a curing temperature curve is set in the dynamic curing processing, and dynamic curing start-up data is output; the dynamic curing start-up data is subjected to chain segment internal stress freezing detection, the freezing degree index is solved, and freezing detection data is generated.

[0024] Determine whether the freezing test data meets the internal stress freezing standard. If it does, proceed with the cooling process. If it does not, adjust the curing time and temperature and return to the curing step.

[0025] The dynamic curing start-up data is subjected to cooling control processing, the cooling rate and gradient are set, and cooling shaping data is generated; the cooling shaping data is subjected to cooling uniformity detection, the temperature distribution is statistically analyzed, and the cooling detection data is output.

[0026] Determine whether the cooling test data meets the cooling uniformity standard. If it does, proceed to the residual stress release step. If it does not, adjust the cooling curve and return to the cooling treatment. Apply surface residual stress release treatment to the cooling shaping data that meets the cooling uniformity standard to generate post-stress release shaping data.

[0027] In a preferred embodiment, the stress-released shaping data is subjected to low-amplitude perturbation pre-activation processing to construct inverse fatigue initial state activation data; the inverse fatigue initial state activation data is subjected to preliminary piezoelectric response detection to extract the piezoelectric response amplitude and response rate, and generate preliminary piezoelectric detection data.

[0028] Determine whether the preliminary piezoelectric detection data meets the inverse fatigue start-up standard. If it does, proceed to the functional verification stage. If it does not, correct the disturbance amplitude parameter and return to the activation step. Apply microstructure stability detection to the inverse fatigue initial state activation data, statistically analyze the crystal region size and chain segment orientation, and construct microstructure detection data.

[0029] Determine whether the microstructure test data meets the stability standard. If it does, register it as functional verification passed data. If it does not, return to the surface stress release step and process it in a loop.

[0030] The functional verification data is subjected to a primary fatigue cycle test, the number of pulsating cycles is loaded, and the primary fatigue test data is output. The primary fatigue test data is then subjected to fatigue performance extraction processing to generate fatigue performance characteristic data.

[0031] In a preferred embodiment, fatigue performance characteristic data are subjected to dynamic deformation response detection to extract natural frequency and strain rebound rate, and output dynamic deformation characteristic data; the dynamic deformation characteristic data are introduced into the fabrication path model to solve the path deviation index and form fabrication path deviation data.

[0032] Determine whether the preparation path deviation data is within the set tolerance range. If it is, output the final preparation path data. If it is not, apply path reverse correction processing. Apply path reverse correction processing to the preparation path deviation data to correct the disturbance frequency, mold cavity parameters and curing window, and generate preparation path correction data.

[0033] The preparation path correction data is applied to the preparation process adjustment process, and the preparation process optimization data is output. The preparation process optimization data is then applied to the process convergence detection, the performance stability is statistically analyzed, and process convergence detection data is generated.

[0034] Determine whether the process convergence detection data meets the convergence criteria. If it does, output the final optimized preparation path. If it does not, return to the path reverse correction process and perform iterative optimization.

[0035] In a preferred embodiment, based on the dispersion data for molding preparation, the flow shear rate, local chain segment energy density, and chain segment rotation orientation are dynamically controlled through pressure and speed control flow treatment and periodic perturbation field loading operations, thereby forming dynamic perturbation retention data under spatiotemporal coordinated driving; let the overall chain segment orientation evolution function of the perturbation-assisted molding process be:

[0036]

[0037] The local perturbation enhancement term is defined as follows:

[0038]

[0039] Where Φ(x,t) is the orientation evolution function of the perturbation-assisted forming chain segment; x is the spatial coordinate position variable inside the mold; and t is the perturbation application time variable. For local flow velocity gradient tensor; denoted as the local flow shear rate tensor; γ(x,t) is the local chain segment stretching energy density; ∈ is the regularization term; μ is the flow shear control exponent; Ω(ω(t),A(t)) is the local perturbation enhancement term; ω(t) is the perturbation frequency; A(t) is the perturbation amplitude; This represents the initial phase offset of the disturbance. ν is the Laplace operator for the local pressure field; β is the local pressure relaxation modulation factor; and ν is the perturbation amplification master index in the above equation.

[0040] In a preferred embodiment, by setting a dynamic curing temperature curve and freezing kinetics, and combining the freezing damping and internal stress dissipation rate of local chain segments, the chain segment arrangement is cured and a prestressed structure is formed. During the cooling process, the residual stress on the surface is released through heat flow control, and the stress release shaping data is generated.

[0041] Let the overall model of freezing dynamics and cooling stress release be:

[0042]

[0043] The cooling stress relief item is:

[0044]

[0045] Where Γ(T,t) is the function for the completion of the freezing and stress release coupling; T is the local temperature field variable; t is the time variable; and σ(x,u) is the local internal stress tensor. η is the local shear rate tensor; η(T(u)) represents the temperature-dependent segmental viscous damping coefficient; δ is the regularization term; λ is the freezing dynamic response exponent; χ(T,t) represents the local residual stress release function during the cooling process. It is the temperature gradient tensor; (x,t) is the temperature Laplace tensor; θ is the heat flux regularization term; ξ is the cooling stress release index.

[0046] In a preferred embodiment, a fabrication path deviation index is constructed based on fatigue performance characteristic data. Combining local strain rebound rate, fatigue gain rate and natural frequency mismatch rate, the fabrication path parameters are dynamically corrected and converged to the final optimized fabrication path through gradient reinforcement and nonlinear alternating step size optimization.

[0047] Let the overall error convergence kinetic equation of the preparation path be:

[0048]

[0049] The path correction iterative formula is:

[0050]

[0051] The dynamic step size is adjusted as follows:

[0052]

[0053] Where Ξ(k) is the comprehensive deviation function of the preparation path in the k-th round; Δ∈(k) is the strain rebound rate deviation in the k-th round; Δγ(k) is the fatigue gain rate deviation in the k-th round; Δf(k) is the natural frequency deviation in the k-th round; ∈ target γ represents the target strain rebound rate; target The target fatigue gain rate; f target ζ represents the target natural frequency; ζ represents the comprehensive bias enhancement index; P k Prepare a path parameter set for the k-th round; To prepare the gradient of the path parameters with respect to the path deviation function; α k β is the dynamic step size factor. k This is the dynamic step size modulation factor.

[0054] The technical effects and advantages of this invention are as follows:

[0055] 1. By applying pressure-controlled and speed-controlled flow and periodic disturbance treatment based on the data of the dispersion liquid for molding preparation, the secondary orientation of chain segments is dynamically induced, and the local stress concentration in the periodic water pressure impact is dispersed, so as to achieve continuous enhancement of the anti-fatigue performance of hollow tubular PVDF materials in dynamic liquid phase environment.

[0056] 2. By applying dynamic perturbation to maintain data, dynamic solidification and cooling control are applied to freeze the chain segment arrangement and simultaneously release residual surface stress, thereby improving the stability of the hollow tube wall structure and ensuring the long-lasting consistency of the piezoelectric catalytic effect under long-cycle water pressure pulsation.

[0057] 3. By applying low-amplitude perturbation pre-activation treatment, the local activation structure inside the chain segment is induced. Combined with piezoelectric function detection and microstructure detection, the inverse fatigue initial state activation data is extracted to establish a sustainable enhanced piezoelectric response mechanism to support energy conversion under dynamic environment.

[0058] 4. By constructing a preparation path deviation index based on fatigue performance characteristic data, applying dynamic deformation response detection and path reverse correction processing, a dynamic adaptive closed-loop optimization path is formed, which improves the consistency of chain segment orientation and the matching of micro-mechanical properties during the molding process.

[0059] 5. By introducing disturbance synchronous induction, dynamic freezing stabilization and path optimization iteration throughout the entire process, the material's structural morphology, chain segment order and piezoelectric output are stabilized in a synergistic manner under periodic pulsating impact environment, thus expanding the application breadth of PVDF piezoelectric catalytic materials in dynamic liquid phase scenarios. Attached Figure Description

[0060] Figure 1 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] Refer to the instruction manual appendix Figure 1 An embodiment of the present invention provides a method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure, comprising:

[0063] By applying drying, dispersion, shearing, degassing and viscosity adjustment treatments to PVDF particle raw material data, a molding preparation dispersion data is constructed, providing a basic material system for the subsequent perturbation molding stage;

[0064] By applying a periodic perturbation field based on the initial filling data, the secondary orientation of the chain segments is dynamically induced, generating dynamic perturbation retention data as an intermediate state for the formation of the inverse fatigue structure.

[0065] Through dynamic solidification and cooling control, the chain segment arrangement in the data is frozen by dynamic disturbance, forming the fixed data after stress release, which is used to ensure the structural stability of the reverse fatigue effect.

[0066] By applying perturbation activation processing and performing piezoelectric function testing, inverse fatigue initial state activation data and function verification pass data are extracted to generate primary fatigue test data for performance characteristic evaluation.

[0067] By extracting dynamic deformation features and correcting path deviations, adaptive closed-loop optimization of the preparation process is achieved, and the final optimized preparation path data is output.

[0068] Collect PVDF raw material data, including particle size distribution, initial moisture content parameter, and melt index parameter. The PVDF raw material data is used to characterize the basic state of the PVDF piezoelectric catalytic raw material.

[0069] Temperature-controlled drying is applied to the PVDF raw material data, and the drying curve parameters are solved during the temperature-controlled drying operation to generate dry PVDF particle data. The dry PVDF particle data is introduced into the solvent system and shear stirring is performed to form homogeneous dispersion data.

[0070] Shear pretreatment is applied to the homogeneous dispersion data to identify the initial orientation direction of the chain segments and generate primary orientation dispersion data of the chain segments. The primary orientation dispersion data of the chain segments is then introduced into a vacuum settling step to remove internal air bubbles and form degassed homogeneous dispersion data.

[0071] Viscosity adjustment is applied to the degassed homogenized dispersion data, the solvent ratio is adjusted, and the target viscosity dispersion data is output. The storage temperature and time of the target viscosity dispersion data are controlled, and the molding preparation dispersion data is output.

[0072] Based on the data of the dispersion liquid for molding preparation, pressure and speed control flow processing is performed to guide the flow trajectory along the axial direction of the hollow tube mold and continuously fill it according to the spatial shape of the mold cavity to generate preliminary mold filling data;

[0073] The initial filling data is subjected to filling uniformity detection processing, and the filling rate and filling pressure parameters in the initial filling data are statistically analyzed to generate filling detection data.

[0074] Determine whether the filling test data meets the preset filling uniformity standard. If it does, apply periodic disturbance processing. If it does not, correct the injection flow rate parameter corresponding to the initial filling data and return to the pressure and speed control flow processing step to re-execute.

[0075] The initial molding data is subjected to a periodic perturbation field loading process, and the perturbation frequency and amplitude parameters are adjusted to output dynamic perturbation molding data. The dynamic perturbation molding data is then subjected to chain segment secondary orientation detection processing to extract the chain segment arrangement direction features in the dynamic perturbation molding data and generate chain segment orientation detection data.

[0076] Determine whether the chain segment orientation detection data meets the set chain segment orientation standard. If it does, apply dynamic solidification processing. If it does not, correct the periodic perturbation field parameters and return to the periodic perturbation field loading process.

[0077] The dynamic perturbation shaping data that conforms to the chain segment orientation standard is subjected to a perturbation persistence process to maintain the duration of the perturbation field and generate dynamic perturbation persistence data.

[0078] The dynamic disturbance holding data is subjected to dynamic curing processing. A curing temperature curve is set in the dynamic curing processing, and dynamic curing start-up data is output. The dynamic curing start-up data is then subjected to chain segment internal stress freezing detection to solve the freezing degree index and generate freezing detection data.

[0079] Determine whether the freezing test data meets the internal stress freezing standard. If it does, proceed with the cooling process. If it does not, adjust the curing time and temperature and return to the curing step.

[0080] The dynamic curing start-up data is subjected to cooling control processing, the cooling rate and gradient are set, and cooling shaping data is generated; the cooling shaping data is subjected to cooling uniformity detection, the temperature distribution is statistically analyzed, and the cooling detection data is output.

[0081] Determine whether the cooling test data meets the cooling uniformity standard. If it does, proceed to the residual stress release step. If it does not, adjust the cooling curve and return to the cooling treatment. Apply surface residual stress release treatment to the cooling shaping data that meets the cooling uniformity standard to generate post-stress release shaping data.

[0082] After stress release, the shaped data is subjected to low-amplitude perturbation pre-activation processing to construct inverse fatigue initial state activation data; the inverse fatigue initial state activation data is subjected to preliminary piezoelectric response detection to extract the piezoelectric response amplitude and response rate, generating preliminary piezoelectric detection data;

[0083] Determine whether the preliminary piezoelectric detection data meets the inverse fatigue start-up standard. If it does, proceed to the functional verification stage. If it does not, correct the disturbance amplitude parameter and return to the activation step. Apply microstructure stability detection to the inverse fatigue initial state activation data, statistically analyze the crystal region size and chain segment orientation, and construct microstructure detection data.

[0084] Determine whether the microstructure test data meets the stability standard. If it does, register it as functional verification passed data. If it does not, return to the surface stress release step and process it in a loop.

[0085] The functional verification data is subjected to a primary fatigue cycle test, the number of pulsating cycles is loaded, and the primary fatigue test data is output. The primary fatigue test data is then subjected to fatigue performance extraction processing to generate fatigue performance characteristic data.

[0086] Dynamic deformation response detection is applied to fatigue performance characteristic data to extract natural frequency and strain rebound rate, and output dynamic deformation characteristic data; the dynamic deformation characteristic data is introduced into the fabrication path model to solve the path deviation index and form fabrication path deviation data.

[0087] Determine whether the preparation path deviation data is within the set tolerance range. If it is, output the final preparation path data. If it is not, apply path reverse correction processing. Apply path reverse correction processing to the preparation path deviation data to correct the disturbance frequency, mold cavity parameters and curing window, and generate preparation path correction data.

[0088] The preparation path correction data is applied to the preparation process adjustment process, and the preparation process optimization data is output. The preparation process optimization data is then applied to the process convergence detection, the performance stability is statistically analyzed, and process convergence detection data is generated.

[0089] Determine whether the process convergence detection data meets the convergence criteria. If it does, output the final optimized preparation path. If it does not, return to the path reverse correction process and perform iterative optimization.

[0090] It should be noted that in the formula structure involved in this scheme, dimensionless terms can be used as proportional or structural adjustment factors. When combined with quantities with units, they only play a role in numerical scaling and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system. This combination of "dimensionless terms and terms with units" can be understood as a composite structural expression commonly used in mathematical physics modeling. It conforms to the principle of dimensional consistency and has a clear physical interpretation basis.

[0091] Secondly, in the formula structure of this scheme, if multiple variables with different physical units are involved, including but not limited to time, mass or energy variables, their joint appearance is to express the collaborative modeling relationship of multiple physical mechanisms. Each variable can form a unified structure through function mapping, ratio combination or normalization adjustment, with clear units and clear meaning. The overall expression conforms to the principle of dimensional consistency and the conventional formula of engineering modeling.

[0092] In this scheme, constants, weights, adjustment factors, threshold parameters, proportional coefficients, etc., are all adjustable control parameters for different application environments. Their values ​​depend on the target equipment configuration, data input characteristics, and performance optimization goals. During the implementation phase, they are converged within a reasonable range through model verification, performance constraints, or engineering calibration. Although these parameters do not have a unique preset value, they have clear adjustment logic and calculation paths. They belong to the deterministic setting process in engineering implementation. The purpose of this setting is to ensure that the scheme is both universally adaptable and reproducible and operable, without affecting its technical clarity and feasibility.

[0093] Based on the dispersion data for molding preparation, the flow shear rate, local segment energy density, and segment rotational orientation are dynamically controlled through pressure and speed control flow treatment and periodic perturbation field loading, thereby forming dynamic perturbation-maintained data under spatiotemporal coordinated driving; let the overall segment orientation evolution function of the perturbation-assisted molding process be:

[0094]

[0095] The local perturbation enhancement term is defined as follows:

[0096]

[0097] Where Φ(x,t) is the perturbation-assisted molding chain segment orientation evolution function, which reflects the temporal and spatial changes in the local chain segment arrangement direction; x is the spatial coordinate position variable inside the mold; t is the perturbation application time variable; For local flow velocity gradient tensor; Let be the local flow shear rate tensor; γ(x,t) be the local chain segment stretching energy density; ∈ be the minimum regularization term, used to prevent division by zero; μ be the flow shear control exponent, used to reflect the nonlinear sensitivity of the chain segment to the flow response; Ω(ω(t),A(t)) be the local perturbation enhancement term, which couples ω(t) and A(t) to form a dynamic superposition perturbation effect; ω(t) be the perturbation frequency, used to control the rate of change of the periodic perturbation; and A(t) be the perturbation amplitude, used to control the perturbation intensity. This represents the initial phase offset of the disturbance. ν is the Laplace operator for the local pressure field, which characterizes the degree of local variation in the pressure field; β is the local pressure relaxation modulation factor; in the above equation, ν is the perturbation amplification master control index, which is used to reflect the degree of nonlinear response of the chain segment to the superimposed dynamic field.

[0098] Explanation of the components of the above formula:

[0099] This part represents the dominant effect of flow shear and tension coupling on the chain segment arrangement, with the shear tensor and tension energy density jointly dominating the initial rotation and arrangement trend of local chain segments;

[0100] This part represents the dynamic impact of the superposition of local disturbances and the relaxation of the pressure field on the correction of the chain segment arrangement, ensuring that the disturbance effect and local flow changes are coordinated and synchronized.

[0101] By setting dynamic curing temperature curves and freezing dynamics, and combining the freezing damping and internal stress dissipation rate of local chain segments, the chain segment arrangement is cured and a prestressed structure is formed. During the cooling process, residual surface stress is released through heat flow control, and post-stress release shaping data is generated.

[0102] Let the overall model of freezing dynamics and cooling stress release be:

[0103]

[0104] The cooling stress relief item is:

[0105]

[0106] Where Γ(T,t) is the freezing and stress release coupling completion function, which describes the degree of synergy between freezing and stress release in chain segment arrangement; T is the local temperature field variable; t is the time variable; σ(x,u) is the local internal stress tensor, which evolves with time u; (x,u) is the local shear rate tensor; η(T(u)) represents the temperature-dependent segmental viscous damping coefficient; δ is the minimum regularization term, which is used to prevent division by zero; λ is the freezing dynamic response exponent, which is used to reflect the degree of nonlinear response of the freezing process to stress dissipation; χ(T,t) represents the local residual stress release function during the cooling process. It is the temperature gradient tensor; ξ is the temperature Laplace tensor; θ is the heat flux regularization term, which is used to prevent division by zero; ξ is the cooling stress release exponent, which is used to describe the nonlinear characteristics of the residual stress release process.

[0107] Explanation of components:

[0108] In the equation Γ(T,t), the first term is... This is used to describe the nonlinear evolution of the chain segment freezing process over time, where shear dissipation and internal stress accumulation work together to drive freezing.

[0109] In the equation Γ(T,t), the second term (1-χ(T,t)) is used to describe the degree of local residual stress release during the cooling process, which suppresses cooling-induced secondary microcracks or structural relaxation.

[0110] Based on fatigue performance characteristic data, a fabrication path deviation index is constructed. Combining local strain rebound rate, fatigue gain rate and natural frequency mismatch rate, the fabrication path parameters are dynamically corrected and converged to the final optimized fabrication path through gradient enhancement and nonlinear alternating step size optimization.

[0111] Let the overall error convergence kinetic equation of the preparation path be:

[0112]

[0113] The path correction iterative formula is:

[0114]

[0115] The dynamic step size is adjusted as follows:

[0116]

[0117] Where Ξ(k) is the comprehensive deviation function of the preparation path in the k-th round; Δ∈(k) is the strain rebound rate deviation in the k-th round; Δγ(k) is the fatigue gain rate deviation in the k-th round; Δf(k) is the natural frequency deviation in the k-th round; ∈ target γ represents the target strain rebound rate; target The target fatigue gain rate; f target ζ represents the target natural frequency; ζ is the comprehensive bias enhancement index, which is used to strengthen the bias convergence; P k For the kth round, a path parameter set is prepared, which includes perturbation frequency, injection flow rate, and cooling curve parameters. (k) is the gradient of the path parameters with respect to the path deviation function; α k This is a dynamic step size factor, which scales dynamically based on the deviation in practical applications; β k This is the dynamic step size modulation factor.

[0118] It is necessary to explain in general that this scheme addresses the fatigue failure problem of traditional sheet-like PVDF piezoelectric catalytic materials caused by local stress concentration in dynamic liquid environment. Based on the systematic regulation of chain segment arrangement and stress evolution mechanism, a set of anti-fatigue material preparation methods with hollow tubular structure as the core has been established.

[0119] The solution first involves drying, shearing dispersion, degassing, and viscosity adjustment of PVDF particle raw material data to form a molding preparation dispersion with stable flowability and preliminary chain segment orientation characteristics, providing a basic system for subsequent pressure and speed control flow processing and periodic disturbance application;

[0120] During the molding stage, pressure and speed control flow processing is performed based on the molding preparation dispersion data to guide continuous stacking and filling along the axial direction of the hollow tube mold. By synchronously superimposing periodic disturbance fields during the filling process, the flow shear rate, local chain segment energy density and chain segment rotation orientation are dynamically controlled to form dynamic disturbance retention data and realize the spatial synchronous induction of secondary chain segment orientation.

[0121] During the curing stage, dynamic disturbance holding data is subjected to dynamic curing treatment. The chain segment arrangement is dynamically frozen through the curing temperature curve. At the same time, cooling control treatment is applied to release residual stress and generate post-stress release shaping data to ensure the freezing and stability of stress within the chain segments and the continuity of the hollow tube wall structure.

[0122] During the functional activation phase, a low-amplitude perturbation pre-activation process is applied based on the post-stress release data to induce the activation of the internal structure of the chain segment. The inverse fatigue initial state activation data and functional verification pass data are extracted through piezoelectric response detection and microstructure detection, providing a basis for fatigue performance characteristic evaluation.

[0123] In the closed-loop stage of the fabrication path, a fabrication path deviation index is constructed based on fatigue performance characteristic data. Combined with strain rebound rate, fatigue gain rate and natural frequency mismatch rate, dynamic deformation response detection and path reverse correction processing are applied. The path parameters are converged through a dynamic step size control mechanism, and finally optimized fabrication path data that meets the requirements of inverse fatigue are output.

[0124] The reason why the whole scheme adopts a progressive logical design of disturbance-assisted molding, dynamic freezing and solidification, low-amplitude activation and path closed-loop optimization is to build up the chain segment sequence and prestress system from the early stage of molding under periodic water pressure disturbance environment. Through dynamic synchronous excitation, the piezoelectric response and fatigue resistance are continuously enhanced, fundamentally overcoming the structural bottleneck of traditional materials that are prone to failure. At the same time, it ensures that the chain segment orientation, micro-stability and macro-morphology are synchronized and coordinated during the preparation process, thereby achieving the "the more impact, the more active" anti-fatigue working mode and meeting the high-efficiency and stable operation requirements in long-term dynamic liquid phase applications.

[0125] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. 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 preparing a PVDF piezoelectric catalytic material with a hollow tubular structure, characterized in that, include: By applying drying, dispersion, shearing, degassing and viscosity adjustment treatments to PVDF particle raw material data, a molding preparation dispersion data is constructed, providing a basic material system for the subsequent perturbation molding stage; By applying a periodic perturbation field based on the initial filling data, the secondary orientation of the chain segments is dynamically induced, generating dynamic perturbation retention data as an intermediate state for the formation of the inverse fatigue structure. Through dynamic curing and cooling control, the chain segment arrangement in the data is frozen by freezing dynamic disturbances, forming data that has been stabilized after stress release. By applying perturbation activation processing and performing piezoelectric function testing, inverse fatigue initial state activation data and function verification pass data are extracted to generate primary fatigue test data for performance characteristic evaluation. By extracting dynamic deformation features and correcting path deviations, adaptive closed-loop optimization of the preparation process is achieved, and the final optimized preparation path data is output.

2. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 1, characterized in that: Collect PVDF raw material data, including particle size distribution, initial moisture content parameter, and melt index parameter. The PVDF raw material data is used to characterize the basic state of the PVDF piezoelectric catalytic raw material. Temperature-controlled drying is applied to the PVDF raw material data, and the drying curve parameters are solved during the temperature-controlled drying operation to generate dry PVDF particle data. The dry PVDF particle data is introduced into the solvent system and shear stirring is performed to form homogeneous dispersion data. Shear pretreatment is applied to the homogeneous dispersion data to identify the initial orientation direction of the chain segments and generate primary orientation dispersion data of the chain segments. The primary orientation dispersion data of the chain segments is then introduced into a vacuum settling step to remove internal air bubbles and form degassed homogeneous dispersion data. Viscosity adjustment is applied to the degassed homogenized dispersion data, the solvent ratio is adjusted, and the target viscosity dispersion data is output. The storage temperature and time of the target viscosity dispersion data are controlled, and the molding preparation dispersion data is output.

3. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 2, characterized in that: Based on the data of the dispersion liquid for molding preparation, pressure and speed control flow processing is performed to guide the flow trajectory along the axial direction of the hollow tube mold and continuously fill it according to the spatial shape of the mold cavity to generate preliminary mold filling data; The initial filling data is subjected to filling uniformity detection processing, and the filling rate and filling pressure parameters in the initial filling data are statistically analyzed to generate filling detection data.

4. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 3, characterized in that: Determine whether the filling test data meets the preset filling uniformity standard. If it does, apply periodic disturbance processing. If it does not, correct the injection flow rate parameter corresponding to the initial filling data and return to the pressure and speed control flow processing step to re-execute. The initial molding data is subjected to periodic perturbation field loading processing, and the perturbation frequency parameters and perturbation amplitude parameters are adjusted to output dynamic perturbation molding data; The dynamic perturbation forming data is subjected to secondary orientation detection processing of chain segments to extract the chain segment arrangement direction features in the dynamic perturbation forming data and generate chain segment orientation detection data. Determine whether the chain segment orientation detection data meets the set chain segment orientation standard. If it does, apply dynamic solidification processing. If it does not, correct the periodic perturbation field parameters and return to the periodic perturbation field loading process. The dynamic perturbation shaping data that conforms to the chain segment orientation standard is subjected to a perturbation persistence process to maintain the duration of the perturbation field and generate dynamic perturbation persistence data.

5. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 4, characterized in that: The dynamic disturbance holding data is subjected to dynamic curing processing. A curing temperature curve is set in the dynamic curing processing, and dynamic curing start-up data is output. The dynamic curing start-up data is then subjected to chain segment internal stress freezing detection to solve the freezing degree index and generate freezing detection data. Determine whether the freezing test data meets the internal stress freezing standard. If it does, proceed with the cooling process. If it does not, adjust the curing time and temperature and return to the curing step. The dynamic curing start-up data is subjected to cooling control processing, the cooling rate and gradient are set, and cooling shaping data is generated; the cooling shaping data is subjected to cooling uniformity detection, the temperature distribution is statistically analyzed, and the cooling detection data is output. Determine whether the cooling test data meets the cooling uniformity standard. If it does, proceed to the residual stress release step. If it does not, adjust the cooling curve and return to the cooling treatment. The cooling and shaping data that meets the cooling uniformity standard is subjected to surface residual stress release treatment to generate post-stress release shaping data.

6. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 5, characterized in that: After stress release, the shaped data is pre-activated by applying a low-amplitude perturbation to construct inverse fatigue initial state activated data. Preliminary piezoelectric response detection is applied to the inverse fatigue initial state activation data, and the piezoelectric response amplitude and response rate are extracted to generate preliminary piezoelectric detection data; Determine whether the preliminary piezoelectric test data meets the inverse fatigue start-up standard. If it does, proceed to the functional verification stage. If it does not, correct the disturbance amplitude parameter and return to the activation step. Microstructure stability detection was performed by applying the inverse fatigue initial state activation data, and the crystal region size and chain segment orientation were statistically analyzed to construct microstructure detection data. Determine whether the microstructure test data meets the stability standard. If it does, register it as functional verification passed data. If it does not, return to the surface stress release step and process it in a loop. Apply the functional verification data to the primary fatigue cycle test, load the number of pulsating cycles, and output the primary fatigue test data; The initial fatigue test data is subjected to fatigue performance extraction processing to generate fatigue performance characteristic data.

7. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 6, characterized in that: The fatigue performance characteristic data are subjected to dynamic deformation response detection to extract the natural frequency and strain rebound rate, and the dynamic deformation characteristic data is output. The dynamic deformation characteristic data is introduced into the fabrication path model to solve the path deviation index and form the fabrication path deviation data. Determine whether the preparation path deviation data is within the set tolerance range. If it is, output the final preparation path data. If it is not, apply path reverse correction processing. The preparation path deviation data is subjected to path reverse correction processing to correct the disturbance frequency, mold cavity parameters and curing window, and generate preparation path correction data. The preparation path correction data is applied to the preparation process adjustment process, and the preparation process optimization data is output. The preparation process optimization data is then applied to the process convergence detection, the performance stability is statistically analyzed, and process convergence detection data is generated. Determine whether the process convergence detection data meets the convergence criteria. If it does, output the final optimized preparation path. If it does not, return to the path reverse correction process and perform iterative optimization.

8. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 7, characterized in that: Based on the dispersion data for molding preparation, the flow shear rate, local segment energy density, and segment rotational orientation are dynamically controlled through pressure and speed control flow treatment and periodic perturbation field loading, thereby forming dynamic perturbation-maintained data under spatiotemporal coordinated driving; let the overall segment orientation evolution function of the perturbation-assisted molding process be: The local perturbation enhancement term is defined as follows: Where Φ(x,t) is the orientation evolution function of the perturbation-assisted forming chain segment; x is the spatial coordinate position variable inside the mold; and t is the perturbation application time variable. For local flow velocity gradient tensor; denoted as the local flow shear rate tensor; γ(x,t) is the local chain segment stretching energy density; ∈ is the regularization term; μ is the flow shear control exponent; Ω(ω(t),A(t)) is the local perturbation enhancement term; ω(t) is the perturbation frequency; A(t) is the perturbation amplitude; This represents the initial phase offset of the disturbance. ν is the Laplace operator for the local pressure field; β is the local pressure relaxation modulation factor; and ν is the perturbation amplification master index in the above equation.

9. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 8, characterized in that: By setting dynamic curing temperature curves and freezing dynamics, and combining the freezing damping and internal stress dissipation rate of local chain segments, the chain segment arrangement is cured and a prestressed structure is formed. During the cooling process, residual surface stress is released through heat flow control, and post-stress release shaping data is generated. Let the overall model of freezing dynamics and cooling stress release be: The cooling stress relief item is: Where Γ(T,t) is the function for the completion of the freezing and stress release coupling; T is the local temperature field variable; t is the time variable; and σ(x,u) is the local internal stress tensor. η is the local shear rate tensor; η(T(u)) represents the temperature-dependent segmental viscous damping coefficient; δ is the regularization term; λ is the freezing dynamic response exponent; χ(T,t) represents the local residual stress release function during the cooling process. It is the temperature gradient tensor; (x,t) is the temperature Laplace tensor; θ is the heat flux regularization term; ξ is the cooling stress release index.

10. The method for preparing a PVDF piezoelectric catalytic material with a hollow tubular structure according to claim 9, characterized in that: Based on fatigue performance characteristic data, a fabrication path deviation index is constructed. Combining local strain rebound rate, fatigue gain rate and natural frequency mismatch rate, the fabrication path parameters are dynamically corrected and converged to the final optimized fabrication path through gradient enhancement and nonlinear alternating step size optimization. Let the overall error convergence kinetic equation of the preparation path be: The path correction iterative formula is: The dynamic step size is adjusted as follows: Where Ξ(k) is the comprehensive deviation function of the preparation path in the k-th round; Δ∈(k) is the strain rebound rate deviation in the k-th round; Δγ(k) is the fatigue gain rate deviation in the k-th round; Δf(k) is the natural frequency deviation in the k-th round; ∈ target γ represents the target strain rebound rate; target The target fatigue gain rate; f target ζ represents the target natural frequency; ζ represents the comprehensive bias enhancement index; P k Prepare a path parameter set for the k-th round; To prepare the gradient of the path parameters with respect to the path deviation function; α k β is the dynamic step size factor. k This is the dynamic step size modulation factor.

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