A method for preparing a postoperative wound intelligent healing patch based on nanomaterials

Abstract

The present invention discloses a method for preparing a postoperative wound intelligent healing patch based on nanomaterials, comprising the following steps: S1, preparing a temperature response layer, using PNIPAM and surface-modified zinc oxide nanoparticles for compounding; S2, preparing a pH response layer, compounding chitosan with surface-modified titanium dioxide nanoparticles; S3, preparing a humidity response layer, using PEGDA and silver nanoparticle composite materials to optimize the release of silver ions; S4, assembling each layer layer by layer through electrospinning technology or lamination technology, and combining meta-learning and transfer learning to control process parameters to complete the overall structure preparation of the patch; S5, embedding temperature, pH and humidity sensors to construct a sensor data set; S6, dynamically adjusting the drug release rate and material phase change behavior to achieve accurate response; S7, after the patch is prepared, combining Bayesian and multi-objective evolutionary algorithms to optimize sterilization parameters. The present invention combines nanomaterials with intelligent algorithms to prepare intelligent healing patches and achieve real-time environmental response.

Description

Technical Field

[0001] The present invention relates to the fields of wound care and intelligent medical technology, and in particular to a method for preparing a nanomaterial-based intelligent wound healing patch after surgery. Background Art

[0002] In modern medicine, postoperative wound healing management has always been a key issue in clinical care. The wound healing process is usually accompanied by complex physiological changes, including dynamic changes in local temperature, pH and humidity, which directly affect the wound healing speed and infection risk. In order to improve the healing effect of postoperative wounds, a variety of wound dressings and patches have been developed in the prior art to protect the wound environment and promote healing. However, traditional wound patches often only have a single protective function, cannot effectively respond to the dynamic changes in the wound environment, and lack intelligent response capabilities, which to a certain extent limits their application in complex wound care.

[0003] Most existing wound dressings rely on the functions of passive absorption and isolation of the external environment of the wound, mainly including gauze, foam dressings, gel dressings, etc. Although these materials can absorb exudate, maintain a moist environment, and isolate external bacteria to a certain extent, they cannot make adaptive adjustments according to the physiological changes of the wound. In addition, traditional dressings are frequently replaced, which can easily lead to secondary damage and infection risks, especially in the middle stage of the healing process, when the temperature and pH value of the wound fluctuate violently. Existing dressings cannot provide sufficient responsiveness, which in turn affects the healing effect.

[0004] In recent years, with the development of materials science and nanotechnology, nanomaterials have begun to be introduced into the field of wound care. Nanoparticles are widely used in biomedical engineering due to their excellent physical and chemical properties, such as high surface area and controllability. Nanomaterials such as zinc oxide, titanium dioxide, and silver nanoparticles have become a hot topic of research due to their good antibacterial, anti-inflammatory and healing properties. However, how to effectively integrate these nanomaterials into wound patches and enable them to play an intelligent response function in the wound environment is still an urgent problem to be solved. Traditional nanomaterial preparation methods mostly focus on the basic properties of materials, but in the actual application of wound patches, the real-time response ability of materials to physiological parameters and the multifunctional integration are still insufficient.

[0005] To solve these problems, existing technologies attempt to introduce temperature-responsive materials, pH-responsive materials, and humidity-responsive materials into wound patches, and enhance functional responses by combining these materials with nanoparticles. However, the implementation of these technologies often relies on a single response mechanism and lacks a systematic multi-level intelligent response structure design. Existing smart wound patches can usually only respond to a single physiological parameter, such as temperature changes, but cannot effectively sense and respond to changes in pH and humidity. The limitations of this single response mechanism make it difficult for wound patches to provide comprehensive protection and therapeutic support in the complex wound healing process, especially when facing multiple infection factors.

[0006] In addition, in the existing technology, although the combination of nanomaterials and smart response materials has been attempted, the control of process parameters still faces great challenges. Electrospinning and lamination technology, as important means of preparing nanocomposite materials, require precise control of process parameters such as spinning voltage, spinning speed, and spinning distance to ensure uniform distribution of materials and interlayer bonding strength. However, existing process control methods often rely on fixed parameter settings and lack flexibility and adaptability, which leads to instability of material properties and consistency of product quality. How to adjust process parameters in real time during the production process to adapt to the characteristics of different materials is a problem that has not yet been solved in the existing technology.

[0007] In the clinical application of wound patches, aseptic handling and sterilization processes are also a major challenge. Existing technologies mostly use gamma-ray irradiation or ethylene oxide sterilization methods, but these methods often cause irreversible damage to the physical and chemical properties of the materials while ensuring the sterilization effect, reducing the mechanical properties and biocompatibility of the patch. The existing sterilization process lacks intelligent optimization means and cannot achieve the best balance between sterilization effect and material protection, which not only affects the clinical effect of the patch, but also limits its promotion and application.

[0008] Therefore, how to provide a method for preparing a postoperative wound intelligent healing patch based on nanomaterials is an urgent problem that technicians in this field need to solve. Summary of the invention

[0009] One purpose of the present invention is to propose a method for preparing a postoperative wound intelligent healing patch based on nanomaterials. The present invention cleverly combines nanomaterials with intelligent algorithms, and prepares a postoperative wound healing patch with multi-level intelligent response through meta-learning, transfer learning and multi-objective optimization technology. The patch can monitor and respond to changes in temperature, pH and humidity in the wound environment in real time, automatically adjust the drug release rate and material phase change behavior, and ensure precise treatment. At the same time, by optimizing the sterilization parameters, the best balance between sterilization effect and material protection is achieved, with the significant advantages of efficient healing, precise treatment, safety and reliability, and excellent material performance, providing an advanced solution for postoperative wound care.

[0010] A method for preparing a nanomaterial-based smart postoperative wound healing patch according to an embodiment of the present invention comprises the following steps:

[0011] S1. Prepare a temperature responsive layer by compounding PNIPAM with surface-modified zinc oxide nanoparticles, and form a structural layer with temperature responsive characteristics by optimizing the distribution and concentration of the nanoparticles;

[0012] S2, preparing a pH responsive layer, compounding chitosan with surface-modified titanium dioxide nanoparticles, and controlling the swelling and drug release behavior of the material to form a response mechanism to pH changes;

[0013] S3, preparing a humidity responsive layer, using a composite material of PEGDA and silver nanoparticles to adjust the water absorption of the hydrogel and optimize the release of silver ions;

[0014] S4. Assemble the temperature response layer, pH response layer and humidity response layer layer by layer through electrospinning technology or lamination technology, and control the process parameters by combining the intelligent algorithm of meta-learning and transfer learning to complete the overall structure preparation of the patch;

[0015] S5, embedding temperature, pH and humidity sensors in the patch, connecting the sensors to the microprocessor through microcircuits, and building a sensor data set;

[0016] S6, combining sensor data sets and historical trends to dynamically adjust drug release rate and material phase change behavior to achieve precise response;

[0017] S7. After the patch is prepared, it is sterilized and the sterilization parameters are optimized by combining Bayesian and multi-objective evolutionary algorithms.

[0018] Optionally, the S1 specifically includes:

[0019] S11, preparing a PNIPAM solution, dissolving the PNIPAM monomer in deionized water in a proportion of 10% to 20%, adding 1% to 5% of ammonium persulfate as an initiator, and adding 0.1% to 1% of MBAA as a cross-linking agent, and under nitrogen protection, controlling the reaction temperature between 0° C. and 10° C. to perform a free radical polymerization reaction until the solution becomes viscous to form a PNIPAM hydrogel prepolymer;

[0020] S12, preparing surface-modified zinc oxide nanoparticles, dispersing the zinc oxide nanoparticles in an ethanol solution at a concentration of 1 mg / ml to 10 mg / ml, preparing zinc oxide sol by a sol-gel method, adding trichloromethylsilane as a surface modifier, controlling the reaction temperature between 25° C. and 40° C., stirring the reaction for 2 to 4 hours to complete the surface modification, then purifying the nanoparticles by centrifugal separation, washing and drying to obtain surface-modified zinc oxide nanoparticles;

[0021] S13, mixing the prepared PNIPAM hydrogel prepolymer with the surface-modified zinc oxide nanoparticles in a mass ratio of 1:0.01 to 1:0.1, and treating at an ultrasonic frequency of 20 kHz to 40 kHz for 5 to 15 minutes to obtain a uniform PNIPAM / zinc oxide mixed solution;

[0022] S14, pouring the PNIPAM / zinc oxide mixed solution into a mold, and performing phase separation treatment by freeze-thaw method, wherein the freezing temperature is controlled between -20°C and -80°C, the thawing temperature is controlled between 0°C and 5°C, and the number of freezing and thawing cycles is 3 to 5 times, to form a PNIPAM / zinc oxide composite structure layer;

[0023] S15, vacuum drying the PNIPAM / zinc oxide composite structure layer, controlling the drying temperature between 30° C. and 50° C. for 12 to 24 hours to obtain a temperature responsive layer;

[0024] S16. Control the thickness of the temperature response layer between 100 μm and 500 μm, and observe the distribution of the nanoparticles in the composite material.

[0025] Optionally, the S2 specifically includes:

[0026] S21, preparing a chitosan solution, dissolving chitosan in a 2% to 5% acetic acid solution at a mass volume ratio of 1% to 3%, and stirring at room temperature for 2 to 4 hours until the chitosan is completely dissolved to form a uniform and transparent chitosan solution;

[0027] S22, preparing surface-modified titanium dioxide nanoparticles, dispersing the titanium dioxide nanoparticles in deionized water at a concentration of 0.5 mg / ml to 5 mg / ml, performing surface modification using a surface modifier, the modification process being carried out at a pH value of 4 to 6, the reaction temperature being controlled between 20° C. and 30° C., the reaction time being 1 to 3 hours, and then separating and purifying the modified titanium dioxide nanoparticles by centrifugation to obtain surface-modified titanium dioxide nanoparticles;

[0028] S23, mixing the prepared chitosan solution with the surface-modified titanium dioxide nanoparticles in a mass ratio of 1:0.01 to 1:0.05, and mixing them by combining mechanical stirring and ultrasonic treatment to form a uniform chitosan / titanium dioxide mixed solution;

[0029] S24, pouring the chitosan / titanium dioxide mixed solution into a mold, performing film-forming treatment by solvent evaporation method, controlling the film-forming temperature between 25° C. and 35° C., and the film-forming time between 12 and 24 hours, until the solvent is completely evaporated to form a chitosan / titanium dioxide composite film;

[0030] S25, soaking the chitosan / titanium dioxide composite film in a phosphate buffer having a pH value of 5 to 7 for 1 to 3 hours, maintaining swelling at a temperature of 20° C. to 30° C., and drying in air to a constant weight to obtain a pH response layer;

[0031] S26. Controlling the thickness of the pH response layer finally obtained, the film thickness is 50 μm to 200 μm.

[0032] Optionally, the S3 specifically includes:

[0033] S31, preparing a PEGDA solution, dissolving the PEGDA solution in deionized water at a mass volume ratio of 10% to 20%, and adding a photoinitiator at a mass volume ratio of 0.1% to 1% of the photoinitiator, stirring evenly to form a PEGDA solution;

[0034] S32, preparing surface-modified silver nanoparticles, dispersing the silver nanoparticles in deionized water at a concentration of 0.5 mg / ml to 5 mg / ml, performing surface modification using a surface modifier, the modification process being performed at room temperature for a reaction time of 1 to 3 hours, and then separating and purifying the modified silver nanoparticles by centrifugation to obtain surface-modified silver nanoparticles;

[0035] S33, mixing the prepared PEGDA solution with the surface-modified silver nanoparticles at a mass ratio of 1:0.01 to 1:0.05, and mixing by ultrasonic treatment to form a uniform PEGDA / silver nanoparticle mixed solution;

[0036] S34, injecting the PEGDA / silver nanoparticle mixed solution into the mold, and crosslinking and curing by ultraviolet light irradiation, wherein the ultraviolet light wavelength is 320 nanometers to 400 nanometers, and the irradiation time is 5 to 15 minutes to form a PEGDA / silver nanoparticle composite hydrogel;

[0037] S35, placing the PEGDA / silver nanoparticle composite hydrogel in an environment with a relative humidity of 50% to 90% for 1 to 3 hours, and then placing it in a dry environment to dry to a constant weight after absorbing enough water, to obtain a humidity responsive layer;

[0038] S36, controlling the thickness of the humidity response layer finally obtained, and the film thickness is 100 μm to 500 μm.

[0039] Optionally, the S4 specifically includes:

[0040] S41, arranging the temperature response layer, the pH response layer and the humidity response layer in sequence, assembling them layer by layer by using electrospinning technology, and evenly distributing each layer of material on the substrate;

[0041] S42, during the electrospinning process, the voltage is controlled between 10 kV and 30 kV, the spinning distance is 10 cm to 20 cm, and the spinning speed is 0.5 ml / hour to 2 ml / hour;

[0042] S43. By combining meta-learning and transfer learning, an adaptive parameter optimization system is formed. The optimization formula is:

[0043]

[0044] R(θ t ,T t )=λ1·Uni(θ t ,T t )+λ2·Adh(θ t ,T t )+λ3·Thk(θ t ,T t );

[0045] Q(θ s ,T t )=δ1·Eff(θ s ,T t )+δ2·Cons(θ s ,T t )+δ3·Exp(θ s ,T t );

[0046] Among them, θ t+1 represents the updated process parameters at the next time t+1, θ trepresents the process parameters at the current time t, α represents the learning rate, γ represents the discount factor, Represents the gradient of the process parameter θ, R(θ t ,T t ) represents the immediate reward function of the current task, T t represents the set of tasks at time t, Uni(θ t ,T t ) represents the uniformity of the material, Adh(θ t ,T t ) represents the interlayer adhesion strength, Thk(θ t ,T t ) represents the precise control of the thickness of each layer, λ1, λ2 and λ3 represent the weight coefficients of the performance index, Q(θ s ,T t ) represents the value function, θ s represents the optimal process parameters of the source task, Eff(θ s ,T t ) represents the efficiency of the spinning process in the source task, Cons(θ s ,T t ) indicates the consistency of process parameters, Exp(θ s ,T t ) represents the experience accumulation in the source task, δ1, δ2 and δ3 represent the weight coefficients of the value index;

[0047] S44, after the electrospinning is completed, the multilayer structure is subjected to a hot pressing treatment, the hot pressing temperature is controlled between 40° C. and 70° C., the pressure is controlled between 5 MPa and 20 MPa, and the hot pressing time is 10 minutes to 30 minutes;

[0048] S45. Check the thickness and uniformity of the assembled multilayer structure. Finally, the total thickness of the patch is controlled between 300 μm and 1000 μm, completing the overall structure preparation of the patch.

[0049] Optionally, the S6 specifically includes:

[0050] S61. After the patch is prepared and embedded with temperature, pH and humidity sensors, use the sensors to collect wound environment data in real time and construct a sensor data set, including time series data of temperature T(t), pH value pH(t) and humidity H(t). Where t represents time and N represents the total number of data points;

[0051] S62. Based on the constructed sensor data set, a dynamic adjustment algorithm is used to dynamically adjust the drug release rate V d (t) and material phase change behavior P(t):

[0052]

[0053] Among them, V d (t) represents the drug release rate at time t, V0 represents the reference drug release rate, η represents the temperature adjustment coefficient, ζ represents the pH adjustment coefficient, ξ represents the humidity adjustment coefficient, T0 represents the reference value of temperature, pH0 represents the reference value of pH, H0 represents the reference value of humidity, represents the variance of temperature, represents the variance of pH value, represents the variance of humidity;

[0054] P(t)=P0+λ T (T(t)-T0)+λ pH ·(pH(t)-pH0)+λ H (H(t)-H0);

[0055] Where P0 represents the reference material state, λ T represents the material response adjustment factor for temperature, λ pH The material response adjustment factor, λ, represents the pH value H The material response adjustment factor representing humidity;

[0056] S63, according to the real-time change of sensor data, the drug release rate V is obtained by calculation d (t) and material phase change behavior P(t) results, dynamically adjusting the release rate of the drug release layer in the patch and the response behavior of the material;

[0057] S64. Through iterative calculation and updating of historical data, the parameters of drug release and material phase change behavior are continuously optimized to gradually form the optimal treatment strategy.

[0058] Optionally, the S7 specifically includes:

[0059] S71. After the overall structure of the patch is prepared, the patch is placed in a sterile environment for sterilization. The sterilization method is selected from gamma ray irradiation or ethylene oxide sterilization, and the temperature, irradiation dose and gas concentration during the sterilization process are monitored in real time;

[0060] S72, using an intelligent optimization system combining Bayesian optimization and multi-objective evolutionary algorithm to adjust sterilization parameters in real time:

[0061]

[0062] in, Indicates the parameter combination with the best comprehensive effect. represents the set of control parameters in the sterilization process, ρ1 represents the weight coefficient of the sterilization effect, ρ2 represents the weight coefficient of material protection, represents the sterilization effect function, represents the material damage function;

[0063] S73. Before each sterilization process, the parameter distribution is gradually updated using the Bayesian optimization model based on the previous sterilization effect and material damage. It indicates the optimal sterilization parameters under given material damage D and sterilization effect E. The posterior distribution probability of :

[0064]

[0065] in, Indicates that given parameters The likelihood function of the sterilization effect under Indicates that given parameters The likelihood function of material damage under represents the prior distribution of sterilization parameters;

[0066] S74. Use a multi-objective evolutionary algorithm to perform multiple iterative simulations on the optimized parameter set to evaluate the sterilization effect and material damage under different parameter combinations, and finally select the parameter combination with the best comprehensive effect.

[0067] S75. After sterilization, the patch is tested for physical and chemical properties, including mechanical strength testing, material degradation rate analysis, and biocompatibility assessment;

[0068] S76. Based on the test results, if the material performance degradation exceeds the preset threshold, the sterilization parameters are further adjusted through Bayesian optimization and multi-objective evolutionary algorithm, and steps S71-S75 are repeated until the optimal sterilization solution is found.

[0069] The beneficial effects of the present invention are:

[0070] First, by designing a multi-level intelligent response structure, the present invention organically combines the temperature response layer, pH response layer and humidity response layer, so that the patch can sense and respond to changes in temperature, pH value and humidity in the wound environment in real time. This multifunctional integrated design greatly enhances the adaptability of the patch in complex wound environments, can provide precise treatment support according to different stages of wound healing, avoids the limitations of a single response mechanism, and significantly improves the speed and quality of wound healing.

[0071] Secondly, the present invention realizes adaptive optimization of process parameters in the production process by introducing intelligent algorithms of meta-learning and transfer learning. The core parameters of electrospinning and lamination technology, such as voltage, spinning speed and spinning distance, are adjusted in real time under the control of intelligent algorithms to ensure the uniform distribution of materials and interlayer bonding strength. This not only improves the consistency and stability of the product, but also effectively reduces the risk of material performance fluctuations during the production process, so that each batch of patches can maintain high quality standards, thereby improving production efficiency and product reliability.

[0072] In terms of sterilization, the present invention combines Bayesian optimization with multi-objective evolutionary algorithms to achieve an intelligent balance between sterilization effect and material protection for the first time. Through real-time optimization of sterilization parameters, the present invention not only ensures the thoroughness of the sterilization process, eliminates the problem of material damage caused by improper parameter settings in traditional sterilization processes, but also significantly improves the mechanical properties and biocompatibility of the patch after sterilization, providing a safer and more effective solution for the clinical application of postoperative wound patches, greatly improving the product's service life and clinical effect.

[0073] In addition, the present invention realizes dynamic monitoring of the wound environment and precise control of drug release through the combination of sensors and intelligent algorithms. The sensor collects wound environment data in real time, and dynamically adjusts the drug release rate and the phase change behavior of the material through the algorithm to ensure the accuracy and personalization of treatment. At the same time, the present invention can effectively prevent infection, accelerate wound healing, and reduce the risk of secondary injury during the treatment process, greatly improving the patient's comfort and overall treatment experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0075] Figure 1 This is a flow chart of a method for preparing a nanomaterial-based smart postoperative wound healing patch proposed by the present invention;

[0076] Figure 2 A schematic diagram of a multi-layer structure patch assembly method for preparing a nanomaterial-based smart postoperative wound healing patch proposed by the present invention;

[0077] Figure 3 This is a sterilization parameter optimization flow chart for a method for preparing a nanomaterial-based smart postoperative wound healing patch proposed in the present invention. DETAILED DESCRIPTION

[0078] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0079] refer to Figure 1-3 A method for preparing a postoperative wound intelligent healing patch based on nanomaterials comprises the following steps:

[0080] S1. Prepare a temperature responsive layer by compounding PNIPAM with surface-modified zinc oxide nanoparticles, and form a structural layer with temperature responsive characteristics by optimizing the distribution and concentration of the nanoparticles;

[0081] S2, preparing a pH responsive layer, compounding chitosan with surface-modified titanium dioxide nanoparticles, and controlling the swelling and drug release behavior of the material to form a response mechanism to pH changes;

[0082] S3, preparing a humidity responsive layer, using a composite material of PEGDA and silver nanoparticles to adjust the water absorption of the hydrogel and optimize the release of silver ions;

[0083] S4. Assemble the temperature response layer, pH response layer and humidity response layer layer by layer through electrospinning technology or lamination technology, and control the process parameters by combining the intelligent algorithm of meta-learning and transfer learning to complete the overall structure preparation of the patch;

[0084] S5, embedding temperature, pH and humidity sensors in the patch, connecting the sensors to the microprocessor through microcircuits, and building a sensor data set;

[0085] S6, combining sensor data sets and historical trends to dynamically adjust drug release rate and material phase change behavior to achieve precise response;

[0086] S7. After the patch is prepared, it is sterilized and the sterilization parameters are optimized by combining Bayesian and multi-objective evolutionary algorithms.

[0087] In this implementation manner, the S1 specifically includes:

[0088] S11, preparing a PNIPAM solution, dissolving the PNIPAM monomer in deionized water in a proportion of 10% to 20%, adding 1% to 5% of ammonium persulfate as an initiator, and adding 0.1% to 1% of MBAA as a cross-linking agent, and under nitrogen protection, controlling the reaction temperature between 0° C. and 10° C. to perform a free radical polymerization reaction until the solution becomes viscous to form a PNIPAM hydrogel prepolymer;

[0089] S12, preparing surface-modified zinc oxide nanoparticles, dispersing the zinc oxide nanoparticles in an ethanol solution at a concentration of 1 mg / ml to 10 mg / ml, preparing zinc oxide sol by a sol-gel method, adding trichloromethylsilane as a surface modifier, controlling the reaction temperature between 25° C. and 40° C., stirring the reaction for 2 to 4 hours to complete the surface modification, then purifying the nanoparticles by centrifugal separation, washing and drying to obtain surface-modified zinc oxide nanoparticles;

[0090] S13, mixing the prepared PNIPAM hydrogel prepolymer with the surface-modified zinc oxide nanoparticles in a mass ratio of 1:0.01 to 1:0.1, and treating at an ultrasonic frequency of 20 kHz to 40 kHz for 5 to 15 minutes to obtain a uniform PNIPAM / zinc oxide mixed solution;

[0091] S14, pouring the PNIPAM / zinc oxide mixed solution into a mold, and performing phase separation treatment by freeze-thaw method, wherein the freezing temperature is controlled between -20°C and -80°C, the thawing temperature is controlled between 0°C and 5°C, and the number of freezing and thawing cycles is 3 to 5 times, to form a PNIPAM / zinc oxide composite structure layer;

[0092] S15, vacuum drying the PNIPAM / zinc oxide composite structure layer, controlling the drying temperature between 30° C. and 50° C. for 12 to 24 hours to obtain a temperature responsive layer;

[0093] S16. Control the thickness of the temperature response layer between 100 μm and 500 μm, and observe the distribution of the nanoparticles in the composite material.

[0094] In this implementation, S2 specifically includes:

[0095] S21, preparing a chitosan solution, dissolving chitosan in a 2% to 5% acetic acid solution at a mass volume ratio of 1% to 3%, and stirring at room temperature for 2 to 4 hours until the chitosan is completely dissolved to form a uniform and transparent chitosan solution;

[0096] S22, preparing surface-modified titanium dioxide nanoparticles, dispersing the titanium dioxide nanoparticles in deionized water at a concentration of 0.5 mg / ml to 5 mg / ml, performing surface modification using a surface modifier, the modification process being carried out at a pH value of 4 to 6, the reaction temperature being controlled between 20° C. and 30° C., the reaction time being 1 to 3 hours, and then separating and purifying the modified titanium dioxide nanoparticles by centrifugation to obtain surface-modified titanium dioxide nanoparticles;

[0097] S23, mixing the prepared chitosan solution with the surface-modified titanium dioxide nanoparticles in a mass ratio of 1:0.01 to 1:0.05, and mixing them by combining mechanical stirring and ultrasonic treatment to form a uniform chitosan / titanium dioxide mixed solution;

[0098] S24, pouring the chitosan / titanium dioxide mixed solution into a mold, performing film-forming treatment by solvent evaporation method, controlling the film-forming temperature between 25° C. and 35° C., and the film-forming time between 12 and 24 hours, until the solvent is completely evaporated to form a chitosan / titanium dioxide composite film;

[0099] S25, soaking the chitosan / titanium dioxide composite film in a phosphate buffer having a pH value of 5 to 7 for 1 to 3 hours, maintaining swelling at a temperature of 20° C. to 30° C., and drying in air to a constant weight to obtain a pH response layer;

[0100] S26. Controlling the thickness of the pH response layer finally obtained, the film thickness is 50 μm to 200 μm.

[0101] In this implementation, S3 specifically includes:

[0102] S31, preparing a PEGDA solution, dissolving the PEGDA solution in deionized water at a mass volume ratio of 10% to 20%, and adding a photoinitiator at a mass volume ratio of 0.1% to 1% of the photoinitiator, stirring evenly to form a PEGDA solution;

[0103] S32, preparing surface-modified silver nanoparticles, dispersing the silver nanoparticles in deionized water at a concentration of 0.5 mg / ml to 5 mg / ml, performing surface modification using a surface modifier, the modification process being performed at room temperature for a reaction time of 1 to 3 hours, and then separating and purifying the modified silver nanoparticles by centrifugation to obtain surface-modified silver nanoparticles;

[0104] S33, mixing the prepared PEGDA solution with the surface-modified silver nanoparticles at a mass ratio of 1:0.01 to 1:0.05, and mixing by ultrasonic treatment to form a uniform PEGDA / silver nanoparticle mixed solution;

[0105] S34, injecting the PEGDA / silver nanoparticle mixed solution into the mold, and crosslinking and curing by ultraviolet light irradiation, wherein the ultraviolet light wavelength is 320 nanometers to 400 nanometers, and the irradiation time is 5 to 15 minutes to form a PEGDA / silver nanoparticle composite hydrogel;

[0106] S35, placing the PEGDA / silver nanoparticle composite hydrogel in an environment with a relative humidity of 50% to 90% for 1 to 3 hours, and then placing it in a dry environment to dry to a constant weight after absorbing enough water, to obtain a humidity responsive layer;

[0107] S36, controlling the thickness of the humidity response layer finally obtained, and the film thickness is 100 μm to 500 μm.

[0108] In this implementation, S4 specifically includes:

[0109] S41, arranging the temperature response layer, the pH response layer and the humidity response layer in sequence, assembling them layer by layer by using electrospinning technology, and evenly distributing each layer of material on the substrate;

[0110] S42, during the electrospinning process, the voltage is controlled between 10 kV and 30 kV, the spinning distance is 10 cm to 20 cm, and the spinning speed is 0.5 ml / hour to 2 ml / hour;

[0111] S43. By combining meta-learning and transfer learning, an adaptive parameter optimization system is formed. The optimization formula is:

[0112]

[0113] R(θ t ,T t )=λ1·Uni(θ t ,T t )+λ2·Adh(θ t ,T t )+λ3·Thk(θ t ,T t );

[0114] Q(θ s ,T t )=δ1·Eff(θ s ,T t )+δ2·Cons(θ s ,T t )+δ3·Exp(θ s ,T t );

[0115] Among them, θ t+1 represents the updated process parameters at the next time t+1, θ t represents the process parameters at the current time t, α represents the learning rate, γ represents the discount factor, Represents the gradient of the process parameter θ, R(θ t ,T t ) represents the immediate reward function of the current task, T t represents the set of tasks at time t, Uni(θ t ,T t ) represents the uniformity of the material, Adh(θ t ,T t) represents the interlayer adhesion strength, Thk(θ t ,T t ) represents the precise control of the thickness of each layer, λ1, λ2 and λ3 represent the weight coefficients of the performance index, Q(θ s ,T t ) represents the value function, θ s represents the optimal process parameters of the source task, Eff(θ s ,T t ) represents the efficiency of the spinning process in the source task, Cons(θ s ,T t ) indicates the consistency of process parameters, Exp(θ s ,T t ) represents the experience accumulation in the source task, δ1, δ2 and δ3 represent the weight coefficients of the value index;

[0116] S44, after the electrospinning is completed, the multilayer structure is subjected to a hot pressing treatment, the hot pressing temperature is controlled between 40° C. and 70° C., the pressure is controlled between 5 MPa and 20 MPa, and the hot pressing time is 10 minutes to 30 minutes;

[0117] S45. Check the thickness and uniformity of the assembled multilayer structure. Finally, the total thickness of the patch is controlled between 300 μm and 1000 μm, completing the overall structure preparation of the patch.

[0118] In this implementation manner, S6 specifically includes:

[0119] S61. After the patch is prepared and embedded with temperature, pH and humidity sensors, use the sensors to collect wound environment data in real time and construct a sensor data set, including time series data of temperature T(t), pH value pH(t) and humidity H(t). Where t represents time and N represents the total number of data points;

[0120] S62. Based on the constructed sensor data set, a dynamic adjustment algorithm is used to dynamically adjust the drug release rate V d (t) and material phase change behavior P(t):

[0121]

[0122] Among them, V d (t) represents the drug release rate at time t, V0 represents the reference drug release rate, η represents the temperature adjustment coefficient, ζ represents the pH adjustment coefficient, ξ represents the humidity adjustment coefficient, T0 represents the reference value of temperature, pH0 represents the reference value of pH, H0 represents the reference value of humidity, represents the variance of temperature, represents the variance of pH value, represents the variance of humidity;

[0123] P(t)=P0+λ T (T(t)-T0)+λ pH ·(pH(t)-pH0)+λ H (H(t)-H0);

[0124] Where P0 represents the reference material state, λ T represents the material response adjustment factor for temperature, λ pH The material response adjustment factor, λ, represents the pH value H The material response adjustment factor representing humidity;

[0125] S63, according to the real-time change of sensor data, the drug release rate V is obtained by calculation d (t) and material phase change behavior P(t) results, dynamically adjusting the release rate of the drug release layer in the patch and the response behavior of the material;

[0126] S64. Through iterative calculation and updating of historical data, the parameters of drug release and material phase change behavior are continuously optimized to gradually form the optimal treatment strategy.

[0127] In this implementation manner, the S7 specifically includes:

[0128] S71. After the overall structure of the patch is prepared, the patch is placed in a sterile environment for sterilization. The sterilization method is selected from gamma ray irradiation or ethylene oxide sterilization, and the temperature, irradiation dose and gas concentration during the sterilization process are monitored in real time;

[0129] S72, using an intelligent optimization system combining Bayesian optimization and multi-objective evolutionary algorithm to adjust sterilization parameters in real time:

[0130]

[0131] in, Indicates the parameter combination with the best comprehensive effect. represents the set of control parameters in the sterilization process, ρ1 represents the weight coefficient of the sterilization effect, ρ2 represents the weight coefficient of material protection, represents the sterilization effect function, represents the material damage function;

[0132] S73. Before each sterilization process, the parameter distribution is gradually updated using the Bayesian optimization model based on the previous sterilization effect and material damage. It indicates the optimal sterilization parameters under given material damage D and sterilization effect E. The posterior distribution probability of :

[0133]

[0134] in, Indicates that given parameters The likelihood function of the sterilization effect under Indicates that given parameters The likelihood function of material damage under represents the prior distribution of sterilization parameters;

[0135] S74. Use a multi-objective evolutionary algorithm to perform multiple iterative simulations on the optimized parameter set to evaluate the sterilization effect and material damage under different parameter combinations, and finally select the parameter combination with the best comprehensive effect.

[0136] S75. After sterilization, the patch is tested for physical and chemical properties, including mechanical strength testing, material degradation rate analysis, and biocompatibility assessment;

[0137] S76. Based on the test results, if the material performance degradation exceeds the preset threshold, the sterilization parameters are further adjusted through Bayesian optimization and multi-objective evolutionary algorithm, and steps S71-S75 are repeated until the optimal sterilization solution is found.

[0138] Embodiment 1:

[0139] In order to verify the feasibility of the present invention in implementation, the present invention was applied to the surgical operating room of a tertiary-level A-class hospital, and the doctor sutured the wound of a patient who underwent surgery for abdominal trauma. After surgery, how to effectively manage the patient's wound healing, especially to prevent infection, accelerate the healing process and reduce scar formation, has become the focus of the nursing team. Although traditional gauze and medical dressings can play a basic protective role, they are often insufficient when dealing with complex wound environments. Specifically, these traditional materials lack the ability to respond to changes in the local environment of the wound, such as temperature increases, pH changes, and humidity fluctuations, and cannot provide personalized treatment plans. Especially in the early postoperative period, the wound is in a highly sensitive period and the risk of infection is greatly increased. How to achieve accurate wound management at this stage is crucial.

[0140] In order to verify the practical application effect of the present invention, the hospital selected 50 postoperative patients aged between 25 and 60, whose postoperative wounds were all located in the abdomen and had similar depth and size. The nursing team applied the nanomaterial-based smart healing patch of the present invention to the wounds of these patients and conducted a 30-day follow-up observation on the healing effect.

[0141] The smart healing patch of the present invention consists of a temperature response layer, a pH response layer and a humidity response layer, and each layer of material has been surface-modified with nanoparticles to achieve an intelligent response to the wound environment. The nursing team applies the patch to the wound within 24 hours after the patient's surgery. The patch monitors the temperature, pH value and humidity of the wound environment in real time through embedded sensors. The data is wirelessly transmitted to the doctor's monitoring equipment, and the nursing team adjusts the drug release rate and material response behavior based on the real-time data.

[0142] Throughout the trial, the nursing team noticed that patients using the smart patch of the present invention healed significantly faster than patients using traditional dressings. Specific data showed that in patients using the smart healing patch of the present invention, the wound infection rate dropped from 15% with traditional dressings to 5% within 7 days after surgery; within 14 days after surgery, 80% of the patients had completely closed wounds, while only 60% of the patients using traditional dressings achieved this level of healing. For all patients, the average healing time was shortened by 2.5 days, from 12 days with traditional dressings to 9.5 days.

[0143] In the later stages of the trial, the nursing team paid special attention to the effects of the aseptic processing and sterilization parameters of the patch on material properties. By combining Bayesian optimization with a multi-objective evolutionary algorithm, the team was able to adjust the parameters before each sterilization process to ensure the best balance between sterilization effect and material protection. After each sterilization, the nursing team tested the mechanical properties and biocompatibility of the patch. The data showed that after optimized sterilization, the mechanical strength of the patch dropped by only 5%, far lower than the 10% to 15% drop after treatment with traditional sterilization methods. At the same time, the biocompatibility of the patch remained good, and within 24 hours after implantation in the body, the local inflammatory response caused by the patch was reduced by about 30% compared with the patch treated with traditional methods.

[0144] In addition to accelerating healing and reducing the risk of infection, the present invention also significantly improves patient comfort through its intelligent response function. On the 10th day of the trial, the nursing team surveyed the patients' pain perception. The data showed that the pain scores of patients using the smart healing patch decreased by an average of 2 points (out of 10 points), and the pain perception was significantly reduced compared with patients using traditional dressings. This result is attributed to the patch's ability to automatically adjust its physical properties according to changes in the wound environment, avoiding discomfort caused by changes in the external environment.

[0145] After the trial, the nursing team conducted a comprehensive evaluation of the patients' overall healing effect and usage experience. The results showed that 90% of the patients were satisfied with the smart healing patch of the present invention and were willing to continue using this new patch in future surgeries. At the same time, the doctors also spoke highly of the ease of use and intelligent functions of the patch, believing that the patch not only improves the efficiency of postoperative care, but also reduces the risk of cross-infection caused by frequent dressing changes during the care process. The following Table 1 intuitively shows the comparative data of postoperative wound healing using the smart healing patch of the present invention and traditional dressings in the form of a table.

[0146] Table 1 Comparison of data between the intelligent healing patch of the present invention and traditional dressings in postoperative wound healing

[0147]

[0148] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for preparing a postoperative wound intelligent healing patch based on nanomaterials, characterized in that: The steps include: S1. Prepare a temperature responsive layer by compounding PNIPAM with surface-modified zinc oxide nanoparticles, and form a structural layer with temperature responsive characteristics by optimizing the distribution and concentration of the nanoparticles; S2, preparing a pH responsive layer, compounding chitosan with surface-modified titanium dioxide nanoparticles, and controlling the swelling and drug release behavior of the material to form a response mechanism to pH changes; S3, preparing a humidity responsive layer, using a composite material of PEGDA and silver nanoparticles to adjust the water absorption of the hydrogel and optimize the release of silver ions; S4. Assemble the temperature response layer, pH response layer and humidity response layer layer by layer through electrospinning technology or lamination technology, and control the process parameters by combining the intelligent algorithm of meta-learning and transfer learning to complete the overall structure preparation of the patch; S5, embedding temperature, pH and humidity sensors in the patch, connecting the sensors to the microprocessor through microcircuits, and building a sensor data set; S6, combining sensor data sets and historical trends to dynamically adjust drug release rate and material phase change behavior to achieve precise response; S7. After the patch is prepared, sterile treatment is performed, and the sterilization parameters are optimized by combining Bayesian and multi-objective evolutionary algorithms; The S4 specifically includes: S41, arranging the temperature response layer, the pH response layer and the humidity response layer in sequence, assembling them layer by layer by using electrospinning technology, and evenly distributing each layer of material on the substrate; S42, during the electrospinning process, the voltage is controlled between 10 kV and 30 kV, the spinning distance is 10 cm to 20 cm, and the spinning speed is 0.5 ml / hour to 2 ml / hour; S43. By combining meta-learning and transfer learning, an adaptive parameter optimization system is formed. The optimization formula is: R(θ t ,T t )=λ1·Uni(θ t ,T t )+λ2·Adh(θ t ,T t )+λ3·Thk(θ t ,T t ); Q(θ s ,T t )=δ1·Eff(θ s ,T t )+δ2·Cons(θ s ,T t )+δ3·Exp(θ s ,T t ); Among them, θ t+1 represents the updated process parameters at the next time t+1, θ t represents the process parameters at the current time t, α represents the learning rate, γ represents the discount factor, Represents the gradient of the process parameter θ, R(θ t ,T t ) represents the immediate reward function of the current task, T t represents the set of tasks at time t, Uni(θ t ,T t ) represents the uniformity of the material, Adh(θ t ,T t ) represents the interlayer adhesion strength, Thk(θ t ,T t ) represents the precise control of the thickness of each layer, λ1, λ2 and λ3 represent the weight coefficients of the performance index, Q(θ s ,T t ) represents the value function, θ s represents the optimal process parameters of the source task, Eff(θ s ,T t ) represents the efficiency of the spinning process in the source task, Cons(θ s ,T t ) indicates the consistency of process parameters, Exp(θ s ,T t ) represents the experience accumulation in the source task, δ1, δ2 and δ3 represent the weight coefficients of the value index; S44, after the electrospinning is completed, the multilayer structure is subjected to a hot pressing treatment, the hot pressing temperature is controlled between 40° C. and 70° C., the pressure is controlled between 5 MPa and 20 MPa, and the hot pressing time is 10 minutes to 30 minutes; S45, inspecting the thickness and uniformity of the assembled multilayer structure, and finally controlling the total thickness of the patch to be between 300 μm and 1000 μm, thus completing the overall structure preparation of the patch; The S6 specifically includes: S61. After the patch is prepared and embedded with temperature, pH and humidity sensors, use the sensors to collect wound environment data in real time and construct a sensor data set, including time series data of temperature T(t), pH value pH(t) and humidity H(t). Where t represents time and N represents the total number of data points; S62. Based on the constructed sensor data set, a dynamic adjustment algorithm is used to dynamically adjust the drug release rate V d (t) and material phase change behavior P(t): Among them, V d (t) represents the drug release rate at time t, V0 represents the reference drug release rate, η represents the temperature regulation coefficient, represents the pH adjustment coefficient, ξ represents the humidity adjustment coefficient, T0 represents the reference value of temperature, pH0 represents the reference value of pH, H0 represents the reference value of humidity, represents the variance of temperature, represents the variance of pH value, represents the variance of humidity; P(t)=P0+λ T ·(T(t)-T0)+λ pH ·(pH(t)-pH0)+λ H ·(H(t)-H0); Where P0 represents the reference material state, λ T represents the material response adjustment factor for temperature, λ pH The material response adjustment factor, λ, represents the pH value H The material response adjustment factor representing humidity; S63, according to the real-time change of sensor data, the drug release rate V is obtained by calculation d (t) and material phase change behavior P(t) results, dynamically adjusting the release rate of the drug release layer in the patch and the response behavior of the material; S64. Through iterative calculation and updating of historical data, the parameters of drug release and material phase change behavior are continuously optimized to gradually form the optimal treatment strategy; The S7 specifically includes: S71. After the overall structure of the patch is prepared, the patch is placed in a sterile environment for sterilization. The sterilization method is selected from gamma ray irradiation or ethylene oxide sterilization, and the temperature, irradiation dose and gas concentration during the sterilization process are monitored in real time; S72, using an intelligent optimization system combining Bayesian optimization and multi-objective evolutionary algorithm to adjust sterilization parameters in real time: Among them, θ opt represents the parameter combination with the best comprehensive effect, θ represents the control parameter set in the sterilization process, ρ1 represents the weight coefficient of the sterilization effect, ρ2 represents the weight coefficient of material protection, E(θ) represents the sterilization effect function, and D(θ) represents the material damage function; S73. Before each sterilization process, the Bayesian optimization model is used to gradually update the parameter distribution P(θ|D,E) based on the previous sterilization effect and material damage. P(θ|D,E) represents the posterior distribution probability of the optimal sterilization parameter θ under given material damage D and sterilization effect E: P(θ|D,E)∝P(E|θ)·P(D|θ)·P(θ); Where P(E|θ) represents the likelihood function of the sterilization effect under a given parameter θ, P(D|θ) represents the likelihood function of material damage under a given parameter θ, and P(θ) represents the prior distribution of the sterilization parameter; S74. Use a multi-objective evolutionary algorithm to perform multiple iterative simulations on the optimized parameter set to evaluate the sterilization effect and material damage under different parameter combinations, and finally select the parameter combination with the best comprehensive effect θ opt ; S75. After sterilization, the patch is tested for physical and chemical properties, including mechanical strength testing, material degradation rate analysis, and biocompatibility assessment; S76. Based on the test results, if the material performance degradation exceeds the preset threshold, the sterilization parameters are further adjusted through Bayesian optimization and multi-objective evolutionary algorithm, and steps S71-S75 are repeated until the optimal sterilization solution is found.

2. The method for preparing a nanomaterial-based postoperative wound intelligent healing patch according to claim 1, characterized in that: The S1 specifically includes: S11, preparing a PNIPAM solution, dissolving the PNIPAM monomer in deionized water in a proportion of 10% to 20%, adding 1% to 5% of ammonium persulfate as an initiator, and adding 0.1% to 1% of MBAA as a cross-linking agent, and under nitrogen protection, controlling the reaction temperature between 0° C. and 10° C. to perform a free radical polymerization reaction until the solution becomes viscous to form a PNIPAM hydrogel prepolymer; S12, preparing surface-modified zinc oxide nanoparticles, dispersing the zinc oxide nanoparticles in an ethanol solution at a concentration of 1 mg / ml to 10 mg / ml, preparing zinc oxide sol by a sol-gel method, adding trichloromethylsilane as a surface modifier, controlling the reaction temperature between 25° C. and 40° C., stirring the reaction for 2 to 4 hours to complete the surface modification, then purifying the nanoparticles by centrifugal separation, washing and drying to obtain surface-modified zinc oxide nanoparticles; S13, mixing the prepared PNIPAM hydrogel prepolymer with the surface-modified zinc oxide nanoparticles in a mass ratio of 1:0.01 to 1:0.1, and treating at an ultrasonic frequency of 20 kHz to 40 kHz for 5 to 15 minutes to obtain a uniform PNIPAM / zinc oxide mixed solution; S14, pouring the PNIPAM / zinc oxide mixed solution into a mold, and performing phase separation treatment by freeze-thaw method, wherein the freezing temperature is controlled between -20°C and -80°C, the thawing temperature is controlled between 0°C and 5°C, and the number of freezing and thawing cycles is 3 to 5 times, to form a PNIPAM / zinc oxide composite structure layer; S15, vacuum drying the PNIPAM / zinc oxide composite structure layer, controlling the drying temperature between 30° C. and 50° C. for 12 to 24 hours to obtain a temperature responsive layer; S16. Control the thickness of the temperature response layer between 100 μm and 500 μm, and observe the distribution of the nanoparticles in the composite material.

3. The method for preparing a nanomaterial-based postoperative wound intelligent healing patch according to claim 1, characterized in that: The S2 specifically includes: S21, preparing a chitosan solution, dissolving chitosan in a 2% to 5% acetic acid solution at a mass volume ratio of 1% to 3%, and stirring at room temperature for 2 to 4 hours until the chitosan is completely dissolved to form a uniform and transparent chitosan solution; S22, preparing surface-modified titanium dioxide nanoparticles, dispersing the titanium dioxide nanoparticles in deionized water at a concentration of 0.5 mg / ml to 5 mg / ml, performing surface modification using a surface modifier, the modification process being carried out at a pH value of 4 to 6, the reaction temperature being controlled between 20° C. and 30° C., the reaction time being 1 to 3 hours, and then separating and purifying the modified titanium dioxide nanoparticles by centrifugation to obtain surface-modified titanium dioxide nanoparticles; S23, mixing the prepared chitosan solution with the surface-modified titanium dioxide nanoparticles in a mass ratio of 1:0.01 to 1:0.05, and mixing them by combining mechanical stirring and ultrasonic treatment to form a uniform chitosan / titanium dioxide mixed solution; S24, pouring the chitosan / titanium dioxide mixed solution into a mold, performing film-forming treatment by solvent evaporation method, controlling the film-forming temperature between 25° C. and 35° C., and the film-forming time between 12 and 24 hours, until the solvent is completely evaporated to form a chitosan / titanium dioxide composite film; S25, soaking the chitosan / titanium dioxide composite film in a phosphate buffer having a pH value of 5 to 7 for 1 to 3 hours, maintaining swelling at a temperature of 20° C. to 30° C., and drying in air to a constant weight to obtain a pH response layer; S26. Controlling the thickness of the pH response layer finally obtained, the film thickness is 50 μm to 200 μm.

4. The method for preparing a nanomaterial-based postoperative wound intelligent healing patch according to claim 1, characterized in that: The S3 specifically includes: S31, preparing a PEGDA solution, dissolving the PEGDA solution in deionized water at a mass volume ratio of 10% to 20%, and adding a photoinitiator at a mass volume ratio of 0.1% to 1% of the photoinitiator, stirring evenly to form a PEGDA solution; S32, preparing surface-modified silver nanoparticles, dispersing the silver nanoparticles in deionized water at a concentration of 0.5 mg / ml to 5 mg / ml, performing surface modification using a surface modifier, the modification process being performed at room temperature for a reaction time of 1 to 3 hours, and then separating and purifying the modified silver nanoparticles by centrifugation to obtain surface-modified silver nanoparticles; S33, mixing the prepared PEGDA solution with the surface-modified silver nanoparticles at a mass ratio of 1:0.01 to 1:0.05, and mixing by ultrasonic treatment to form a uniform PEGDA / silver nanoparticle mixed solution; S34, injecting the PEGDA / silver nanoparticle mixed solution into the mold, and crosslinking and curing by ultraviolet light irradiation, wherein the ultraviolet light wavelength is 320 nanometers to 400 nanometers, and the irradiation time is 5 to 15 minutes to form a PEGDA / silver nanoparticle composite hydrogel; S35, placing the PEGDA / silver nanoparticle composite hydrogel in an environment with a relative humidity of 50% to 90% for 1 to 3 hours, and then placing it in a dry environment to dry to a constant weight after absorbing enough water, to obtain a humidity responsive layer; S36, controlling the thickness of the humidity response layer finally obtained, and the film thickness is 100 μm to 500 μm.

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