Intelligent nano metal powder preparation and control method driven by liquid drop microfluidic technology

Abstract

The invention discloses an intelligent preparation and control method for nano metal powder driven by a droplet microfluidic technology, relates to the technical field of metal powder processing, and aims to accurately control particle distribution, calculate particle size distribution uniformity Dcv and dispersion stability coefficient Dsf and optimize a shear flow field through a droplet interface dynamic model in combination with a dynamic light scattering and adaptive optimization algorithm. The particle size uniformity is ensured, the powder stacking density and the conductive stability are improved, and the problem that particle size distribution is difficult to accurately control in an existing method is solved; a high-sensitivity sensor is adopted to monitor environmental interference, Fourier transform analysis and Gaussian process regression reinforcement learning are combined to optimize temperature and flow velocity, by-products and pollutants are reduced, powder purity is ensured, long-term stability of electronic devices is improved, and resistance drift or local overheating is avoided. And aiming at the agglomeration problem, an agglomeration trend value Pag is calculated and graded evaluation is carried out, so that particle agglomeration is effectively inhibited, and the stability and reliability of the conductive material are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of metal powder processing, and in particular to a method for intelligent preparation and control of nano-metal powder driven by droplet microfluidics technology. Background Art

[0002] The preparation and control technology of nanometal powders originated in the late 20th century and has gradually matured with the development of nanotechnology. Due to its unique physical and chemical properties, such as high specific surface area and quantum size effect, it is widely used in catalysts, magnetic materials, conductive materials and biomedicine. Common preparation methods include chemical reduction, vapor deposition, mechanical ball milling and electric explosion. By precisely controlling the reaction conditions and process parameters, the particle size, morphology and dispersibility of nanopowders can be adjusted to meet the needs of different applications.

[0003] However, the existing technology for the preparation and control of nano-metal powders used in conductive materials has the following technical disadvantages:

[0004] 1. Uneven particle size distribution: Existing preparation methods (such as chemical reduction and mechanical ball milling) make it difficult to accurately control the particle size distribution of nanoparticles, resulting in large deviations in particle size, affecting the bulk density and contact resistance of the powder, and making the overall conductivity of the conductive material unstable;

[0005] 2. Difficulty in purity control: During the preparation process, by-products, unreacted precursors, and external contaminants may be mixed into the metal powder, affecting its conductivity. Even with washing and purification, it is still difficult to completely remove trace impurities, which may cause resistance drift or local overheating in the long-term operation of electronic devices.

[0006] 3. Agglomeration and dispersion control difficulties: Due to the high surface energy of nanopowders, particles are easily attracted to each other by van der Waals forces and agglomerated, affecting the dispersion of the particles, thereby causing the conductive network to be interrupted and the conductive performance of the material to decrease. This problem is particularly prominent in applications such as conductive inks and electronic pastes. Summary of the Invention

[0007] In response to the deficiencies in the prior art, the present invention provides an intelligent preparation and control method for nano-metal powder driven by droplet microfluidics technology, which solves the technical shortcomings mentioned in the background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for intelligent preparation and control of nano-metal powder driven by droplet microfluidics, comprising the following steps:

[0009] S1. Extracting and preprocessing data related to the physical properties of metal precursor particles from the droplet microfluidic system to construct a data set of particle physical properties; then tracking and recording data related to vibration interference from the external environment and data related to the reaction temperature gradient, and constructing a data set of preparation environment interference after preprocessing;

[0010] S2. Based on the particle physical property data set, a convolutional neural network combined with a long short-term memory network is used to construct a droplet interface dynamics model. Based on the preparation environment interference data set, a thermodynamic growth model is constructed by combining the Gaussian process regression reinforcement learning algorithm.

[0011] S3. Calculate and evaluate the particle size distribution uniformity Dcv within the droplet. Based on the evaluation of the particle size distribution uniformity Dcv, further calculate and evaluate the dispersion stability coefficient Dsf of the current preparation process. Finally, by combining an adaptive optimization algorithm with microfluidic shear flow field information, a nanometal powder preparation optimization model is constructed. After training, the dispersion stability coefficient Dsf is fitted with the particle size distribution uniformity Dcv to obtain the final nanoparticle dispersion stability index Pds.

[0012] S4. Compare the preset dispersion stability threshold Lds with the nanoparticle dispersion index Pds to comprehensively judge whether the particle dispersion state is within a stable and controllable range during the current nanometal powder preparation process; if it is not within the controllable range, calculate and evaluate the powder agglomeration trend value Pag in real time and issue dynamic adjustment instructions of different levels.

[0013] Preferably, S1 specifically includes the following contents:

[0014] Extract data related to the physical properties of metal precursor particles, including particle size distribution, specific surface area, morphology parameters, and crystal structure information. Use light scattering analysis, laser particle size analysis, transmission electron microscopy, and X-ray diffraction analysis to collect and analyze the data related to the physical properties of particles in real time to construct a particle physical property data set.

[0015] Through high-sensitivity vibration sensors, high-precision thermocouple arrays and environmental pressure sensors, environmental interference data is collected in real time. Three-axis vibration sensors and fiber optic gyroscopes are used to monitor the vibration interference level of the external environment in real time, and Fourier transform is used to analyze the vibration spectrum characteristics, record environmental vibration data, and form a set of environmental interference data.

[0016] Preferably, S2 specifically includes the following contents:

[0017] In the process of intelligent preparation of nano-metal powder driven by droplet microfluidics, the convolutional neural network is first used to extract the particle distribution characteristics in the droplet based on the particle physical property data set, and the long short-term memory network is combined to process the time series data of fluid dynamics to construct a droplet interface dynamics model; in the specific operation process, the microfluidic optical microscope system is used to perform high-precision imaging of the particle distribution inside the droplet, and the Brownian diffusion effect of the particles is monitored in real time by combining dynamic light scattering to obtain relevant parameters including fluid shear rate, droplet surface tension and interface fluidity. At the same time, a rheological sensor is used to measure the change in droplet shear force and input it into the CNN- The LSTM combination model is used for learning and training; secondly, based on the preparation environment interference data set, Gaussian process regression is used to analyze the nonlinear relationship between temperature changes and nanoparticle nucleation and growth rate, and reinforcement learning is combined to adjust the growth environment of nanoparticles to construct a thermodynamic growth model; in the specific implementation process, a high-precision thermocouple array is used to monitor the temperature gradient changes of droplets in real time, and the external disturbance level is collected by combining the environmental pressure sensor. The GPR-RL combination strategy is used to fit and optimize the input data, and finally the optimal growth conditions of nanometal powders are predicted, and key parameters including temperature and flow rate of the microfluidic system are dynamically adjusted.

[0018] Preferably, S3 specifically includes:

[0019] Fluid dynamics data were collected in real time using a microfluidic optical detection system and dynamic light scattering, followed by thermodynamic data collected in real time using a high-precision thermocouple array and infrared thermal imaging. Based on the droplet interface dynamics model and thermodynamic growth model, the fluid dynamics data and thermodynamics data sets were analyzed to construct a fluid dynamics data set and a thermodynamics data set, respectively.

[0020] After extracting relevant data from the fluid mechanics data set and performing dimensionless processing, the droplet interface stability factor Dif and the nanoparticle nucleation rate Poe are obtained using the following calculation formulas:

[0021]

[0022] Wherein, Dia represents the surface tension of the droplet in the fluid mechanics data set, Dib represents the fluid shear rate in the fluid mechanics data set, Dic represents the interfacial fluidity in the fluid mechanics data set, and Did represents the droplet morphology stability index in the fluid mechanics data set; a1 is the correction factor for the influence of shear flow, and a2 is the correction factor for the influence of interfacial flow, where 0.1≤a1<1, 0.05<a2≤0.5, and a1+a2=1; e represents the base of the natural logarithm, and β is the exponential weighting factor of the influence of the droplet morphology stability index Did on the droplet interface stability factor Dif;

[0023]

[0024] Where Poa represents the critical energy parameter for nucleation in the thermodynamic data set, Pob represents the supersaturation influence coefficient in the thermodynamic data set, Poc represents the growth rate adjustment factor in the thermodynamic data set, and Pod represents the temperature gradient distribution parameter in the thermodynamic data set.

[0025] Preferably, S3 specifically further includes: extracting the droplet interface stability factor Dif and the nanoparticle nucleation rate Poe and performing normalization processing, and then obtaining the particle size distribution uniformity Dcv by fitting the following calculation formula:

[0026]

[0027] Where α and β are adjustment factors for the droplet interface stability factor Dif and the nanoparticle nucleation rate Poe, respectively, which are used to match the contribution ratio of the droplet interface stability and the particle nucleation rate to the particle size uniformity; m and n are nonlinear adjustment exponents, which are fitted according to experimental data to optimize the influence relationship between the two.

[0028] Based on application requirements, experimental data and optimization algorithms, a particle size distribution uniformity threshold E is preset and compared with the particle size distribution uniformity Dcv for evaluation. The specific evaluation contents are as follows:

[0029] When the particle size distribution uniformity Dcv ≥ the particle size distribution uniformity threshold E, it means that the particle size distribution uniformity is qualified and the particle size deviation meets the preparation requirements. At this time, the subsequent processes, including dispersion optimization and surface modification, can be directly entered;

[0030] When the particle size distribution uniformity Dcv is less than the particle size distribution uniformity threshold E, it means that the particle size distribution uniformity is unqualified and the particle size deviation does not meet the preparation requirements. At this time, the process should be adjusted and the particle size distribution uniformity Dcv should be recalculated and evaluated.

[0031] Preferably, S3 specifically further includes: based on the evaluation content of the particle size distribution uniformity Dcv, when the particle size distribution uniformity is qualified, by extracting relevant data from the thermodynamic data set, respectively calculating the temperature gradient influence coefficient Phe and the shear flow induced particle uniform distribution factor Dnf, the specific calculation formula is as follows:

[0032]

[0033] Wherein, Pha represents the temperature change rate in the fluid mechanics data set, Phb represents the heat conductivity coefficient in the fluid mechanics data set, Phc represents the heat exchange factor in the fluid mechanics data set, and Phd represents the local heat exchange correction factor in the fluid mechanics data set, which is used to characterize the influence of temperature gradients at different locations. Specifically, the heat exchange rate at the droplet interface and the internal core area is selected for weighted calculation or acquisition; local heat exchange correction factor;

[0034]

[0035] Where Dna represents the Brownian diffusion influence factor of the particles in the thermodynamic data set, Dnb represents the shear flow particle migration index in the thermodynamic data set, specifically the shear flow migration rate of the particles, which is obtained by fluid optical imaging analysis, Dnc represents the particle gravity sedimentation correction factor in the thermodynamic data set, and Dnd represents the droplet internal Reynolds number in the thermodynamic data set.

[0036] Preferably, S3 specifically further includes:

[0037] The temperature gradient influence coefficient Phe and the shear flow induced particle uniform distribution factor Dnf were extracted and normalized, and the dispersion stability coefficient Dsf was calculated using the following formula:

[0038]

[0039] The dispersion stability threshold F is preset based on the target application requirements, experimental data, and historical preparation experience, and compared with the dispersion stability coefficient Dsf for evaluation. The specific evaluation contents are as follows:

[0040] When the dispersion stability coefficient Dsf ≥ the dispersion stability threshold F, it indicates that the particle dispersion stability during the preparation process is normal, the particles have not agglomerated, and the dispersion state meets the application requirements; then proceed to the subsequent process;

[0041] When the dispersion stability coefficient Dsf is less than the dispersion stability threshold F, it indicates that the particle dispersion stability during the preparation process is abnormal, and the particles agglomerate or partially settle, affecting the final powder performance. At this time, adjust the process parameters, including adjusting the shear flow intensity and temperature gradient uniformity.

[0042] Preferably, S3 specifically further includes:

[0043] The nano-metal powder preparation optimization model acquires microfluidic shear flow field information in real time, collects shear rate, droplet morphology parameters, and particle migration rate, and uses a temperature sensor to collect the droplet temperature gradient. Then, based on the adaptive gradient optimization algorithm, the following objective optimization function is constructed:

[0044] maxPds=f(Dsf, Dcv);

[0045] The gradient descent method is used to train the model parameters so that the nanoparticle dispersion stability index Pds gradually converges to the optimal value. Secondly, the microfluidic shear flow field information is used to optimize the shear flow rate, particle nucleation rate, and temperature control parameters, and the matching relationship between the dispersion stability coefficient Dsf and the particle size distribution uniformity Dcv is adjusted. Finally, the final nanoparticle dispersion stability index Pds is calculated using the nanometal powder preparation optimization model. The specific calculation formula is as follows:

[0046]

[0047] Where x is the non-uniformity correction coefficient, which is used to suppress the deviation between the dispersion stability coefficient Dsf and the particle size distribution uniformity Dcv.

[0048] Preferably, S4 specifically includes:

[0049] Based on the application requirements, historical preparation data, and dynamic optimization results of the target nanopowder, a first dispersion stability threshold Lds-1 and a second dispersion stability threshold Lds-2 are preset and compared with the nanoparticle dispersion index Pds to comprehensively determine whether the particle dispersion state is within a stable and controllable range during the current nanometal powder preparation process; the first dispersion stability threshold Lds-1 is greater than the second dispersion stability threshold Lds-2.

[0050] The specific evaluation contents are as follows:

[0051] The first dispersion stability threshold Lds-1 ≥ the dispersion stability threshold Lds ≥ the second dispersion stability threshold Lds-2, indicating that the dispersion state of the nanoparticles is stable and controllable, the nanoparticles are evenly dispersed during the microfluidic preparation process, and the overall dispersion meets the application requirements;

[0052] At this point, the nanoparticle powder enters the subsequent process, including surface modification, drying and storage, or direct application in the field of conductive materials;

[0053] When the nanoparticle dispersibility index Pds is greater than the first dispersion stability threshold Lds-1, or the second dispersion stability threshold Lds-2 is greater than the nanoparticle dispersibility index Pds, it indicates that the dispersion state of the nanoparticles is beyond the controllable range, and the nanoparticles agglomerate, locally settle, and increase in particle size deviation during the microfluidic preparation process, affecting the final powder performance;

[0054] At this point, the powder agglomeration trend is further analyzed and evaluated.

[0055] Preferably, S4 specifically further includes:

[0056] When the dispersion state of nanoparticles in nanometal powder exceeds the controllable range, the interparticle van der Waals interaction index Pvd, particle Brownian diffusion influence factor Pbd, and particle shear dispersion correction factor Psd are obtained through real-time monitoring using atomic force microscopy, dynamic light scattering, and rheological sensors.

[0057] After extracting the inter-particle van der Waals interaction index Pvd, the particle Brownian diffusion influence factor Pbd, and the particle shear dispersion correction factor Psd and performing dimensionless processing, the powder agglomeration tendency value Pag is obtained by real-time calculation. The specific calculation formula is as follows:

[0058]

[0059] Based on the application requirements, historical experimental data, and rheological properties of the target nanopowders, particle dispersion experiments were used to determine the key parameter ranges under different agglomeration trends. Acceptable agglomeration risk thresholds were then established and preset in conjunction with a process optimization model. These thresholds included a first powder agglomeration trend threshold, Pbg1, and a second powder agglomeration trend threshold, Pbg2. These thresholds were then compared and evaluated with the powder agglomeration trend, Pag, to issue dynamic adjustment instructions at different levels. The first powder agglomeration trend threshold, Pbg1, was greater than the second powder agglomeration trend threshold, Pbg2.

[0060] The specific evaluation contents are as follows:

[0061] When the powder agglomeration trend Pag is less than the second powder agglomeration trend threshold Pbg2, it means that the particles are beginning to agglomerate or settle, but the risk is still within the controllable range. At this time, low-level adjustment instructions are triggered, including increasing the shear flow intensity by 10% and adjusting the temperature field uniformity.

[0062] Second powder agglomeration trend threshold Pbg2 ≤ Powder agglomeration trend Pag < First powder agglomeration trend threshold Pbg1: This indicates that the current particle agglomeration phenomenon or size deviation is on the rise, which will affect the powder performance. At this time, the intermediate adjustment instructions are triggered, including increasing the microfluidic channel shear rate by 10% on the original basis, adjusting the precursor solution concentration, and introducing a surface modifier to adjust the electrostatic repulsion between particles.

[0063] Powder agglomeration trend Pag ≥ first powder agglomeration trend threshold Pbg1: This indicates that the current particle agglomeration phenomenon or size deviation has exceeded the controllable range and affected the powder performance. At this time, advanced adjustment instructions are triggered, including forcibly increasing the shear rate by 20% on the original basis, dispersing the agglomerates through ultrasound or high-frequency oscillation, adjusting the reaction time and cooling rate, and finally reconfiguring the chemical environment and adjusting the precursor concentration and solvent polarity.

[0064] The present invention provides a method for intelligent preparation and control of nano-metal powders driven by droplet microfluidics. It has the following beneficial effects:

[0065] (1) The droplet microfluidic technology-driven intelligent preparation and control method for nano-metal powders can accurately control the particle distribution characteristics inside the droplet by using a droplet interface dynamics model that combines a convolutional neural network with a long short-term memory network. The Brownian diffusion effect of particles is monitored in real time by combining dynamic light scattering to obtain parameters such as fluid shear rate, droplet surface tension, and interface fluidity. The adaptive optimization algorithm is combined with the microfluidic shear flow field information to construct an optimization model for nano-metal powder preparation. Finally, by calculating the particle size distribution uniformity Dcv and the dispersion stability coefficient Dsf, the particle size deviation is ensured to meet the preparation requirements, solving the problem that existing methods such as chemical reduction and mechanical ball milling are difficult to accurately control the particle size distribution of nanoparticles, thereby improving the bulk density and contact resistance stability of the powder, and making the overall conductivity of the conductive material more stable.

[0066] (2) The droplet microfluidic technology-driven intelligent preparation and control method of nano-metal powders collects preparation environment interference data in real time through high-sensitivity vibration sensors, high-precision thermocouple arrays and environmental pressure sensors, and uses three-axis vibration sensors and fiber optic gyroscopes to monitor the vibration interference level of the external environment in real time; combines Fourier transform to analyze the vibration spectrum characteristics, records environmental vibration data, and forms a preparation environment interference data set; through the thermodynamic growth model combined with Gaussian process regression reinforcement learning algorithm, dynamically adjusts key parameters such as temperature and flow rate of the microfluidic system, effectively reducing the mixing of by-products, unreacted precursors and external pollutants; at the same time, the microfluidic optical microscope system is used to perform high-precision imaging of the particle distribution inside the droplet, and the dynamic light scattering is combined to monitor the Brownian diffusion effect of the particles in real time, further ensuring the purity of the nano-metal powder, solving the problem that the existing methods are difficult to completely remove trace impurities, thereby avoiding problems such as resistance drift or local overheating that may be caused by electronic devices during long-term operation;

[0067] (3) The droplet microfluidic technology-driven intelligent preparation and control method of nano-metal powders addresses the difficulties in agglomeration and dispersion control. By extracting the inter-particle van der Waals interaction index Pvd, the particle Brownian diffusion influence factor Pbd, and the particle shear dispersion correction factor Psd, and performing dimensionless processing, the powder agglomeration trend value Pag is calculated and evaluated in real time. Based on the application requirements, historical experimental data, and rheological properties of the target nanopowders, the particle dispersion experiment is used to determine the key parameter ranges under different agglomeration trends, and an acceptable agglomeration risk threshold is preset in combination with the process optimization model, including the first powder agglomeration trend threshold Pbg1 and The second powder agglomeration trend threshold Pbg2; based on the comparative evaluation of the powder agglomeration trend Pag and the preset threshold, dynamic adjustment instructions of different levels are issued, including adjusting the shear flow intensity, temperature field uniformity, microfluidic channel shear rate, precursor solution concentration, and introducing surface modifiers, etc.; through these measures, the van der Waals force between nanoparticles is effectively suppressed, and the problem that nanopowders are easy to agglomerate due to high surface energy is solved, thereby ensuring the dispersion of particles, avoiding the interruption of the conductive network, and significantly improving the conductive performance of the material. Especially in applications such as conductive inks and electronic pastes, this problem has been effectively solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a schematic flow chart of the steps of the intelligent preparation and control method of nano-metal powder driven by droplet microfluidics technology of the present invention. DETAILED DESCRIPTION

[0069] 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.

[0070] Example 1

[0071] See also Figure 1 The present invention provides a method for intelligent preparation and control of nano-metal powder driven by droplet microfluidics technology, comprising the following steps:

[0072] S1. Extracting and preprocessing data related to the physical properties of metal precursor particles from the droplet microfluidic system to construct a data set of particle physical properties; then tracking and recording data related to vibration interference from the external environment and data related to the reaction temperature gradient, and constructing a data set of preparation environment interference after preprocessing;

[0073] S2. Based on the particle physical property data set, a convolutional neural network combined with a long short-term memory network is used to construct a droplet interface dynamics model. Based on the preparation environment interference data set, a thermodynamic growth model is constructed by combining the Gaussian process regression reinforcement learning algorithm.

[0074] S3. Calculate and evaluate the particle size distribution uniformity Dcv within the droplet. Based on the evaluation of the particle size distribution uniformity Dcv, further calculate and evaluate the dispersion stability coefficient Dsf of the current preparation process. Finally, by combining an adaptive optimization algorithm with microfluidic shear flow field information, a nanometal powder preparation optimization model is constructed. After training, the dispersion stability coefficient Dsf is fitted with the particle size distribution uniformity Dcv to obtain the final nanoparticle dispersion stability index Pds.

[0075] S4. Compare the preset dispersion stability threshold Lds with the nanoparticle dispersion index Pds to comprehensively judge whether the particle dispersion state is within a stable and controllable range during the current nanometal powder preparation process; if it is not within the controllable range, calculate and evaluate the powder agglomeration trend value Pag in real time and issue dynamic adjustment instructions of different levels.

[0076] In this embodiment, the preparation efficiency and quality of nano-metal powder are significantly improved by organically combining steps S1 to S4. In S1, this stage is the mixing stage, in which the metal precursor solution is mixed with other reactants. At this time, the extraction and preprocessing of the data related to the physical properties of the particles mainly occur in the mixing stage. By means of light scattering analysis, laser particle size analysis, transmission electron microscopy and X-ray diffraction analysis, the particle size distribution, specific surface area, morphology parameters and crystal structure information of the particles are collected and analyzed in real time to construct a data set of the physical properties of the particles. At the same time, in the mixing stage, the sample is a mixture of the metal precursor solution and other reactants, and nanoparticles have not yet been formed. The current stage mainly collects data related to the physical properties of the particles, including particle size distribution, specific surface area and morphology parameters.

[0077] In S2, this stage is the shearing stage, in which the shear flow field in the droplet microfluidic system shears the droplet to form nanoparticles. At this time, based on the particle physical property data set, a convolutional neural network (CNN) is combined with a long short-term memory network (LSTM) to construct a droplet interface dynamics model. At the same time, based on the preparation environment interference data set, a thermodynamic growth model is constructed by combining Gaussian process regression (GPR) with reinforcement learning (RL). In the shearing stage, the sample is the particle distribution state inside the droplet, and the nanoparticles begin to nucleate and grow. The shearing stage mainly collects fluid mechanics data such as particle distribution inside the droplet, fluid shear rate, droplet surface tension, interface fluidity, and thermodynamic data such as temperature gradient changes.

[0078] In S3, this stage is the reaction stage, where the nanoparticles further grow and stabilize. At this time, the microfluidic optical detection system and dynamic light scattering are used to collect real-time fluid dynamics data, and the high-precision thermocouple array and infrared thermal imaging are used to collect real-time thermodynamics data. Based on the droplet interface dynamics model and the thermodynamic growth model, the fluid dynamics data and thermodynamics data are analyzed to construct a fluid dynamics data set and a thermodynamic data set respectively. In the reaction stage, the sample is a dispersed system of nanoparticles, and the particles further grow and stabilize. The current stage mainly collects the uniformity of the particle size distribution inside the droplet, the dispersion stability coefficient, the temperature gradient influence coefficient, and the shear flow induced particle uniform distribution factor.

[0079] In S4, this stage is the evaluation and adjustment stage. Based on the application requirements, historical preparation data and dynamic optimization results of the target nanopowder, the dispersion stability threshold Lds is preset and compared with the nanoparticle dispersion index Pds to comprehensively judge whether the particle dispersion state is within a stable and controllable range during the current preparation process of the nanometal powder. In the evaluation and adjustment stage, the sample is the final prepared nanometal powder, and the particle dispersion state has been formed. At this stage, the main data collected are the van der Waals interaction index between particles, the particle Brownian diffusion influence factor, the particle shear dispersion correction factor, etc., which are used to evaluate the powder agglomeration trend value Pag.

[0080] Example 2

[0081] S1 specifically includes the following:

[0082] Extract data related to the physical properties of metal precursor particles, including particle size distribution, specific surface area, morphology parameters, and crystal structure information. Use light scattering analysis, laser particle size analysis, transmission electron microscopy, and X-ray diffraction analysis to collect and analyze the data related to the physical properties of particles in real time to construct a particle physical property data set.

[0083] Through high-sensitivity vibration sensors, high-precision thermocouple arrays and environmental pressure sensors, environmental interference data is collected in real time. Three-axis vibration sensors and fiber optic gyroscopes are used to monitor the vibration interference level of the external environment in real time, and Fourier transform is used to analyze the vibration spectrum characteristics, record environmental vibration data, and form a set of environmental interference data.

[0084] S2 specifically includes the following:

[0085] In the process of intelligent preparation of nano-metal powder driven by droplet microfluidics, the convolutional neural network is first used to extract the particle distribution characteristics in the droplet based on the particle physical property data set, and the long short-term memory network is combined to process the time series data of fluid dynamics to construct a droplet interface dynamics model; in the specific operation process, the microfluidic optical microscope system is used to perform high-precision imaging of the particle distribution inside the droplet, and the Brownian diffusion effect of the particles is monitored in real time by combining dynamic light scattering to obtain relevant parameters including fluid shear rate, droplet surface tension and interface fluidity. At the same time, a rheological sensor is used to measure the change in droplet shear force and input it into the CNN- The LSTM combination model is used for learning and training; secondly, based on the preparation environment interference data set, Gaussian process regression is used to analyze the nonlinear relationship between temperature changes and nanoparticle nucleation and growth rate, and reinforcement learning is combined to adjust the growth environment of nanoparticles to construct a thermodynamic growth model; in the specific implementation process, a high-precision thermocouple array is used to monitor the temperature gradient changes of droplets in real time, and the external disturbance level is collected by combining the environmental pressure sensor. The GPR-RL combination strategy is used to fit and optimize the input data, and finally the optimal growth conditions of nanometal powders are predicted, and key parameters including temperature and flow rate of the microfluidic system are dynamically adjusted.

[0086] In this embodiment, in step one, by extracting data related to the physical properties of the particles of the metal precursor (including particle size distribution, specific surface area, morphology parameters and crystal structure information), and using light scattering analysis, laser particle size analysis, transmission electron microscopy and X-ray diffraction analysis to collect and analyze them in real time, a particle physical property data set is constructed; at the same time, a high-sensitivity vibration sensor, a high-precision thermocouple array and an environmental pressure sensor are used to collect and prepare environmental interference data in real time, and a three-axis vibration sensor and a fiber optic gyroscope are used to monitor the vibration interference level of the external environment in real time, and the vibration spectrum characteristics are analyzed in combination with Fourier transform, and the environmental vibration data is recorded to form a preparation environment interference data set; the significance of this step is to provide a comprehensive and accurate data foundation for subsequent model construction, ensure the controllability and predictability of the physical properties of the particles and the external environmental interference in the preparation process of the nano-metal powder, thereby laying a data support for improving the uniformity of the particle size distribution, purity and dispersion stability of the nano-metal powder;

[0087] In the second step, based on the particle physical property data set, a convolutional neural network (CNN) is used to extract the particle distribution characteristics in the droplet, and the long short-term memory network (LSTM) is combined to process the time series data of fluid dynamics to construct a droplet interface dynamics model; the particle distribution inside the droplet is imaged with high precision through a microfluidic optical microscope system, and the Brownian diffusion effect of the particles is monitored in real time by dynamic light scattering to obtain parameters such as fluid shear rate, droplet surface tension and interface fluidity. At the same time, a rheological sensor is used to measure the change in droplet shear force, and the result is input into the CNN-LSTM combined model for learning and training; secondly, based on the preparation environment interference data set, Gaussian process regression (GPR) is used to analyze the relationship between temperature change and nanoparticle nucleation and growth. The nonlinear relationship between the growth rate and the growth environment of the nanoparticles is calculated, and reinforcement learning (RL) is combined to adjust the growth environment of the nanoparticles and construct a thermodynamic growth model; the temperature gradient changes of the droplets are monitored in real time through a high-precision thermocouple array, and the external disturbance level is collected in combination with an environmental pressure sensor. The input data is fitted and optimized using a GPR-RL combination strategy, and the optimal growth conditions of the nanometal powder are finally predicted, and key parameters such as the temperature and flow rate of the microfluidic system are dynamically adjusted; the significance of this step lies in the accurate prediction and dynamic regulation of the particle distribution characteristics inside the droplets and the growth environment of the nanoparticles, which solves the problems of uneven particle size distribution, difficult purity control, and agglomeration and dispersion control in traditional methods, thereby significantly improving the preparation efficiency and quality of nanometal powders.

[0088] Example 3

[0089] S3 specifically includes:

[0090] Fluid dynamics data were collected in real time using a microfluidic optical detection system and dynamic light scattering, followed by thermodynamic data collected in real time using a high-precision thermocouple array and infrared thermal imaging. Based on the droplet interface dynamics model and thermodynamic growth model, the fluid dynamics data and thermodynamics data sets were analyzed to construct a fluid dynamics data set and a thermodynamics data set, respectively.

[0091] After extracting relevant data from the fluid mechanics data set and performing dimensionless processing, the droplet interface stability factor Dif and the nanoparticle nucleation rate Poe are obtained using the following calculation formulas:

[0092]

[0093] Wherein, Dia represents the surface tension of the droplet in the fluid mechanics data set, Dib represents the fluid shear rate in the fluid mechanics data set, Dic represents the interfacial fluidity in the fluid mechanics data set, and Did represents the droplet morphology stability index in the fluid mechanics data set; a1 is the correction factor for the influence of shear flow, and a2 is the correction factor for the influence of interfacial flow, where 0.1≤a1<1, 0.05<a2≤0.5, and a1+a2=1; e represents the base of the natural logarithm, and β is the exponential weighting factor of the influence of the droplet morphology stability index Did on the droplet interface stability factor Dif;

[0094]

[0095] Where Poa represents the critical energy parameter for nucleation in the thermodynamic data set, Pob represents the supersaturation influence coefficient in the thermodynamic data set, Poc represents the growth rate adjustment factor in the thermodynamic data set, and Pod represents the temperature gradient distribution parameter in the thermodynamic data set.

[0096] S3 specifically includes: extracting the droplet interface stability factor Dif and the nanoparticle nucleation rate Poe and normalizing them, and then obtaining the particle size distribution uniformity Dcv by fitting the following calculation formula:

[0097]

[0098] Where α and β are adjustment factors for the droplet interface stability factor Dif and the nanoparticle nucleation rate Poe, respectively, which are used to match the contribution ratio of the droplet interface stability and the particle nucleation rate to the particle size uniformity; m and n are nonlinear adjustment exponents, which are fitted according to experimental data to optimize the influence relationship between the two.

[0099] Based on application requirements, experimental data and optimization algorithms, a particle size distribution uniformity threshold E is preset and compared with the particle size distribution uniformity Dcv for evaluation. The specific evaluation contents are as follows:

[0100] When the particle size distribution uniformity Dcv ≥ the particle size distribution uniformity threshold E, it means that the particle size distribution uniformity is qualified and the particle size deviation meets the preparation requirements. At this time, the subsequent processes, including dispersion optimization and surface modification, can be directly entered;

[0101] When the particle size distribution uniformity Dcv is less than the particle size distribution uniformity threshold E, it means that the particle size distribution uniformity is unqualified and the particle size deviation does not meet the preparation requirements. At this time, the process should be adjusted and the particle size distribution uniformity Dcv should be recalculated and evaluated.

[0102] S3 specifically also includes: based on the evaluation content of particle size distribution uniformity Dcv, when the particle size distribution uniformity is qualified, by extracting relevant data from the thermodynamic data set, respectively calculating the temperature gradient influence coefficient Phe and the shear flow induced particle uniform distribution factor Dnf. The specific calculation formula is as follows:

[0103]

[0104] Wherein, Pha represents the temperature change rate in the fluid mechanics data set, Phb represents the heat conductivity coefficient in the fluid mechanics data set, Phc represents the heat exchange factor in the fluid mechanics data set, and Phd represents the local heat exchange correction factor in the fluid mechanics data set, which is used to characterize the influence of temperature gradients at different locations. Specifically, the heat exchange rate at the droplet interface and the internal core area is selected for weighted calculation or acquisition; local heat exchange correction factor;

[0105]

[0106] Where Dna represents the Brownian diffusion influence factor of the particles in the thermodynamic data set, Dnb represents the shear flow particle migration index in the thermodynamic data set, specifically the shear flow migration rate of the particles, which is obtained by fluid optical imaging analysis, Dnc represents the particle gravity sedimentation correction factor in the thermodynamic data set, and Dnd represents the droplet internal Reynolds number in the thermodynamic data set.

[0107] S3 specifically includes:

[0108] The temperature gradient influence coefficient Phe and the shear flow induced particle uniform distribution factor Dnf were extracted and normalized, and the dispersion stability coefficient Dsf was calculated using the following formula:

[0109]

[0110] The dispersion stability threshold F is preset based on the target application requirements, experimental data, and historical preparation experience, and compared with the dispersion stability coefficient Dsf for evaluation. The specific evaluation contents are as follows:

[0111] When the dispersion stability coefficient Dsf ≥ the dispersion stability threshold F, it indicates that the particle dispersion stability during the preparation process is normal, the particles have not agglomerated, and the dispersion state meets the application requirements; then proceed to the subsequent process;

[0112] When the dispersion stability coefficient Dsf is less than the dispersion stability threshold F, it indicates that the particle dispersion stability during the preparation process is abnormal, and the particles agglomerate or partially settle, affecting the final powder performance. At this time, adjust the process parameters, including adjusting the shear flow intensity and temperature gradient uniformity.

[0113] S3 specifically includes:

[0114] The nano-metal powder preparation optimization model acquires microfluidic shear flow field information in real time, collects shear rate, droplet morphology parameters, and particle migration rate, and uses a temperature sensor to collect the droplet temperature gradient. Then, based on the adaptive gradient optimization algorithm, the following objective optimization function is constructed:

[0115] maxPds=f(Dsf, Dcv);

[0116] The gradient descent method is used to train the model parameters so that the nanoparticle dispersion stability index Pds gradually converges to the optimal value. Secondly, the microfluidic shear flow field information is used to optimize the shear flow rate, particle nucleation rate, and temperature control parameters, and the matching relationship between the dispersion stability coefficient Dsf and the particle size distribution uniformity Dcv is adjusted. Finally, the final nanoparticle dispersion stability index Pds is calculated using the nanometal powder preparation optimization model. The specific calculation formula is as follows:

[0117]

[0118] Where x is the non-uniformity correction coefficient, which is used to suppress the deviation between the dispersion stability coefficient Dsf and the particle size distribution uniformity Dcv.

[0119] In this embodiment, fluid mechanics-related data are collected in real time through a microfluidic optical detection system and dynamic light scattering, and thermodynamics-related data are collected in real time in combination with a high-precision thermocouple array and infrared thermal imaging. The data are analyzed based on a droplet interface dynamics model and a thermodynamic growth model to construct a fluid mechanics data set and a thermodynamics data set, respectively. The droplet interface stability factor Dif and the nanoparticle nucleation rate Poe are calculated by extracting relevant data from the fluid mechanics data set and performing dimensionless processing. The particle size distribution uniformity Dcv is further obtained by normalization fitting and compared with a preset particle size distribution uniformity threshold E to ensure that the particle size deviation meets the preparation requirements.

[0120] When the particle size distribution uniformity is qualified, relevant data from the thermodynamic data set are extracted to calculate the temperature gradient influence coefficient Phe and the shear flow induced particle uniform distribution factor Dnf. The dispersion stability coefficient Dsf is calculated through normalization processing and compared with the preset dispersion stability threshold F to ensure normal particle dispersion stability. This process effectively suppresses particle agglomeration and local sedimentation through real-time monitoring and dynamic adjustment, ensuring the dispersion stability of the nanometal powder.

[0121] Finally, the microfluidic shear flow field information is obtained in real time through the nano-metal powder preparation optimization model, the droplet temperature gradient is collected in combination with the temperature sensor, the target optimization function is constructed based on the adaptive optimization algorithm and the model parameters are trained using the gradient descent method to optimize the shear flow rate, particle nucleation rate and temperature control parameters, adjust the matching relationship between the dispersion stability coefficient Dsf and the particle size distribution uniformity Dcv, and calculate the final nanoparticle dispersion stability index Pds (where x is the non-uniform correction coefficient, which is used to suppress the deviation effect between Dsf and Dcv); the significance of this step is to achieve the optimization of nano-metal powder particles through multi-dimensional data collection and real-time calculation and evaluation. Precise control of particle size distribution uniformity, dispersion stability, and growth environment solves the problems of uneven particle size distribution, particle agglomeration, and poor dispersion stability in traditional methods, thereby significantly improving the preparation quality and performance of nanometal powders and providing reliable guarantees for subsequent processes (such as dispersion optimization, surface modification, and conductive material applications). During the process, the target optimization function maxPds is to find the optimal matching point between the nanoparticle dispersion stability Dsf and the particle size uniformity Dcv. The microfluidic shear flow field parameters are continuously adjusted through the gradient optimization algorithm to optimize the nanoparticle dispersion stability index Pds, ultimately improving the quality and dispersion stability of the nanometal powders.

[0122] Among them, the supersaturation influence coefficient specifically refers to the effect of supersaturation in the solution on the particle nucleation rate, which is related to the metal ion concentration, solvent polarity and nucleation driving force; the growth rate adjustment factor specifically refers to the parameter that measures the growth rate of particles, which depends on the diffusion rate, temperature and nucleation rate; the temperature gradient distribution parameter specifically refers to the temperature change in the droplet or microfluidic channel, which determines the uniformity of particle growth;

[0123] In addition, the calculation formula for the dispersion stability coefficient Dsf reflects the dual effects of temperature gradient uniformity and shear flow intensity on particle dispersion. The square root operation can avoid the excessive influence of a single factor on the calculation results, making the calculation results more stable. Moreover, the larger the shear flow-induced particle uniform distribution factor Dnf, the stronger the shear flow field and the more uniform the distribution of particles in the fluid, which can effectively resist the agglomeration phenomenon caused by van der Waals forces and improve the dispersion stability of the powder.

[0124] Finally, the adaptive gradient optimization algorithm optimizes the microfluidic shear flow field parameters in real time through four steps: data acquisition, gradient calculation, error feedback, and parameter update, so that the nanoparticle dispersion stability index Pds gradually converges to the optimal value.

[0125] Example 4

[0126] S4 specifically includes:

[0127] Based on the application requirements, historical preparation data, and dynamic optimization results of the target nanopowder, a first dispersion stability threshold Lds-1 and a second dispersion stability threshold Lds-2 are preset and compared with the nanoparticle dispersion index Pds to comprehensively determine whether the particle dispersion state is within a stable and controllable range during the current nanometal powder preparation process; the first dispersion stability threshold Lds-1 is greater than the second dispersion stability threshold Lds-2.

[0128] The specific evaluation contents are as follows:

[0129] The first dispersion stability threshold Lds-1 ≥ the dispersion stability threshold Lds ≥ the second dispersion stability threshold Lds-2, indicating that the dispersion state of the nanoparticles is stable and controllable, the nanoparticles are evenly dispersed during the microfluidic preparation process, and the overall dispersion meets the application requirements;

[0130] At this point, the nanoparticle powder enters the subsequent process, including surface modification, drying and storage, or direct application in the field of conductive materials;

[0131] When the nanoparticle dispersibility index Pds is greater than the first dispersion stability threshold Lds-1, or the second dispersion stability threshold Lds-2 is greater than the nanoparticle dispersibility index Pds, it indicates that the dispersion state of the nanoparticles is beyond the controllable range, and the nanoparticles agglomerate, locally settle, and increase in particle size deviation during the microfluidic preparation process, affecting the final powder performance;

[0132] At this point, the tendency of powder agglomeration is further analyzed and evaluated.

[0133] S4 specifically includes:

[0134] When the dispersion state of nanoparticles in nanometal powder exceeds the controllable range, the interparticle van der Waals interaction index Pvd, particle Brownian diffusion influence factor Pbd, and particle shear dispersion correction factor Psd are obtained through real-time monitoring using atomic force microscopy, dynamic light scattering, and rheological sensors.

[0135] After extracting the inter-particle van der Waals interaction index Pvd, the particle Brownian diffusion influence factor Pbd, and the particle shear dispersion correction factor Psd and performing dimensionless processing, the powder agglomeration tendency value Pag is obtained by real-time calculation. The specific calculation formula is as follows:

[0136]

[0137] Based on the application requirements, historical experimental data, and rheological properties of the target nanopowders, particle dispersion experiments were used to determine the key parameter ranges under different agglomeration trends. Acceptable agglomeration risk thresholds were then established and preset in conjunction with a process optimization model. These thresholds included a first powder agglomeration trend threshold, Pbg1, and a second powder agglomeration trend threshold, Pbg2. These thresholds were then compared and evaluated with the powder agglomeration trend, Pag, to issue dynamic adjustment instructions at different levels. The first powder agglomeration trend threshold, Pbg1, was greater than the second powder agglomeration trend threshold, Pbg2.

[0138] The specific evaluation contents are as follows:

[0139] When the powder agglomeration trend Pag is less than the second powder agglomeration trend threshold Pbg2, it means that the particles are beginning to agglomerate or settle, but the risk is still within the controllable range. At this time, low-level adjustment instructions are triggered, including increasing the shear flow intensity by 10% and adjusting the temperature field uniformity.

[0140] Second powder agglomeration trend threshold Pbg2 ≤ Powder agglomeration trend Pag < First powder agglomeration trend threshold Pbg1: This indicates that the current particle agglomeration phenomenon or size deviation is on the rise, which will affect the powder performance. At this time, the intermediate adjustment instructions are triggered, including increasing the microfluidic channel shear rate by 10% on the original basis, adjusting the precursor solution concentration, and introducing a surface modifier to adjust the electrostatic repulsion between particles.

[0141] Powder agglomeration trend Pag ≥ first powder agglomeration trend threshold Pbg1: This indicates that the current particle agglomeration phenomenon or size deviation has exceeded the controllable range and affected the powder performance. At this time, advanced adjustment instructions are triggered, including forcibly increasing the shear rate by 20% on the original basis, dispersing the agglomerates through ultrasound or high-frequency oscillation, adjusting the reaction time and cooling rate, and finally reconfiguring the chemical environment and adjusting the precursor concentration and solvent polarity.

[0142] In this embodiment, in step S4, based on the application requirements, historical preparation data and dynamic optimization results of the target nanopowder, a dispersion stability threshold Lds is preset and compared with the nanoparticle dispersibility index Pds to comprehensively judge whether the particle dispersion state in the current nanometal powder preparation process is within a stable and controllable range; when the nanoparticle dispersibility index Pds ≥ the dispersion stability threshold Lds, it indicates that the nanoparticle dispersion state is stable and controllable, the particles are uniformly dispersed and meet the application requirements, and the powder enters the subsequent process (including surface modification, drying and storage or direct application in the field of conductive materials); when the nanoparticle dispersion index Pds is greater than or equal to the dispersion stability threshold Lds, it indicates that the nanoparticle dispersion state is stable and controllable, the particles are uniformly dispersed and meet the application requirements, and the powder enters the subsequent process (including surface modification, drying and storage or direct application in the field of conductive materials); When the particle dispersibility index Pds is less than the dispersion stability threshold Lds, it means that the particle dispersion state is beyond the controllable range and may experience agglomeration, local sedimentation, or increased particle size deviation. At this time, the inter-particle van der Waals interaction index Pvd, particle Brownian diffusion influence factor Pbd, and particle shear dispersion correction factor Psd are monitored in real time through atomic force microscopy, dynamic light scattering, and rheological sensors. The powder agglomeration trend value Pag is calculated after dimensionless processing. The significance of this step is to ensure the dispersion stability of the nanometal powder through real-time monitoring and evaluation of the particle dispersion state, providing reliable support for subsequent processes.

[0143] Based on the application requirements, historical experimental data and rheological properties of the target nanopowder, the particle dispersion experiment is used to determine the key parameter ranges under different agglomeration trends, and an acceptable agglomeration risk threshold (including the first powder agglomeration trend threshold Pbg1 and the second powder agglomeration trend threshold Pbg2) is preset and constructed in combination with the process optimization model. The thresholds are compared and evaluated with the powder agglomeration trend Pag, and dynamic adjustment instructions of different levels are issued. When the powder agglomeration trend Pag is less than the second powder agglomeration trend threshold Pbg2, low-level adjustment instructions are triggered (including increasing the shear flow intensity by 10% and adjusting the temperature field uniformity). When the second powder agglomeration trend threshold Pbg2 is less than or equal to the powder agglomeration trend Pag, the low-level adjustment instructions are triggered (including increasing the shear flow intensity by 10% and adjusting the temperature field uniformity). Pbg1, the intermediate adjustment instructions are triggered (including increasing the shear rate of the microfluidic channel by 10%, adjusting the concentration of the precursor solution and introducing a surface modifier); when the powder agglomeration trend Pag ≥ the first powder agglomeration trend threshold Pbg1, the advanced adjustment instructions are triggered (including forcibly increasing the shear rate by 20%, dispersing the agglomerates by ultrasound or high-frequency oscillation, adjusting the reaction time and cooling rate, and reconfiguring the chemical environment); the significance of this step is to suppress the particle agglomeration trend in real time through multi-level dynamic adjustment instructions, optimize the particle dispersion state, ensure the stable performance of the nano-metal powder, solve the problem of particle agglomeration and dispersion control in traditional methods, and significantly improve the application performance of nano-metal powder in conductive materials and other fields.

[0144] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent preparation and control of nano-metal powders driven by droplet microfluidics, characterized by: The following steps are involved: S1. Extracting and preprocessing data related to the physical properties of metal precursor particles from the droplet microfluidic system to construct a particle physical properties data set; then tracking and recording data related to external environmental vibration interference and reaction temperature gradient, and constructing a preparation environment interference data set after preprocessing; S2. Based on the particle physical property data set, a convolutional neural network combined with a long short-term memory network is used to construct a droplet interface dynamics model. Based on the preparation environment interference data set, a thermodynamic growth model is constructed by combining the Gaussian process regression reinforcement learning algorithm. S3. Calculate and evaluate the particle size distribution uniformity Dcv within the droplet. Based on the evaluation of the particle size distribution uniformity Dcv, further calculate and evaluate the dispersion stability coefficient Dsf of the current preparation process. Finally, by combining an adaptive optimization algorithm with microfluidic shear flow field information, a nanometal powder preparation optimization model is constructed. After training, the dispersion stability coefficient Dsf is fitted with the particle size distribution uniformity Dcv to obtain the final nanoparticle dispersion stability index Pds. S4. Compare the preset dispersion stability threshold Lds with the nanoparticle dispersion index Pds to comprehensively judge whether the particle dispersion state is within a stable and controllable range during the current nanometal powder preparation process; if it is not within the controllable range, calculate and evaluate the powder agglomeration trend value Pag in real time and issue dynamic adjustment instructions of different levels.

2. The method for intelligent preparation and control of nano-metal powder driven by droplet microfluidics according to claim 1, characterized in that: S1 specifically includes the following: Extract data related to the physical properties of metal precursor particles, including particle size distribution, specific surface area, morphology parameters, and crystal structure information. Use light scattering analysis, laser particle size analysis, transmission electron microscopy, and X-ray diffraction analysis to collect and analyze the data related to the physical properties of particles in real time to construct a particle physical property data set. Through high-sensitivity vibration sensors, high-precision thermocouple arrays and environmental pressure sensors, environmental interference data is collected in real time. Three-axis vibration sensors and fiber optic gyroscopes are used to monitor the vibration interference level of the external environment in real time, and Fourier transform is used to analyze the vibration spectrum characteristics, record environmental vibration data, and form a set of environmental interference data.

3. The method for intelligent preparation and control of nano-metal powder driven by droplet microfluidics according to claim 1, characterized in that: S2 specifically includes the following: Based on a data set of particle physical properties, a droplet interface dynamics model was constructed. A convolutional neural network was used to extract the distribution characteristics of particles within the droplet, and a long-short-term memory network was combined to process the time series data of fluid dynamics to calculate the fluid shear rate, droplet surface tension, and interfacial fluidity. Based on a dataset of preparation environment disturbances, a nanoparticle thermodynamic growth model was constructed. Gaussian process regression was used to analyze the nonlinear relationship between temperature changes and nanoparticle nucleation and growth rates. Reinforcement learning was then combined to adjust the growth environment to optimize the growth conditions of nanometal powders. According to the droplet interface dynamics model and the nanoparticle thermodynamic growth model, target adjustment values ​​of key growth parameters including droplet temperature and flow rate are calculated and output to the microfluidic system to optimize the nanometal powder preparation process.

4. The method for intelligent preparation and control of nano-metal powders driven by droplet microfluidics according to claim 1, characterized in that: S3 specifically includes: Fluid dynamics data were collected in real time using a microfluidic optical detection system and dynamic light scattering, followed by thermodynamic data collected in real time using a high-precision thermocouple array and infrared thermal imaging. Based on the droplet interface dynamics model and thermodynamic growth model, the fluid dynamics data and thermodynamics data sets were analyzed to construct a fluid dynamics data set and a thermodynamics data set, respectively. After extracting relevant data from the fluid mechanics data set and performing dimensionless processing, the droplet interface stability factor Dif and the nanoparticle nucleation rate Poe are obtained using the following calculation formulas: Wherein, Dia represents the surface tension of the droplet in the fluid mechanics data set, Dib represents the fluid shear rate in the fluid mechanics data set, Dic represents the interfacial fluidity in the fluid mechanics data set, and Did represents the droplet morphology stability index in the fluid mechanics data set; a1 is the correction factor for the influence of shear flow, and a2 is the correction factor for the influence of interfacial flow, where 0.1≤a1<1, 0.05<a2≤0.5, and a1+a2=1; e represents the base of the natural logarithm, and β is the exponential weighting factor of the influence of the droplet morphology stability index Did on the droplet interface stability factor Dif; Where Poa represents the critical energy parameter for nucleation in the thermodynamic data set, Pob represents the supersaturation influence coefficient in the thermodynamic data set, Poc represents the growth rate adjustment factor in the thermodynamic data set, and Pod represents the temperature gradient distribution parameter in the thermodynamic data set.

5. The method for intelligent preparation and control of nano-metal powder driven by droplet microfluidics according to claim 4, characterized in that: S3 specifically includes: extracting the droplet interface stability factor Dif and the nanoparticle nucleation rate Poe and normalizing them, and then obtaining the particle size distribution uniformity Dcv by fitting the following calculation formula: Where α and β are adjustment factors for the droplet interface stability factor Dif and the nanoparticle nucleation rate Poe, respectively, which are used to match the contribution ratio of the droplet interface stability and the particle nucleation rate to the particle size uniformity; m and n are nonlinear adjustment exponents, which are fitted according to experimental data to optimize the influence relationship between the two. Based on application requirements, experimental data and optimization algorithms, a particle size distribution uniformity threshold E is preset and compared with the particle size distribution uniformity Dcv for evaluation. The specific evaluation contents are as follows: When the particle size distribution uniformity Dcv ≥ the particle size distribution uniformity threshold E, it means that the particle size distribution uniformity is qualified and the particle size deviation meets the preparation requirements. At this time, the subsequent processes, including dispersion optimization and surface modification, can be directly entered; When the particle size distribution uniformity Dcv is less than the particle size distribution uniformity threshold E, it means that the particle size distribution uniformity is unqualified and the particle size deviation does not meet the preparation requirements. At this time, the process should be adjusted and the particle size distribution uniformity Dcv should be recalculated and evaluated.

6. The method for intelligent preparation and control of nano-metal powders driven by droplet microfluidics according to claim 5, characterized in that: S3 specifically also includes: based on the evaluation content of particle size distribution uniformity Dcv, when the particle size distribution uniformity is qualified, by extracting relevant data from the thermodynamic data set, respectively calculating the temperature gradient influence coefficient Phe and the shear flow induced particle uniform distribution factor Dnf. The specific calculation formula is as follows: Where Pha represents the temperature change rate in the fluid mechanics data set, Phb represents the heat transfer coefficient in the fluid mechanics data set, Phc represents the heat exchange factor in the fluid mechanics data set, and Phd represents the local heat exchange correction factor in the fluid mechanics data set, which is used to characterize the influence of temperature gradients at different locations. Specifically, the heat exchange rate at the droplet interface and the internal core area is selected for weighted calculation or acquisition; Where Dna represents the Brownian diffusion influence factor of the particles in the thermodynamic data set, Dnb represents the shear flow particle migration index in the thermodynamic data set, specifically the shear flow migration rate of the particles, which is obtained by fluid optical imaging analysis, Dnc represents the particle gravity sedimentation correction factor in the thermodynamic data set, and Dnd represents the droplet internal Reynolds number in the thermodynamic data set.

7. The method for intelligent preparation and control of nano-metal powders driven by droplet microfluidics according to claim 6, characterized in that: S3 specifically includes: The temperature gradient influence coefficient Phe and the shear flow induced particle uniform distribution factor Dnf were extracted and normalized, and the dispersion stability coefficient Dsf was calculated using the following formula: The dispersion stability threshold F is preset based on the target application requirements, experimental data, and historical preparation experience, and compared with the dispersion stability coefficient Dsf for evaluation. The specific evaluation contents are as follows: When the dispersion stability coefficient Dsf ≥ the dispersion stability threshold F, it indicates that the particle dispersion stability during the preparation process is normal, the particles have not agglomerated, and the dispersion state meets the application requirements; then proceed to the subsequent process; When the dispersion stability coefficient Dsf is less than the dispersion stability threshold F, it indicates that the particle dispersion stability during the preparation process is abnormal, and the particles agglomerate or partially settle, affecting the final powder performance. At this time, adjust the process parameters, including adjusting the shear flow intensity and temperature gradient uniformity.

8. The method for intelligent preparation and control of nano-metal powder driven by droplet microfluidics according to claim 1, characterized in that: S3 specifically includes: The nano-metal powder preparation optimization model acquires microfluidic shear flow field information in real time, collects shear rate, droplet morphology parameters, and particle migration rate, and uses a temperature sensor to collect the droplet temperature gradient. Then, based on the adaptive gradient optimization algorithm, the following objective optimization function is constructed: maxPds=f(Dsf, Dcv); The gradient descent method is used to train the model parameters so that the nanoparticle dispersion stability index Pds gradually converges to the optimal value. Secondly, the microfluidic shear flow field information is used to optimize the shear flow rate, particle nucleation rate, and temperature control parameters, and the matching relationship between the dispersion stability coefficient Dsf and the particle size distribution uniformity Dcv is adjusted. Finally, the final nanoparticle dispersion stability index Pds is calculated using the nanometal powder preparation optimization model. The specific calculation formula is as follows: Where x is the non-uniformity correction coefficient, which is used to suppress the deviation between the dispersion stability coefficient Dsf and the particle size distribution uniformity Dcv.

9. The method for intelligent preparation and control of nano-metal powders driven by droplet microfluidics according to claim 1, characterized in that: S4 specifically includes: Based on the application requirements, historical preparation data, and dynamic optimization results of the target nanopowder, a first dispersion stability threshold Lds-1 and a second dispersion stability threshold Lds-2 are preset and compared with the nanoparticle dispersion index Pds to comprehensively determine whether the particle dispersion state is within a stable and controllable range during the current nanometal powder preparation process; the first dispersion stability threshold Lds-1 is greater than the second dispersion stability threshold Lds-2. The specific evaluation contents are as follows: The first dispersion stability threshold Lds-1 ≥ the dispersion stability threshold Lds ≥ the second dispersion stability threshold Lds-2, indicating that the dispersion state of the nanoparticles is stable and controllable, the nanoparticles are evenly dispersed during the microfluidic preparation process, and the overall dispersion meets the application requirements; At this point, the nanoparticle powder enters the subsequent process, including surface modification, drying and storage, or direct application in the field of conductive materials; When the nanoparticle dispersibility index Pds is greater than the first dispersion stability threshold Lds-1, or the second dispersion stability threshold Lds-2 is greater than the nanoparticle dispersibility index Pds, it indicates that the dispersion state of the nanoparticles is beyond the controllable range, and the nanoparticles agglomerate, locally settle, and increase in particle size deviation during the microfluidic preparation process, affecting the final powder performance; At this point, the powder agglomeration trend is further analyzed and evaluated.

10. The method for intelligent preparation and control of nano-metal powder driven by droplet microfluidics according to claim 9, characterized in that: S4 specifically includes: When the dispersion state of nanoparticles in nanometal powder exceeds the controllable range, the interparticle van der Waals interaction index Pvd, particle Brownian diffusion influence factor Pbd, and particle shear dispersion correction factor Psd are obtained through real-time monitoring using atomic force microscopy, dynamic light scattering, and rheological sensors. After extracting the inter-particle van der Waals interaction index Pvd, the particle Brownian diffusion influence factor Pbd, and the particle shear dispersion correction factor Psd and performing dimensionless processing, the powder agglomeration tendency value Pag is obtained by real-time calculation. The specific calculation formula is as follows: Based on the application requirements, historical experimental data, and rheological properties of the target nanopowders, particle dispersion experiments were used to determine the key parameter ranges under different agglomeration trends. Acceptable agglomeration risk thresholds were then established and preset in conjunction with a process optimization model. These thresholds included a first powder agglomeration trend threshold, Pbg1, and a second powder agglomeration trend threshold, Pbg2. These thresholds were then compared and evaluated with the powder agglomeration trend, Pag, to issue dynamic adjustment instructions at different levels. The first powder agglomeration trend threshold, Pbg1, was greater than the second powder agglomeration trend threshold, Pbg2. The specific evaluation contents are as follows: When the powder agglomeration trend Pag is less than the second powder agglomeration trend threshold Pbg2, it means that the particles are beginning to agglomerate or settle, but the risk is still within the controllable range. At this time, low-level adjustment instructions are triggered, including increasing the shear flow intensity by 10% and adjusting the temperature field uniformity. Second powder agglomeration trend threshold Pbg2 ≤ Powder agglomeration trend Pag < First powder agglomeration trend threshold Pbg1: This indicates that the current particle agglomeration phenomenon or size deviation is on the rise, which will affect the powder performance. At this time, the intermediate adjustment instructions are triggered, including increasing the microfluidic channel shear rate by 10% on the original basis, adjusting the precursor solution concentration, and introducing a surface modifier to adjust the electrostatic repulsion between particles. Powder agglomeration trend Pag ≥ first powder agglomeration trend threshold Pbg1: This indicates that the current particle agglomeration phenomenon or size deviation has exceeded the controllable range and affected the powder performance. At this time, advanced adjustment instructions are triggered, including forcibly increasing the shear rate by 20% on the original basis, dispersing the agglomerates through ultrasound or high-frequency oscillation, adjusting the reaction time and cooling rate, and finally reconfiguring the chemical environment and adjusting the precursor concentration and solvent polarity.

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