Uniform temperature control superparamagnetic iron oxide nanoparticle preparation equipment and method

Abstract

The invention relates to the technical field of production of nano composite materials, in particular to equipment and a method for preparing superparamagnetic iron oxide nano particles with uniform temperature control. Comprising a thermocouple series layered thermoelectric module, a stainless steel cylindrical reaction kettle, a flexible heat-conducting gasket, a temperature sensor, an aluminum oxide ceramic isolation layer, a stirrer, a supporting and transmission device, a numerical control pulse power supply and a control module, when the superparamagnetic ferrite nano-particles are prepared, the temperature uniformity is ensured while the temperature rise rate is met in the temperature rise process, and the requirements for particle size distribution width, particle shape and dispersity control and large-scale requirements are met when the superparamagnetic ferrite nano-particles are prepared through high-temperature thermal decomposition.

Description

Technical Field

[0001] This invention relates to the field of nanocomposite material production technology, specifically to a temperature-controlled equipment and method for preparing superparamagnetic iron oxide nanoparticles. Background Technology

[0002] High-temperature pyrolysis is a mature and efficient method for preparing superparamagnetic ferrite nanoparticles, which exhibit low defect rates and excellent crystallinity. The heating process and parameter control in this method are crucial for determining the particle size, morphology, and magnetic properties.

[0003] The heating rate affects the balance between nucleation and growth, as well as the size of nanoparticles. Rapid heating above 5°C / min can lead to incomplete thermal decomposition of the precursor, lower supersaturation of the reaction solution, and difficulty in separating nucleation and growth processes, resulting in larger particle sizes and wider size distributions. Slow heating below 2°C / min prolongs crystal growth time, making it difficult to control the maturation stage during crystal growth, leading to the dissolution of small particles and the growth of larger particles, further widening the particle size distribution. The heating rate also affects magnetic properties; when the temperature is controlled between 2°C / min and 5°C / min, the specific saturation magnetization decreases with increasing rate. Temperature uniformity affects particle morphology control; uneven temperature fields, with a temperature difference greater than 10°C between the reactor edge and center, can lead to local overgrowth, forming non-spherical particles and reducing dispersion stability. Temperature uniformity also affects crystal structure integrity; a uniform temperature field can reduce lattice stress and prevent the aberrant phase transition from Fe3O4 to γ-Fe2O3.

[0004] Existing technologies using silicone oil systems as the heating system for high-temperature pyrolysis in the preparation of superparamagnetic ferrite nanoparticles employ a heating system. Silicone oil, acting as a heat transfer medium, transfers heat to the reactor through circulation. Due to the limited heat exchange area, local temperature differences exceeding ±5°C are easily generated. Temperature control can only be achieved by switching external heating elements and a refrigeration compressor, with temperature then conducted through the silicone oil. Furthermore, due to the working mechanism of the silicone oil system, temperature control is limited to silicone oil circulation, resulting in a slow response when suppressing local temperature differences. Thermoelectric modules, used for cooling electronic devices such as CPUs and for cold chain transportation of blood in medical applications, offer advantages such as short temperature control response time and high control precision. For industrial heating and temperature control in reactors, the precision can reach ±1°C, with a temperature control range between +50°C and +150°C.

[0005] Currently, both silicone oil systems and thermoelectric module systems have compatibility issues when used as heating devices for the high-temperature pyrolysis preparation of superparamagnetic ferrite nanoparticles. Silicone oil systems have slightly lower temperature control accuracy, longer temperature control response time, weaker ability to regulate local temperature differences, and high requirements for system sealing. While thermoelectric module systems are more suitable for the process requirements of high-temperature pyrolysis preparation of superparamagnetic ferrite nanoparticles in terms of temperature control accuracy and response time, they also have problems with high-temperature and local temperature control not meeting the requirements.

[0006] In order to improve the production capacity of superparamagnetic ferrite nanoparticles while avoiding the problems of temperature uniformity, control precision and flexible matching of heating rate when the existing technology is used for large-scale production, it is necessary to provide a superparamagnetic iron oxide nanoparticle preparation equipment with uniform temperature control that can ensure temperature uniformity while meeting the heating rate requirements, and meet the requirements of magnetic balance, dispersion and scalability of superparamagnetic ferrite nanoparticles. Summary of the Invention

[0007] The present invention discloses a temperature-controlled superparamagnetic iron oxide nanoparticle preparation device, which employs multi-level thermocouples connected in series and layered thermoelectric modules installed around the outer wall of a cylindrical reactor, with each layer independently controlled. Axial temperature uniformity is achieved by dynamically and independently adjusting the power of different layer modules. When local temperature differences are too large, local independent cooling can also be achieved by controlling the current direction, thereby ensuring temperature uniformity while achieving heating rate.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control, characterized in that it includes:

[0009] S1, Determine the physical parameters, including the effective volume V of the cylindrical reactor. ef ,satisfy:

[0010]

[0011] Where D is the diameter of the cylindrical reactor in meters (m), H is the height in meters (m), η is the effective filling coefficient used to control heat overflow, and the number of layers of the thermocouple-connected layered thermoelectric module is n. The heating power range of a single-layer module is... Unit W, i = 1, ..., n, the maximum power of a single-layer module satisfy:

[0012]

[0013] Where k is the thermal coefficient of the cylindrical reactor, in units of W / (m·K), L i The length of the heat transfer path for the i-th layer module, in meters. The maximum allowable temperature rise for the i-th layer module, in K. For minimum power, the thermal inertia time adjustment constant relative to a single-layer module Unit s, satisfying:

[0014]

[0015] Where ρ is the sampling time interval for colloidal density, in kg / m³. 3 c p Specific heat capacity (J / (kg·K)) and h are convective heat transfer coefficients (J / (s·m)). 2 ·k), the temperature response hysteresis parameter relative to a single-layer module Unit s, satisfying:

[0016]

[0017] Where 'a' is the thermal diffusivity of the cylindrical reactor material, in meters (m). 2 / s;

[0018] S2. Temperature sensor calibration: The temperature sensor is determined to be n based on the number of layers in the thermocouple series-connected layered thermoelectric module, and the linearity error range meets the requirements. Response speed meets

[0019] S3, Feedback variable input, including temperature error e and error change rate ec, where e satisfies:

[0020] e = e o -e i (5)

[0021] Among them, e o For the target temperature value, e i The measured temperature is given by the temperature sensor, and the unit is °C. The ec condition satisfies:

[0022] Among them, e k e represents the temperature error at the current moment. k-1 The temperature error at the previous moment is expressed in °C / s, and Δt satisfies the following conditions:

[0023] S4. Fuzzification processing: The continuous input variables e and ec are converted into fuzzy quantities through the triangular membership function;

[0024] S5. Establish a fuzzy rule base. Based on experimental data, establish a fuzzy rule base and infer and output the proportional adjustment coefficient ΔK of the optimal PID adjustment. p Adjustment factor ΔK for integral term i Differential adjustment coefficient ΔK d ;

[0025] S6. PID initial parameter setting, determining the basic proportional term K p0 Integral term K i0 Differential term K d0 The value;

[0026] S7. Parameter adaptive, updating the initial parameters of PID through a fuzzy rule base;

[0027] S8. Execute the output and output the target adjustment power P(t) of the thermocouple series layered thermoelectric module based on the updated PID initial parameters.

[0028] Preferably, step S4, which converts the continuous input variables e and ec into fuzzy quantities using a triangular membership function, includes the following steps:

[0029] S401, determine the universe of discourse of e and ec, e of e max and ec of ec max They respectively satisfy:

[0030] e max =ΔT i max +ε (7)

[0031]

[0032] The domain of e is [-e] max ,e max The domain of ec is [-ec]. max ,ec max ];

[0033] S402, partition the fuzzy set and triangular membership function. The fuzzy set is {negative large (NB), negative small (NS), zero (ZO), positive small (PS), positive large (PB)}. The parameters of the triangular membership function for the temperature error e satisfy: NB e ={a=-e max b = -e max c = -e max / 2}, NS e ={a=-e max / 2,b=-e max / 4,c=0},ZO e ={a=-e max / 4, b = 0, c = e max / 4},PS e ={a=0,b=e} max / 4,c=e max / 2},PB e ={a=e max / 2,b=e max c = emax The fuzzy set of temperature error e satisfies: A = {NB} e NS e ZO e PS e ,PB e The triangular membership function parameters of the error rate of change ec satisfy: NB ec ={a1=-ec max b1 = -ec max c1 = -ec max / 2}, NS ec ={a1=-ec max / 2,b1=-ec max / 4,c1=0},ZO ec ={a1=-ec max / 4, b1 = 0, c1 = ec max / 4},PS ec ={a1=0,b1=ec max / 4,c1=ec max / 2},PB ec ={a1=ec max / 2,b1=ec max c1 = ec max The fuzzy set of the error change rate ec satisfies: B = {NB} ec NS ec ZO ec PS ec ,PB ec};

[0034] S403, by calculating the membership degree μ of the input e and ec using trigonometric functions, the membership degree μ of e and ec relative to the fuzzy set is obtained. A,i (e) and μ B,j (ec) respectively satisfy:

[0035]

[0036] Preferably, in step S5, a fuzzy rule base is established based on experimental data, and the proportional term adjustment coefficient ΔK is output through inference. p Adjustment factor ΔK for integral term i Differential adjustment coefficient ΔK d Includes the following steps:

[0037] S501, Data Acquisition, the acquired data includes the viscosity μ of different i-th layer colloids. i And different heat transfer path lengths L of the i-th layer i All operating condition data, including the input e and ec, and the proportional adjustment coefficient ΔK of the optimal PID adjustment at the output. pAdjustment factor ΔK for integral term i Differential adjustment coefficient ΔK d ,satisfy:

[0038]

[0039] Where: K p0 K is the initial scaling factor. i0 K is the initial integration coefficient. d0 These are the initial differential coefficients. To optimize the output scaling term for the experiment, To optimize the output integral term for the experiment, To optimize the output differential term for the experiment;

[0040] S502, Rule Output Value Calculation, Proportional Term Rule The output satisfies:

[0041]

[0042] Integral Rules The output satisfies:

[0043]

[0044] Differential term rules The output satisfies:

[0045]

[0046] S503, Rule activation strength calculation, single rule R ij satisfy:

[0047]

[0048] Among them: A i ∈A,B j ∈B, the regular activation strength of the k-th data group satisfy

[0049]

[0050] Preferably, step S7 updates the initial PID parameters using a fuzzy rule base, satisfying: K p =K p0 +ΔK p ,K i =K i0 +ΔK i ,K d =K d0 +ΔK d , or K p =K p0 ·ΔK p,K i =K i0 gΔK i ,K d =K d0 ·ΔK d , where K p It is the updated scaling factor, K i It is the updated integral coefficient, K d These are the updated differential coefficients, K p0 It is the initial proportionality coefficient, K i0 K is the initial integral coefficient. d0 These are the initial differential coefficients.

[0051] Preferably, the target adjustable power P(t) of the output thermocouple series layered thermoelectric module of S8 satisfies:

[0052]

[0053] And it is controlled by pulse width modulation.

[0054] In a second aspect, an apparatus for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control comprises: a thermocouple-connected layered thermoelectric module, a stainless steel cylindrical reactor, a flexible thermally conductive pad, a temperature sensor, an alumina ceramic isolation layer, a stirrer, a support and transmission device, a digitally controlled pulse power supply, and a control module. The thermocouple-connected layered thermoelectric module is arranged in a ring array with vertically layered thermoelectric modules, evenly spaced along the circumference of the stainless steel cylindrical reactor and tightly fitted to the outer wall of the reactor via the flexible thermally conductive pad. An alumina ceramic isolation layer is embedded between each layer of the thermocouple-connected layered thermoelectric module. The temperature sensor is evenly distributed along the axial and / or circumferential direction of the stainless steel cylindrical reactor. The stirrer is arranged along the axial direction of the stainless steel cylindrical reactor and connected to the support and transmission device. Each layer of the thermocouple-connected layered thermoelectric module is electrically connected to the digitally controlled pulse power supply. The support and transmission device, the digitally controlled pulse power supply, and the temperature sensor are electrically connected to the control module.

[0055] Preferably, the thermoelectric module of the thermocouple series-connected layered thermoelectric module is one or more of lead telluride module, silicon-germanium alloy module, and bismuth-antimony-telluride module.

[0056] Preferably, the outer wall of the stainless steel cylindrical reactor has a honeycomb-like micro-protrusion structure.

[0057] Preferably, the outer wall of the stainless steel cylindrical reactor is coated with aluminum nitride ceramic or silicon carbide.

[0058] Preferably, the flexible thermal pad is a flexible graphene or a composite thermal pad made of graphene and metal foil.

[0059] Preferably, the temperature sensor is a ceramic-encapsulated PT1000 temperature sensor or a PT1000 temperature sensor with a stainless steel sheath.

[0060] Preferably, the agitator is one of an anchor agitator, a frame agitator, a screw agitator, or a turbine agitator.

[0061] Preferably, the numerical control pulse power supply is a unidirectional numerical control pulse power supply or a high-frequency unidirectional numerical control pulse power supply.

[0062] Preferably, the control module is a PLC control system or an embedded microcontroller system.

[0063] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0064] 1. It can achieve high-precision temperature control. Steady-state accuracy can be achieved by connecting thermocouples in series with layered thermoelectric modules. It is compatible with the narrow size distribution of superparamagnetic nanoparticles. Compared with silicone oil systems, it can improve the energy efficiency ratio while meeting the heating rate requirements.

[0065] 2. A PID control method for temperature uniformity control of thermocouple series-connected layered thermoelectric modules was implemented. The target adjustment power is determined based on the temperature difference, and the driving voltage or current of the thermocouple series-connected layered thermoelectric modules is adjusted in separate circuits, which meets the requirements of temperature response hysteresis and colloidal viscosity sensitivity in the preparation process of superparamagnetic particles.

[0066] 3. It can achieve rapid temperature control response. Through digitally controlled pulse power supply and phase change thermal storage material, the heating / cooling rate can be ≥5℃ / min, which meets the dynamic process requirements for the preparation of superparamagnetic ferrite nanoparticles by high-temperature thermal decomposition.

[0067] 4. The equipment uses thermoelectric modules to achieve heat transfer through the Peltier effect of semiconductor materials, eliminating the need for liquid media such as silicone oil, thus fundamentally avoiding equipment pollution and performance degradation caused by silicone oil leakage. Attached Figure Description

[0068] Figure 1 This is a schematic diagram of the structure of a temperature-controlled superparamagnetic iron oxide nanoparticle preparation device according to Embodiment 1 of the present invention.

[0069] Figure 2 This is a top view of the stainless steel cylindrical reactor of Embodiment 1 of the present invention.

[0070] Figure 3 This is a flowchart of a high-temperature thermal decomposition method for preparing superparamagnetic ferrite nanoparticles according to the present invention.

[0071] In the diagram: 1. Thermocouple series layered thermoelectric module, 2. Stainless steel cylindrical reactor, 3. Flexible thermal conductive pad, 4. Temperature sensor, 5. Alumina ceramic isolation layer, 6. Stirrer, 7. Support and transmission device, 8. CNC pulse power supply, 9. Control module. Detailed Implementation

[0072] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] Example 1

[0074] To achieve the above objectives, the present invention provides the following technical solution: a method for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control, characterized in that it includes:

[0075] S1, Determine the physical parameters, including the effective volume V of the cylindrical reactor. ef ,satisfy:

[0076]

[0077] Where D = 0.8 is the diameter of the cylindrical reactor (m), H = 1.0 is the height (m), η = 0.7 is the effective filling coefficient used to control heat overflow, and the number of layers in the thermocouple-connected layered thermoelectric module is n = 3. The heating power range of the first single-layer module is... The unit is W, representing the maximum power of a single-layer module. satisfy:

[0078]

[0079] Where k = 0.6 is the thermal coefficient of the cylindrical reactor, in W / (m·K), L1 = 0.03 is the heat transfer path length of the first-layer module, in meters, and ΔT1 max =80 is the maximum allowable temperature rise for the first layer, in Kelvin (K). For minimum power, the thermal inertia time adjustment constant relative to a single-layer module Unit s, satisfying:

[0080]

[0081] Where ρ = 1250 is the sampling time interval for colloidal density, in kg / m³. 3 c p=3800 is the specific heat capacity J / (kg·K), h=850 is the convective heat transfer coefficient, in J / (s·m). 2 ·k), the temperature response hysteresis parameter relative to a single-layer module Unit s, satisfying:

[0082]

[0083] Where, a = 1.5 × 10 -5 The thermal diffusivity of the cylindrical reactor material is given in meters (m). 2 / s;

[0084] S2. Temperature sensor calibration: Based on the number of layers in the thermocouple series-connected layered thermoelectric module, the temperature sensor is determined to be 3. The linearity error range meets ε=±0.1%, and the response speed meets τ. sen =0.5s;

[0085] S3, Feedback variable input, including temperature error e and error change rate ec, where e satisfies:

[0086] e = e o -e i =80-78=2℃ (22)

[0087] Among them, e o =80 is the target temperature value, e i =78 represents the actual measured temperature from the temperature sensor, both in °C, and EC satisfies:

[0088]

[0089] Among them, e k =2 represents the temperature error at the current moment, e k-1 =1.5 represents the temperature error at the previous moment, with units of ℃ / s, and Δt satisfies Δt=1;

[0090] S4. Fuzzification processing: The continuous input variables e and ec are converted into fuzzy quantities through the triangular membership function;

[0091] S5. Establish a fuzzy rule base. Based on experimental data, establish a fuzzy rule base and infer and output the proportional adjustment coefficient ΔK of the optimal PID adjustment. p =1.2, integral term adjustment factor ΔK i =0.8, differential term adjustment factor ΔK d =1.1;

[0092] S6. PID initial parameter setting, determining the basic proportional term K p0 =1, Integral term K i0 =0.1, differential term K d0 = 0.01;

[0093] S7. Parameter adaptive design: Updates the initial PID parameters using a fuzzy rule base, satisfying: K p =K p0 ·ΔK p =1.2,K i =K i0 gΔK i =0.08,K d =K d0 ·ΔK d =0.011;

[0094] S8. Execute the output. Based on the updated PID initial parameters, output the target adjustment power P(t) of the thermocouple series layered thermoelectric module, satisfying:

[0095] Example 2

[0096] To achieve the above objectives, the present invention provides the following technical solution: a method for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control, characterized in that, in step S4, the continuous input variables e and ec are converted into fuzzy quantities through a triangular membership function, with a target temperature of 80℃, a current temperature of 77℃, and a temperature error e. k =3℃, temperature error e from the previous second k-1 =2.6℃, ec = (3-2.6) / 1 = 0.4℃ / s includes the following steps:

[0097] S401, determine the universe of discourse of e and ec, e of e max =4℃ and EC of EC max = 2℃ / s, the universe of discourse of e is [-e max ,e max ] = [-4,4], and the universe of discourse of ec is [-2,2];

[0098] S402, partition the fuzzy set and triangular membership function. The fuzzy set is {negative large (NB), negative small (NS), zero (ZO), positive small (PS), positive large (PB)}. The parameters of the triangular membership function for the temperature error e satisfy: NB e ={a=-e max b = -e max c = -e max / 2}, NS e ={a=-e max b = -e max / 2,c=0},ZO e ={a=-e max / 2, b = 0, c = e max / 2},PS e ={a=0,b=e}max / 2,c=e max}, PB e ={a=e max / 2,b=e max c = e max The fuzzy set of temperature error e is detailed in Table 1, and the triangular membership function parameters of the error change rate ec satisfy: NB ec ={a1=-ec max b1 = -ec max c1 = -ec max / 2}, NS ec ={a1=-ec max b1 = -ec max / 2,c1=0},ZO ec ={a1=-ec max / 2, b1 = 0, c1 = ec max / 2},PS ec ={a1=0,b1=ec max / 2,c1=ec max}, PB ec ={a1=ec max / 2,b1=ec max c1 = ec max The fuzzy set of the error change rate ec is detailed in Table 2.

[0099] Table 1

[0100] Fuzzy set a b c <![CDATA[NB e ]]> -4 -4 -2 <![CDATA[NS e ]]> -4 -2 0 <![CDATA[ZO e ]]> -2 0 2 <![CDATA[PS e ]]> 0 2 4 <![CDATA[PB e ]]> 2 4 4

[0101] Table 2

[0102] Fuzzy set a1 b1 c1 <![CDATA[NB ec ]]> -2 -2 -1 <![CDATA[NS ec ]]> -2 -1 0 <![CDATA[ZO ec ]]> -1 0 1 <![CDATA[PS ec ]]> 0 1 2 <![CDATA[PB ec ]]> 1 2 2

[0103] S403 calculates the inputs e and ec using trigonometric functions, e k =3℃, in PS e Between b and c and PB e Between a and b, according to:

[0104]

[0105] Then e is the membership degree of the relative fuzzy set. ec = 0.4℃ / s, at ZO ec Between b and c and PS ec Between a and b, according to:

[0106]

[0107] Then the membership degree of the relative fuzzy set is ec

[0108] Example 3

[0109] In one embodiment of the present invention, considering the thermal inertia characteristics of a thermocouple series-connected layered thermoelectric module, step S6 involves setting the initial parameters for the PID controller and determining the basic proportional term K based on the Ziegler-Nichols method. p Integral term K i Differential term K d To determine the value, first turn off integration and differentiation, then gradually increase the proportionality coefficient K. p Continue until the system exhibits constant-amplitude oscillations, and record the critical gain K at this point. c and oscillation period P c Calculate the initial parameter K according to the formula: p =0.65K c ,T i =0.5P c ,T d =0.15P c ,T i It is the integration time constant, T d It is the differential time constant. K d =K p ×T d Actual measured data K c =5,P c = 900 seconds, the theoretical parameter is K p =3.25,T i =450,T d =135. Based on the thermal conductivity characteristics of the layered structure, the parameters should be fine-tuned, and the differential term should be appropriately enhanced to compensate for the hysteresis effect caused by the multilayer heat capacity.

[0110] Example 4

[0111] As attached Figure 2 , 3As shown, a temperature-controlled superparamagnetic iron oxide nanoparticle preparation device includes: a thermocouple-connected layered thermoelectric module 1, a stainless steel cylindrical reactor 2, a flexible thermally conductive pad 3, a temperature sensor 4, an alumina ceramic isolation layer 5, a stirrer 6, a support and transmission device 7, a digitally controlled pulse power supply 8, and a control module 9. The thermocouple-connected layered thermoelectric module 1 is arranged in a ring array with vertically layered thermoelectric modules evenly spaced along the circumference of the stainless steel cylindrical reactor 2 and tightly fitted to the outer wall of the stainless steel cylindrical reactor 2 by the flexible thermally conductive pad 3. An alumina ceramic isolation layer 5 is embedded between the layers of the thermocouple-connected layered thermoelectric module 1. The temperature sensor 4 is evenly distributed along the axial and / or circumferential directions of the stainless steel cylindrical reactor 2. The stirrer 6 is arranged along the axial direction of the stainless steel cylindrical reactor 2 and connected to the support and transmission device 7. Each layer of the thermocouple-connected layered thermoelectric module 1 is electrically connected to the digitally controlled pulse power supply 8. The support and transmission device 7, the digitally controlled pulse power supply 8, and the temperature sensor 4 are electrically connected to the control module 9.

[0112] This embodiment represents the most basic implementation. In this embodiment, the thermocouple-connected layered thermoelectric module 1 is arranged in a ring array with vertically layered thermoelectric modules, evenly spaced along the circumference of the stainless steel cylindrical reactor 2. The thermocouples connected in series in the layered thermoelectric module 1 can achieve bidirectional temperature control through the Seebeck effect while increasing the temperature regulation upper limit of existing thermoelectric module systems. The circumferentially evenly spaced layered structure allows for axial temperature balance and local control by dynamically adjusting the power of different layers of the thermocouple-connected layered thermoelectric module 1. Flexible thermally conductive pads 3 are tightly fitted to the outer wall of the stainless steel cylindrical reactor 2, reducing contact thermal resistance and minimizing temperature fluctuations. Alumina ceramic isolation layers 5 are embedded between the layers of the thermocouple-connected layered thermoelectric module 1, preventing mutual interference of thermoelectric potentials between layers and providing mechanical support. After power-on, the control module 9 controls the thermocouple-connected layered thermoelectric module 1 to work through the CNC pulse power supply 8 to heat the stainless steel cylindrical reactor 2, and controls the stirrer 6 to work. The temperature sensor 4 collects the temperature in real time and transmits it to the control module 9. The control module 9 controls the heating rate by adjusting the output power of the CNC pulse power supply 8 according to the heating rate and local temperature difference. It also controls the cooling rate or regulates the large temperature difference by adjusting the current direction of the CNC pulse power supply 8, and achieves small temperature difference by adjusting the speed of the stirrer 6. After the target temperature is reached, the control module 9 stops the electric stirrer 6 and the CNC pulse power supply 8.

[0113] The control module 9, embedded with the aforementioned high-temperature thermal decomposition method for preparing superparamagnetic ferrite nanoparticles, utilizes real-time acquisition of the target temperature, actual temperature, and temperature error. It then employs a three-stage synergistic mechanism of proportional, integral, and derivative actions to regulate the power of the thermocouple-connected layered thermoelectric module. The proportional stage directly adjusts the power output based on the current error intensity; the larger the error, the greater the adjustment. In the large temperature difference region (e>5℃), the power is fully activated to accelerate heating. In the small temperature difference region (e<5℃), integral and derivative actions are employed for finer adjustment. The integral stage eliminates steady-state deviations by accumulating historical errors for heat preservation. The derivative stage predicts temperature change trends and suppresses overshoot and oscillation. When the actual temperature rapidly approaches the set value, the derivative action reduces the power in advance to prevent overshoot. In the thermocouple-connected layered thermoelectric module, power regulation manifests as dynamic adjustment of the PWM duty cycle or current intensity, ultimately achieving precise temperature control.

[0114] The above embodiments are provided merely for the purpose of describing the present invention and are not intended to limit the scope of the invention. All equivalent substitutions and modifications made without departing from the spirit and principles of the invention should be covered within the scope of the invention.

Claims

1. A method for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control, characterized in that, include: S1, Determine the physical parameters, including the effective volume V of the cylindrical reactor. ef ,satisfy: Where D is the diameter of the cylindrical reactor in meters (m), H is the height in meters (m), η is the effective filling coefficient used to control heat overflow, and the number of layers of the thermocouple-connected layered thermoelectric module is n. The heating power range of a single-layer module is... The maximum power of a single-layer module, unit W, i = 1, ..., n. satisfy: Where k is the thermal coefficient of the cylindrical reactor, in units of W / (m·K), L i The length of the heat transfer path for the i-th layer module, in meters. The maximum allowable temperature rise for the i-th layer module, in K. For minimum power, the thermal inertia time adjustment constant relative to a single-layer module Unit s, satisfying: Where ρ is the sampling time interval for colloidal density, in kg / m³. 3 c p Specific heat capacity (J / (kg·K)) and h are convective heat transfer coefficients (J / (s·m)). 2 ·k), the temperature response hysteresis parameter relative to a single-layer module Unit s, satisfying: Where 'a' is the thermal diffusivity of the cylindrical reactor material, in meters (m). 2 / s; S2. Temperature sensor calibration: The temperature sensor is determined to be n based on the number of layers in the thermocouple series-connected layered thermoelectric module, and the linearity error range meets the requirements. Response speed meets S3, Feedback variable input, including temperature error e and error change rate ec, where e satisfies: e = e o -e i (5) Where, e o For the target temperature value, e i The measured temperature is given by the temperature sensor, and the unit is °C. The ec condition satisfies: Among them, e k e represents the temperature error at the current moment. k-1 The temperature error at the previous moment is expressed in °C / s, and Δt satisfies the following conditions: S4. Fuzzification processing: The continuous input variables e and ec are converted into fuzzy quantities through the triangular membership function; S5. Establish a fuzzy rule base. Based on experimental data, establish a fuzzy rule base and infer and output the proportional adjustment coefficient ΔK of the optimal PID adjustment. p The integral term adjustment factor ΔK i Differential adjustment factor ΔK d ; S6. PID initial parameter setting, determining the basic proportional term K p0 Integral term K i0 Differential term K d0 The value; S7. Parameter adaptive, updating the initial parameters of PID through a fuzzy rule base; S8. Execute the output and output the target adjustment power P(t) of the thermocouple series layered thermoelectric module based on the updated PID initial parameters.

2. The method for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control according to claim 1, characterized in that: The step S4, which transforms continuous input variables e and ec into fuzzy quantities using a triangular membership function, includes the following steps: S401, determine the universe of discourse of e and ec, e of e max and ec of ec max They respectively satisfy: The domain of e is [-e] max ,e max The domain of ec is [-ec]. max ,ec max ]; S402, partition the fuzzy set and triangular membership function. The fuzzy set is {negative large (NB), negative small (NS), zero (ZO), positive small (PS), positive large (PB)}. The parameters of the triangular membership function for the temperature error e satisfy: NB e ={a=-e max b = -e max c = -e max / 2}, NS e ={a=-e max / 2,b=-e max / 4,c=0},ZO e ={a=-e max / 4, b = 0, c = e max / 4},PS e ={a=0,b=e} max / 4,c=e max / 2},PB e ={a=e max / 2,b=e max c = e max The fuzzy set of temperature error e satisfies: A = {NB} e NS e ZO e PS e ,PB e The triangular membership function parameters of the error rate of change ec satisfy: NB ec ={a1=-ec max b1 = -ec max c1 = -ec max / 2}, NS ec ={a1=-ec max / 2,b1=-ec max / 4,c1=0},ZO ec ={a1=-ec max / 4, b1 = 0, c1 = ec max / 4},PS ec ={a1=0,b1=ec max / 4,c1=ec max / 2},PB ec ={a1=ec max / 2,b1=ec max c1 = ec max The fuzzy set of the error change rate ec satisfies: B = {NB} ec NS ec ZO ec PS ec ,PB ec }; S403, by calculating the membership degree μ of the input e and ec using trigonometric functions, the membership degree μ of e and ec relative to the fuzzy set is obtained. A,i (e) and μ B,j (ec) respectively satisfy:

3. The method for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control according to claim 1, characterized in that: The S7 updates the initial parameters of the PID using a fuzzy rule base, satisfying: K p =K p0 +ΔK p ,K i =K i0 +ΔK i ,K d =K d0 +ΔK d , or K p =K p0 ·ΔK p ,K i =K i0 gΔK i ,K d =K d0 ·ΔK d , where K p It is the updated scaling factor, K i It is the updated integral coefficient, K d These are the updated differential coefficients, K p0 It is the initial proportionality coefficient, K i0 K is the initial integral coefficient. d0 These are the initial differential coefficients.

4. The method for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control according to claim 1, characterized in that: The target adjustable power P(t) of the S8 output thermocouple series layered thermoelectric module satisfies:

5. A method for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control according to claim 1 or claim 2, characterized in that: The S5 establishes a fuzzy rule base based on experimental data and infers and outputs the proportional term adjustment coefficient ΔK. p The integral term adjustment factor ΔK i Differential adjustment factor ΔK d Includes the following steps: S501, Data Acquisition, the acquired data includes the viscosity μ of different i-th layer colloids. i And different heat transfer path lengths L of the i-th layer i All operating condition data, including the input e and ec, and the proportional adjustment coefficient ΔK of the optimal PID adjustment at the output. p The integral term adjustment factor ΔK i Differential adjustment factor ΔK d ,satisfy: Where: K p0 K is the initial scaling factor. i0 K is the initial integration coefficient. d0 These are the initial differential coefficients. To optimize the output scaling term for the experiment, To optimize the output integral term for the experiment, To optimize the output differential term for the experiment; S502, Rule Output Value Calculation, Proportional Term Rule The output satisfies: Integral Rules The output satisfies: Differential term rules The output satisfies: S503, Rule activation strength calculation, single rule R ij satisfy: Among them: A i ∈A,B j ∈B, the regular activation strength of the k-th data group satisfy 6. A temperature-controlled superparamagnetic iron oxide nanoparticle preparation apparatus, used to perform the temperature-controlled superparamagnetic iron oxide nanoparticle preparation method according to claim 1, comprising: The system comprises a thermocouple-connected layered thermoelectric module, a stainless steel cylindrical reactor, a flexible thermally conductive pad, a temperature sensor, an alumina ceramic insulating layer, a stirrer, a support and transmission device, a digitally controlled pulse power supply, and a control module. The thermocouple-connected layered thermoelectric module is arranged in a ring array with vertically layered thermoelectric modules evenly spaced along the circumference of the stainless steel cylindrical reactor and tightly fitted to the outer wall of the reactor via a flexible thermally conductive pad. An alumina ceramic insulating layer is embedded between each layer of the thermocouple-connected layered thermoelectric module. The temperature sensor is evenly distributed along the axial and / or circumferential direction of the stainless steel cylindrical reactor. The stirrer is arranged along the axial direction of the stainless steel cylindrical reactor and connected to the support and transmission device. Each layer of the thermocouple-connected layered thermoelectric module is electrically connected to the digitally controlled pulse power supply. The support and transmission device, the digitally controlled pulse power supply, and the temperature sensor are electrically connected to the control module.

7. The equipment for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control according to claim 6, characterized in that: The thermoelectric module of the thermocouple series-connected layered thermoelectric module is one or more of lead telluride module, silicon-germanium alloy module, and bismuth-antimony-telluride module.

8. The equipment for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control according to claim 6, characterized in that: The outer wall of the stainless steel cylindrical reactor has a honeycomb-like micro-protrusion structure, and the outer wall of the stainless steel cylindrical reactor is coated with aluminum nitride ceramic or silicon carbide.

9. The equipment for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control according to claim 6, characterized in that: The flexible thermal pad is a flexible graphene or a composite thermal pad made of graphene and metal foil.

10. The equipment for preparing superparamagnetic iron oxide nanoparticles with uniform temperature control according to claim 6, characterized in that: The temperature sensor is a ceramic-encapsulated PT1000 temperature sensor or a PT1000 temperature sensor with a stainless steel sheath.

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