New energy carbon-based material purification process monitoring system based on spectral analysis

Abstract

The application discloses a new energy carbon-based material purification process monitoring system based on spectral analysis, and relates to the field of purification process monitoring, and comprises a sampling module, which is used for layered sampling of materials in each reaction area in a purification process and gradient dispersion pretreatment of the sampling samples simultaneously; and an excitation module, which is used for emitting a laser beam with periodic energy modulation to excite impurity characteristic spectra of the pretreated samples; the application realizes accurate monitoring of the whole process of new energy carbon-based material purification through spectral fusion and dynamic regulation logic, amplifies structural signal differences by means of interference effects of specific frequency coherent light beams, analyzes impurity morphology transformation by means of third derivative spectral analysis, and converts multi-dimensional parameters into process regulation instructions through a three-dimensional regulation matrix.

Description

Technical Field

[0001] This invention relates to the field of purification process monitoring technology, specifically to a purification process monitoring system for new energy carbon-based materials based on spectral analysis. Background Technology

[0002] Purification processes for new energy carbon-based materials remove impurities from the materials through physical or chemical methods, improve conductivity and stability, and optimize performance in fields such as batteries and energy storage. This is a crucial step before the materials are applied.

[0003] In the current purification process of new energy carbon-based materials, there is no targeted and highly intelligent monitoring system to monitor this process.

[0004] To address this, a monitoring system for the purification process of new carbon-based materials based on spectral analysis was proposed. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a new energy carbon-based material purification process monitoring system based on spectral analysis, which can effectively solve the problems of the existing technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions;

[0007] This invention discloses a monitoring system for the purification process of new carbon-based materials based on spectral analysis, comprising:

[0008] The sampling module is used to perform stratified sampling of materials in each reaction zone of the purification process, and simultaneously perform gradient dispersion pretreatment on the sampled samples; the excitation module is used to emit a periodically energy-modulated laser beam to excite the characteristic spectra of impurities on the pretreated samples; the analysis module is used to receive the characteristic spectral signals of impurities and analyze the types, contents, and morphological changes of metallic impurities; the acquisition module is used to simultaneously emit two coherent beams of different frequencies on the pretreated samples to obtain the composite structural characteristic spectra of the materials; the inversion module is used to receive the composite structural characteristic spectral signals and invert the microstructural state of the materials; the control module is used to acquire the output signals of the analysis module and the inversion module, and generate dynamic process adjustment commands based on the output signals.

[0009] Furthermore, the reaction zones in the purification process include: a liquid reaction zone, an acoustic field zone, a temperature gradient zone, and a solid-liquid contact zone;

[0010] The gradient dispersion pretreatment of the sampled material is achieved through pressure gradient changes within nested conical channels:

[0011] During the flow of the sample in the nested conical channel, it is divided into several sub-streams step by step. The flow velocity of each sub-stream increases or decreases linearly with the change of the channel cross-section, so that the solid particles in the sample form a continuous gradient distribution from high concentration to low concentration in the dispersion medium.

[0012] During the pretreatment process, the amount of dispersion medium injected will be adaptively adjusted according to the initial concentration of the sample. The higher the initial concentration of the sample, the more dispersion medium is injected, and vice versa.

[0013] The dispersion medium is a fluid that does not chemically react with the material and can uniformly suspend the solid particles of the material, and is injected into the nested conical channel simultaneously with the sample.

[0014] Furthermore, the excitation module integrates a laser generator, a pulse modulation component, and a focusing component. During the operation of the excitation module, the laser generator inside the excitation module generates an initial laser beam, which is then converted into a pulsed laser beam with a preset period by the pulse modulation component. The energy of the pulsed laser beam is periodically changed by adjusting the duty cycle of the pulse, and the duty cycle changes continuously from the initial value according to a preset nonlinear function within one period. The modulated pulsed laser beam is focused onto the pretreated sample surface by the focusing component to obtain the impurity characteristic spectrum.

[0015] Furthermore, the preset nonlinear function is a piecewise composite function, expressed as:

[0016] ;

[0017] In the formula: This is the initial gain coefficient; It is a nonlinear modulation factor; It is a time variable; This is the initial offset; It is the exponential gain coefficient; It is the attenuation factor; This is the middle offset; , , These are the polynomial coefficients; It is a constant; For a unit period;

[0018] At the start of the cycle, the duty cycle starts from the initial value and proceeds according to... The change pattern is modulated by the square of the sine curve, so that the duty cycle shows a trend of first increasing rapidly and then decreasing slowly;

[0019] After entering the middle stage of the cycle, the duty cycle switches to The change pattern is exponentially modulated to make the duty cycle show a trend of first slowly increasing and then rapidly decreasing.

[0020] At the end of the cycle, the duty cycle switches back to... The change pattern is modulated by three polynomials, so that the duty cycle shows a trend of first a smooth transition and then a sharp change to the end value of the cycle. It is set at the junction of the three stages, and the duty cycle change rate and acceleration at the end of the previous stage are consistent with those at the beginning of the next stage.

[0021] Furthermore, during the operation of the analysis module, the types of metal impurities are determined by the characteristic wavelength peak positions of the metal impurities, and the content of various metal impurities is calculated by the integral area and half-width and height of the characteristic peaks.

[0022] The morphological change analysis process of the metallic impurities in the analysis module is as follows:

[0023] By continuously collecting the third derivative spectra of the characteristic peaks of the same metallic impurity at different times, the asymmetry factor x and peak position drift rate v of the characteristic peaks are extracted.

[0024] ;

[0025] In the formula: The width and height are the right half of the peak's width and height, and the left half of the peak's width and height. For the second moment The wavelength value of the characteristic peak of the metal impurity detected at the first moment The wavelength value of the characteristic peak of the metal impurity detected at that time;

[0026] The morphological transformation index is calculated by combining the asymmetry factor x and the peak shift rate v.

[0027] ;

[0028] In the formula: These are material property coefficients; The initial radiation intensity; Real-time radiation intensity;

[0029] Among them, the higher the lattice energy of metallic impurities, the better. The larger the value, the lower the value, and vice versa; the higher the ion hydration energy of the metallic impurity. The larger the value, the smaller the value. The initial default value range is 0.8 to 1.2. The initial default value range is 0.3~0.7, when the morphological transformation index Greater than the critical conversion index When determining the transformation of impurities from particulate to ionic state, the state transformation index is... < The determination is made when impurities polymerize from the ionic state to the particulate state.

[0030] Furthermore, the two different frequency coherent beams used during the operation of the acquisition module conform to:

[0031] The frequency of the first type of coherent beam follows: ;

[0032] In the formula: This is the bandgap correction factor for the material; The intrinsic bandgap width of the material; This refers to the width of the defect energy level that is dynamically generated during the purification process; It is Planck's constant;

[0033] The frequency of the second type of coherent beam follows: ;

[0034] In the formula: The fundamental frequency offset coefficient; A factor related to amorphous carbon content;

[0035] in, ∈[1.1, 1.3], fundamental frequency offset coefficient ∈[0.05, 0.2], the phase difference between the two coherent beams remains constant and is an integer multiple of π / 2.

[0036] Furthermore, the process of retrieving the microstructure state of the material in the inversion module is as follows:

[0037] Extract the frequency shift, phase change, and light intensity attenuation coefficient of the scattered light from the characteristic spectrum of the composite structure;

[0038] Among them, the frequency offset is represented by the frequency difference between the scattered light and the incident light, the phase change is represented by the phase lag of the scattered light relative to the incident light, and the light intensity attenuation coefficient is represented by the negative natural logarithm of the ratio of the scattered light intensity to the incident light intensity.

[0039] Calculate crystal structure integrity and amorphous carbon residue parameters:

[0040] ;

[0041] In the formula: For the integrity of the crystal structure; The crystal integrity coefficient; This represents the frequency offset of the scattered light; This represents the standard frequency offset of a complete carbon-based crystal. This refers to the phase change. The parameters are for amorphous carbon residue. The characteristic coefficient of amorphous carbon; The light intensity attenuation coefficient;

[0042] in, ∈[1.2, 1.5], ∈[0.8,1], the larger the C value, the more complete the crystal structure. A higher value indicates a higher amount of amorphous carbon residue.

[0043] Furthermore, the integrity of the crystal structure With amorphous carbon residual parameters Correction based on thermodynamic parameter correlation factor Q:

[0044] ;

[0045] The thermodynamic parameter correlation factor Q takes values ​​that conform to:

[0046] When the real-time temperature T of the carbon-based material is in the first temperature range hour, z represents the temperature sensitivity coefficient, with values ​​ranging from [0.3, 0.5].

[0047] When the real-time temperature T is in the second temperature range hour, r is the high-temperature correction factor, with a value in the range of [0.4, 0.6].

[0048] The first temperature range consists of the lattice vibration threshold temperature of carbon-based materials and the critical temperature at which amorphous carbon begins to decompose. The second temperature range consists of the highest temperature at which the crystal structure of carbon-based materials stabilizes and the critical temperature at which amorphous carbon begins to decompose. The value of Q is dynamically set according to the type of carbon-based material, and the value of Q always satisfies 1.0 ≤ Q ≤ 1.5. (The corrected value is...) This refers to the microstructure state of the material.

[0049] Furthermore, during the operation phase of the control module, the logical representation of generating dynamic process adjustment commands based on the output signals is as follows:

[0050] Transformation index Crystal structure integrity and amorphous carbon residue rate As input variables, a three-dimensional adjustment matrix is ​​constructed, where the matrix element values ​​correspond to the adjustment amounts of process parameters; when > At the same time, the adjustment amount and There is a positive correlation, and the acid treatment concentration should be increased preferentially, while the concentration adjustment range meets the requirements. ;

[0051] In the formula: This is the concentration adjustment coefficient; As a crystal integrity correlation factor;

[0052] when < At the same time, the adjustment amount and There is a positive correlation, so the ultrasound power should be increased preferentially, and the power adjustment range should meet the requirements. ;

[0053] In the formula: This is the power regulation coefficient; Amorphous carbon influencing factor;

[0054] when When the ratio of the crystal structure integrity to the amorphous carbon residue of the carbon-based material is denoted as R, three threshold values ​​R1, R2, and R3 are set, where R1 < R2 < R3. When R > R3, the annealing temperature adjustment range is... , This is the high-temperature adjustment coefficient;

[0055] When R1≤R≤R3 , This is the medium temperature adjustment coefficient;

[0056] When R < R1 , This is the low-temperature adjustment coefficient;

[0057] The temperature change at each step does not exceed 50℃, and the adjustment interval shortens as the absolute value of the deviation between R and the threshold increases.

[0058] Furthermore, the sampling module is interconnected with the excitation module and the analysis module via a wireless network. The analysis module is interconnected with the acquisition module via a wireless network. The acquisition module is interconnected with the inversion module and the control module via a wireless network.

[0059] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects:

[0060] This invention provides a monitoring system for the purification process of new carbon-based materials based on spectral analysis. During operation, the system achieves precise monitoring of the entire purification process of new carbon-based materials through spectral fusion and dynamic control logic. It amplifies structural signal differences by utilizing the interference effect of coherent beams at specific frequencies, analyzes impurity morphology transformation using third-order derivative spectroscopy, and converts multi-dimensional parameters into process control commands through a three-dimensional adjustment matrix. This system can track changes in the morphology of metallic impurities and the integrity of the carbon-based structure in real time, improving the accuracy of impurity residue control. At the same time, it reduces acid and energy consumption and shortens the purification cycle through adaptive parameter adjustment, eliminating the lag and limitations of traditional monitoring and single indicators, and providing efficient and intelligent support for the large-scale preparation of high-purity carbon-based materials. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0062] Figure 1 This is a schematic diagram of a monitoring system for the purification process of new carbon-based materials based on spectral analysis. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0064] The present invention will be further described below with reference to embodiments.

[0065] Example:

[0066] This embodiment describes a monitoring system for the purification process of new energy carbon-based materials based on spectral analysis, such as... Figure 1 As shown, it includes:

[0067] The sampling module is used to perform stratified sampling of materials in each reaction zone of the purification process and simultaneously perform gradient dispersion pretreatment on the sampled materials.

[0068] The purification process includes the following reaction zones: liquid reaction zone, acoustic field zone, temperature gradient zone, and solid-liquid contact zone.

[0069] Gradient dispersion pretreatment of the sampled material is achieved through pressure gradient changes within nested conical channels:

[0070] During the flow of the sample in the nested conical channel, it is divided into several sub-streams step by step. The flow velocity of each sub-stream increases or decreases linearly with the change of the channel cross-section, so that the solid particles in the sample form a continuous gradient distribution from high concentration to low concentration in the dispersion medium.

[0071] During the pretreatment process, the amount of dispersion medium injected will be adaptively adjusted according to the initial concentration of the sample. The higher the initial concentration of the sample, the more dispersion medium is injected, and vice versa.

[0072] The dispersion medium is a fluid that does not chemically react with the material and can uniformly suspend the solid particles of the material, and is injected into the nested conical channel simultaneously with the sample.

[0073] Stratified sampling operation:

[0074] At least three sampling ports are set up along the height of the reaction vessel. Each sampling port is connected to the main channel through an independent nested conical channel with the taper of the conical channel increasing from top to bottom. When the carbon-based material mixture flows through the sampling area, the nested conical channel forms a progressively increasing pressure gradient due to the change in the channel cross-section. This causes samples from different reaction areas to enter the corresponding sampling ports under the pressure difference. A micron-sized filter is installed at the channel inlet of the upper sampling port, while a nano-sized filter and a submicron-sized filter are installed at the channel inlet of the middle and lower sampling ports, respectively. Through the synergistic effect of the filter pore size and the pressure gradient, the carbon-based material in different depth reaction areas is layered and intercepted. At the same time, the sampling amount is dynamically adjusted by a deformable elastic throttle valve installed in each channel. The degree of deformation of the elastic throttle valve is related to the preset threshold of the impurity concentration in the corresponding area. When the impurity concentration in a certain area exceeds the threshold, the elastic throttle valve corresponding to that area deforms to increase the channel cross-sectional area, thereby increasing the sampling amount in that area.

[0075] The following example illustrates a specific structure of a nested tapered channel:

[0076] The nested conical channel contains 3 to 5 coaxial conical tubes, each with a cone angle of 15°-30°. The larger diameter end (5 to 10 mm) faces the reaction zone, and the smaller diameter end (1 to 3 mm) is offset by 0.5 to 2 mm along the sampling direction.

[0077] The inner wall of the outer tube has a spiral ridge with a height of 0.2-0.5 mm and a pitch of 2-5 mm. The middle tube has 3-6 diversion holes with a diameter of 0.3-0.8 mm on its wall, with the hole spacing increasing by 0.5-1 mm along the axial direction. The end of the inner tube has an elastic throttling diaphragm with a thickness of 0.1-0.3 mm. The initial diaphragm orifice diameter is 0.2-0.4 mm and can expand to 2-3 times the initial value with pressure changes.

[0078] The annular flow channel formed by adjacent tubes has a cross-sectional area that decreases gradually from the outer layer to the inner layer by a factor of 2-3, in order to create a continuous pressure gradient field. This allows the sample to flow in layers along the radial direction and achieve multi-stage flow splitting through the splitting holes.

[0079] The excitation module is used to emit a periodically energy-modulated laser beam to the pretreated sample to excite the characteristic spectra of impurities.

[0080] The excitation module integrates a laser generator, a pulse modulation component, and a focusing component. During the operation of the excitation module, the laser generator inside the excitation module generates an initial laser beam, which is then converted into a pulsed laser beam with a preset period by the pulse modulation component. The energy of the pulsed laser beam is periodically changed by adjusting the duty cycle of the pulse, and the duty cycle changes continuously from the initial value according to a preset nonlinear function within one period. The modulated pulsed laser beam is focused onto the pretreated sample surface by the focusing component to obtain the characteristic spectrum of impurities.

[0081] The nonlinear function is assumed to be a piecewise composite function, with the expression:

[0082] ;

[0083] In the formula: This is the initial gain coefficient; It is a nonlinear modulation factor; It is a time variable; This is the initial offset; It is the exponential gain coefficient; It is the attenuation factor; This is the middle offset; , , These are the polynomial coefficients; It is a constant; For a unit period;

[0084] At the start of the cycle, the duty cycle starts from the initial value and proceeds according to... The change pattern is modulated by the square of the sine curve, so that the duty cycle shows a trend of first increasing rapidly and then decreasing slowly;

[0085] After entering the middle stage of the cycle, the duty cycle switches to The change pattern is exponentially modulated to make the duty cycle show a trend of first slowly increasing and then rapidly decreasing.

[0086] At the end of the cycle, the duty cycle switches back to... The change pattern is modulated by three polynomials to make the duty cycle show a trend of first smooth transition and then rapid change to the end value of the cycle. It is set at the junction of the three stages, and the duty cycle change rate and change acceleration at the end of the previous stage are consistent with the beginning of the next stage.

[0087] In the above formula, The reaction rate is set according to the purification process of carbon-based materials; the faster the reaction, the better. The smaller the value, This is a positive constant, expressed in units of duty cycle per millisecond², and its value is positively correlated with the mass fraction of the lowest boiling point metallic impurities in carbon-based materials. For every 0.1% increase in mass fraction, [the value increases by 0.1%]. Increase by 0.05, It is a positive constant, with units of (1 / millisecond²), and is positively correlated with the lowest boiling point value (unit: °C) of the target metallic impurity. At the lowest boiling point, The value is taken as the product of 0.0002 and the lowest boiling point value. This is a positive constant, expressed as a duty cycle (%), and its value is equal to the minimum duty cycle threshold required to induce characteristic light radiation from the lowest boiling point metallic impurity. This threshold was determined through previous experiments. This is a positive constant, expressed in units of duty cycle / e^(milliseconds), and its value is negatively correlated with the number of target metal impurities, n. =10 / n (n is a positive integer) A constant less than zero, in units of 1 / millisecond, is negatively correlated with the average boiling point of the target metallic impurity (in °C). When the average boiling point is t_avg, The value is -0.00005 × t_avg. It is a constant greater than zero, expressed in terms of duty cycle (%), and its value is determined by... The function value at time t = T / 3 is determined, that is... , This is a non-zero constant, expressed in units of duty cycle per millisecond (m³), and its absolute value is positively correlated with the difference between the highest and lowest boiling points of the target metallic impurity. This is a constant, measured in units of duty cycle per millisecond², and its value is adjusted according to the content of medium-boiling-point metallic impurities in the carbon-based material. The higher the content, the larger the absolute value of b. This is a constant, expressed in units of duty cycle per millisecond, used for correction. The rate of change of the slope of the curve is positively correlated with the viscosity coefficient of the carbon-based material. It is a constant greater than zero, expressed in terms of duty cycle (%), and its value is equal to... The function value at time t = 2T / 3;

[0088] The above formula achieves continuous change of the duty cycle from the initial value according to a preset law through quadratic modulation of the sine curve in the initial stage, exponential modulation in the middle stage, and cubic polynomial modulation in the final stage. It also ensures that the rate of change and acceleration are consistent at the junction points of the three stages, so that the laser beam energy is periodically modulated to excite the characteristic spectra of impurities with different properties. This piecewise composite function modulation of the laser duty cycle not only accurately matches the impurity excitation requirements through the function characteristics of different stages, but also ensures the smoothness of energy modulation through the continuous condition of rate and acceleration at the junction points. It is different from the limitation of single function modulation in insufficient excitation of complex impurity systems, and improves the efficiency and completeness of obtaining the characteristic spectra of impurities.

[0089] The analysis module is used to receive the characteristic spectral signals of impurities and analyze the types, contents, and morphological changes of the metallic impurities.

[0090] During the analysis module's operation phase, the types of metal impurities are determined by the characteristic wavelength peak positions of the metal impurities, and the content of each metal impurity is calculated by the integral area and half-width and height of the characteristic peaks.

[0091] The analysis process for the morphological changes of metallic impurities in the analysis module is as follows:

[0092] By continuously collecting the third derivative spectra of the characteristic peaks of the same metallic impurity at different times, the asymmetry factor x and peak position drift rate v of the characteristic peaks are extracted.

[0093] ;

[0094] In the formula: The width and height are the right half of the peak's width and height, and the left half of the peak's width and height. For the second moment The wavelength value of the characteristic peak of the metal impurity detected at the first moment The wavelength value of the characteristic peak of the metal impurity detected at that time;

[0095] In the formula for calculating the asymmetry factor x, the ratio of the right half-width to the left half-width of the characteristic peak is used to quantify the morphological asymmetry of the characteristic peak of the metal impurity. Its numerical change reflects the isomerization trend of the impurity during the purification process, providing a basic parameter for judging the transformation of the impurity morphology. This formula captures the subtle changes in the morphology of the impurity in a simple quantitative way. Compared with the traditional qualitative description of peak shape, it is more objective and can sensitively reflect the morphological distortion of the impurity during the transformation process from particulate state to ionic state (or vice versa), thus improving the accuracy of morphological analysis.

[0096] In the formula for calculating the peak drift rate v, the difference between the characteristic peak wavelengths at the second and first moments is divided by the time difference. This is used to characterize the wavelength shift speed of the characteristic peak of the metal impurity over time. Its magnitude and direction reflect the rate of change of the chemical environment of the impurity during the purification process. By dynamically tracking the wavelength changes of the characteristic peak, the change of the chemical environment of the impurity is transformed into a quantifiable rate index, overcoming the limitations of static peak position analysis and realizing real-time monitoring of the dynamic process of impurity morphology transformation.

[0097] The morphological transformation index is calculated by combining the asymmetry factor x and the peak shift rate v.

[0098] ;

[0099] In the formula: These are material property coefficients; The initial radiation intensity; Real-time radiation intensity;

[0100] Among them, the higher the lattice energy of metallic impurities, the better. The larger the value, the lower the value, and vice versa; the higher the ion hydration energy of the metallic impurity. The larger the value, the smaller the value. The initial default value range is 0.8 to 1.2. The initial default value range is 0.3~0.7, when the morphological transformation index Greater than the critical conversion index When determining the transformation of impurities from particulate to ionic state, the state transformation index is... < The time is used to determine whether impurities have polymerized from an ionic state to a particulate state;

[0101] The above formula constructs a morphology transformation index through multi-parameter coupling, transforming the complex process of impurity morphology transformation into a single quantitative indicator. This overcomes the one-sidedness of single-parameter judgment and achieves accurate determination of the direction and degree of impurity morphology transformation.

[0102] The acquisition module is used to simultaneously emit two coherent beams of different frequencies onto the pre-processed sample to obtain the composite structure characteristic spectrum of the material.

[0103] The two different frequency coherent beams used during the acquisition module's operation phase obey:

[0104] The frequency of the first type of coherent beam follows: ;

[0105] In the formula: This is the bandgap correction factor for the material; The intrinsic bandgap width of the material; This refers to the width of the defect energy level that is dynamically generated during the purification process; It is Planck's constant;

[0106] The above formula incorporates the defect energy level width of the material into the design of the coherent beam frequency, so that the beam frequency can dynamically match the structural changes of the material during the purification process. Compared with a fixed frequency beam, it can more accurately excite the composite structural characteristics of the material and improve the targeting of the structural spectrum.

[0107] The frequency of the second type of coherent beam follows: ;

[0108] In the formula: The fundamental frequency offset coefficient; Amorphous carbon content is a related factor;

[0109] in, ∈[1.1, 1.3], fundamental frequency offset coefficient ∈[0.05, 0.2], the phase difference between the two coherent beams remains constant and is an integer multiple of π / 2;

[0110] The above formula introduces an amorphous carbon content correlation factor to design a second beam frequency, so that the two coherent beams focus on the characteristic excitation of the material's crystal structure and amorphous carbon respectively, forming a complementary composite spectral acquisition mode. This overcomes the problem that a single frequency beam cannot fully resolve complex structures and improves the comprehensiveness of material structure analysis.

[0111] It should be noted that the amorphous carbon content correlation factor When the proportion of amorphous carbon is in the range of 0-20%, The value ranges from 1.0 to 1.3, and the amorphous carbon content is between 20% and 50%. The range is 1.3-1.7, when the proportion of amorphous carbon is higher than 50%. The range is set between 1.7 and 2.5;

[0112] The inversion module is used to receive the characteristic spectral signals of the composite structure and invert the microstructure state of the material.

[0113] The process of retrieving the microstructure state of materials in the inversion module is as follows:

[0114] Extract the frequency shift, phase change, and light intensity attenuation coefficient of the scattered light from the characteristic spectrum of the composite structure;

[0115] Among them, the frequency offset is represented by the frequency difference between the scattered light and the incident light, the phase change is represented by the phase lag of the scattered light relative to the incident light, and the light intensity attenuation coefficient is represented by the negative natural logarithm of the ratio of the scattered light intensity to the incident light intensity.

[0116] Calculate crystal structure integrity and amorphous carbon residue parameters:

[0117] ;

[0118] In the formula: For the integrity of the crystal structure; The crystal integrity coefficient; This represents the frequency offset of the scattered light; This represents the standard frequency offset of a complete carbon-based crystal. This refers to the phase change. The parameters are for amorphous carbon residue. The characteristic coefficient of amorphous carbon; The light intensity attenuation coefficient;

[0119] in, ∈[1.2, 1.5], ∈[0.8,1], the larger the C value, the more complete the crystal structure. A higher value indicates a higher amount of amorphous carbon residue;

[0120] In the above formula, the frequency shift and phase change of the scattered light are combined and the crystal integrity coefficient is introduced for correction, thus constructing a quantitative index of crystal structure integrity. Compared with traditional XRD peak intensity analysis, it can better reflect the subtle differences in microstructure, realize the accurate assessment of the degree of crystal structure perfection, and use the light intensity attenuation coefficient to characterize the amount of amorphous carbon residue. Combined with the amorphous carbon characteristic coefficient for calibration, the residue of amorphous carbon is transformed into a parameter that can be directly measured by spectroscopy. Compared with traditional methods such as gravimetric method, it is more real-time and improves the efficiency and dynamism of amorphous carbon residue analysis.

[0121] Crystal structure integrity With amorphous carbon residual parameters Correction based on thermodynamic parameter correlation factor Q:

[0122] ;

[0123] The thermodynamic parameter correlation factor Q takes values ​​that follow the following rules:

[0124] When the real-time temperature T of the carbon-based material is in the first temperature range hour, z represents the temperature sensitivity coefficient, with values ​​ranging from [0.3, 0.5].

[0125] When the real-time temperature T is in the second temperature range hour, r is the high-temperature correction factor, with a value in the range of [0.4, 0.6].

[0126] The first temperature range consists of the lattice vibration threshold temperature of carbon-based materials and the critical temperature at which amorphous carbon begins to decompose. The second temperature range consists of the highest temperature at which the crystal structure of carbon-based materials stabilizes and the critical temperature at which amorphous carbon begins to decompose. The value of Q is dynamically set according to the type of carbon-based material, and the value of Q always satisfies 1.0 ≤ Q ≤ 1.5. (The corrected value is...) This refers to the microstructure state of the material;

[0127] In the above formula, a segmented correlation factor Q was designed to address the differences in thermodynamic properties across different temperature ranges. This enabled temperature-adaptive corrections for C and A, overcoming the limitations of traditional isothermal analysis and making the measurement of microstructural parameters more accurate over a wide temperature range.

[0128] The control module is used to acquire the output signals of the analysis module and the inversion module, and generate dynamic process adjustment commands based on the output signals.

[0129] During the operation of the control module, the logical representation of generating dynamic process adjustment commands based on the output signals is as follows:

[0130] Transformation index Crystal structure integrity and amorphous carbon residue rate As input variables, a three-dimensional adjustment matrix is ​​constructed, where the matrix element values ​​correspond to the adjustment amounts of process parameters; when > At the same time, the adjustment amount and There is a positive correlation, and the acid treatment concentration should be increased preferentially, while the concentration adjustment range meets the requirements. ;

[0131] In the formula: This is the concentration adjustment coefficient; As a crystal integrity correlation factor;

[0132] when < At the same time, the adjustment amount and There is a positive correlation, so the ultrasound power should be increased preferentially, and the power adjustment range should meet the requirements. ;

[0133] In the formula: This is the power regulation coefficient; Amorphous carbon influencing factor;

[0134] when When the ratio of the crystal structure integrity to the amorphous carbon residue of the carbon-based material is denoted as R, three threshold values ​​R1, R2, and R3 are set, where R1 < R2 < R3. When R > R3, the annealing temperature adjustment range is... , This is the high-temperature adjustment coefficient;

[0135] When R1≤R≤R3 , This is the medium temperature adjustment coefficient;

[0136] When R < R1 , This is the low-temperature adjustment coefficient;

[0137] Among them, the temperature change of each step does not exceed 50℃, and the adjustment interval shortens as the absolute value of the deviation between R and the threshold increases;

[0138] middle:

[0139] This indicates the adjustment range of the acid treatment concentration, expressed in mol / L. Related to the type of carbon-based material, taking graphene as an example, 0.02≤ ≤0.05 (L / (mol·exponential unit)) and Dimensionless The value range is 0.3 to 0.5. Follow It increases and decreases linearly, and 0.8 ≤ ≤1.2 (dimensionless) The value ranges from 0 to 1 (dimensionless, 1 represents a complete crystal structure).

[0140] middle:

[0141] This indicates the adjustment range of the ultrasonic power, measured in W. For carbon nanotube materials, 5≤ ≤10 (W / exponential unit) The value ranges from 1.5 to 2.5 (dimensionless). The value ranges from 0 to 1 (dimensionless, 1 indicates that it is completely amorphous carbon).

[0142] , , middle:

[0143] This indicates the adjustment range of the annealing temperature, expressed in degrees Celsius (°C). The value range is 30~50 (℃ / (dimensionless ln unit)), R is dimensionless, and R3 represents the upper limit threshold of the ratio R, with a value range of 3~5 (dimensionless). The value range is -40 to -20 (℃ / dimensionless), and the value range of R2 is 1 to 2 (dimensionless). The value range is 25~45 (℃ / dimensionless), and R1 represents the lower limit threshold of the ratio R, with a value range of 0.2~0.5 (dimensionless).

[0144] The sampling module is interconnected with the excitation module and the analysis module via a wireless network. The analysis module is interconnected with the acquisition module via a wireless network. The acquisition module is interconnected with the inversion module and the control module via a wireless network.

[0145] In this embodiment, the sampling module performs stratified sampling of materials in each reaction region of the purification process, and simultaneously performs gradient dispersion pretreatment on the sampled materials. The excitation module then emits a periodically energy-modulated laser beam to excite the impurity characteristic spectrum of the pretreated sample. The analysis module receives the impurity characteristic spectrum signal and analyzes the type, content, and morphological changes of the metallic impurities. The acquisition module further emits two coherent beams of different frequencies simultaneously on the pretreated sample to obtain the composite structure characteristic spectrum of the material. The inversion module receives the composite structure characteristic spectrum signal and inverts the microstructure state of the material. Finally, the control module obtains the output signals of the analysis module and the inversion module and generates dynamic process adjustment commands based on the output signals.

[0146] In the above embodiments, the system dynamically adjusts the beam frequency and process parameters, captures changes in impurity morphology and carbon-based material structure in real time, and generates adjustment instructions through multi-parameter correlation calculations. This can accurately improve impurity removal efficiency, reduce acid and energy waste, and reduce damage to the crystal structure of carbon-based materials. As a result, the purity of the purified material and the stability of the process are effectively improved, providing more targeted and intelligent monitoring and management services for the purification process of new energy carbon-based materials.

[0147] It should be noted that the system in the above embodiments is particularly applicable to acid treatment processes that require precise control of the layered removal of metal impurities in the purification of new energy carbon-based materials, high-temperature annealing processes that require the step-by-step removal of metal impurities according to boiling point differences, ultrasonic-assisted purification processes that require amplification of the signal differences between carbon-based materials and amorphous carbon, and mixed acid co-purification processes that require the combination of microstructure and thermodynamic parameter regulation.

[0148] The sampling module, with its nested conical channels and pressure gradient-driven multi-stage flow splitting design, achieves dynamic adaptive changes in sample volume according to impurity concentration. Unlike traditional fixed-point, fixed-flow sampling methods, it can accurately acquire representative samples from different reaction regions, providing comprehensive and targeted data for subsequent analysis. The excitation module sequentially excites metallic impurities with different boiling points through a periodically energy-modulated laser beam and unique adjustment of the duty cycle. The radiation intensity exhibits a non-linear mapping relationship with the impurity content, which is significantly different from the traditional constant-energy laser excitation mode, allowing for the extraction of richer impurity information. The analysis module comprehensively analyzes multiple parameters such as the half-width, height, integral area, and second derivative characteristics of the spectral peak, introducing a dynamic evolution coefficient of the spectral peak shape. This breaks through the limitations of existing technologies that rely on single or a few parameters for analysis, enabling more precise and in-depth determination of impurities. The acquisition module amplifies signal differences by utilizing the interference effect of two coherent beams of different frequencies, and the frequency difference can be adjusted in real time. This is fundamentally different from common single-frequency Raman excitation techniques, creating a new approach for obtaining more refined composite structural characteristic spectra of carbon-based materials. The inversion module inverts the microstructural state based on the frequency shift and phase change of scattered light, incorporating material thermodynamic parameter correlation factors. This is more comprehensive and in-depth than existing technologies that rely on only a single or a few factors. The control module constructs a ternary mapping model of spectral characteristics, process parameters, and material properties, enabling the control command to exhibit nonlinear step changes and be positively correlated with the rate of change of the spectral signal. This differs from existing simple linear control or fixed-mode control, and can more efficiently and accurately adjust the purification process dynamically according to the real-time state of the material.

[0149] In summary, the system in the above embodiments achieves precise monitoring of the entire purification process of new energy carbon-based materials through spectral fusion and dynamic control logic. It also amplifies structural signal differences by leveraging the interference effect of coherent beams at specific frequencies, analyzes impurity morphology transformation using third-order derivative spectroscopy, and converts multi-dimensional parameters into process control commands through a three-dimensional adjustment matrix. This enables real-time tracking of changes in the morphology of metallic impurities and the integrity of the carbon-based structure, improving the accuracy of impurity residue control. Simultaneously, it reduces acid and energy consumption through adaptive parameter adjustment, shortens the purification cycle, and eliminates the lag and limitations of traditional monitoring and single indicators, providing efficient and intelligent support for the large-scale preparation of high-purity carbon-based materials.

[0150] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A monitoring system for the purification process of new carbon-based materials based on spectral analysis, characterized in that, include: The sampling module is used to perform stratified sampling of materials in each reaction zone of the purification process and simultaneously perform gradient dispersion pretreatment on the sampled materials. The excitation module is used to emit a periodically energy-modulated laser beam to the pretreated sample to excite the characteristic spectra of impurities. The analysis module is used to receive the characteristic spectral signals of impurities and analyze the types, contents, and morphological changes of the metallic impurities. During the operation of the analysis module, the types of metal impurities are determined by the characteristic wavelength peak positions of the metal impurities, and the content of each metal impurity is calculated by the integral area and half-width and height of the characteristic peaks. The morphological change analysis process of the metallic impurities in the analysis module is as follows: By continuously collecting the third derivative spectra of the characteristic peaks of the same metallic impurity at different times, the asymmetry factor x and peak position drift rate v of the characteristic peaks are extracted. ； In the formula: The width and height are the right half of the peak's width and height, and the left half of the peak's width and height. For the second moment The wavelength value of the characteristic peak of the metal impurity detected at the first moment The wavelength value of the characteristic peak of the metal impurity detected at that time; The morphological transformation index is calculated by combining the asymmetry factor x and the peak shift rate v. ； In the formula: These are material property coefficients; The initial radiation intensity; Real-time radiation intensity; Among them, the higher the lattice energy of metallic impurities, the better. The larger the value, the lower the value, and vice versa; the higher the ion hydration energy of the metallic impurity. The larger the value, the smaller the value. The initial default value range is 0.8 to 1.

2. The initial default value range is 0.3~0.7, when the morphological transformation index Greater than the critical conversion index When determining the transformation of impurities from particulate to ionic state, the state transformation index is... < The time is used to determine whether impurities have polymerized from an ionic state to a particulate state; The acquisition module is used to simultaneously emit two coherent beams of different frequencies onto the pre-processed sample to obtain the composite structure characteristic spectrum of the material. The inversion module is used to receive the characteristic spectral signals of the composite structure and invert the microstructure state of the material. The control module is used to acquire the output signals of the analysis module and the inversion module, and generate dynamic process control commands based on the output signals.

2. The new energy carbon-based material purification process monitoring system based on spectral analysis according to claim 1, characterized in that, The purification process includes the following reaction zones: liquid reaction zone, acoustic field zone, temperature gradient zone, and solid-liquid contact zone. The gradient dispersion pretreatment of the sampled material is achieved through pressure gradient changes within nested conical channels: During the flow of the sample in the nested conical channel, it is divided into several sub-streams step by step. The flow velocity of each sub-stream increases or decreases linearly with the change of the channel cross-section, so that the solid particles in the sample form a continuous gradient distribution from high concentration to low concentration in the dispersion medium. During the pretreatment process, the amount of dispersion medium injected will be adaptively adjusted according to the initial concentration of the sample. The higher the initial concentration of the sample, the more dispersion medium is injected, and vice versa. The dispersion medium is a fluid that does not chemically react with the material and can uniformly suspend the solid particles of the material, and is injected into the nested conical channel simultaneously with the sample.

3. The new energy carbon-based material purification process monitoring system based on spectral analysis according to claim 1, characterized in that, The excitation module integrates a laser generator, a pulse modulation component, and a focusing component. During the operation of the excitation module, the laser generator inside the excitation module generates an initial laser beam, which is then converted into a pulsed laser beam with a preset period by the pulse modulation component. The energy of the pulsed laser beam is periodically changed by adjusting the duty cycle of the pulse, and the duty cycle changes continuously from the initial value according to a preset nonlinear function within one period. The modulated pulsed laser beam is focused onto the pretreated sample surface by the focusing component to obtain the characteristic spectrum of impurities.

4. The new energy carbon-based material purification process monitoring system based on spectral analysis according to claim 3, characterized in that, The preset nonlinear function is a piecewise composite function, expressed as: ； In the formula: This is the initial gain coefficient; It is a nonlinear modulation factor; It is a time variable; This is the initial offset; It is the exponential gain coefficient; It is the attenuation factor; This is the middle offset; , , These are the polynomial coefficients; It is a constant; For a unit period; At the start of the cycle, the duty cycle starts from the initial value and proceeds according to... The change pattern is modulated by the square of the sine curve, so that the duty cycle shows a trend of first increasing rapidly and then decreasing slowly; After entering the middle stage of the cycle, the duty cycle switches to The change pattern is exponentially modulated to make the duty cycle show a trend of first slowly increasing and then rapidly decreasing. At the end of the cycle, the duty cycle switches back to... The change pattern is modulated by three polynomials, so that the duty cycle shows a trend of first a smooth transition and then a sharp change to the end value of the cycle. It is set at the junction of the three stages, and the duty cycle change rate and acceleration at the end of the previous stage are consistent with those at the beginning of the next stage.

5. The new energy carbon-based material purification process monitoring system based on spectral analysis according to claim 1, characterized in that, The two different frequency coherent beams used during the operation of the acquisition module follow the following rules: The frequency of the first type of coherent beam follows: ; In the formula: This is the bandgap correction factor for the material; The intrinsic bandgap width of the material; This refers to the width of the defect energy level that is dynamically generated during the purification process; It is Planck's constant; The frequency of the second type of coherent beam follows: ; In the formula: The fundamental frequency offset coefficient; A factor related to amorphous carbon content; in, ∈[1.1, 1.3], fundamental frequency offset coefficient ∈[0.05, 0.2], the phase difference between the two coherent beams remains constant and is an integer multiple of π / 2.

6. The new energy carbon-based material purification process monitoring system based on spectral analysis according to claim 1, characterized in that, The process of retrieving the microstructure state of the material in the inversion module is as follows: Extract the frequency shift, phase change, and light intensity attenuation coefficient of the scattered light from the characteristic spectrum of the composite structure; Among them, the frequency offset is represented by the frequency difference between the scattered light and the incident light, the phase change is represented by the phase lag of the scattered light relative to the incident light, and the light intensity attenuation coefficient is represented by the negative natural logarithm of the ratio of the scattered light intensity to the incident light intensity. Calculate crystal structure integrity and amorphous carbon residue parameters: ； In the formula: For the integrity of the crystal structure; The crystal integrity coefficient; This represents the frequency offset of the scattered light; This represents the standard frequency offset of a complete carbon-based crystal. This refers to the phase change. The parameters are for amorphous carbon residue. The characteristic coefficient of amorphous carbon; The light intensity attenuation coefficient; in, ∈[1.2, 1.5], ∈[0.8,1], the larger the C value, the more complete the crystal structure. A higher value indicates a higher amount of amorphous carbon residue.

7. The new energy carbon-based material purification process monitoring system based on spectral analysis according to claim 6, characterized in that, Crystal structure integrity With amorphous carbon residual parameters Correction based on thermodynamic parameter correlation factor Q: ； The thermodynamic parameter correlation factor Q takes values ​​that conform to: When the real-time temperature T of the carbon-based material is in the first temperature range hour, z represents the temperature sensitivity coefficient, with values ​​ranging from [0.3, 0.5]. When the real-time temperature T is in the second temperature range hour, r is the high-temperature correction factor, with a value in the range of [0.4, 0.6]. The first temperature range consists of the lattice vibration threshold temperature of carbon-based materials and the critical temperature at which amorphous carbon begins to decompose. The second temperature range consists of the highest temperature at which the crystal structure of carbon-based materials stabilizes and the critical temperature at which amorphous carbon begins to decompose. The value of Q is dynamically set according to the type of carbon-based material, and the value of Q always satisfies 1.0 ≤ Q ≤ 1.

5. (The corrected value is...) This refers to the microstructure state of the material.

8. The new energy carbon-based material purification process monitoring system based on spectral analysis according to claim 1, characterized in that, During the operation of the control module, the logical representation of generating dynamic process adjustment commands based on the output signals is as follows: Transformation index Crystal structure integrity and amorphous carbon residue rate As input variables, a three-dimensional adjustment matrix is ​​constructed, where the matrix element values ​​correspond to the adjustment amounts of process parameters; when > At the same time, the adjustment amount and There is a positive correlation, and the acid treatment concentration should be increased preferentially, while the concentration adjustment range meets the requirements. ; In the formula: This is the concentration adjustment coefficient; As a crystal integrity correlation factor; when < At the same time, the adjustment amount and There is a positive correlation, so the ultrasound power should be increased preferentially, and the power adjustment range should meet the requirements. ; In the formula: This is the power regulation coefficient; Amorphous carbon influencing factor; when When the ratio of the crystal structure integrity to the amorphous carbon residue of the carbon-based material is denoted as R, three threshold values ​​R1, R2, and R3 are set, where R1 < R2 < R3. When R > R3, the annealing temperature adjustment range is... , This is the high-temperature adjustment coefficient; When R1≤R≤R3 , This is the medium temperature adjustment coefficient; When R < R1 , This is the low-temperature adjustment coefficient; The temperature change at each step does not exceed 50℃, and the adjustment interval shortens as the absolute value of the deviation between R and the threshold increases.

9. The new energy carbon-based material purification process monitoring system based on spectral analysis according to claim 1, characterized in that, The sampling module is interconnected with the excitation module and the analysis module via a wireless network. The analysis module is interconnected with the acquisition module via a wireless network. The acquisition module is interconnected with the inversion module and the control module via a wireless network.

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