Intelligent preparation process of cobalt hydroxide nanocomposite

Abstract

This application relates to the field of cobalt hydroxide preparation technology, specifically to an intelligent preparation process for cobalt hydroxide nanocomposite materials. The process includes: weighing cobalt nitrate and polyvinylpyrrolidone into a mixing container, adding deionized water, and performing ultrasonic mixing; collecting the temperature at each set position and adjustment interval within the mixing container at each time point; determining a first adjustment coefficient for each position within each adjustment interval; determining the significant value of the temperature change response at each position, and combining this with the first adjustment coefficient to obtain a second adjustment coefficient for each position within each adjustment interval; using a PLC control system to regulate the temperature during the ultrasonic mixing process; passing argon gas through the ultrasonically mixed solution, adding sodium borohydride solution, and then adding an equal volume of potassium tetrachloropalladate and potassium tetrachloroplatinate solutions, followed by centrifugation, washing, and drying to obtain the cobalt hydroxide nanocomposite material. This application improves the accuracy of detecting residues on the wafer cleaning surface.

Description

Technical Field

[0001] This application relates to the field of cobalt hydroxide preparation technology, specifically to an intelligent preparation process for cobalt hydroxide nanocomposite materials. Background Technology

[0002] Cobalt hydroxide nanocomposites, with their unique physicochemical properties, have shown significant application potential in energy fields such as supercapacitors, battery electrodes, and electrocatalysis, becoming a research hotspot in scientific research and industry in recent years. However, single cobalt hydroxide materials have inherent defects such as insufficient conductivity and poor structural stability. By constructing multi-component nanocomposites, their conductivity, cycle stability, and reactivity can be significantly improved through synergistic effects, meeting the stringent requirements of high-performance energy storage devices for electrode materials.

[0003] However, during the preparation of cobalt hydroxide nanocomposites, the cavitation effect caused by ultrasonic dispersion often leads to differences in local temperature monitoring response, causing local micro-region temperature field imbalance. Excessively high temperatures may decrease the thermal stability of cobalt hydroxide, or even cause decomposition or destruction of the crystal structure, thus affecting the material's performance. Simultaneously, temperature imbalance also affects the dispersibility of nanoparticles; localized overheating may lead to particle aggregation or sintering, thereby reducing the uniformity of the composite material and ultimately affecting the preparation quality of the cobalt hydroxide nanocomposites. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides an intelligent preparation process for cobalt hydroxide nanocomposites to solve the existing issues.

[0005] The intelligent preparation process of cobalt hydroxide nanocomposite material proposed in this application adopts the following technical solution:

[0006] One embodiment of this application provides an intelligent preparation process for cobalt hydroxide nanocomposites, the method comprising the following steps:

[0007] Weigh cobalt nitrate and polyvinylpyrrolidone and pour them into a mixing container. Add deionized water and mix ultrasonically.

[0008] During ultrasonic mixing, the temperature at each set position and adjustment interval within the mixing container is collected at each time; the temperature distribution difference between each position and the other positions within each adjustment interval is analyzed to determine the first adjustment coefficient of each position in each adjustment interval.

[0009] The filter window length is corrected based on the first adjustment coefficient, and the filter is used to denoise the temperature of each position and each adjustment range.

[0010] By comparing the denoised temperature at all locations with the target temperature at each time point, the deviation set at each time point is determined. The similarity of the deviation sets at different times within each adjustment interval and the numerical distribution in the deviation sets at each time point are analyzed to classify all times within each adjustment interval. Based on the distribution of the time intervals between each category and the denoised temperature difference between each location and other locations at corresponding times within each category, the significant value of the temperature change response at each location is determined. Combined with the first adjustment coefficient, the second adjustment coefficient of each location in each adjustment interval is obtained.

[0011] The proportion of the second adjustment coefficient in all positions is used to correct the temperature deviation at each position and time, which is used to regulate the temperature in the ultrasonic mixing process.

[0012] After ultrasonic mixing, the solution was transferred to a reaction vessel and argon gas was introduced. Sodium borohydride solution was added dropwise while stirring. After the reaction, equal volumes of potassium tetrachloropalladate and potassium tetrachloroplatinate solutions were added. Finally, the reaction solution was centrifuged, washed, and dried to obtain cobalt hydroxide nanocomposite material.

[0013] In one embodiment, the concentrations of cobalt nitrate solution and polyvinylpyrrolidone in the ultrasonically mixed solution are 0.2 mmol / L and 2~4 g / L, respectively.

[0014] In one embodiment, the process of determining the first adjustment coefficient is as follows:

[0015] The difference in temperature dispersion between each location and the other locations at all times within each adjustment interval is denoted as the first difference. The mean of the first difference between each location and all other locations is calculated. The first adjustment coefficient is the product of the temperature dispersion of each location at all times within each adjustment interval and the mean.

[0016] In one embodiment, the modified filter window length includes:

[0017] Calculate the product of the first adjustment coefficient of each position in each adjustment interval and the preset initial filter window length, and record it as the first product. Take the integer result of the sum of the first product and the preset initial filter window length as the corrected filter window length of each position in each adjustment interval.

[0018] In one embodiment, a first deviation coefficient is determined for each time step based on the similarity, and a second deviation coefficient is determined for each time step based on the numerical distribution, wherein the second deviation coefficient is the mean of all values ​​in the deviation set for each time step.

[0019] In one embodiment, classifying all times within each adjustment interval includes:

[0020] The first deviation coefficient and the second deviation coefficient at each time point are combined to form a two-dimensional array for each time point. The two-dimensional arrays for all times points within each adjustment interval are clustered to obtain the classification results for all times points within each adjustment interval.

[0021] In one embodiment, determining the significant values ​​of the temperature change response at each location includes:

[0022] Arrange all times in each class in ascending order, and determine the first eigenvalue of each class based on the degree of dispersion of the interval between all adjacent times in each class;

[0023] Extract the denoised temperature of any location at the corresponding time in each category to form the feature sequence of the location in each category. Calculate the mean of the metric distances between the feature sequences of any location in each category and all other locations, and use this as the second feature value of the location in each category.

[0024] For each adjustment interval, the ratio of the first feature value of each class to the sum of the first feature values ​​of all classes is calculated. The product of the second feature value of any position in each class and the ratio is calculated and denoted as the second product. The sum of the second products of any position in all classes is taken as the significant value of the temperature change response at any position.

[0025] In one embodiment, regulating the temperature during the ultrasonic mixing process includes:

[0026] For each adjustment interval, the product of the significant value of the temperature change response at each position and the first adjustment coefficient is calculated as the second adjustment coefficient at each position; the percentage is recorded as the confidence weight at each position.

[0027] Calculate the difference between the temperature at each position and the target temperature at the next moment in each adjustment interval. Sum the products of the differences at all positions and their corresponding confidence weights as the feedback deviation signal value of the PLC control system at the next moment. Use the feedback deviation signal value to regulate the temperature.

[0028] In one embodiment, the flow rate of the introduced argon gas is 0-500 mL / min, and the gas introduction time is not less than 30 minutes; the addition of sodium borohydride solution makes the molar ratio of cobalt nitrate to sodium borohydride in the final mixed solution 1:10-25, the addition time is controlled within 5 minutes, and the reaction continues for 10-15 minutes after the addition is completed; in the mixed solution after adding equal volumes of potassium tetrachloropalladate and potassium tetrachloroplatinate solutions, the molar ratio of cobalt nitrate to potassium tetrachloropalladate is 1:20-80, and the molar ratio of cobalt nitrate to potassium tetrachloroplatinate is 1:20-80.

[0029] In one embodiment, the centrifugation time is 10-15 minutes, then the supernatant is discarded, deionized water is added to the precipitate, and the mixture is shaken and washed for 1-2 minutes.

[0030] This application has at least the following beneficial effects:

[0031] This application collects the temperature at each set location and adjustment interval within the mixing container at each time point during the ultrasonic mixing process; analyzes the difference in temperature dispersion between each location and other locations at all times within each adjustment interval, and determines the first adjustment coefficient for each location in each adjustment interval based on the dispersion of each location; through multi-location temperature dispersion difference analysis, it captures local solvation heat anomalies in real time, improves the predictive ability of ultrasonic mixing uniformity, enhances temperature distribution uniformity, provides spatial weight basis for subsequent filtering, and avoids local temperature gradients being masked by the overall mean; based on the first adjustment coefficient, it corrects the filter window length for each location in each adjustment interval, and uses the filter to denoise the temperature of each location in each adjustment interval; Effectively suppressing transient noise caused by ultrasonic cavitation, avoiding temperature signal distortion caused by filtering waves, improving the targeting of data denoising, and enhancing signal fidelity; based on the denoised temperature data, by the difference between the temperature at all locations at each time point and the target temperature, the deviation set at each time point is determined, realizing global temperature consistency quantification and comprehensively reflecting the deviation of the thermodynamic state of the mixed system; analyzing the similarity between the deviation sets at each time point and other time points within each adjustment interval, the first deviation coefficient at each time point is determined; based on the numerical distribution in the deviation sets at each time point, the second deviation coefficient at each time point is obtained; the first deviation coefficient enhances the ability to capture sudden interference, accurately identifying transient temperature pulses caused by ultrasonic cavitation, and the second deviation coefficient quantifies... The cumulative effect of gradual drift reflects the overall temperature deviation. Combining the first and second deviation coefficients, all times within each adjustment interval are classified, and all times in each category are arranged in ascending order. Based on the dispersion of the intervals between all adjacent times, a first characteristic value for each category is determined. The first characteristic value reflects the severity of temperature change. The denoised temperature at any location within each category is extracted to form a feature sequence for that location within that category. The difference between the feature sequences of that location and all other locations within each category is analyzed to determine a second characteristic value for that location within each category. The second characteristic value helps to locate sensitive areas within the mixing container and highlights the degree of temperature anomalies at each location. By integrating the first feature value and the second feature value, the significant value of the temperature change response at any position is determined, and the significance of the temperature change response difference at different stages and positions in the actual preparation process is accurately extracted. Combined with the first adjustment coefficient, the second adjustment coefficient of each position in each adjustment interval is obtained. Using the proportion of the second adjustment coefficient of each position in all positions within each adjustment interval, the temperature deviation of each position at each time is corrected. The temperature in the ultrasonic mixing process is regulated by the PLC control system, realizing precise and intelligent control of local temperature anomalies caused by cavitation effect, ensuring the temperature uniformity and process stability of the solution mixing system, and significantly improving the preparation accuracy and product performance consistency of cobalt hydroxide nanocomposite materials. Attached Figure Description

[0032] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart illustrating the steps of an intelligent preparation process for cobalt hydroxide nanocomposite materials provided in this application;

[0034] Figure 2 This is a flowchart of ultrasonic mixing temperature control. Detailed Implementation

[0035] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation methods, structures, features, and effects of an intelligent preparation process for cobalt hydroxide nanocomposite materials proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0037] The following description, in conjunction with the accompanying drawings, details the specific scheme of an intelligent preparation process for a cobalt hydroxide nanocomposite material provided in this application.

[0038] Example 1

[0039] Please see Figure 1 It illustrates a flowchart of the intelligent preparation process of a cobalt hydroxide nanocomposite material provided in Embodiment 1 of this application, the process including:

[0040] First, prepare the raw materials, including: prepare cobalt nitrate. Polyvinylpyrrolidone (PVP), Sodium borohydride Potassium tetrachloropalladium Potassium tetrachloroplatinate The components include deionized water, anhydrous ethanol, and high-purity argon gas, with a purity ≥99.99%. and Prepare a 0.5 mmol / L mixed solution by mixing equal volumes before use.

[0041] Next, weigh cobalt nitrate and PVP and pour them into a mixing container. Add deionized water and mix using an ultrasonic mixer to make the concentrations of cobalt nitrate solution and PVP 0.2 mmol / L and 2 g / L, respectively. Set the ultrasonic time to 20 minutes and control the temperature at 25°C using a PLC control system. The target temperature is 25°C. The cavitation effect of ultrasound accelerates the dissolution of the solute and promotes uniform dispersion, forming a homogeneous and transparent mixed solution.

[0042] Uneven ultrasonic dispersion during ultrasonic mixing can lead to incomplete dissolution of cobalt nitrate and PVP, affecting initial reaction conditions, causing uneven particle size distribution during nanoparticle nucleation, resulting in abnormal reaction kinetics, uneven noble metal deposition, decreased product purity, increased impurity risk, reduced process repeatability, and batch stability. Therefore, precise temperature control during ultrasonic mixing is necessary to ensure uniform dispersion. Specific temperature control procedures include:

[0043] (1) Collect the temperature at each time in each set position and each adjustment interval in the mixing container; analyze the difference in the degree of dispersion of the temperature at each position and the other positions at all times in each adjustment interval; combine the degree of dispersion of each position to determine the first adjustment coefficient of each position in each adjustment interval; based on the first adjustment coefficient, correct the filter window length of each position in each adjustment interval; and use the filter to denoise the temperature of each position in each adjustment interval.

[0044] In this embodiment, during the ultrasonic mixing stage of the solution in the intelligent production of cobalt hydroxide nanocomposite materials, n infrared temperature sensors are evenly and uniformly arranged on the inner wall of the mixing container that is not in contact with the solution. The specific number of infrared temperature sensors is determined according to the amount of material used in the actual production process and the size of the mixing container. In this embodiment, n=10.

[0045] Compared to traditional intelligent monitoring and control processes, this embodiment addresses the localized temperature anomalies caused by cavitation during ultrasonic dispersion. Infrared temperature sensors are uniformly deployed during the ultrasonic mixing stage to collect temperature data. The collected temperature data is then analyzed to understand the solution temperature distribution during ultrasonic dispersion. This avoids significant discrepancies between the monitored and actual temperatures caused by cavitation, which could negatively impact the final material's performance. Specifically:

[0046] This embodiment first sets up adjustment intervals. At intervals between each adjustment interval, the temperature during the ultrasonic mixing process is adjusted and calibrated. Specifically, at the next moment after each adjustment interval, the temperature of the ultrasonically mixed solution is adjusted and calibrated. In this embodiment, the length of the adjustment interval is set to 5 minutes. The temperature at each location within the mixing container is collected synchronously at a time interval of 0.1 seconds. The length of the adjustment interval and the time interval can be set by the implementer according to actual conditions; this embodiment does not impose any restrictions on these settings.

[0047] This embodiment takes the j-th adjustment interval as an example. Considering the influence of equipment and environmental interference during the actual preparation and production process, resulting in poor quality of the collected temperature data, all temperature data collected at each position within the j-th adjustment interval are used as input, and a mean filter is used for filtering and noise reduction to obtain the noise-reduced temperature data at each position. It should be noted that during the filtering and noise reduction process, the cavitation effect is considered, leading to differences in temperature change characteristics at different positions in the ultrasonic mixing solution. Therefore, for situations where interference causes significant differences in temperature change at different positions, the filtering window is adjusted based on the temperature difference at each position compared to other positions under interference. The specific adjustment method is as follows:

[0048] Taking the i-th position in the mixing container as an example, calculate the dispersion of the temperature at all times collected at the i-th position within the j-th adjustment interval, calculate the difference between the dispersion of the i-th position and the dispersion of each of the other positions in the j-th adjustment interval, and denote it as the first difference. Multiply the average of all the first differences corresponding to the i-th position by the dispersion of the i-th position, and use it as the first adjustment coefficient of the i-th position in the j-th adjustment interval.

[0049] It should be noted that the specific calculation methods for dispersion include variance, standard deviation, coefficient of variation, etc. This embodiment does not limit this, and uses the coefficient of variation as the calculation method for dispersion. Difference represents the degree of difference between two variables, and can be calculated by difference, absolute value of difference, square of difference, ratio, etc. In this example, the absolute value of difference is used as the calculation method for the first difference.

[0050] It should be understood that, based on prior knowledge of quantifying the differences in temperature distribution at multiple locations in existing calculations, this application analyzes the differences in temperature dispersion between each location and the other locations within each adjustment interval at all times, and fully considers the local temperature gradient characteristics caused by ultrasonic cavitation effect. It constructs a calculation method for the first adjustment coefficient, which is used to adjust the length of the filter window during subsequent filtering processing. This allows the length of the filter window to be adaptively adjusted according to the interference of temperature data at each location, thereby improving the accuracy of determining the length of the filter window.

[0051] The first adjustment coefficient represents the degree of temperature deviation at the i-th position during the ultrasonic mixing process due to interference during temperature acquisition. A larger first adjustment coefficient indicates a greater impact of interference on the temperature data at the i-th position, requiring a larger filter window for processing to reduce the influence of environmental interference on local temperature data. In this embodiment, the filter window is adjusted as follows: In the formula, Indicates the first The length of the filtering window when filtering temperature data collected at location j in the j-th adjustment interval. This indicates the initial filter window length set in this embodiment. The implementer may set the parameters according to the actual situation, and this embodiment does not impose any restrictions on this. Indicates the first The first adjustment coefficient at position j in the adjustment interval This represents the floor function. This is denoted as the first product.

[0052] (2) Based on the denoised temperature data, the deviation set at each time is determined by the difference between the temperature at all locations at each time and the target temperature; the similarity between the deviation sets at each time and the other times in each adjustment interval is analyzed to determine the first deviation coefficient at each time; and the second deviation coefficient at each time is obtained according to the numerical distribution in the deviation set at each time.

[0053] Based on the above analysis and processing, the temperature data collected at each location within the mixing container during the preparation and production process in each adjustment interval are subjected to targeted filtering. Based on the filtered temperature data, the impact of the cavitation effect on the consistency of local temperature changes during ultrasonic mixing is analyzed. Specifically, to effectively analyze the accuracy of the real-time control response to temperature deviations at different locations during ultrasonic mixing, for each temperature acquisition moment during the preparation and production process, the difference between the temperature data at all locations at each acquisition moment and the target temperature is calculated and denoted as the first difference. The set of all first differences at each acquisition moment is taken as the deviation set for each moment.

[0054] Furthermore, taking the j-th adjustment interval as an example, the similarity of the deviation set between each acquisition time in the j-th adjustment interval and every other acquisition time is calculated. The mean of the similarity between each acquisition time in the j-th adjustment interval and all acquisition times of the sound is used as the first deviation coefficient for each acquisition time in the j-th adjustment interval. Secondly, the mean of all elements in the deviation set of each acquisition time is used as the second deviation coefficient for each acquisition time. In this embodiment, the similarity is calculated using the Jaccard coefficient. Implementers can choose other existing feasible methods for calculating the similarity between evaluation sets; this embodiment does not impose any restrictions on this.

[0055] (3) Combining the first deviation coefficient and the second deviation coefficient, classify all times in each adjustment interval, arrange all times in each category in ascending order, and determine the first characteristic value of each category based on the degree of dispersion of the interval between all adjacent times.

[0056] Using the first deviation coefficient at each acquisition time within the j-th adjustment interval as the abscissa and the second deviation coefficient at each acquisition time as the ordinate, a two-dimensional Cartesian coordinate system is constructed. That is, the first deviation coefficient and the second deviation coefficient at each time constitute a two-dimensional array for each time. Therefore, in the two-dimensional Cartesian coordinate system, each time within the j-th adjustment interval corresponds to a data point. The data points at all times within the j-th adjustment interval are clustered using the DBSCAN algorithm to obtain each cluster, denoted as a class. In other words, all times within the j-th adjustment interval are divided into classes. The minimum sample size in the DBSCAN algorithm is 5, and the neighborhood radius is determined using the k-distance graph method. The DBSCAN algorithm is a known existing technology; implementers can choose other feasible existing clustering algorithms, and this embodiment does not impose any restrictions on this.

[0057] To accurately analyze the discontinuous characteristics of the temporal response changes of temperature data collected at different locations in an ultrasonically mixed solution under cavitation effect, the sample data in each cluster after the above clustering was analyzed. For each cluster, all data points in the cluster were sorted according to the chronological order of their corresponding acquisition times. The absolute value of the difference between the acquisition times of adjacent data points after sorting was calculated and recorded as the first absolute value of the difference. The variance of all first absolute values ​​of the difference in each category was used as the first characteristic value of each category.

[0058] It should be understood that the first eigenvalue quantifies the temporal discontinuity of temperature changes during ultrasonic mixing, thereby improving the accuracy of feature extraction from temperature data.

[0059] (4) Extract the denoised temperature of any position at the corresponding time in each class to form the feature sequence of the position in each class; analyze the difference between the feature sequences of the position in each class and all other positions to determine the second feature value of the position in each class.

[0060] Taking the i-th position in the j-th adjustment interval as an example, the denoised temperatures corresponding to all times in any cluster at the i-th position are arranged in chronological order to form the feature sequence of the i-th position in that cluster. For example, the j-th adjustment interval includes times 1, 2, 3, 4, ..., m. In any cluster, times 2, 5, and 10 are included. Therefore, the feature sequence of the i-th position in any cluster is a sequence composed of the denoised temperature data of the i-th position at times 2, 5, and 10, where m is the number of times contained in the j-th adjustment interval. Ultimately, the i-th position corresponds to a feature sequence in each cluster.

[0061] Taking any of the clusters as an example, the DTW distance between the feature sequences of the i-th position and other positions is calculated, and the mean of all the DTW distances is used as the second feature value of the i-th position in any cluster. The DTW distance reflects the difference between the feature sequences of the i-th position and other positions. The implementer can choose other feasible distance measurement calculation methods, and this embodiment does not limit this.

[0062] Based on prior knowledge of the quantification of temperature sequence differences at multiple locations in existing calculations, this application constructs a method for calculating the second eigenvalue by analyzing the mean metric distance between the feature sequences of the i-th location and all other locations, fully considering the spatial differences in temperature response at different locations.

[0063] (5) Combine the first feature value and the second feature value to determine the significant value of the temperature change response at any position, and combine the first adjustment coefficient to obtain the second adjustment coefficient of each position in each adjustment interval; use the proportion of the second adjustment coefficient of each position in all positions within each adjustment interval to correct the temperature deviation of each position at each time, and use the PLC control system to regulate the temperature in the ultrasonic mixing process.

[0064] By combining the first feature value and the second feature value, the significant value of the temperature change response at each location in the mixing container within each adjustment range is determined. The specific calculation method is as follows:

[0065] In the formula, Let be the significant value of the temperature change response at the i-th position in the j-th adjustment interval. Let be the second feature value at the i-th position in the v-th class divided by the j-th adjustment interval. Let the first feature value of class v be the value of the j-th adjustment interval. The sum of the first feature values ​​of all classes in the j-th adjustment interval partition. Let be the number of classes divided at all times in the j-th adjustment interval. This is denoted as the second product.

[0066] To address the issue that traditional single features cannot comprehensively evaluate temperature response characteristics, this application combines the temporal and spatial characteristics of temperature changes during ultrasonic mixing to construct a significant value for temperature change response. This significant value reflects the sensitivity of temperature deviation at each location under cavitation effects. The larger the significant value, the greater the influence of temperature change response on the temperature deviation at the corresponding location under cavitation effects.

[0067] In evaluating the interference level of temperature filtering at different locations within the mixing container, the first adjustment coefficient for each location within each adjustment interval is calculated to reflect the degree of deviation of the temperature data at that location from interference. This allows for the analysis of the confidence level of the temperature monitoring response deviation at each location. A larger first adjustment coefficient indicates a more significant difference in consistency between the temperature monitoring response at that location under cavitation effects and other locations, thus increasing the accuracy of the temperature response analysis. Therefore, the product of the significant temperature change response value at each location within each adjustment interval and the first adjustment coefficient is calculated as the second adjustment coefficient for that location within each adjustment interval. The second adjustment coefficient integrates the significant temperature change response value and the first adjustment coefficient, enabling it to comprehensively reflect the temperature change patterns at each location and improving the accuracy and reliability of subsequent temperature control. Furthermore, for each adjustment interval, the ratio of the second adjustment coefficient at each location to the sum of the second adjustment coefficients at all locations is used as the confidence weight for that location within each adjustment interval. A larger confidence weight indicates a higher probability of temperature fluctuations at that location under cavitation effects, and a greater weight in the impact of its temperature deviation on the intelligent temperature control adjustment during the overall solution preparation process.

[0068] To effectively analyze the impact of cavitation effect on temperature monitoring during ultrasonic mixing, this embodiment uses the total ultrasonic mixing time as a benchmark and sets the duration of each adjustment interval. Based on the calculated confidence weights, the real-time temperature deviation is weighted. Specifically, taking the j-th adjustment interval as an example, the difference between the temperature at each position and the target temperature at the next moment of the j-th adjustment interval is calculated and recorded as the second difference. The sum of the products of the second differences at all positions and the corresponding confidence weights is used as the feedback deviation signal value of the PLC control system at the next moment of the j-th adjustment interval. The feedback deviation signal value drives the PLC control system to generate a temperature control signal. The generated temperature control signal adjusts the heating power of the heating equipment and the cooling water flow rate of the condensing equipment during the ultrasonic mixing process, thereby completing the dynamic calibration of the temperature during the ultrasonic mixing process. This achieves precise and intelligent control of local temperature anomalies caused by cavitation effect, ensuring the temperature uniformity and process stability of the solution mixing system. The ultrasonic mixing temperature control flowchart is as follows. Figure 2 As shown.

[0069] Then, the ultrasonically mixed solution was transferred to a reaction vessel, and an argon gas inlet device was connected. The argon gas flow rate ranged from 0 to 500 mL / min; in this embodiment, 100 mL / min was used to purge air from the reaction system. The aeration time was initially ensured to be at least 30 minutes to fully remove air from the reaction vessel. After the air in the reaction vessel was fully removed, the solution was continuously stirred using a magnetic stirrer at a speed of 500 r / min until the reaction was complete. The temperature was controlled at 20°C using a PLC control system. Sodium borohydride solution was slowly added dropwise using a constant-pressure dropping funnel, ensuring that the molar ratio of cobalt nitrate to added sodium borohydride in the mixed solution was 1:10. The dropping time was controlled within 5 minutes, and the reaction continued for 10 minutes after the addition was complete. After the reaction was finished, an equal volume of 0.5 mmol / L [a specific solution] was added through the constant-pressure dropping funnel. and Solution, in which, add and After solution, cobalt nitrate and The molar ratio of cobalt nitrate to cobalt nitrate is 1:20. The molar ratio of the substances is 1:20. After reacting for 20 minutes, the argon gas valve is closed to stop the gas flow. Then, the mixture is stirred for another 10 minutes to ensure that the reaction proceeds fully.

[0070] Finally, after the reaction was complete, the reaction solution was transferred to a 50 mL centrifuge tube and centrifuged using a high-speed centrifuge at 8000 rpm for 10 minutes, allowing the nanomaterials to precipitate at the bottom of the tube. The supernatant was discarded, and 20 mL of deionized water was added to the precipitate. The mixture was vortexed for 1 minute and then centrifuged again. This washing step was repeated twice, followed by washing three times with anhydrous ethanol using the same method to remove residual reaction reagents and impurities. Finally, the washed product was placed in a vacuum drying oven and dried at 60 °C for 12 hours to obtain cobalt / platinum palladium / cobalt hydroxide nanomaterials.

[0071] Example 2

[0072] Please see Figure 1 It shows a flowchart of the intelligent preparation process of a cobalt hydroxide nanocomposite material provided in Embodiment 2 of this application, the process including:

[0073] First, prepare the raw materials, including: prepare cobalt nitrate. Polyvinylpyrrolidone (PVP), Sodium borohydride Potassium tetrachloropalladium Potassium tetrachloroplatinate The components include deionized water, anhydrous ethanol, and high-purity argon gas, with a purity ≥99.99%. and Prepare a 0.5 mmol / L mixed solution by mixing equal volumes before use.

[0074] Next, weigh cobalt nitrate and PVP and pour them into a mixing container. Add deionized water and mix using an ultrasonic mixer to make the concentrations of cobalt nitrate solution and PVP 0.2 mmol / L and 3 g / L, respectively. Set the ultrasonic time to 20 minutes and control the temperature at 25°C using a PLC control system. The target temperature is 25°C. The cavitation effect of ultrasound accelerates the dissolution of the solute and promotes uniform dispersion, forming a homogeneous and transparent mixed solution.

[0075] It should be noted that during the ultrasonic mixing process, the temperature inside the mixing container is controlled using the exact same method as in Embodiment 1 of this application.

[0076] Then, the ultrasonically mixed solution was transferred to a reaction vessel, and an argon gas inlet device was connected. The argon gas flow rate ranged from 0 to 500 mL / min; in this embodiment, argon gas was introduced at a flow rate of 200 mL / min to purge air from the reaction system. The aeration time was initially ensured to be no less than 30 minutes to fully remove air from the reaction vessel. After the air in the reaction vessel was fully removed, the solution in the reaction vessel was continuously stirred using a magnetic stirrer at a speed of 500 r / min until the reaction was complete. The temperature was controlled at 20°C using a PLC control system. Sodium borohydride solution was slowly added dropwise using a constant-pressure dropping funnel, ensuring that the molar ratio of cobalt nitrate to added sodium borohydride in the mixed solution was 1:15. The dropping time was controlled within 5 minutes, and the reaction continued for 13 minutes after the addition was complete. After the reaction was complete, an equal volume of 0.5 mmol / L [a specific solution] was added through the constant-pressure dropping funnel. and Solution, in which, add and After solution, cobalt nitrate and The molar ratio of cobalt nitrate to cobalt nitrate is 1:50. The molar ratio of the substances is 1:50. After reacting for 20 minutes, the argon gas valve is closed to stop the gas flow. Then, the mixture is stirred for another 13 minutes to ensure that the reaction proceeds fully.

[0077] Finally, after the reaction was complete, the reaction solution was transferred to a 50 mL centrifuge tube and centrifuged using a high-speed centrifuge at 8000 rpm for 13 minutes, allowing the nanomaterials to precipitate at the bottom of the tube. The supernatant was discarded, and 20 mL of deionized water was added to the precipitate. The mixture was vortexed for 2 minutes and then centrifuged again. This washing step was repeated twice, followed by washing three times with anhydrous ethanol using the same method to remove residual reaction reagents and impurities. Finally, the washed product was placed in a vacuum drying oven and dried at 60 °C for 12 hours to obtain cobalt / platinum palladium / cobalt hydroxide nanomaterials.

[0078] Example 3

[0079] Please see Figure 1 It shows a flowchart of the intelligent preparation process of a cobalt hydroxide nanocomposite material provided in Embodiment 3 of this application, the process including:

[0080] First, prepare the raw materials, including: prepare cobalt nitrate. Polyvinylpyrrolidone (PVP), Sodium borohydride Potassium tetrachloropalladium Potassium tetrachloroplatinate The components include deionized water, anhydrous ethanol, and high-purity argon gas, with a purity ≥99.99%. and Prepare a 0.5 mmol / L mixed solution by mixing equal volumes before use.

[0081] Next, weigh cobalt nitrate and PVP and pour them into a mixing container. Add deionized water and mix using an ultrasonic mixer to make the concentrations of cobalt nitrate solution and PVP 0.2 mmol / L and 4 g / L, respectively. Set the ultrasonic time to 20 minutes and control the temperature at 25°C using a PLC control system. The target temperature is 25°C. The cavitation effect of ultrasound accelerates the dissolution of the solute and promotes uniform dispersion, forming a homogeneous and transparent mixed solution.

[0082] It should be noted that during the ultrasonic mixing process, the temperature inside the mixing container is controlled using the exact same method as in Embodiment 1 of this application.

[0083] Then, the ultrasonically mixed solution was transferred to a reaction vessel, and an argon gas inlet device was connected. The argon gas flow rate ranged from 0 to 500 mL / min; in this embodiment, argon gas was introduced at a flow rate of 300 mL / min to purge air from the reaction system. The aeration time was initially ensured to be no less than 30 minutes to fully remove air from the reaction vessel. After the air in the reaction vessel was fully removed, the solution in the reaction vessel was continuously stirred using a magnetic stirrer at a speed of 500 r / min until the reaction was complete. The temperature was controlled at 20°C using a PLC control system. Sodium borohydride solution was slowly added dropwise using a constant-pressure dropping funnel, ensuring that the molar ratio of cobalt nitrate to added sodium borohydride in the mixed solution was 1:25. The dropping time was controlled within 5 minutes, and the reaction continued for 15 minutes after the addition was complete. After the reaction was complete, an equal volume of 0.5 mmol / L [a specific solution] was added through the constant-pressure dropping funnel. and Solution, in which, add and After solution, cobalt nitrate and The molar ratio of cobalt nitrate to cobalt nitrate is 1:80. The molar ratio of the substances is 1:80. After reacting for 20 minutes, the argon gas valve is closed to stop the gas flow. Then, the mixture is stirred for another 15 minutes to ensure that the reaction proceeds fully.

[0084] Finally, after the reaction was complete, the reaction solution was transferred to a 50 mL centrifuge tube and centrifuged using a high-speed centrifuge at 8000 rpm for 15 minutes, allowing the nanomaterials to precipitate at the bottom of the tube. The supernatant was discarded, and 20 mL of deionized water was added to the precipitate. The mixture was then vortexed for 2 minutes and centrifuged again. This washing step was repeated twice, followed by washing three times with anhydrous ethanol using the same method to remove residual reaction reagents and impurities. Finally, the washed product was placed in a vacuum drying oven and dried at 60 °C for 12 hours to obtain cobalt / platinum palladium / cobalt hydroxide nanomaterials.

[0085] Comparative Example 1

[0086] Comparative Example 1 will not adjust the temperature during the ultrasonic mixing process, and the remaining processes will be carried out in the same manner and with the same parameters as in Example 1 of this application, to obtain the cobalt / platinum palladium / cobalt hydroxide nanomaterials prepared in Comparative Example 1.

[0087] Comparative Example 2

[0088] Comparative Example 2 will not adjust the temperature during the ultrasonic mixing process, and the remaining processes will be carried out in the same manner and with the same parameters as in Example 2 of this application, to obtain the cobalt / platinum palladium / cobalt hydroxide nanomaterials prepared in Comparative Example 2.

[0089] Comparative Example 3

[0090] Comparative Example 3 will not adjust the temperature during the ultrasonic mixing process, and the remaining processes will be carried out in the same manner and with the same parameters as in Example 3 of this application, to obtain the cobalt / platinum palladium / cobalt hydroxide nanomaterials prepared in Comparative Example 3.

[0091] Table 1 shows the performance comparison results of the cobalt / platinum palladium / cobalt hydroxide nanomaterials prepared in the embodiments of this application and the comparative examples.

[0092]

[0093] As shown in Table 1, by controlling the temperature during the ultrasonic mixing process, the cobalt hydroxide nanomaterials prepared in this application have higher tap density and lower average particle size. Higher tap density allows for denser particle packing, reduces catalyst dispersion loss, facilitates magnetic separation and recovery, and enhances the corrosion resistance of the cobalt hydroxide protective layer. Smaller particle size results in a larger specific surface area and more catalytic active sites; conversely, excessively large particle size leads to a decrease in specific surface area and affects catalytic efficiency.

[0094] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0095] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0096] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An intelligent preparation process for cobalt hydroxide nanocomposite materials, characterized in that, The process includes: Weigh cobalt nitrate and polyvinylpyrrolidone and pour them into a mixing container. Add deionized water and mix ultrasonically. During ultrasonic mixing, the temperature at each set position and adjustment interval within the mixing container is collected at each time; the temperature distribution difference between each position and the other positions within each adjustment interval is analyzed to determine the first adjustment coefficient of each position in each adjustment interval. The filter window length is corrected based on the first adjustment coefficient, and the filter is used to denoise the temperature of each position and each adjustment range. By comparing the denoised temperature at all locations with the target temperature at each time point, the deviation set at each time point is determined. The similarity of the deviation sets at different times within each adjustment interval and the numerical distribution in the deviation sets at each time point are analyzed to classify all times within each adjustment interval. Based on the distribution of the time intervals between each category and the denoised temperature difference between each location and other locations at corresponding times within each category, the significant value of the temperature change response at each location is determined. Combined with the first adjustment coefficient, the second adjustment coefficient of each location in each adjustment interval is obtained. The proportion of the second adjustment coefficient in all positions is used to correct the temperature deviation at each position and time, which is used to regulate the temperature in the ultrasonic mixing process. After the ultrasonically mixed solution was transferred to the reaction vessel, argon gas was introduced. Sodium borohydride solution was added dropwise while stirring. After the reaction, equal volumes of potassium tetrachloropalladate and potassium tetrachloroplatinate solutions were added. Finally, the reaction solution was centrifuged, washed, and dried to obtain cobalt hydroxide nanocomposite material. The determination of the significant values ​​of the temperature change response at each location includes: Arrange all times in each class in ascending order, and determine the first eigenvalue of each class based on the degree of dispersion of the interval between all adjacent times in each class; Extract the denoised temperature of any location at the corresponding time in each category to form the feature sequence of the location in each category. Calculate the mean of the metric distances between the feature sequences of any location in each category and all other locations, and use this as the second feature value of the location in each category. For each adjustment interval, the ratio of the first feature value of each class to the sum of the first feature values ​​of all classes is calculated. The product of the second feature value of any position in each class and the ratio is calculated and denoted as the second product. The sum of the second products of any position in all classes is taken as the significant value of the temperature change response at any position.

2. The intelligent preparation process of cobalt hydroxide nanocomposite material as described in claim 1, characterized in that, The concentrations of cobalt nitrate solution and polyvinylpyrrolidone in the ultrasonically mixed solution are 0.2 mmol / L and 2~4 g / L, respectively.

3. The intelligent preparation process of cobalt hydroxide nanocomposite material as described in claim 1, characterized in that, The process for determining the first adjustment coefficient is as follows: The difference in temperature dispersion between each location and the other locations at all times within each adjustment interval is denoted as the first difference. The mean of the first difference between each location and all other locations is calculated. The first adjustment coefficient is the product of the temperature dispersion of each location at all times within each adjustment interval and the mean.

4. The intelligent preparation process of cobalt hydroxide nanocomposite material as described in claim 1, characterized in that, The modified filter window length includes: Calculate the product of the first adjustment coefficient of each position in each adjustment interval and the preset initial filter window length, and record it as the first product. Take the integer result of the sum of the first product and the preset initial filter window length as the corrected filter window length of each position in each adjustment interval.

5. The intelligent preparation process of cobalt hydroxide nanocomposite material as described in claim 1, characterized in that, Based on the similarity, a first deviation coefficient is determined for each time moment, and based on the numerical distribution, a second deviation coefficient is determined for each time moment, wherein the second deviation coefficient is the mean of all values ​​in the deviation set for each time moment.

6. The intelligent preparation process of cobalt hydroxide nanocomposite material as described in claim 5, characterized in that, The classification of all times within each adjustment interval includes: The first deviation coefficient and the second deviation coefficient at each time point are combined to form a two-dimensional array for each time point. The two-dimensional arrays for all times points within each adjustment interval are clustered to obtain the classification results for all times points within each adjustment interval.

7. The intelligent preparation process of cobalt hydroxide nanocomposite material as described in claim 1, characterized in that, The temperature control during the ultrasonic mixing process includes: For each adjustment interval, the product of the significant value of the temperature change response at each position and the first adjustment coefficient is calculated as the second adjustment coefficient at each position; the percentage is recorded as the confidence weight at each position. Calculate the difference between the temperature at each position and the target temperature at the next moment in each adjustment interval. Sum the products of the differences at all positions and their corresponding confidence weights as the feedback deviation signal value of the PLC control system at the next moment. Use the feedback deviation signal value to regulate the temperature.

8. The intelligent preparation process of cobalt hydroxide nanocomposite material as described in claim 1, characterized in that, The flow rate of the introduced argon gas is 0-500 mL / min, and the gas introduction time is not less than 30 minutes; The sodium borohydride solution is added dropwise to make the molar ratio of cobalt nitrate to sodium borohydride in the final mixed solution 1:10~25, and the dropwise addition time is controlled within 5 minutes. After the dropwise addition is completed, the reaction continues for 10~15 minutes. In the mixed solution after adding equal volumes of potassium tetrachloropalladate and potassium tetrachloroplatinate solutions, the molar ratio of cobalt nitrate to potassium tetrachloropalladate is 1:20~80, and the molar ratio of cobalt nitrate to potassium tetrachloroplatinate is 1:20~80.

9. The intelligent preparation process of cobalt hydroxide nanocomposite material as described in claim 1, characterized in that, The centrifugation time is 10-15 minutes. Then, the supernatant is discarded, deionized water is added to the precipitate, and the mixture is shaken and washed for 1-2 minutes.

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