Automatic manufacturing method of nano magnesium-based hydrogen storage material

Abstract

The invention relates to the technical field of metal material spectral analysis, in particular to an automatic manufacturing method of a nano magnesium-based hydrogen storage material, which comprises the following steps: mixing a metal oxide and a nano graphene sheet to prepare a mixed solution A and a mixed solution B, dropwise adding the mixed solution A into the mixed solution B, stirring, and obtaining spectral data in the stirring process, based on the difference degree of reflection intensity corresponding to pixels in the hyperspectral gel data and the contour information of the potential defect area, determining a floccule evaluation coefficient, and adjusting the stirring temperature; determining the bubble confidence coefficient of the potential defect area based on the change condition of the reflection intensity of the pixel, and controlling the stirring time to obtain carbon gel; the preparation method comprises the following steps: grinding carbon gel to obtain a composite catalyst, carrying out ball milling on magnesium powder and the composite catalyst, synthesizing a powdery magnesium-based hydrogen storage material through a hydrogenation combustion method, and carrying out ball milling again to obtain the nano magnesium-based hydrogen storage material. The preparation quality of the hydrogen storage material can be improved.

Description

Technical Field

[0001] This application relates to the technical field of material spectral analysis, and specifically relates to an automated manufacturing method for nano magnesium-based hydrogen storage materials. Background Art

[0002] Hydrogen storage materials refer to materials that can store and release hydrogen, and have important application values in the fields of energy storage, hydrogen energy vehicles, and industrial gas treatment. The production process of metal powder generally involves selecting raw materials, melting and crushing, and then ball milling and screening to obtain the required refined metal powder. In the preparation process of hydrogen storage materials, the nano-confinement method is mainly used to prepare carbon gels containing metal catalysts, and nano-MgH2 is uniformly loaded in the three-dimensional network structure of the carbon gels to improve the hydrogen absorption and desorption thermodynamic properties of nano-Mg.

[0003] However, in the preparation process of the gel composite catalyst, the gel time needs to be strictly controlled according to the reaction temperature and acidity. During the gel process, hydrolysis reaction and polycondensation reaction occur simultaneously. If the gel time is short and the hydrolysis speed is too fast, white flocculent precipitates will be produced, resulting in the failure of gel preparation, or the pores in the prepared wet gel are small, which is not conducive to the discharge of moisture during subsequent drying; if the gel time is long, the hydrolysis reaction will be inhibited and the polycondensation reaction will be promoted, resulting in an extended gel time, and the pores of the obtained wet gel are large, reducing the contact area with nano-MgH2 and affecting the hydrogen storage capacity of nano magnesium-based materials. Summary of the Invention

[0004] To solve the above technical problems, this application provides an automated manufacturing method for nano magnesium-based hydrogen storage materials to solve the existing problems.

[0005] The automated manufacturing method for nano magnesium-based hydrogen storage materials of this application adopts the following technical solutions: An embodiment of this application provides an automated manufacturing method for nano magnesium-based hydrogen storage materials, and this method includes the following steps: Mix metal oxide and nano-graphene sheets, and obtain functionalized graphene through treatment; add the functionalized graphene and tetrabutyl titanate into isopropanol, and magnetically stir for 1 h to 3 h to obtain a mixed solution A; Add scandium nitrate monohydrate into isopropanol, adjust the acidity, stir and mix evenly to obtain a mixed solution B, and place it in a constant temperature bath at 10°C to 20°C; drop the mixed solution A into the mixed solution B at a speed of 1 to 5 drops per second and stir, and analyze the gel state during the stirring process to control the stirring time. The specific process is as follows: Obtain hyperspectral gel data at each sampling moment during the stirring process, and determine the flocculation evaluation coefficient of each potential defect region based on the difference degree of the reflection intensities corresponding to the pixels within each potential defect region in the hyperspectral gel data and the contour information of each potential defect region, so as to adjust the temperature of the stirring reaction process; Determine the bubble confidence of each potential defect region based on the change of the reflection intensity of the pixels in each potential defect region, extract the real bubble region, and control the stirring time in combination with the number of real bubble regions at each sampling moment to obtain carbon gel; Grind the carbon gel to obtain a composite catalyst. After ball-milling magnesium powder with the composite catalyst, synthesize a powdery magnesium-based hydrogen storage material by the hydrogenation combustion method, and perform ball-milling treatment again to obtain a nano magnesium-based hydrogen storage material.

[0006] Furthermore, the process for obtaining the functionalized graphene is as follows: Put the nano graphene sheets into metal oxides, reflux at 150°C to 200°C for 5h to 7h, cool to room temperature and wash with ionic water until neutral, and dry at 70°C to 90°C to obtain functionalized graphene.

[0007] Furthermore, the method for determining the flocculation evaluation coefficient of each potential defect region is as follows: Each potential defect region is each connected domain in the pseudo-color image corresponding to the hyperspectral gel data; Construct the floc weight of each potential defect region based on the shape characteristics of each potential defect region; Determine the difference result of each pixel in each potential defect region based on the difference between the reflection intensities of each pixel in each potential defect region; Determine the difference disorder coefficient of each potential defect region based on the symmetry degree of the difference results of all pixels within each potential defect region, where the difference disorder coefficient is negatively correlated with the symmetry degree; Determine the flocculation evaluation coefficient of each potential defect region through the floc weight and difference disorder coefficient of each potential defect region, where the flocculation evaluation coefficient is the positive fusion of the floc weight and difference disorder coefficient.

[0008] Furthermore, the method for constructing the floc weight of each potential defect region is as follows: Determine the floc weight of each potential defect region based on the aspect ratio and roundness of the boundary shape of each potential defect region, where the floc weight is positively correlated with the aspect ratio of the boundary shape and negatively correlated with the roundness.

[0009] Further, the method for determining the difference results of each pixel in each potential defect area is as follows: Obtain the neighborhood pixels of each pixel, form a pixel sequence of each pixel with the reflection intensities of each pixel at different wavelengths, and calculate the difference between the pixel sequence of each pixel and the pixel sequences of its neighborhood pixels, which is denoted as the difference result of each pixel.

[0010] Further, to adjust the temperature of the stirring reaction process, the specific method is as follows: For the flocculation evaluation coefficient of each potential defect area at each sampling moment, analyze the proportion of potential defect areas with a flocculation evaluation coefficient greater than the preset flocculation threshold among all potential defect areas at each sampling moment; When the proportion is greater than the preset ratio, lower the temperature of the stirring reaction process by a preset value.

[0011] Further, the method for determining the bubble confidence of each potential defect area is as follows: Fit the pixel sequences of each pixel in each potential defect area to construct a pixel curve for each pixel in each potential defect area; Obtain the bubble confidence of each potential defect area based on the curve smoothness of each pixel in each potential defect area. Among them, the bubble confidence of each potential defect area is positively correlated with the curve smoothness of each pixel therein.

[0012] Further, the process of controlling the stirring time by combining the number of real bubble areas at each sampling moment is as follows: Statistically analyze the appearance rate of the real bubble areas corresponding to each sampling moment. When the appearance rate is less than the preset percentage, the quality of the carbon gel at the corresponding sampling moment is qualified. When the appearance rates at multiple consecutive sampling moments are all less than the preset percentage, stop stirring.

[0013] Further, the method for obtaining the composite catalyst is as follows: Put the carbon gel into an oven and dry it for 12 h to 24 h, grind it into a powder, and calcine it at 600 °C to 650 °C in an air atmosphere for 3 h to 6 h, and then cool it to room temperature.

[0014] Further, after ball-milling the magnesium powder and the composite catalyst, synthesize a powdered magnesium-based hydrogen storage material by the hydrogenation combustion method, and then perform ball-milling treatment again to obtain a nano-magnesium-based hydrogen storage material. Specifically: The ball-milling time of the magnesium powder and the composite catalyst is 6 h to 12 h, and the ball-milling speed is 400 to 600 rpm; during the synthesis by the hydrogenation combustion method, heat it up to 550 °C to 600 °C under a hydrogen atmosphere of 3 MPa, keep it warm for 1.5 h to 3 h, then cool it down to 300 °C to 350 °C and keep it warm for 4 h to 6 h again, and cool it to room temperature to obtain a powdered magnesium-based hydrogen storage material. Then, under a hydrogen atmosphere of 0.1 MPa, perform ball-milling for 12 h to 24 h, and the ball-milling speed is 400 to 600 rpm.

[0015] The present application has at least the following beneficial effects: By analyzing the hyperspectral data of the blend solution in the glass container, and analyzing according to the characteristics of flocs, air vortex, bubble region, and interference region during the gelation process, the gelation process is micro-controlled. Finally, the proportion of bubbles is reflected by the bubble confidence at each sampling moment to determine the termination time of the gelation process. In the prior art, by controlling the material ratio, it is possible to achieve batch production of the hydrogen storage material of scandium sesquioxide and titanium dioxide loaded with nano-magnesium / graphene. However, in the process of preparing the carbon gel composite catalyst, it is easy to cause improper reaction control, resulting in the failure of carbon gel preparation or uneven pores of the carbon gel, causing the inability to load more magnesium-based materials and the defect of a low hydrogen storage upper limit. By analyzing the characteristics of flocs and bubbles that are likely to appear during the gelation process, the present application can control the gelation duration, improve the loading capacity of the prepared nano-magnesium-based material, and improve the thermodynamic properties and hydrogen storage capacity of the material. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of the steps of an automated manufacturing method for a nano-magnesium-based hydrogen storage material provided by the present application; Figure 2 It is a schematic diagram of the determination process of the floc evaluation coefficient for each potential defect area; Figure 3 It is a flowchart of the temperature adjustment during the stirring reaction process; Figure 4 It is a flowchart of the bubble confidence analysis for each potential defect area; Figure 5 It is a flowchart of the stirring time control during the carbon gel formation process. Detailed Embodiments

[0018] In order to further elaborate on the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific embodiments, structures, features, and effects of an automated manufacturing method for a nano-magnesium-based hydrogen storage material proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, terms such as "including", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, such that a circuit structure, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the article or device comprising the element. Additionally, the term "and / or" as used herein includes any and all combinations of one or more of the related listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application pertains.

[0020] The following specifically describes the specific solution of an automated manufacturing method for a nano magnesium-based hydrogen storage material provided by this application in conjunction with the accompanying drawings.

[0021] An automated manufacturing method for a nano magnesium-based hydrogen storage material provided by an embodiment of this application. Specifically, the following is an automated manufacturing method for a nano magnesium-based hydrogen storage material. Please refer to Figure 1 , this application is mainly a nano magnesium-based hydrogen storage material with a carbon gel-supported trace metal composite catalyst. The manufacturing method includes the following steps: Catalyst preparation: a) Put nano graphene sheets into a 65% scandium oxide HNO3 solution and reflux at 150 °C for 7 h. Then cool it to room temperature and wash it with ionized water until neutral, and dry the nano graphene sheets at 70 °C to obtain functionalized graphene, where the purity of the nano graphene sheets is required to be above 99.5%. Pickling is to remove impurities on the surface of nano graphene and generate some active groups on its surface, which is beneficial to oxide adsorption; In another embodiment of this application, for the reflux temperature in step a), it is 200 °C, the reflux duration is 5 h, and the drying temperature is 90 °C; In other embodiments of this application, for the reflux temperature in step a), it is 170 °C, the reflux duration is 6 h, and the drying temperature is 80 °C.

[0022] b) Add 0.4 g of functionalized graphene and 1.2768 g of tetrabutyl titanate to 10 mL of isopropanol reagent, accelerate the mixing using ultrasound, and stir magnetically for 1 h to obtain a mixed solution A; add 1.08 g of scandium nitrate monohydrate to 10 ml of isopropanol, then add 8 ml of pure water, adjust the acidity by adding concentrated nitric acid with a mass percentage of 69%, and stir and mix evenly to obtain a mixed solution B; In another embodiment of the present application, the magnetic stirring duration in the process of b) is 2 h; In other embodiments of the present application, the magnetic stirring duration in the process of b) is 3 h.

[0023] c) Place the mixed solution B in a glass container and place it in a constant temperature bath at 10°C, and perform magnetic stirring on the mixed solution B. Slowly drop the mixed solution A into the mixed solution B at a rate of 1 drop per second until a carbon gel is formed; In another embodiment of the present application, for the constant temperature bath in c), the temperature is 15°C, and the dropping rate of the mixed solution A is 3 drops per second; In other embodiments of the present application, for the constant temperature bath in c), the temperature is 20°C, and the dropping rate of the mixed solution A is 5 drops per second.

[0024] During the stirring process, control the stirring duration to be 1 h to 5 h. In order to ensure the quality of the carbon gel, control the termination of the stirring during the carbon gel formation process. The specific process is as follows: Step 1: Use a hyperspectral acquisition device to obtain gel data during the gelation process.

[0025] In order to control the magnetic stirring duration of the carbon gel, the present application needs to obtain the gel spectral data during the gelation process in real time. The present application places the hyperspectral acquisition device in the constant temperature bath so that the position of the hyperspectral camera is at the same horizontal height as the glass container of the mixed solution B, thereby continuously collecting the gel data during the gelation process. In this embodiment, the sampling frequency of the hyperspectral camera is set to 10 s.

[0026] Thus, the acquisition of spectral data during the gelation process is realized.

[0027] Step 2: Obtain a floc assessment coefficient by using the morphological characteristics of flocs and air vortices in the blend solution and the spectral pixel differences.

[0028] Under ideal conditions, by mixing the mixed solutions A and B to form a blend solution, the carbon gel formed under magnetic stirring in the constant temperature bath presents a state of uniform texture, without bubbles and precipitation. However, in the actual gelation process, it is easy to cause the hydrolysis reaction in the blend solution to be too fast due to improper control, resulting in the formation of white flocculent precipitates. And as the magnetic stirring proceeds, the blend solution on the surface layer in the glass container is likely to entrain a part of the air into the solution, resulting in bubbles in the gel, affecting the size of the microscopic pores of the carbon gel and reducing the loading amount of nano magnesium-based materials.

[0029] When flocs are generated in a glass container, they often originate from the middle of the container, and show a ribbon-like distribution as stirring progresses, gradually descending and precipitating to the bottom of the container. However, since the blended solution exhibits a liquid characteristic when adding the mixed solution A, air vortices are likely to form in the middle of the container during magnetic stirring. Both the air vortices and the flocs will cause fluctuations in the reflection intensity of the gel data, but the air vortices will gradually disappear as the gel formation progresses, so the air vortex area will interfere with the floc area.

[0030] To detect the gel state during the gel formation process, the gel data at each sampling moment is subjected to true color synthesis using ENVI software to obtain a pseudo-color image of the gel data. And the pseudo-color image is used to obtain the potential defect areas at the current sampling moment through edge detection and connected component analysis methods, that is, each connected component is regarded as each potential defect area. Therefore, several potential defect areas can be obtained according to a single sampling moment. Based on the relative position relationship of the pixels in the pseudo-color image and the hyperspectral gel data, the potential defect areas in the hyperspectral gel data are obtained. Based on the pixel characteristics of the potential defect areas, the distinction between flocs and air vortices is realized.

[0031] When a single potential defect area corresponds to flocs, the overall shape of this area should show a filamentous ribbon-like distribution. And due to the different overall thickness and material content of the flocs, the difference in the reflection intensity of the pixels within the floc area is relatively large; if a single potential defect area corresponds to an air vortex, since it is caused by magnetic rotation, the air vortex is often in the middle of the glass container and its shape is circular.

[0032] Based on the morphological characteristics and pixel characteristics of flocs and air vortices, a floc evaluation coefficient is constructed to evaluate the floc situation in the hyperspectral gel data at the current sampling moment. The reflection intensities of each pixel at different wavelengths are used to form the pixel sequence of each pixel. The construction process of the floc evaluation coefficient is as follows: Based on the morphological characteristics of flocs and air vortices, for the boundary shape characteristics constructed from the relative positions of the boundary pixels in each potential defect area, the floc weights of each potential defect area are obtained using the boundary shapes of each potential defect area. Among them, the floc weight is positively correlated with the aspect ratio of the boundary shape and negatively correlated with the roundness.

[0033] In this embodiment, the floc weight of each pixel is the product of the aspect ratio of the boundary shape and a preset scale factor greater than 1, and the scale factor is 5.

[0034] It should be understood that if a filamentous ribbon-like distribution appears within the potential defect area at the current sampling moment, the larger the aspect ratio of the boundary shape corresponding to the potential defect area and the smaller the roundness, the greater the floc weight obtained, indicating that the potential defect area is more likely to be a floc area.

[0035] Further, in this embodiment, each pixel in the potential defect region is divided into an 8-neighborhood, the neighborhood pixels of each pixel are obtained, and the difference between the pixel sequence of each pixel and its neighborhood pixels is calculated, which is recorded as the difference result of each pixel. In this embodiment, the DTW distance between sequences is used to measure the difference. Implementers can also use other methods for measuring the difference between sequences, and this application does not make special restrictions.

[0036] When the potential defect region is a floc, due to the different thicknesses and contents in different parts of the floc region, the pixel differences within the region change disorderly, resulting in a larger difference disorder coefficient. On the contrary, when the potential defect region is an air vortex, since the air vortex is usually thicker in the middle and thinner on both sides, the change of the pixel sequence will show a symmetric situation with larger differences in the middle and smaller differences on both sides, resulting in a smaller difference disorder coefficient. Therefore, based on the difference results of each pixel in the potential defect region, the difference disorder coefficient of the potential defect region is analyzed.

[0037] Among them, the difference disorder coefficient of each potential defect region is negatively correlated with the symmetry degree of the difference results of all pixels in each potential defect region. The negative correlation means that the change trends of the variables are opposite, that is, the higher the symmetry degree, the smaller the difference disorder coefficient.

[0038] Preferably, in this embodiment, the skewness and variance of the difference results of all pixels in each potential defect region are calculated, and the difference disorder coefficient of each potential defect region is the product of the skewness and variance of the difference results of all pixels in each potential defect region.

[0039] Based on the floc weight and the difference disorder coefficient of each potential defect region, the floc evaluation coefficient of each potential defect region is determined. Among them, the floc evaluation coefficient is the positive fusion of the floc weight and the difference disorder coefficient.

[0040] It should be noted that positive fusion means that the change trends of the variables are the same. The specific relationship can be addition, multiplication, square, etc., and this application does not make special restrictions.

[0041] Preferably, in this embodiment, the positive fusion is an addition relationship, and the floc evaluation coefficient is the product result of the floc weight and the difference disorder coefficient.

[0042] In the actual application process, as another embodiment, the positive fusion is a multiplication relationship, and the floc evaluation coefficient is the sum value of the floc weight and the difference disorder coefficient.

[0043] It should be understood that when the boundary shape of the current potential defect area presents a banded filamentous shape, the larger the obtained flocculent weight value, and at the same time, the greater the fluctuation of the difference change between the pixels in the area, the greater the value of the difference disorder coefficient, making the flocculent evaluation coefficient larger, indicating that the current potential defect area is more likely to be a flocculent area.

[0044] Specifically, for the schematic diagram of the determination process of the flocculent evaluation coefficient of each potential defect area, please refer to Figure 2 .

[0045] For each sampling moment, taking the current sampling moment as an example, traverse all potential defect areas at the current sampling moment, and normalize each flocculent evaluation coefficient. Further, set a flocculent threshold, and count the proportion of potential defect areas at the current sampling moment that are greater than the flocculent threshold among all potential defect areas at the current sampling moment. When the proportion is greater than the preset ratio, that is, flocculents appear in the glass container, it indicates that the hydrolysis reaction in the blend solution at the corresponding sampling moment is relatively fast. In order to inhibit the hydrolysis reaction and improve the gel quality, at this time, it is necessary to appropriately reduce the constant temperature, lower the temperature by a preset value, and the implementer can set the preset value of the temperature reduction according to the actual application scenario.

[0046] Preferably, in this embodiment, the flocculent threshold is set to 0.9. When the proportion is greater than 5%, the temperature is lowered by 2°C.

[0047] In the actual application scenario, the implementer can also set it by himself. As other implementation methods, when the proportion is greater than 10%, the temperature is lowered by 5°C.

[0048] Specifically, for the flow chart of temperature adjustment during the stirring reaction process, please refer to Figure 3 .

[0049] Furthermore, as the gel forms, the blend solution will gradually turn into a gel state. At this time, the surface blend solution will entrain air into the gel and form bubbles in the gel. Due to the presence of bubbles, it is easy to cause the microscopic pores of the wet gel to increase, which is not conducive to the loading of nano magnesium-based materials by the gel. At this time, the potential defect area corresponds to the bubble area and the interference area in the hyperspectral gel data. It is necessary to exclude the interference of local noise on bubble detection to improve the accuracy of gel analysis during the formation of carbon gel and ensure the accuracy of stirring time control.

[0050] Considering that the inside of the bubble area is air, due to the influence of scattering and reflection, there are differences in the reflection intensity of each wavelength. However, the adjacent wavelengths in the hyperspectral data are relatively short, so the pixel curve change in the bubble area is relatively smooth. In the interference area, due to the randomness of noise, the noise may act randomly on each wavelength, so the pixel curve in the interference area is relatively tortuous.

[0051] Based on the characteristic differences between the bubble regions and the interference regions, the bubble confidence levels of each potential defect region are analyzed. The specific calculation process is as follows: Based on the pixel sequences of each pixel, curve fitting is performed to obtain the pixel curves of each pixel. For each pixel within a single said potential defect region, the smoothness of its pixel curve is calculated. The smoothness of the curve can be analyzed by existing technologies, and this application does not make special limitations on this.

[0052] In this embodiment, the smoothness of the curve can be analyzed by the slope of the pixel curve, and the reciprocal of the curve slope is used as the smoothness of the curve.

[0053] In the actual application process, for the smoothness of the curve, the implementer can also analyze it through the rate of change of the curve, and use the reciprocal of the curve rate of change as the smoothness of the curve.

[0054] It should be understood that if the current region is an interference region, since the pixels inside the region are affected by noise and change relatively violently, the pixel curve is relatively tortuous, and the value of the curve smoothness situation obtained is small; on the contrary, if the current region is a bubble region, since it is only affected by internal air scattering and refraction, the change in the reflection intensity of the pixel sequence is small, the pixel curve is relatively smooth, and the value of the curve smoothness situation obtained is large.

[0055] Based on the smoothness of each pixel within each potential defect region, the bubble confidence levels of each potential defect region are obtained. Among them, the bubble confidence levels of each potential defect region are positively correlated with the smoothness of each pixel within it.

[0056] It should be noted that the said positive correlation means that the change trends between variables are the same. The larger the dependent variable, the larger the independent variable increases, and the smaller the dependent variable, correspondingly, the smaller the independent variable decreases. The specific relationship is determined according to the actual application scenario.

[0057] In this embodiment, the bubble confidence level of the potential defect region can be analyzed through the mean and variance of the smoothness of all pixels within the potential defect region. The ratio of the mean to the variance of the smoothness of all pixels within each potential defect region is used as the bubble confidence level of each potential defect region.

[0058] It should be understood that for the bubble region, since the pixels inside the region are generally smooth as a whole, the smoothness of each pixel curve is better, the larger the mean, the smaller the variance, and the higher the obtained bubble confidence level. For the interference region, the degree of interference of each pixel by noise is different, so the smoothness of each pixel curve is poor, the mean is small, the variance is large, and the obtained bubble confidence level is small.

[0059] Specifically, for the flowchart of analyzing the bubble confidence level of each potential defect region, please refer to Figure 4 .

[0060] Step 3: Determine the gel termination moment by using the bubble confidence level and the bubble threshold.

[0061] The bubble confidence level can measure the possibility that a single potential defect area is a bubble at the current sampling moment. The bubble confidence levels of all potential defect areas at the current sampling moment are used as the input of the Otsu method, and the algorithm outputs the bubble threshold. Among them, the Otsu method is also the maximum inter-class variance method. In this embodiment, it is used to perform threshold segmentation on the bubble confidence levels of all potential areas at the current sampling moment, and the segmentation threshold obtained after threshold segmentation is used as the bubble threshold. It should be noted that the specific process of performing threshold segmentation using the Otsu method is prior art and will not be elaborated in this embodiment. The potential defect areas greater than the bubble threshold are marked as real bubble areas, and the potential defect areas less than or equal to the bubble threshold are marked as interference areas. Thus, the occurrence rate of real bubble areas at each sampling moment is statistically calculated, and the stirring time is controlled based on the occurrence rate corresponding to each sampling moment.

[0062] In this embodiment, the occurrence rate of real bubble areas at each sampling moment is the ratio of the total number of pixels of all real bubble areas at each sampling moment to the total number of pixels in the hyperspectral gel data. As other embodiments, the implementer can analyze the occurrence rate of real bubble areas at each sampling moment through the ratio of the number of real bubble areas to the number of potential defect areas. Specifically, this application does not make special restrictions. If the occurrence rate is less than 3%, it indicates that the bubble content in the gel of the glass container corresponding to the current sampling moment is small, and the quality of the carbon gel meets the requirements. At the same time, to avoid the accidental randomness of a single sampling moment, if the bubble occurrence rates at multiple consecutive (6 consecutive in this embodiment) sampling moments meet the requirements, the isothermal stirring can be stopped to obtain the carbon gel.

[0063] Specifically, for the flowchart of controlling the stirring time during the formation process of the carbon gel, please refer to Figure 5 .

[0064] d) Put the obtained carbon gel into an oven and dry it in the oven at 55°C for 24 h, and grind it into a powder. Then, calcine the dried powder at 600°C for 6 h in an air atmosphere, and finally cool it naturally to room temperature to obtain the composite catalyst; As another embodiment of this application, in d), the drying duration is 17 h, the calcination temperature is 620°C, and the calcination duration is 3 h; As other embodiments of this application, in d), the drying duration is 12 h, the calcination temperature is 650°C, and the calcination duration is 4.5 h.

[0065] Loading magnesium: In this embodiment, 9.5 g of magnesium powder and 0.5 g of The prepared composite catalyst is pretreated by ball milling. It should be noted that the ball milling is carried out under the protection of an argon atmosphere. The ball milling duration is set to 6 h and the ball milling speed is 600 rpm, thus obtaining magnesium / composite catalyst powder. It is required that the purity of the magnesium powder is above 99% and the particle size is 40 or less; As another embodiment of the present application, in step the ball milling duration is 8 h and the ball milling speed is 500 rpm; As other embodiments of the present application, in step the ball milling duration is 12 h and the ball milling speed is 400 rpm.

[0066] Preparation of powdered hydrogen storage material: In this embodiment, the magnesium / composite catalyst powder is synthesized by hydrogenation combustion method. During the synthesis process, the magnesium / composite catalyst powder is heated to 550 °C under a hydrogen atmosphere of 3 MPa, the heat preservation duration is set to 3 h, then it is cooled to 320 °C and heat-preserved again for 6 h, and finally cooled to room temperature, thus obtaining a powdered magnesium-based hydrogen storage material; As another embodiment of the present application, in step the heating temperature is 570 °C, the heat preservation duration is 2 h, it is cooled to 300 °C, and the heat preservation time is 5 h again; As other embodiments of the present application, in step the heating temperature is 600 °C, the heat preservation duration is 1.5 h, it is cooled to 350 °C, and the heat preservation time is 4 h again.

[0067] Preparation of nano hydrogen storage material: In this embodiment, 2 g of the prepared powdered magnesium-based hydrogen storage material is put into a ball milling tank. Preferably, it is ball milled for 18 h under a hydrogen atmosphere of 0.1 MPa, and the ball milling speed is set to 400 rpm. Thus, a nano magnesium-based hydrogen storage material can be obtained; As another embodiment of the present application, in step the powdered magnesium-based hydrogen storage material is put into a ball milling tank, the ball milling time is 182, and the ball milling speed is 500 rpm; As other embodiments of the present application, in step the ball milling time is 24 h and the ball milling speed is 600 rpm.

[0068] So far, according to the above embodiments and methods of the present application, an automated manufacturing of a nano magnesium-based hydrogen storage material can be realized. During the preparation process, the formation process of the carbon gel is controlled, the stirring duration of the carbon gel formation process is controlled, the quality of the carbon gel is ensured, and thus the preparation quality of the nano magnesium-based hydrogen storage material is improved.

[0069] It is understood that references to "one embodiment" or "some embodiments" or the like described in the specification of the present application mean that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the present application. Thus, when "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. appear in different places in this specification, they do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0070] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. At the same time, the magnitudes of the sequence numbers of the steps in the embodiments do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments in this specification.

[0071] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. An automated manufacturing method for a nano magnesium-based hydrogen storage material, characterized in that The method includes the following steps: Mix the metal oxide and the nano-graphene sheets, and obtain functionalized graphene through treatment; add the functionalized graphene and tetrabutyl titanate into isopropanol, and magnetically stir for 1 h to 3 h to obtain a mixed solution A; Add scandium nitrate monohydrate into isopropanol, adjust the acidity, stir and mix evenly to obtain a mixed solution B, and place it in a constant temperature bath at 10°C to 20°C; drop the mixed solution A into the mixed solution B at a rate of 1 to 5 drops per second and stir, and analyze the gel state during the stirring process to control the stirring time. The specific process is as follows: Obtain the hyperspectral gel data at each sampling moment during the stirring process, and determine the flocculation evaluation coefficient of each potential defect region based on the difference degree of the reflection intensities corresponding to the pixels in each potential defect region in the hyperspectral gel data and the contour information of each potential defect region, so as to adjust the temperature of the stirring reaction process; Determine the bubble confidence of each potential defect region based on the change of the reflection intensity of the pixels in each potential defect region, extract the real bubble region, and control the stirring time in combination with the number of real bubble regions at each sampling moment to obtain carbon gel; Grind the carbon gel to obtain a composite catalyst, ball-mill the magnesium powder and the composite catalyst, synthesize a powdery magnesium-based hydrogen storage material by the hydrogenation combustion method, and perform ball-milling treatment again to obtain a nano magnesium-based hydrogen storage material.

2. The automated manufacturing method of a nano magnesium-based hydrogen storage material according to claim 1, characterized in that, The process for obtaining the functionalized graphene is as follows: Put the nano-graphene sheets into the metal oxide, reflux at 150°C to 200°C for 5 h to 7 h, cool to room temperature, wash with ionized water until neutral, and dry at 70°C to 90°C to obtain functionalized graphene.

3. The automated manufacturing method of a nano magnesium-based hydrogen storage material as described in claim 1, characterized in that, The method for determining the flocculation evaluation coefficient of each potential defect region is as follows: Each potential defect region is each connected domain in the pseudo-color image corresponding to the hyperspectral gel data; Construct the floc weight of each potential defect region based on the shape characteristics of each potential defect region; Determine the difference result of each pixel in each potential defect region based on the difference between the reflection intensities of each pixel in each potential defect region; Determine the difference disorder coefficient of each potential defect region based on the symmetry degree of the difference results of all pixels in each potential defect region, wherein the difference disorder coefficient is negatively correlated with the symmetry degree; Determine the flocculation evaluation coefficient of each potential defect region through the floc weight and the difference disorder coefficient of each potential defect region, wherein the flocculation evaluation coefficient is the positive fusion of the floc weight and the difference disorder coefficient.

4. The automated manufacturing method of a nano magnesium-based hydrogen storage material according to claim 3, characterized in that, The method for constructing the floc weight of each potential defect region is as follows: Determine the floc weight of each potential defect region based on the aspect ratio and roundness of the boundary shape of each potential defect region, wherein the floc weight is positively correlated with the aspect ratio of the boundary shape and negatively correlated with the roundness.

5. The automated manufacturing method of a nano magnesium-based hydrogen storage material according to claim 3, characterized in that, The method for determining the difference result of each pixel in each potential defect region is as follows: obtain the neighborhood pixels of each pixel, form the pixel sequence of each pixel with the reflection intensities of each pixel at different wavelengths, and calculate the difference between the pixel sequence of each pixel and the pixel sequence of its neighborhood pixels, which is recorded as the difference result of each pixel.

6. The automated manufacturing method of a nano magnesium-based hydrogen storage material as described in claim 1, characterized in that, To adjust the temperature of the stirring reaction process, the specific method is as follows: For the floc evaluation coefficient of each potential defect area at each sampling moment, analyze the proportion of potential defect areas with flocs greater than the preset floc threshold among all potential defect areas at each sampling moment; When the proportion is greater than the preset ratio, lower the temperature of the stirring reaction process by a preset value.

7. The automated manufacturing method of a nano magnesium-based hydrogen storage material according to claim 5, characterized in that, The method for determining the bubble confidence of each potential defect area is as follows: Fit the pixel sequences of each pixel in each potential defect area to construct a pixel curve for each pixel in each potential defect area; Obtain the bubble confidence of each potential defect area based on the curve smoothness of each pixel in each potential defect area, where the bubble confidence of each potential defect area is positively correlated with the curve smoothness of each pixel therein.

8. The automated manufacturing method of a nano magnesium-based hydrogen storage material according to claim 1, characterized in that, The process of controlling the stirring time by combining the number of real bubble areas at each sampling moment is as follows: Statistically analyze the appearance rate of the real bubble areas corresponding to each sampling moment. When the appearance rate is less than the preset percentage, the quality of the carbon gel at the corresponding sampling moment is qualified. When the appearance rates at multiple consecutive sampling moments are all less than the preset percentage, stop stirring.

9. The automated manufacturing method of a nano magnesium-based hydrogen storage material according to claim 1, characterized in that The method for obtaining the composite catalyst is as follows: Put the carbon gel into an oven and dry it for 12h - 24h, grind it into powder, and calcine it at 600°C - 650°C in an air atmosphere for 3h - 6h, and then cool it to room temperature.

10. The automated manufacturing method of a nano magnesium-based hydrogen storage material according to claim 1, characterized in that, After ball-milling the magnesium powder with the composite catalyst, synthesize a powdery magnesium-based hydrogen storage material by the hydrogenation combustion method, and then perform ball-milling treatment again to obtain a nano-magnesium-based hydrogen storage material. Specifically: The ball-milling time of the magnesium powder and the composite catalyst is 6h - 12h, and the ball-milling speed is 400 - 600rpm; during the synthesis process by the hydrogenation combustion method, heat it to 550°C - 600°C under a hydrogen atmosphere of 3MPa, keep it warm for 1.5h - 3h, then cool it to 300°C - 350°C and keep it warm for 4h - 6h again, and cool it to room temperature to obtain a powdery magnesium-based hydrogen storage material. Then, under a hydrogen atmosphere of 0.1MPa, perform ball-milling for 12h - 24h, and the ball-milling speed is 400 - 600rpm.

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