Comprehensive detection method for graphene oxide

Abstract

The invention discloses a comprehensive detection method for graphene oxide, and particularly relates to the field of new material detection.The comprehensive detection method comprises the steps that in the heating process, an infrared characteristic spectrogram of a graphene oxide sample in a set coverage wave band is collected, and a spectrum change track forming a time sequence is recorded; calculating a characteristic peak intensity change rate sequence of adjacent spectrograms in the spectrum change track based on the target coverage wave band; constructing a three-dimensional change point cloud which takes a target coverage wave band and temperature as plane coordinates and characteristic peak intensity as elevation, and performing curved surface fitting processing on the single sample point cloud to generate a single sample functional group dynamic evolution fitting curved surface set; performing secondary fitting on the single-sample fitting curved surface set to generate a multi-sample functional group dynamic evolution comprehensive fitting curved surface; based on the change characteristics of the first-order derivative and the second-order derivative of the comprehensive fitting curved surface, the fracture, migration and mutual conversion evolution behaviors of the functional group under the temperature control condition are extracted, and the dynamic detection of the dynamic evolution process of the graphene oxide sample is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of new material detection, and more specifically, to a comprehensive detection method for graphene oxide. Background Art

[0002] In the existing preparation and application processes of graphene oxide, Fourier transform infrared spectroscopy is often used to detect the distribution states of functional groups such as carboxyl, hydroxyl, and epoxy. However, traditional detection means are usually limited to collecting static infrared response spectra at specific time points, and it is difficult to effectively track the dynamic evolution behaviors of functional groups under thermal stimulation or processing conditions.

[0003] In actual production, for graphene oxide raw materials of different batches and qualities (such as sample sheet size, proportion of oxygen-containing functional groups, and surface defect density), although the functional group conversion processes show consistent overall trends, there are still significant subtle-scale differences in their specific conversion temperature ranges, evolution rates, and final-state distributions. If only relying on static spectral data for process adjustment, it is impossible to accurately capture the breakage, mutual conversion, and migration changes of functional groups during heating or processing. This detection blind area of the dynamic evolution state of functional groups will lead to deviations in the setting of control parameters during production, and further cause problems such as unstable product performance and decline in material quality.

[0004] To solve the above problems, a technical solution is provided as follows. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a comprehensive detection method for graphene oxide to solve the problems proposed in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solution: A comprehensive detection method for graphene oxide, comprising the following steps: S1: Set at least one temperature control time period, and perform staged heating on the graphene oxide sample; S2: During each temperature control time period, collect the infrared characteristic spectra of the graphene oxide sample in the set coverage band, and record the spectral change trajectory of the time series; S3: Based on the set target coverage band, calculate the characteristic peak intensity change rate sequence of adjacent spectra in the spectral change trajectory; S4: Construct a three-dimensional change point cloud with the target coverage band and temperature as the plane coordinates and the characteristic peak intensity as the elevation, perform surface fitting processing on the three-dimensional change point cloud, and generate a single-sample functional group dynamic evolution fitting surface set; S5: Perform quadratic fitting on the single-sample fitting surface set to generate a multi-sample functional group dynamic evolution comprehensive fitting surface; S6: Extract the breaking, migration, and mutual transformation evolution behaviors of functional groups based on the local first-order derivative and second-order derivative change characteristics of the comprehensive fitting surface.

[0007] In a preferred embodiment, in S1, setting at least one temperature control period and performing staged heating on the graphene oxide sample specifically includes: Select several graphene oxide samples of the current batch, and perform differential screening based on the measurement results of the sample sheet size, oxygen-containing functional group ratio, and surface defect density; Divide at least one temperature control period, set the temperature range and corresponding heating rate of each temperature control period, perform heating treatment on the screened graphene oxide samples, and simultaneously record the temperature changes during the heating process in real time.

[0008] In a preferred embodiment, in S2, within each temperature control period, collecting the infrared characteristic spectra of the graphene oxide sample in the set coverage band and recording the spectral change trajectory of the time series specifically includes: Bind the start and end times of each temperature control period to the start time node of infrared spectrum collection; Configure the infrared collection instruction for the set coverage band, and set the target coverage band range to the band covering the characteristic absorption intervals of carboxyl, hydroxyl, and epoxy functional groups in the graphene oxide sample; Correspond the infrared characteristic spectra of the target coverage band obtained each time to the time series nodes of the temperature control period; Arrange the infrared characteristic spectra collected in each temperature control stage in the order of temperature control time to form a set of spectral change trajectories of a continuous time series, and simultaneously mark the attribution relationship of the temperature control period of each trajectory segment.

[0009] In a preferred embodiment, in S3, based on the set target coverage band, calculating the characteristic peak intensity change rate sequence of adjacent spectra in the spectral change trajectory specifically includes: Extract the absorption peak intensity values of the target coverage band from the spectral change trajectory, construct the intensity time series of the target coverage band changing with time, and calculate the intensity change rate between adjacent time nodes; Synchronize the intensity change rate of the target coverage band with the temperature control period index, and establish the corresponding relationship of time, temperature, and change rate parameters.

[0010] In a preferred embodiment, the calculation of the intensity change rate between adjacent time nodes is specifically to extract the absorption peak area in the target coverage band interval, and calculate the ratio of the peak area change amount to the time interval between adjacent time nodes in the order of increasing time to generate a continuous peak area change rate sequence of the target coverage band.

[0011] In a preferred embodiment, in S4, constructing a three-dimensional variable point cloud with the target coverage band and temperature as the planar coordinates and the characteristic peak intensity as the elevation, and performing surface fitting processing on the three-dimensional variable point cloud to generate a single-sample functional group dynamic evolution fitting surface set specifically includes: Perform scale elimination processing on the absorption peak intensities of the target characteristic bands of all samples, and establish a normalized characteristic peak intensity expressed based on the intensity change rate; Extract the characteristic peak intensities and corresponding temperature values of the target coverage band in each graphene oxide sample at different temperature control periods; Taking the target coverage band as the horizontal axis, temperature as the vertical axis, and characteristic peak intensity as the elevation value, construct a single-sample three-dimensional variable point cloud and establish a unified spatial coordinate system; Based on the single-sample three-dimensional variable point cloud, perform global surface fitting processing using polynomial regression fitting to form a preliminary fitting surface with the characteristic peak intensity varying continuously with the band and temperature; Perform residual distribution analysis on the preliminary fitting surface, identify high-deviation regions and perform locally weighted regression adjustment to generate a single-sample fitting surface, and store it in the functional group dynamic evolution fitting surface set.

[0012] In a preferred embodiment, in S5, performing a second fitting on the single-sample fitting surface set to generate a multi-sample functional group dynamic evolution comprehensive fitting surface specifically includes: Extract the continuous change expressions corresponding to the band, temperature, and characteristic peak intensity in each single-sample functional group dynamic evolution fitting surface, and unify the spatial resolution; Set the weight parameters of each single-sample fitting surface based on the surface residuals, and multiply the characteristic peak intensities of the corresponding nodes of each surface by the weights in the standardized coordinate system; Sum the node intensities of all weighted single-sample fitting surfaces under the band and temperature indices to form a comprehensive variable point cloud; Calculate the average intensity distribution of the comprehensive variable point cloud based on the node summation result to generate a functional group dynamic evolution comprehensive fitting surface.

[0013] In a preferred embodiment, in S6, based on the local first-order derivative and second-order derivative change characteristics of the comprehensive fitting surface, extracting the cleavage, migration, and interconversion evolution behaviors of the functional groups specifically includes: Under the target coverage band, calculate the first-order derivative distribution of the characteristic peak intensity with respect to temperature change based on a continuous temperature interval with a set differential length; Establish a sliding window, extract the cumulative characteristic peak intensity change rate of the first-order derivative distribution within the sliding window, and mark the temperature interval corresponding to the sliding window with a cumulative characteristic peak intensity change rate greater than or equal to the set threshold as the candidate area for functional group migration behavior; Calculate the first-order variation of the change rate of the characteristic peak intensity in the candidate area of the functional group migration behavior to generate a second derivative distribution; Extract the temperature intervals corresponding to the extreme values in the second derivative distribution, label the temperature interval corresponding to the maximum value as the new peak generation area of the characteristic peak, and label the temperature interval corresponding to the minimum value as the fracture occurrence area of the characteristic peak; Integrate the temperature interval indexes extracted from the first derivative and the second derivative, and output the dynamic evolution feature set of the functional group fracture, migration and interconversion behaviors under the target coverage band.

[0014] The technical effects and advantages of an integrated detection method for graphene oxide of the present invention: By continuously collecting the infrared characteristic spectra of graphene oxide samples in the set coverage band under multiple temperature control periods, extracting the change rate of the characteristic peak intensity based on the target coverage band, constructing a three-dimensional change point cloud related to temperature and band and performing surface fitting to form a single-sample dynamic evolution expression. Further, through the weighted fusion of the fitting surfaces of multiple samples, a unified comprehensive dynamic evolution surface is generated. Based on the continuous derivative analysis of the comprehensive fitting surface, the change rate and abnormal change rate of the characteristic peak intensity are extracted to accurately identify the fracture, migration and interconversion behaviors of functional groups.

[0015] Compared with the traditional static detection method, the present invention can dynamically track the evolution process of functional groups of different batches of graphene oxide, fully reflect the subtle differences in the material processing or application process, effectively support the optimization of production process parameters and the control of quality stability, and improve the accuracy of functional group state determination and the consistency of material application. Brief Description of the Drawings

[0016] Figure 1 It is a schematic diagram of an integrated detection method for graphene oxide of the present invention. Detailed Embodiment

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment 1 Figure 1 An integrated detection method for graphene oxide of the present invention is given, which includes the following steps: S1: Set at least one temperature control period and perform staged heating on the graphene oxide sample; S2: During each temperature control period, collect the infrared characteristic spectra of the graphene oxide sample in the set coverage band and record the spectral change trajectory of the time series; S3: Based on the set target coverage band, calculate the characteristic peak intensity change rate sequence of adjacent spectra in the spectral change trajectory; S4: Construct a three-dimensional change point cloud with the target coverage band and temperature as plane coordinates and the characteristic peak intensity as elevation, perform surface fitting on the three-dimensional change point cloud, and generate a single sample functional group dynamic evolution fitting surface set; S5: Perform quadratic fitting on the single-sample fitting surface set to generate a comprehensive fitting surface for the dynamic evolution of functional groups in multiple samples; S6: Based on the change characteristics of the local first-order derivatives and second-order derivatives of the comprehensive fitting surface, the breakage, migration and interconversion evolution behaviors of functional groups are extracted.

[0019] In S1 , at least one set of temperature control periods is set to perform stage heating on the graphene oxide sample.

[0020] Several graphene oxide (GO) samples from the same production batch were selected, with a minimum of 10 samples to account for the production fluctuations within the batch. For each GO sample, the flake size distribution was measured using transmission electron microscopy (TEM) combined with image processing software. Size statistics were performed on at least 100 single-layer or low-layer regions of the sample, with the minimum, maximum, and average sizes recorded.

[0021] For each sample, X-ray photoelectron spectroscopy (XPS) was used to determine the proportion of oxygen-containing functional groups. The test parameters were set as The C1s peak was analyzed using a source (1486.6 eV) with an energy step of 0.1 eV. The area ratios of functional groups such as carboxyl, hydroxyl, and epoxy groups relative to the total carbon peak were extracted based on peak decomposition fitting, and the total oxygen-containing functional group ratio was calculated from this. Based on the differences in the detected oxygen content, a screening threshold was set to retain samples within the target range (e.g., oxygen content between 28% and 40%).

[0022] To test surface defect density, Raman spectroscopy (532nm laser source) was used to measure the intensity ratio of the D-peak to the G-peak for each GO sample. Measurements were taken from at least five different areas of each sample, and the average value was calculated. The surface defect density of each sample was determined based on the ID / IG ratio. Screening criteria were set (for example, requiring an ID / IG ratio between 0.9 and 1.3) to eliminate samples with abnormally high or low defect densities. A differential screening method was employed based on three indicators: flake size, oxygen-containing functional group ratio, and surface defect density. Samples within the preset parameter window were retained, while samples that deviated significantly from the standard were eliminated to form a sample group for subsequent processing.

[0023] After completing sample screening, set up a heating treatment plan for the screened GO samples. First, divide at least one set of temperature control periods. The following is a specific example: Three groups of time intervals are set, namely: the first stage is 150 - 200 °C, the second stage is 200 - 250 °C, and the third stage is 250 - 300 °C. The heating rate of each time period is set separately. The heating rate of the first stage is set to 2 °C / min, the heating rate of the second stage is set to 1 °C / min, and the heating rate of the third stage is set to 0.5 °C / min to control the dynamic precision of heat treatment in different temperature zones.

[0024] During the sample heating process, a high-precision programmable heating stage (temperature control accuracy ±0.5 °C) is used, and the sample is spread on a quartz glass slide under the protection of an inert atmosphere. During the heating process, temperature data is collected in real time through a K-type thermocouple embedded near the surface of the slide. The data sampling frequency is set to record once every 2 seconds, and the temperature control system synchronously records the temperature change curve to generate a time-temperature data table.

[0025] Each group of samples is independently heat-treated. Before treatment, ensure that the initial moisture of the sample is removed (vacuum drying at 80 °C for 12 hours) to avoid affecting the temperature control accuracy due to the volatilization of residual moisture during the heating process. The temperature control program sets stage stop points (such as maintaining for 10 minutes at the starting temperature and ending temperature of each stage) to ensure the temperature uniformity within each stage and the full development of dynamic functional group changes.

[0026] In S2, within each group's temperature control time period, the infrared characteristic spectrogram of the graphene oxide sample is collected in the set coverage wavelength band, and the spectral change trajectory of the time series is recorded.

[0027] During the temperature control heating process of the graphene oxide sample, first set clear start and end time points for each temperature control time period, and input the starting temperature, target end temperature, and heating rate of each temperature control time period into the temperature control system during the initialization stage to automatically generate a theoretical heating curve. Based on the generated heating curve, the theoretical start and end time nodes corresponding to each temperature control time period are derived, and these time nodes are used as the time anchors for spectral acquisition synchronization binding.

[0028] Configure the infrared spectrum acquisition parameters, and clearly set the target coverage wavelength band range during the acquisition instruction configuration stage. The target coverage wavelength band range needs to cover the characteristic absorption intervals of typical functional groups in the graphene oxide sample, where the carboxyl group is mainly located at ,the hydroxyl group is mainly located at ,and the characteristic peak of the epoxy group is located at . Therefore, the acquisition instruction sets the target coverage wavelength band to ,covering all the above characteristic sections, and sets the spectral resolution to be better than to ensure the detection ability of fine peak position drift.

[0029] During the heating-up process, when the system time reaches the starting node of each set temperature control period, immediately start the infrared spectrum acquisition task within the target coverage band range and record the corresponding first characteristic spectrogram. The spectrum acquisition adopts the real-time acquisition mode, with the set time resolution of acquiring one complete spectrogram every 60 seconds until the termination node of the corresponding temperature control period, ensuring the formation of a complete continuous spectral data stream within each temperature control stage. All the acquired characteristic spectrograms are numbered according to the acquisition timestamp and are bidirectionally bound to the start and end time nodes of the temperature control period, forming a time series index system.

[0030] After completing the acquisition of all temperature control stages, organize the spectral sequences within each stage in the order of temperature control time to generate a complete set of continuous time series spectral change trajectories. Each trajectory segment within the trajectory set is respectively assigned a temperature control period attribution label, such as "Stage 1: 150–200°C", "Stage 2: 200–250°C", etc.

[0031] In S3, based on the set target coverage band, calculate the characteristic peak intensity change rate sequence of adjacent spectrograms in the spectral change trajectory.

[0032] For the characteristic absorption peaks in the target coverage band interval, carry out the extraction of the intensity change rate and the establishment of the parameter sequence. First, in each frame of the acquired spectrogram, extract the absorption peak characteristics within the preset target coverage band. The spectral data is intercepted point by point within the target coverage band range and processed using the baseline subtraction method to eliminate the influence of the background signal. The baseline region is selected in the flat section without obvious absorption signals on both sides of the absorption feature.

[0033] After baseline subtraction, use the integration method to extract the absorption area of each target peak. The selected integration interval covers the sections extended before and after the maximum intensity point of the typical absorption peak. The peak area obtained by integration is the intensity characterization value of the characteristic absorption peak in this band at the current time node. According to the order of continuous spectral acquisition, each frame of spectrum generates a corresponding peak area data point, forming a peak area time series that changes with time covering the target band. To calculate the absorption intensity change rate, for the peak area time series, extract the data of any two adjacent time nodes in ascending order of time, calculate the ratio of the change in peak area between the two points to the corresponding time interval, and generate a set of continuous peak area change rate sequences, with each change rate corresponding to a time period.

[0034] After calculating the change rate sequence, synchronously bind the change rate value corresponding to each time period with its temperature control period index. The specific operation is as follows: According to the start time node corresponding to each change rate, find the temperature control period where this time node is located, and mark the temperature control stage label in the change rate sequence. Synchronously generate a triple data set of time, temperature (deduced from the real-time temperature control record corresponding to the time node), and change rate, forming a complete time-temperature-change rate correspondence table.

[0035] In S4, construct a three-dimensional change point cloud with the target coverage band and temperature as the plane coordinates and the characteristic peak intensity as the elevation. Perform surface fitting processing on the three-dimensional change point cloud to generate a single-sample functional group dynamic evolution fitting surface set.

[0036] Perform scale elimination processing on the absorption peak intensities of the target coverage bands of all graphene oxide samples. The scale elimination adopts a normalization strategy, and normalizes the peak intensities within the target coverage bands of each sample according to the change rate. The specific processing is as follows: Use the maximum change amplitude in the peak intensity change rate sequence of each sample as the normalization denominator, divide the original peak intensity value by this maximum change amplitude, and generate a normalized characteristic peak intensity sequence expressed based on the change rate. This processing step eliminates the fitting deviation caused by the absolute value difference of the original peak intensities of different samples, making the subsequent surface fitting process better reflect the actual functional group evolution trend rather than the initial magnitude difference.

[0037] After normalization, for each graphene oxide sample, extract the characteristic peak intensity value corresponding to the target coverage band in each temperature control period and the temperature value corresponding to the acquisition moment. The sampling strategy remains consistent with the spectral acquisition stage. The temperature value is from the real-time temperature control system record curve, and the sampling interval is synchronized with the spectral acquisition frequency to ensure the one-to-one correspondence between temperature and peak intensity data.

[0038] Organize the extracted data into a standard data structure: Use the target coverage band (unit ) as the horizontal axis, temperature (unit °C) as the vertical axis, and the normalized characteristic peak intensity as the elevation value to construct a single-sample three-dimensional change point cloud. Each data point in the point cloud contains three attributes: (band value, temperature value, intensity value). Establish a unified spatial coordinate system, set fixed step intervals for the band dimension and temperature dimension. For example, the band steps by 1 , and the temperature steps by 1 °C to ensure data density and uniformity during subsequent surface fitting.

[0039] After the three-dimensional change point cloud is constructed, use the polynomial regression fitting method for preliminary surface fitting processing. Select a second-order polynomial regression model, in the form as follows: where Z is the characteristic peak intensity, X is the band, Y is the temperature, to is the fitting coefficient. The regression fitting uses the least squares method to solve the coefficient group, minimizing the sum of the squared fitting residuals of the overall surface in space. To prevent overfitting, a penalty factor is set during the fitting process, and the iteration stops when the fitting residual decrease rate is lower than the set threshold.

[0040] After the preliminary fitting surface is generated, residual distribution analysis is carried out. The residual values of each fitting point (the difference between the fitting predicted value and the actual point cloud value) are extracted and recorded, and a residual heat map is plotted. The high-deviation regions of the residual heat map are analyzed, that is, the point set where the residual is greater than the global mean plus twice the standard deviation, and marked as the local high-deviation area. Within the high-deviation area, local optimization is performed again in the form of locally weighted regression. The weighted regression adopts the Gaussian kernel weighting strategy, and the weights are inversely proportional to the residual size to enhance the local fitting accuracy.

[0041] Finally, the final single-sample fitting surface is generated, the expression of the fitting surface and the optimized weight parameters are saved, and the single-sample fitting surface is stored in the set of fitting surfaces for the dynamic evolution of functional groups, serving as the data basis for subsequent multi-sample surface integration and comprehensive dynamic modeling.

[0042] In S5, a second fitting is performed on the set of single-sample fitting surfaces to generate a multi-sample comprehensive fitting surface for the dynamic evolution of functional groups.

[0043] In the set of fitting surfaces for the dynamic evolution of single-sample functional groups, first, the continuous change expressions of the band, temperature, and characteristic peak intensity are extracted from each single-sample surface. Since there may be slight spatial discreteness differences among different samples during data acquisition and surface fitting, to ensure the consistency of subsequent weighted fusion, the spatial resolution of all single-sample fitting surfaces is unified. The spatial unification standard is set as a step interval of 1 in the band direction and a step interval of 1°C in the temperature direction. For each single-sample surface, interpolation is performed at the specified standard band and temperature index positions, and the cubic spline interpolation algorithm is used to fill the gaps between the original data points, generating a continuous characteristic peak intensity expression under the unified coordinate grid system.

[0044] After the unified spatial grid is completed, based on the residual analysis results of each single-sample fitting surface, the weight parameter corresponding to each sample is set. The specific method is as follows: The sum of the squared residuals (RSS) is calculated for all nodes of the single-sample fitting surface on the unified grid, and the reciprocal of the RSS value of each sample is used as the preliminary weight index. To avoid extreme weight deviation, a maximum and minimum weight limit range is set (for example, the weight is set between 0.5 - 2 times the initial mean), and the preliminary weight is normalized.

[0045] Under the standardized coordinate system, node intensity weighting processing is performed on each single-sample fitting surface. Specifically: at the unified band and temperature index positions, multiply the single-sample characteristic peak intensity value by the corresponding weight coefficient to form a weighted characteristic peak intensity node matrix. The weighted characteristic peak intensity node matrices of all samples are superimposed one by one according to the node index positions, and the node intensity summation operation is carried out. After the summation is completed, a comprehensive change point cloud containing a triple of band, temperature, and cumulative intensity is obtained by accumulation at each standard node position. After the comprehensive change point cloud is constructed, the average characteristic peak intensity of the node is calculated based on the node summation result. The node average intensity value is obtained by dividing the total cumulative intensity of the nodes in the comprehensive change point cloud by the number of superimposed samples.

[0046] Based on the node average intensity distribution, a comprehensive fitting surface for the dynamic evolution of functional groups is generated. The comprehensive fitting surface forms a continuous, smooth, and non-abrupt three-dimensional change expression within the entire target band and temperature range, which can be used as a unified data platform for subsequent dynamic evolution feature extraction (such as derivative change, migration trend recognition, fracture behavior extraction).

[0047] In S6, based on the local first-order and second-order derivative change characteristics of the comprehensive fitting surface, the fracture, migration, and interconversion evolution behaviors of functional groups are extracted.

[0048] Within the target coverage band interval, dynamic behavior extraction processing is performed on the data of the characteristic peak intensity varying with temperature. First, set the differential length, define the differential step size of the continuous temperature interval, and select a differential step size of 0.5 °C as the basic temperature increment unit for the first-order derivative calculation. For each fixed band value, extract the change in the characteristic peak intensity within the continuous temperature sequence. This operation is sequentially performed on all temperature nodes within the target coverage band interval to generate a first-order derivative distribution sequence of the characteristic peak intensity with respect to temperature change.

[0049] After generating the first-order derivative distribution sequence, establish a sliding window mechanism. The window width is set to a temperature span of 15 °C, and the window step size is set to 5 °C. For each sliding window, within the continuous first-order derivative data covered by the window, accumulate the characteristic peak intensity change rate, that is, sum the first-order derivatives within the window. Set a cumulative change rate threshold (specifically set according to the length and value of the change rate sequence, and the default setting is the average value of the change rate sequence). When the cumulative change rate within the sliding window is greater than or equal to the cumulative change rate threshold, mark the corresponding temperature interval as a candidate area for the migration behavior of functional groups. All eligible window intervals are merged to form a set of continuous temperature migration candidate segments.

[0050] For each extracted candidate region of the functional group migration behavior, a second derivative calculation is further performed. In each candidate region, the first-order change amount is calculated for the extracted first derivative sequence, and a second derivative distribution sequence of the characteristic peak intensity in the corresponding temperature range is generated through this operation. In the second derivative distribution sequence, extreme points are extracted, and the extreme values are defined as local maxima or local minima. Detection rules are set. If there is a point within the range of five temperature points in the neighborhood whose second derivative value is greater than the adjacent two points and greater than the set extreme value threshold (specifically set according to the change rate of the second derivative value, and the default setting is 1 / 10 of the sum of the absolute values of the second derivative values of five temperature points), it is marked as a maximum value; if it is less than the adjacent two points and less than the negative extreme value threshold (specifically set according to the change rate of the second derivative value, and the default setting is the opposite of the extreme value threshold), it is marked as a minimum value. The temperature range corresponding to the second derivative maximum value is marked as the new peak generation area of the characteristic peak, and the temperature range corresponding to the minimum value is marked as the fracture occurrence area of the characteristic peak.

[0051] Integrate all candidate regions of the functional group migration behavior extracted by the first derivative sliding window, the new peak generation area and the fracture occurrence area calibrated by the second derivative extreme values, and construct a complete set of dynamic evolution characteristics of the functional group under the target coverage band. Each element of the characteristic set contains the band index, the temperature range, and the annotation of the change behavior (fracture / migration / interconversion).

[0052] The above formulas are all calculated by taking the numerical values without dimension. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0053] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0054] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0055] Those skilled in the art can clearly understand that for the sake of convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0056] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical functional division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0057] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical module, and it may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0058] In addition, in each embodiment of this application, each functional module can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0059] If the above functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0060] As described above, this is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

[0061] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A comprehensive detection method for graphene oxide, characterized in that It includes the following steps: S1: Set at least one temperature control period and perform staged heating on the graphene oxide sample; S2: During each temperature control period, collect the infrared characteristic spectrograms of the graphene oxide sample in the set coverage band and record the spectral change trajectory of the time series; S3: Based on the set target coverage band, calculate the characteristic peak intensity change rate sequence of adjacent spectrograms in the spectral change trajectory; S4: Construct a three-dimensional change point cloud with the target coverage band and temperature as the plane coordinates and the characteristic peak intensity as the elevation, perform surface fitting on the three-dimensional change point cloud, and generate a single-sample functional group dynamic evolution fitting surface set; S5: Perform quadratic fitting on the single-sample fitting surface set to generate a multi-sample functional group dynamic evolution comprehensive fitting surface; S6: Based on the local first-order derivative and second-order derivative change characteristics of the comprehensive fitting surface, extract the breaking, migration, and mutual conversion evolution behaviors of the functional groups.

2. The comprehensive detection method of graphene oxide according to claim 1, wherein, In S1, setting at least one temperature control period and performing staged heating on the graphene oxide sample specifically includes: Select several graphene oxide samples of the current batch and perform differential screening based on the measurement results of the sample sheet size, oxygen-containing functional group ratio, and surface defect density; Divide at least one temperature control period, set the temperature range and corresponding heating rate of each temperature control period, perform heating treatment on the screened graphene oxide samples, and record the temperature change during the heating process in real time.

3. The comprehensive detection method of graphene oxide according to claim 1, wherein In S2, during each temperature control period, collecting the infrared characteristic spectrograms of the graphene oxide sample in the set coverage band and recording the spectral change trajectory of the time series specifically includes: Bind the start and end times of each temperature control period to the start time node of the infrared spectrum collection; Configure the infrared collection instruction in the set coverage band, and set the target coverage band range to cover the characteristic absorption intervals of carboxyl, hydroxyl, and epoxy functional groups in the graphene oxide sample; Correspond the infrared characteristic spectrogram of the target coverage band obtained each time to the time series node of the temperature control period; Arrange the infrared characteristic spectrograms collected in each temperature control stage in the order of temperature control time to form a spectral change trajectory set of a continuous time series, and mark the temperature control period attribution relationship of each trajectory segment.

4. The comprehensive detection method of graphene oxide according to claim 3, characterized in that, In S3, based on the set target coverage band, calculating the characteristic peak intensity change rate sequence of adjacent spectrograms in the spectral change trajectory specifically includes: Extract the absorption peak intensity values of the target coverage band from the spectral change trajectory, construct an intensity time series of the target coverage band changing with time, and calculate the intensity change rate between adjacent time nodes; Synchronize the intensity change rate of the target coverage band with the temperature control period index to establish the corresponding relationship of time, temperature, and change rate parameters.

5. The comprehensive detection method of graphene oxide according to claim 4, characterized in that The specific calculation of the intensity change rate between adjacent time nodes is to extract the absorption peak area in the target coverage band interval, calculate the ratio of the peak area change amount to the time interval between adjacent time nodes in the order of increasing time, and generate a continuous peak area change rate sequence of the target coverage band.

6. The comprehensive detection method of graphene oxide according to claim 1, wherein, In S4, a three-dimensional variable point cloud with the target coverage band and temperature as the plane coordinates and the characteristic peak intensity as the elevation is constructed, and the three-dimensional variable point cloud is subjected to surface fitting processing to generate a single-sample functional group dynamic evolution fitting surface set, which specifically includes: Perform scale elimination processing on the absorption peak intensities of the target characteristic bands of all samples, and establish a normalized characteristic peak intensity expressed based on the intensity change rate; Extract the characteristic peak intensities of the target coverage band and the corresponding temperature values of each graphene oxide sample at different temperature control time periods; With the target coverage band as the horizontal axis, temperature as the vertical axis, and characteristic peak intensity as the elevation value, construct a single-sample three-dimensional variable point cloud and establish a unified spatial coordinate system; Based on the single-sample three-dimensional variable point cloud, perform global surface fitting processing using polynomial regression fitting to form a preliminary fitting surface with the characteristic peak intensity changing continuously with the band and temperature; Perform residual distribution analysis on the preliminary fitting surface, identify high-deviation regions and perform locally weighted regression adjustment to generate a single-sample fitting surface, and store it in the functional group dynamic evolution fitting surface set.

7. A comprehensive detection method for graphene oxide according to claim 1, characterized in that In S5, perform quadratic fitting on the single-sample fitting surface set to generate a multi-sample functional group dynamic evolution comprehensive fitting surface, which specifically includes: Extract the continuous change expressions corresponding to the band, temperature, and characteristic peak intensity in each single-sample functional group dynamic evolution fitting surface, and unify the spatial resolution; Set the weight parameters of each single-sample fitting surface based on the surface residuals, and multiply the characteristic peak intensities of the corresponding nodes of each surface by the weights in the standardized coordinate system; Sum the node intensities of all weighted single-sample fitting surfaces under the band and temperature indices to form a comprehensive variable point cloud; Calculate the average intensity distribution of the comprehensive variable point cloud based on the node summation result to generate a functional group dynamic evolution comprehensive fitting surface.

8. A comprehensive detection method for graphene oxide according to claim 1, characterized in that, In S6, based on the local first-order derivative and second-order derivative change characteristics of the comprehensive fitting surface, extract the cleavage, migration, and interconversion evolution behaviors of the functional groups, which specifically includes: Under the target coverage band, calculate the first-order derivative distribution of the characteristic peak intensity with respect to the temperature change based on a continuous temperature interval with a set differential length; Establish a sliding window, extract the cumulative characteristic peak intensity change rate of the first-order derivative distribution within the sliding window, and mark the temperature interval corresponding to the sliding window with a cumulative characteristic peak intensity change rate greater than or equal to the set threshold as the candidate area for functional group migration behavior; Calculate the first-order change amount of the characteristic peak intensity change rate within the candidate area for functional group migration behavior to generate a second-order derivative distribution; Extract the temperature intervals corresponding to the extreme values in the second-order derivative distribution, mark the temperature interval corresponding to the maximum value as the new peak generation area of the characteristic peak, and mark the temperature interval corresponding to the minimum value as the cleavage occurrence area of the characteristic peak; Integrate the temperature interval indices extracted from the first-order derivative and the second-order derivative, and output the dynamic evolution characteristic set of the cleavage, migration, and interconversion behaviors of the functional groups under the target coverage band.

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