Carbon nanotube Raman spectrum batch simulation method based on Gaussian light spot modeling and related device

Abstract

The invention discloses a carbon nano tube Raman spectrum batch simulation method based on Gaussian light spot modeling and a related device, and relates to the technical field of high-flux carbon nano tube characterization, the method comprises the following steps: constructing a laser light spot area with light spot intensity conforming to Gaussian distribution based on a Gaussian model; randomly generating a plurality of simulation space coordinates in the region, and enabling the simulation space coordinates to correspond to Raman spectrum data of the carbon nanotubes with different chiral indexes; obtaining the effective chord length of the carbon nanotube, and calculating the signal intensity of the single tube in combination with the light spot intensity distribution; calculating the spatial weight of each carbon nanotube by taking the signal intensity of the central position of the light spot as a reference; and obtaining corresponding Raman spectrum data from a pre-established single-spectrum database in combination with the chiral index, and carrying out weight superposition to obtain total Raman spectrum data. According to the scheme, an actual detection scene can be accurately reproduced, and time consumed by single-time carbon nanotube Raman spectrum superposition is lt; and multi-chirality combination is supported, the simulation precision and efficiency are remarkably improved, and the characterization requirement of the high-flux carbon nano tube is met.

Description

Technical Field

[0001] This application relates to the field of high-throughput carbon nanotube characterization technology, and in particular to a method and related apparatus for batch simulation of carbon nanotube Raman spectroscopy based on Gaussian spot modeling. Background Technology

[0002] In the field of high-throughput carbon nanotube characterization, Raman spectroscopy is a core method for predicting the properties of carbon nanotube materials (such as defect degree, structural and electrical properties), and the accuracy of its simulation results directly affects the efficiency of material screening and application development. Currently, existing carbon nanotube spectral simulation schemes only target the Raman spectrum of a single carbon nanotube, without considering the signal superposition effect of multiple carbon nanotubes within a laser spot in actual detection scenarios. Therefore, they cannot include the Raman spectra of carbon nanotubes with different chiral configurations, resulting in large deviations between simulation results and actual detection data, poor scalability, and difficulty in meeting the needs of high-throughput characterization. Summary of the Invention

[0003] The purpose of this application is to provide a batch simulation method and related apparatus for carbon nanotube Raman spectroscopy based on Gaussian spot modeling, which is compatible with batch simulation schemes for multiple chiral configurations.

[0004] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a batch simulation method for the Raman spectra of carbon nanotubes based on Gaussian spot modeling, comprising the following steps: Based on the Gaussian model, a laser spot region with a beam intensity distribution conforming to a Gaussian distribution is constructed.

[0005] Several simulated spatial coordinates are randomly generated within the laser spot area, and corresponding carbon nanotubes are constructed accordingly; each carbon nanotube has a different chirality index.

[0006] For carbon nanotubes generated at any simulated spatial coordinate, the signal intensity of the carbon nanotubes is calculated based on their positional distribution and light spot intensity distribution.

[0007] The spatial weight of each carbon nanotube is calculated based on the signal intensity at the center of the laser spot region.

[0008] Based on the chiral index of each carbon nanotube, the corresponding Raman spectral data are obtained from a pre-established single-spectrum database, and the Raman spectral data of each carbon nanotube are superimposed according to spatial weights to obtain the total Raman spectral data of carbon nanotubes.

[0009] Optionally, a laser spot region with a Gaussian distribution of intensity can be constructed according to the following formula: .

[0010] in, For the light spot intensity distribution, The intensity of the laser beam at the center of the laser beam region. r Let be the distance from any point within the laser spot area to the center of the spot. The radius of the light spot waist is denoted as .

[0011] Optionally, after constructing a laser spot region whose intensity distribution conforms to a Gaussian distribution based on a Gaussian model, the method further includes a step to verify whether the laser spot region conforms to the characteristics of a Gaussian distribution, specifically including the following steps: Single carbon nanotubes were tested using a Raman optics platform to obtain signals containing the intensity of the G peak.

[0012] A spatial response function is constructed using the intensity of the G peak to determine whether the intensity distribution of the laser spot region conforms to a Gaussian distribution, thus obtaining the first judgment result.

[0013] If the first judgment result is negative, then the laser spot area is reconstructed.

[0014] If the first judgment result is yes, then proceed with the subsequent process.

[0015] Alternatively, the signal intensity of the carbon nanotube can be calculated according to the following formula: .

[0016] in, For the first i The signal intensity of carbon nanotubes For the light spot intensity distribution, r Let be the distance from any point on the chord length to the center of the light spot. Used to indicate the positional distribution of carbon nanotubes.

[0017] Optionally, the spatial weight of carbon nanotubes can be calculated according to the following formula; .

[0018] in, For the first i The spatial weight of carbon nanotubes For the first i The signal intensity of carbon nanotubes The signal intensity is when the carbon nanotube is located at the center of the laser spot region.

[0019] Optionally, the Raman spectral data of each carbon nanotube can be superimposed according to the following formula: .

[0020] in, The total Raman spectrum data of carbon nanotubes, For the firsti The spatial weight of carbon nanotubes For the first i Raman spectral data of carbon nanotubes.

[0021] Secondly, this application provides a batch simulation system for carbon nanotube Raman spectroscopy based on Gaussian spot modeling, including the following functional modules: The laser spot region modeling module is used to construct a laser spot region whose intensity distribution conforms to a Gaussian distribution based on a Gaussian model.

[0022] The carbon nanotube distribution simulation module is used to randomly generate several simulated spatial coordinates within the laser spot area and construct corresponding carbon nanotubes; each carbon nanotube has a different chirality index.

[0023] The measurement and signal strength quantization module is used to calculate the signal strength of carbon nanotubes generated at arbitrary simulated spatial coordinates, based on the positional distribution of the carbon nanotubes and the intensity distribution of the light spot.

[0024] The spatial weight calculation module is used to calculate the spatial weight of each carbon nanotube based on the signal intensity at the center of the laser spot region.

[0025] The Raman spectral data overlay module is used to obtain the corresponding Raman spectral data from a pre-established single-spectrum database based on the chiral index of each carbon nanotube, and overlay the Raman spectral data of each carbon nanotube according to spatial weights to obtain the total Raman spectral data of carbon nanotubes.

[0026] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling described above.

[0027] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling described above.

[0028] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling described above.

[0029] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method and related apparatus for batch simulation of Raman spectra of carbon nanotubes based on Gaussian spot modeling. In this method, a laser spot region is constructed based on a Gaussian model, which can accurately reproduce the spatial distribution characteristics of the laser spot intensity in actual detection, providing an accurate theoretical basis for the simulation of signal superposition of multiple carbon nanotubes. Subsequently, the random dispersion state of multiple carbon nanotubes with different chiral indices in the actual detection scenario is simulated, which provides the prerequisite for the superposition of Raman spectral signals of multi-chiral configuration carbon nanotubes. By combining the positional distribution of carbon nanotubes with the spot intensity distribution, the actual overlap length between carbon nanotubes and laser spots is accurately quantified, making the signal intensity calculation of a single carbon nanotube more consistent with the actual excitation scenario and improving the accuracy of single-tube signal quantization. Subsequently, using the signal intensity of carbon nanotubes at the center of the laser spot as a benchmark, the influence of light intensity gradient on carbon nanotubes at different spatial locations is accurately reflected, making the spatial weight allocation more reasonable and ensuring the accuracy of the intensity ratio when multiple tube signals are superimposed. Finally, based on the chiral index of each carbon nanotube, the corresponding Raman spectral data are obtained from a pre-established single-spectrum database and superimposed according to the spatial weight. This approach not only accommodates the Raman spectral characteristics of carbon nanotubes with different chiral configurations through the single-spectrum database, but also simulates the superposition effect of multiple tube signals in actual detection by combining spatial weight. This enables accurate batch Raman spectral simulation of multi-chiral carbon nanotubes, making the simulation results closer to the actual test data and effectively meeting the data requirements for high-throughput carbon nanotube characterization. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating a batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling, provided as an embodiment of this application.

[0032] Figure 2 This is a schematic diagram of a translation test performed on a Raman optics platform in a batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling, provided in an embodiment of this application.

[0033] Figure 3 This is a schematic diagram of the Gaussian spot signal intensity and formula used in a batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling, provided in an embodiment of this application.

[0034] Figure 4This is a schematic diagram showing the G peak intensity-spatial position relationship curve and the obtained beam waist radius and beam radius in a batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian beam spot modeling provided in an embodiment of this application.

[0035] Figure 5 This is a schematic diagram of the simulated light spot intensity distribution and carbon nanotube position distribution in a batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian light spot modeling, provided in an embodiment of this application.

[0036] Figure 6 This is a schematic diagram illustrating the superposition of Raman spectra of different chiralities in a batch simulation method for carbon nanotube Raman spectra based on Gaussian spot modeling, provided as an embodiment of this application.

[0037] Figure 7 This is a schematic diagram of the functional modules of a batch simulation system for carbon nanotube Raman spectroscopy based on Gaussian spot modeling, provided in an embodiment of this application.

[0038] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] This application provides a batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling. In an exemplary embodiment, such as... Figure 1 As shown, it includes the following steps: A1. Based on the Gaussian model, construct a laser spot region whose intensity distribution conforms to a Gaussian distribution. The Gaussian distribution is as follows: Figure 2 As shown. In this embodiment, a laser spot region with a Gaussian distribution of intensity is constructed according to the following formula: .

[0042] in, For the light spot intensity distribution, The intensity of the laser beam at the center of the laser beam region. rLet be the distance from any point within the laser spot area to the center of the spot. The radius of the light spot waist is denoted as .

[0043] To ensure that the Raman spectroscopy simulation of carbon nanotubes is consistent with reality, after step A1, a step is also included to verify whether the laser spot region conforms to the Gaussian distribution characteristics, which specifically includes the following steps: B1. Single carbon nanotubes are tested using a Raman optics platform to obtain signals including the intensity of the G peak. For example... Figure 3 As shown, the Raman optics platform can realize laser spot translation, and the measured spot radius d and spot waist radius can be used to achieve this. and central light intensity In one exemplary embodiment, the light spot was moved in 0.1 μm steps, using a ×100 (NA=0.90) lens, 25% laser power, and a measurement was taken at 1592 cm⁻¹. -1 The intensity change of peak G was obtained by plotting the graph. , ,like Figure 4 As shown.

[0044] B2. Construct a spatial response function using the G peak intensity to determine whether the laser spot intensity distribution in the laser spot region conforms to a Gaussian distribution, obtaining the first judgment result. If the first judgment result is negative, proceed to step B3; if the first judgment result is positive, proceed directly to the subsequent process.

[0045] B3. Reconstruct the laser spot area.

[0046] A2. Randomly generate several simulated spatial coordinates within the laser spot area and construct corresponding carbon nanotubes for each; each carbon nanotube has a different chirality index. For example... Figure 5 As shown, the figure uses a two-dimensional Cartesian coordinate system (XY), with the coordinate axes all in micrometers (μm), ranging from -0.6μm ≤ X, Y ≤ 0.6μm, encompassing the effective light spot intensity. The light spot intensity is represented by a grayscale gradient; the dashed line is a circle with a radius d for the light spot, and the color bar on the right represents light intensity (unit: W / m²). 2 The quantitative relationship between grayscale and light intensity is determined, with a scale range of 0.00~1.00W / m. 2 The light intensity distribution exhibits a centrally symmetrical radial attenuation characteristic. The three vertical lines in the figure represent the positions of three randomly simulated carbon nanotubes. Since carbon nanotubes are one-dimensional nanomaterials, they are usually arranged in parallel in a horizontal array sample. Therefore, in the XY plane, they are represented by vertical lines with a fixed X coordinate and extending along the Y axis. The number of nanotubes can represent the density and can be set according to the simulation requirements.

[0047] A3. For carbon nanotubes generated at any simulated spatial coordinate, calculate the signal intensity of the carbon nanotubes based on their positional distribution and light spot intensity distribution. In this embodiment, the signal intensity of the carbon nanotubes is calculated using the following formula: .

[0048] in, For the first i The signal intensity of carbon nanotubes For the light spot intensity distribution, r Let be the distance from any point on the chord length to the center of the light spot. Used to indicate the positional distribution of carbon nanotubes.

[0049] A4. Using the signal intensity at the center of the laser spot region as a benchmark, calculate the spatial weight of each carbon nanotube. In this embodiment, the spatial weight of the carbon nanotube is calculated according to the following formula; .

[0050] in, For the first i The spatial weight of carbon nanotubes For the first i The signal intensity of carbon nanotubes This represents the signal intensity when the carbon nanotube is located at the center of the laser spot region. The spatial weight reflects the sensitivity of the carbon nanotube's spatial position to the light intensity gradient. For example... Figure 5 As shown, the numbers near the vertical line (0.15, 0.99, 0.05) correspond to the signal weights at the locations of the carbon nanotubes, which are related to the light spot intensity at those locations.

[0051] A5. Based on the chiral index of each carbon nanotube, obtain the corresponding Raman spectral data from a pre-established single-spectrum database, and then superimpose the Raman spectral data of each carbon nanotube according to spatial weights to obtain the total Raman spectral data of the carbon nanotubes. In this embodiment, the Raman spectral data of each carbon nanotube are superimposed according to the following formula: .

[0052] in, The total Raman spectrum data of carbon nanotubes, For the first i The spatial weight of carbon nanotubes For the first i Raman spectral data of carbon nanotubes. A pre-established single-spectrum database stores chirality indices and corresponding Raman spectral data, supporting the superposition of Raman spectra for different chiral combinations. The final superimposed output of the total Raman spectral data of carbon nanotubes is illustrated in the diagram below. Figure 6As shown in the figure, (14,4), (11,10) and (19,10) are the chiral indices of different carbon nanotubes.

[0053] The batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling provided in the above embodiments of this application can accurately reproduce the actual detection scenario. The time for a single carbon nanotube Raman spectrum superposition is less than 3 seconds. It supports multiple chiral combinations, significantly improves simulation accuracy and efficiency, and meets the needs of high-throughput carbon nanotube characterization.

[0054] Based on the same inventive concept, this application also provides a system for implementing the above-described method for batch simulation of carbon nanotube Raman spectroscopy based on Gaussian spot modeling. The solution provided by this system is similar to the solution described in the above method. In an exemplary embodiment, such as... Figure 7 As shown, a batch simulation system for carbon nanotube Raman spectroscopy based on Gaussian spot modeling is provided, including the following functional modules: The laser spot region modeling module is used to construct a laser spot region whose intensity distribution conforms to a Gaussian distribution based on a Gaussian model.

[0055] The carbon nanotube distribution simulation module is used to randomly generate several simulated spatial coordinates within the laser spot area and construct corresponding carbon nanotubes; each carbon nanotube has a different chirality index.

[0056] The measurement and signal strength quantization module is used to calculate the signal strength of carbon nanotubes generated at arbitrary simulated spatial coordinates, based on the positional distribution of the carbon nanotubes and the intensity distribution of the light spot.

[0057] The spatial weight calculation module is used to calculate the spatial weight of each carbon nanotube based on the signal intensity at the center of the laser spot region.

[0058] The Raman spectral data overlay module is used to obtain the corresponding Raman spectral data from a pre-established single-spectrum database based on the chiral index of each carbon nanotube, and overlay the Raman spectral data of each carbon nanotube according to spatial weights to obtain the total Raman spectral data of carbon nanotubes.

[0059] certainly, Figure 7 The architecture shown is merely exemplary; it can be omitted as needed when implementing different functionalities. Figure 7 One or at least two components of the system shown.

[0060] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 8As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database stores data from a pre-established single-spectrum database. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it can implement the batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling provided in the previous embodiment.

[0061] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0062] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0063] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0064] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0065] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0066] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0067] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0068] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A batch simulation method for carbon nanotube Raman spectra based on Gaussian spot modeling, characterized in that, include: Based on the Gaussian model, a laser spot region whose intensity distribution conforms to a Gaussian distribution is constructed; Several simulated spatial coordinates are randomly generated within the laser spot area, and corresponding carbon nanotubes are constructed accordingly; each carbon nanotube has a different chirality index. For a carbon nanotube generated at any simulated spatial coordinate, the signal intensity of the carbon nanotube is calculated based on the position distribution of the carbon nanotube and the intensity distribution of the light spot. The spatial weight of each carbon nanotube is calculated based on the signal intensity at the center of the laser spot region. Based on the chiral index of each carbon nanotube, the corresponding Raman spectral data are obtained from a pre-established single-spectrum database, and the Raman spectral data of each carbon nanotube are superimposed according to spatial weights to obtain the total Raman spectral data of carbon nanotubes.

2. The method for batch simulation of carbon nanotube Raman spectra based on Gaussian spot modeling according to claim 1, characterized in that, The laser spot region with a Gaussian intensity distribution is constructed according to the following formula: ； in, For the light spot intensity distribution, The intensity of the laser beam at the center of the laser beam region. r Let be the distance from any point within the laser spot area to the center of the spot. The radius of the light spot waist is denoted as .

3. The method for batch simulation of carbon nanotube Raman spectra based on Gaussian spot modeling according to claim 1, characterized in that, After constructing a laser spot region whose intensity distribution conforms to a Gaussian distribution based on a Gaussian model, the process also includes a step to verify whether the laser spot region conforms to the Gaussian distribution characteristics. Specifically, this includes: Single carbon nanotubes were tested using a Raman optics platform to obtain signals including the intensity of the G peak. A spatial response function is constructed using the intensity of the G peak to determine whether the intensity distribution of the laser spot region conforms to a Gaussian distribution, thus obtaining the first judgment result. If the first judgment result is negative, then the laser spot area is reconstructed; If the first judgment result is yes, then proceed with the subsequent process.

4. The method for batch simulation of carbon nanotube Raman spectra based on Gaussian spot modeling according to claim 1, characterized in that, The signal intensity of carbon nanotubes is calculated using the following formula: ； in, For the first i The signal intensity of carbon nanotubes For the light spot intensity distribution, r Let be the distance from any point on the chord length to the center of the light spot. Used to indicate the positional distribution of carbon nanotubes.

5. The method for batch simulation of carbon nanotube Raman spectroscopy based on Gaussian spot modeling according to claim 1, characterized in that, The spatial weight of carbon nanotubes is calculated according to the following formula; ； in, For the first i The spatial weight of carbon nanotubes For the first i The signal intensity of carbon nanotubes The signal intensity is when the carbon nanotube is located at the center of the laser spot region.

6. The method for batch simulation of carbon nanotube Raman spectra based on Gaussian spot modeling according to claim 1, characterized in that, The Raman spectra of each carbon nanotube are superimposed according to the following formula: ； in, The total Raman spectrum data of carbon nanotubes, For the first i The spatial weight of carbon nanotubes For the first i Raman spectral data of carbon nanotubes.

7. A batch simulation system for carbon nanotube Raman spectroscopy based on Gaussian spot modeling, characterized in that, include: The laser spot region modeling module is used to construct a laser spot region whose spot intensity distribution conforms to a Gaussian distribution based on a Gaussian model. A carbon nanotube distribution simulation module is used to randomly generate several simulated spatial coordinates within the laser spot area and construct corresponding carbon nanotubes; each carbon nanotube has a different chirality index. The measurement and signal strength quantization module is used to calculate the signal strength of carbon nanotubes generated at any simulated spatial coordinates, based on the position distribution and light spot intensity distribution of the carbon nanotubes. The spatial weight calculation module is used to calculate the spatial weight of each carbon nanotube based on the signal intensity at the center of the laser spot region. The Raman spectral data overlay module is used to obtain the corresponding Raman spectral data from a pre-established single-spectrum database based on the chiral index of each carbon nanotube, and overlay the Raman spectral data of each carbon nanotube according to spatial weights to obtain the total Raman spectral data of carbon nanotubes.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the batch simulation method for carbon nanotube Raman spectroscopy based on Gaussian spot modeling as described in any one of claims 1-6.

Images