A method, device and equipment for detecting sterilizing nanofilm

Abstract

The present invention discloses a method, device, and equipment for detecting bactericidal nanofilms, belonging to the field of optical measurement. The detection method includes performing detection pretreatment on a vanadium dioxide nanofilm to obtain a sample to be detected; scanning the sample to be detected at room temperature and at a phase transition temperature to obtain spectral data and construct a spectral database; extracting the transmittance and reflectivity of the vanadium dioxide nanofilm from the spectral database, calculating the refractive index and extinction coefficient, and storing the calculated data in an optical constant database; and fitting the data in the spectral database and the optical constant database to obtain surface defect results for the vanadium dioxide nanofilm. The present invention utilizes the phase transition characteristics of vanadium dioxide to achieve accurate identification of surface defects in the vanadium dioxide nanofilm through two scans.

Description

Technical Field

[0001] The present invention relates to the field of optical measurement, and in particular to a method, device and equipment for detecting a bactericidal nanofilm. Background Art

[0002] Nanofilms refer to thin film materials with a thickness in the nanoscale range, and their properties are closely related to their surface and interface defects. Nanofilms have unique physical and chemical properties, and the latest research has found that two-dimensional ultra-thin vanadium dioxide has excellent broad-spectrum antibacterial properties. The new ultra-thin vanadium dioxide film provides a new strategy for treating infected wounds, greatly expanding the application range of nanofilms and extending the application of nanocatalytic drugs to the anti-infection field. However, surface defects are prone to occur during the production of nanofilms, and the presence of these surface defects can seriously affect their performance, especially for vanadium dioxide films that need to be used in the field of medical sterilization. Surface defects not only affect the mechanical properties, optical properties, and electrical behavior of the film, but may also lead to a reduction in the sterilization effect of the material. Therefore, the detection of surface defects in nanofilms is particularly important.

[0003] Currently, the most common method for detecting surface defects in nanofilms is atomic force microscopy, a high-resolution imaging technique that uses the interaction forces between atoms to detect the surface topography of a sample. AFM can detect surface roughness and defects in nanofilms with nanometer-scale resolution. However, its scanning speed is slow, and the sample surface condition can affect measurement results, leading to inaccurate detection of subtle defects. Therefore, the development of efficient and accurate detection methods is crucial for the research and application of nanofilms. Summary of the Invention

[0004] One of the purposes of the present invention is to provide a method for detecting bactericidal nanofilms and to solve the shortcomings of the prior art of slow nanofilm detection and low detection accuracy.

[0005] The present invention is achieved through the following technical solution: a method for detecting a bactericidal nanofilm, comprising: S100, performing detection pretreatment on a vanadium dioxide nanofilm to obtain a sample to be detected; S200, scanning the sample to be detected at room temperature and phase transition temperature to obtain spectral data, and constructing a spectral database; S300, extracting the transmittance and reflectivity of the vanadium dioxide nanofilm from the spectral database, calculating the refractive index and extinction coefficient, and storing the calculated data in an optical constant database; S400, fitting the data in the spectral database and the optical constant database to obtain surface defect results of the vanadium dioxide nanofilm.

[0006] Furthermore, the sample to be tested is obtained by uniformly coating a vanadium dioxide nanofilm on a transparent substrate, and the transparent substrate is lead-free glass.

[0007] Furthermore, constructing a spectral database includes using a Fourier transform infrared spectrometer to scan a sample to be tested at room temperature, scanning the test sample in three wavelength ranges of near infrared, mid infrared and far infrared by the Fourier transform infrared spectrometer to obtain first spectral data, and constructing a normal temperature spectral sub-database after noise reduction processing of the first spectral data; heating the same sample to be tested to 68°C, scanning the test sample heated to 68°C in three wavelength ranges of near infrared, mid infrared and far infrared by the Fourier transform infrared spectrometer to obtain second spectral data, and constructing a phase change spectral sub-database after noise reduction processing of the second spectral data; standardizing the data in the normal temperature spectral sub-database and the phase change spectral sub-database; then performing independent component analysis on the data in the spectral database to obtain independent components of the normal temperature spectrum and independent components of the phase change spectrum, comparing the independent components of the normal temperature spectrum and the phase change spectrum, and storing the comparison results in a comparison sub-database; the normal temperature spectral sub-database, the phase change spectral sub-database and the comparison sub-database together constitute the spectral database.

[0008] Furthermore, the wavelength of the near infrared is 1µm, the wavelength of the mid infrared is 10µm, and the wavelength of the far infrared is 100µm.

[0009] Furthermore, the noise reduction process includes removing background noise from the spectral data by comparing it with a background spectrum.

[0010] Furthermore, the substrate spectrum includes a room temperature substrate spectrum and a phase change substrate spectrum; wherein, the room temperature substrate spectrum is obtained by scanning the transparent substrate at room temperature using near-infrared light with a wavelength of 1µm, mid-infrared light with a wavelength of 10µm, and far-infrared light with a wavelength of 100µm, and the phase change substrate spectrum is obtained by heating the transparent substrate to the phase change temperature and scanning using near-infrared light with a wavelength of 1µm, mid-infrared light with a wavelength of 10µm, and far-infrared light with a wavelength of 100µm.

[0011] Furthermore, the data in the room temperature spectrum sub-database and the phase change spectrum sub-database are standardized by using Z-score standardization. The Z-score standardization includes: , , ; Where Z is the output data after standardization, dimensionless; x is the data in the database, dimensionless; μ is the mean of all the data in the database, dimensionless; σ is the standard deviation of the data, dimensionless; M is the total number of data set samples in the database, dimensionless; x i is the data value of the i-th sample in the database, dimensionless.

[0012] Furthermore, independent component analysis includes performing the following operations on the room temperature spectrum sub-database and the phase change spectrum sub-database respectively: combining the three groups of spectral data of near infrared, mid infrared and far infrared into a data matrix, in which each row represents a sample and each column represents a wavelength point; calculating the covariance matrix and performing eigendecomposition; and finding independent components using the FastICA algorithm.

[0013] Furthermore, comparing the independent components of the room temperature spectrum and the phase change spectrum includes using a fast Fourier transform (FFT) algorithm to perform Fourier transform on each independent component of the room temperature spectrum and the phase change spectrum to convert the time domain signal into the frequency domain; drawing spectrum diagrams under room temperature and phase change states, and checking the changes in frequency components by comparing the frequency domain amplitude and phase information of the room temperature spectrum and the phase change spectrum; and identifying abnormal changes in the spectrum as frequency components indicating changes in material properties or defects.

[0014] Furthermore, calculating the transmittance and reflectance of the vanadium dioxide nanofilm includes obtaining the transmittance T and reflectance R at different wavelengths from a spectrum database, and calculating the refractive index n and the extinction coefficient k based on the transmittance T and the reflectance R; , , where R is the reflectivity, T is the transmittance, n is the refractive index, k is the extinction coefficient, α is the light absorption coefficient, λ is the wavelength of light, and d is the thickness of the film. The thickness of the film is 50 to 70 nm.

[0015] Furthermore, the fitting includes fitting the data in the comparison sub-database of the spectral database and the data in the optical constant database through the Lorentz model. First, the data in the two databases are preprocessed, and then the Lorentz model is used to fit the data in the comparison sub-database and the optical constant data to obtain the abnormal peak of the spectrum, the abnormal change data of the refractive index and the extinction coefficient, and thus obtain the surface defect results.

[0016] On the other hand, the present invention provides a bactericidal nanofilm detection device, which includes a preprocessing unit, a spectral database construction unit, an optical constant database construction unit and a fitting unit, wherein the preprocessing unit is configured to perform detection preprocessing on the vanadium dioxide nanofilm to obtain a sample to be detected; the spectral database construction unit is connected to the preprocessing unit and is configured to scan the sample to be detected at room temperature and phase change temperature to obtain spectral data, and construct a spectral database; the optical constant database construction unit is connected to the spectral database construction unit and is configured to extract the transmittance and reflectivity of the vanadium dioxide nanofilm from the spectral database, calculate the refractive index and extinction coefficient, and store the calculated data in the optical constant database; the fitting unit is connected to the optical constant database construction unit and is configured to fit the data in the spectral database and the optical constant database to obtain surface defect results of the vanadium dioxide nanofilm.

[0017] In another aspect, the present invention provides a computer device, comprising: a processor; and a memory storing a computer program. When the computer program is executed by the processor, the above-mentioned bactericidal nanofilm detection method is implemented.

[0018] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0019] 1. The present invention utilizes the phase change characteristics of vanadium dioxide to scan the vanadium dioxide nanofilm twice and compares the results of the two scans to achieve accurate identification of surface defects of the vanadium dioxide nanofilm.

[0020] 2. This invention analyzes the optical properties of vanadium dioxide nanofilms, scans them at two temperatures using three specific wavelengths of infrared light, and comprehensively analyzes the optical properties to more accurately detect surface defects, thereby improving overall detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0022] Figure 1 A flow chart of a method provided for exemplary embodiment 1 of the present invention. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0024] Example 1:

[0025] Figure 1 The flowchart of the method of this embodiment is shown. This embodiment provides a method for detecting bactericidal nanofilms.

[0026] This embodiment includes the following steps:

[0027] Step 1: The nanofilm is pre-treated to obtain a sample to be tested.

[0028] Specifically, The nano film is evenly coated on a transparent substrate to obtain a sample to be tested, and the transparent substrate is lead-free glass.

[0029] Step 2: Scan the sample to be tested at room temperature and phase transition temperature to obtain spectral data and build a spectral database.

[0030] Specifically, the spectral database includes three sub-databases, namely the normal temperature spectrum sub-database, the phase change spectrum sub-database and the comparison sub-database.

[0031] The spectral data in the ambient temperature spectral sub-database and the phase change spectral sub-database are obtained by scanning the samples under test using a Fourier transform infrared spectrometer at different temperatures. Specifically, at room temperature (25-30°C), the samples are scanned using near-infrared light with a wavelength of 1000nm (1µm), mid-infrared light with a wavelength of 10µm, and far-infrared light with a wavelength of 100µm. The spectral data obtained using these three infrared scans are compared with the background spectrum to remove background noise, and then stored in the ambient temperature spectral sub-database.

[0032] Heat the sample to be tested to The phase transition temperature of the nanofilm (68°C) was determined by scanning the heated sample using near-infrared light with a wavelength of 1000nm (1µm), mid-infrared light with a wavelength of 10µm, and far-infrared light with a wavelength of 100µm. The spectral data obtained by these three infrared scans were compared with the substrate spectrum to remove background noise from the spectral data and stored in the phase transition spectrum sub-database.

[0033] The base spectrum data is obtained by scanning a transparent substrate (in this example, a lead-free glass plate) at room temperature using near-infrared light at a wavelength of 1000nm (1µm), mid-infrared light at a wavelength of 10µm, and far-infrared light at a wavelength of 100µm. This is used to obtain a room-temperature base spectrum, which is used to remove background noise from the room-temperature spectrum data. Similarly, the transparent substrate is heated to the phase transition temperature and scanned using near-infrared light at a wavelength of 1000nm (1µm), mid-infrared light at a wavelength of 10µm, and far-infrared light at a wavelength of 100µm. This is used to obtain a phase transition base spectrum, which is used to remove background noise from the phase transition spectrum data.

[0034] Then, the data of the room temperature spectrum sub-database and the phase change spectrum sub-database are standardized by using Z-score standardization. The Z-score standardization includes:

[0035] ,

[0036] ,

[0037] ;

[0038] Among them, Z is the data output after standardization, dimensionless; x is the data in the database, dimensionless; μ is the mean of all the data in the database, dimensionless; σ is the standard deviation of the data, dimensionless; M is the total number of data set samples in the database, dimensionless; x i is the data value of the i-th sample in the database, dimensionless.

[0039] After standardization, independent component analysis (ICA) was performed on the data in the room-temperature and phase-change spectral sub-databases. Specifically, the ICA process involved combining the near-infrared, mid-infrared, and far-infrared spectral data into a data matrix, where each row represents a sample and each column represents a wavelength. The covariance matrix was calculated and eigenvalue decomposition was performed. Finally, the FastICA algorithm was used to identify independent components.

[0040] It should be noted that for the three sets of spectral data (near-infrared, mid-infrared, and far-infrared spectral data), each set of data may contain some common signal components (such as flatness information). Through independent component analysis, these spectral data can be decomposed into independent components. For example, the data can be divided into independent signals related to defect characteristics and independent signals related to flatness, so that the defect characteristics of the sample can be analyzed more clearly and intuitively. The FastICA algorithm is then used to separate independent components from the mixed spectral data and analyze the main features in the independent components. This allows for a better understanding and analysis. Spectral properties of nanofilms.

[0041] After obtaining the independent components of the room-temperature spectrum and the independent components of the phase change spectrum, the fast Fourier transform (FFT) algorithm is used to perform Fourier transform on each independent component of the room-temperature spectrum and the phase change spectrum to convert the time domain signal into the frequency domain; the spectrum diagrams under the room-temperature and phase change states are drawn, and the changes in the frequency components are checked by comparing the frequency domain amplitude and phase information of the room-temperature spectrum and the phase change spectrum; abnormal changes in the spectrum are identified as frequency components indicating changes in material properties or defects, and these abnormal change data are saved in the comparison sub-database for future use.

[0042] It should be noted that large errors in the spectrum may indicate that the material has undergone physical or chemical changes during phase change. These large errors may lead to surface or structural defects in the film. Theoretically, detection can be achieved at this step. The purpose of detecting surface defects of nanofilms is to detect the surface defects of nanofilms, but in fact this method still has errors. Therefore, in order to more accurately detect surface defects of nanofilms, the present invention calculates the optical characteristics and then fits the comparison results with the optical characteristics, so as to more accurately determine the surface defects of nanofilms. The defects on the surface of nanofilm can be effectively improved.

[0043] Step 3: Extract from the spectral database The transmittance and reflectivity of the nanofilm are calculated, and the refractive index and extinction coefficient are calculated, and the calculated data are stored in the optical constant database.

[0044] Specifically, calculation The transmittance and reflectance of the nanofilm include obtaining the transmittance T and reflectance R at different wavelengths from a spectrum database, and calculating the refractive index n and the extinction coefficient k based on the transmittance T and the reflectance R.

[0045] ,

[0046] ,

[0047] Among them, R is the reflectivity, T is the transmittance, n is the refractive index, k is the extinction coefficient, α is the light absorption coefficient, λ is the wavelength of light, and d is the thickness of the film. The thickness of the film is generally 50 to 70 nm.

[0048] Substituting the reflectivity R and transmittance T at a specific wavelength λ into the above refractive index formula, the refractive index n is calculated. After calculating the refractive index n, the known thickness d and transmittance T of the film are used to calculate the extinction coefficient k.

[0049] It should be noted that the spectral data at different wavelengths in the room temperature spectrum sub-database and the phase change spectrum sub-database need to be calculated separately, and the calculated results are stored in the optical constant database to facilitate the next fitting operation.

[0050] Step 4: Fit the data in the spectral database and the optical constant database to obtain Nanofilm surface defect results.

[0051] Specifically, the data in the contrast sub-database of the spectral database and the data in the optical constant database are fitted using the Lorentz model, including:

[0052] First, the data in the comparison sub-database and the optical constant database were preprocessed to remove missing values ​​and outliers, and then the data were standardized.

[0053] The purpose of eliminating missing values ​​and outliers is to ensure the integrity and accuracy of the data. The purpose of standardization is to facilitate effective comparison and analysis of fitting algorithms.

[0054] The Lorentz model is then used to fit the data in the comparison sub-database and the optical constant data to obtain the abnormal peak of the spectrum, the abnormal change data of the refractive index and the extinction coefficient, thereby obtaining more accurate surface defect detection results.

[0055] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting bactericidal nanofilms, characterized in that: The method comprises, S100, performing detection pretreatment on the vanadium dioxide nanofilm to obtain a sample to be detected; S200, scanning the sample to be tested at room temperature and phase transition temperature to obtain spectral data, and constructing a spectral database; S300, extracting transmittance data and reflectance data of the vanadium dioxide nanofilm from the spectrum database, calculating the refractive index and extinction coefficient, and storing the calculated data in the optical constant database; S400, fitting the data in the spectrum database and the optical constant database to obtain the surface defect results of the vanadium dioxide nanofilm; The constructing of the spectrum database comprises: A Fourier transform infrared spectrometer is used to scan the sample to be tested at room temperature, and the sample is scanned in the near infrared, mid infrared and far infrared wavelength ranges by the Fourier transform infrared spectrometer to obtain first spectral data, and the first spectral data is subjected to noise reduction processing to construct a room temperature spectral sub-database; The same sample to be tested is heated to 68°C, and the sample heated to 68°C is scanned by a Fourier transform infrared spectrometer in the near infrared, mid-infrared, and far infrared wavelength ranges to obtain second spectral data. The second spectral data is subjected to noise reduction processing to construct a phase change spectrum sub-database; performing standardization processing on the data in the room temperature spectrum sub-database and the phase change spectrum sub-database; Then, independent component analysis is performed on the data in the spectral database to obtain independent components of the normal temperature spectrum and independent components of the phase change spectrum, and the independent components of the normal temperature spectrum and the phase change spectrum are compared, and the comparison results are stored in a comparison sub-database; The normal temperature spectrum sub-database, phase change spectrum sub-database and comparison sub-database together constitute the spectrum database; The data of the room temperature spectrum sub-database and the phase change spectrum sub-database were standardized by using Z-score standardization. Independent component analysis includes performing the following operations on the room temperature spectrum sub-database and the phase change spectrum sub-database respectively: combining the three sets of spectral data (near infrared, mid infrared, and far infrared) into a data matrix, where each row represents a sample and each column represents a wavelength point; calculating the covariance matrix and performing eigendecomposition; and finding independent components using the FastICA algorithm; Comparing the independent components of the room temperature spectrum and the phase change spectrum includes performing a Fourier transform on each independent component of the room temperature spectrum and the phase change spectrum using a fast Fourier transform algorithm to convert the time domain signal into the frequency domain; plotting the spectrum at room temperature and phase change state, and comparing the frequency domain amplitude and phase information of the room temperature spectrum and the phase change spectrum to view the change in frequency components; and identifying abnormal changes in the spectrum as frequency components indicating changes in material properties or defects. The calculation of the transmittance data and reflectance data of the vanadium dioxide nanofilm includes obtaining the transmittance T and reflectance R at different wavelengths from a spectrum database, and calculating the refractive index n and the extinction coefficient k based on the transmittance T and the reflectance R; ， ， Among them, R is reflectivity, T is transmittance, n is refractive index, k is extinction coefficient, α is light absorption coefficient, λ is wavelength of light, d is thickness of film, and the thickness of film is 50-70nm.

2. The bactericidal nanofilm detection method according to claim 1, characterized in that: The sample to be tested is prepared by uniformly coating a vanadium dioxide nanofilm on a transparent substrate, wherein the transparent substrate is lead-free glass.

3. The bactericidal nanofilm detection method according to claim 1, characterized in that: The wavelength of the near infrared is 1µm; the wavelength of the mid-infrared is 10µm; and the wavelength of the far infrared is 100µm. The noise reduction processing includes removing background noise of the spectral data by comparing it with a base spectrum.

4. The bactericidal nanofilm detection method according to claim 1, characterized in that: The fitting includes fitting the data in the comparison sub-database of the spectrum database and the data in the optical constant database by using the Lorentz model, First, the data in the two databases are preprocessed. Then, the Lorentz model is used to fit the data in the comparison sub-database and the optical constant data to obtain the abnormal peak of the spectrum, the abnormal change data of the refractive index and the extinction coefficient, and thus the surface defect results are obtained.

5. A computer device, characterized in that: The device comprises: processor; The memory stores a computer program, and when the computer program is executed by the processor, the bactericidal nano film detection method according to any one of claims 1 to 4 is implemented.

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