Method for determining solution parameter of electrolyte solution

Abstract

The invention relates to a method for measuring solution parameters of an electrolyte solution based on near infrared spectroscopy. Real-time online detection of the solution parameters in the production process of the electrolyte solution is achieved through the near infrared spectroscopy.

Description

Technical Field

[0001] The present invention relates to the technical field of detection and analysis, and in particular to a method for measuring solution parameters of an electrolyte solution based on near-infrared spectroscopy. Background Art

[0002] Electrolyte is an important component of secondary batteries. Commercially available electrolytes include solid form and solution form. In the process of producing electrolyte solution, it is necessary to monitor the solution parameters of the electrolyte solution in a timely manner, such as electrolyte content and solution density, to determine whether the electrolyte solution meets the use standard to improve production efficiency. At present, the method for determining the solution parameters of the electrolyte solution, such as the ion chromatography method for determining the electrolyte content, cannot achieve real-time online detection, and the densimeter method for determining density, for example, cannot achieve real-time online detection of the solution density at a limited temperature.

[0003] Therefore, there is an urgent need to develop a method for determining solution parameters of an electrolyte solution that can achieve real-time online detection. Summary of the invention

[0004] In view of the problems existing in the background technology, the present application provides a method for measuring solution parameters of an electrolyte solution based on near-infrared spectroscopy. The measurement method of the present application can detect the solution parameters of the electrolyte solution in real time online during the production process.

[0005] The method for measuring solution parameters of an electrolyte solution based on near infrared spectroscopy provided in the present application comprises the following steps:

[0006] Selecting a solution parameter to be measured of the electrolyte solution sample to be measured;

[0007] Determine solution parameters of calibration set samples to obtain measured values, where the calibration set samples include electrolytes and solvents;

[0008] Scan the near infrared spectra of the samples in the calibration set;

[0009] Based on the partial least squares method, a modeling band and a spectrum preprocessing method are selected to establish a mathematical model, wherein the modeling band covers at least part of the near-infrared absorption band of the solvent;

[0010] The spectral data of the electrolyte solution sample to be tested is collected, and the solution parameters of the electrolyte solution sample to be tested are calculated in combination with a mathematical model.

[0011] The present application uses near-infrared spectroscopy to achieve real-time online detection of solution parameters during the production process of electrolyte solutions. The present application uses at least part of the near-infrared absorption band of the solvent as the modeling band, and indirectly reflects the solution parameters of the electrolyte solution through the characteristic absorption peak of the solvent; and does not directly reflect the solution parameters of the electrolyte solution through the characteristic absorption peak of the electrolyte. Therefore, the method of the present application has nothing to do with whether the electrolyte has a characteristic absorption peak in the near-infrared region, and the temperature has little effect on the near-infrared spectrum of the solvent, so the test has high accuracy and strong universality.

[0012] In addition, the determination method of the present application also has the advantages of high efficiency, rapidity, non-destructiveness, environmental protection, low cost, etc. In addition, the determination method of the present application has good accuracy and repeatability.

[0013] In some embodiments, according to the first aspect, a first example of the first aspect is provided, wherein the solution parameter is electrolyte content.

[0014] The method of the present application is suitable for determining the electrolyte content of an electrolyte solution. The mathematical model has a good prediction effect, can realize real-time online detection of the electrolyte content, and has strong universality.

[0015] In some embodiments, according to the first aspect, a second example of the first aspect is provided, and the electrolyte is LiFSI.

[0016] The present application utilizes near-infrared spectroscopy and takes at least part of the near-infrared absorption band of the solvent as the modeling band. The characteristic absorption peak of the solvent is used to indirectly reflect the content of LiFSI, thereby solving the problem that the LiFSI content in the LiFSI solution cannot be directly determined by near-infrared spectroscopy.

[0017] In some embodiments, according to the first aspect, a third example of the first aspect is proposed, and the modeling band is 12492cm -1 ~4524cm -1 Alternatively, the modeling band is 9400cm -1 ~7500cm -1 and 6100cm -1 ~5448cm -1 .

[0018] The selection of the modeling band is very important for the prediction effect of the mathematical model. By optimizing the modeling band, it is beneficial to further improve the prediction effect of the mathematical model. Since the full band contains a lot of information, useless information or baseline noise will interfere with the prediction effect of the model. When the object of measurement is the electrolyte content, in order to improve the prediction effect of the mathematical model, the present application selects the spectral band with effective information of the solvent in the near-infrared region, that is, the above-mentioned modeling band for modeling, thereby reducing the intake of useless information and improving the prediction effect of the model.

[0019] In some embodiments, according to the first aspect, a fourth example of the first aspect is proposed, the calibration set sample contains water, and the modeling band is 6052cm -1 ~4600cm -1 .

[0020] By optimizing the modeling band, it is helpful to further improve the prediction effect of the model.

[0021] In some embodiments, according to the first aspect, a fifth example of the first aspect is proposed, and the spectral preprocessing method includes one or more of the first-order derivative, the second-order derivative, the multivariate scattering correction, and the standard normalization method.

[0022] Since some physical states of electrolyte solution samples, such as color and turbidity, can cause interferences such as baseline drift or translation and scattering, making the effective information of the band unclear, this application selects a spectral preprocessing method that can effectively reduce interference to improve the model prediction accuracy.

[0023] In some embodiments, according to the first aspect, a sixth example of the first aspect is provided, wherein the solution parameter is density.

[0024] Commercially available electrolyte solutions need to meet the density standards at a certain limited temperature (such as 25°C). The density of the electrolyte solution is greatly affected by the solution temperature. The temperature of the electrolyte solution will change during the production process, so how to monitor the density of the electrolyte solution in real time during the production process becomes a problem. At present, most methods for determining the density of electrolyte solutions use a density meter method for measurement, which can only detect the density of the electrolyte solution at the current temperature during the production process in real time. If you want to detect the density of the electrolyte solution at a certain limited temperature (such as 25°C), you need to take a sample to the laboratory to adjust the solution temperature and then use a density meter to measure it. Since the solution temperature needs to be adjusted, the density meter method cannot achieve real-time online detection of the density of the electrolyte solution at a certain limited temperature (such as 25°C) during the production process. This application applies near-infrared spectroscopy to the density determination of electrolyte solutions, using at least part of the near-infrared absorption band of the solvent as the modeling band, and reflects the solution density through the characteristic absorption peak of the solvent. Since the temperature has little effect on the near-infrared spectrum of the solvent, there is no need to adjust the temperature of the electrolyte solution, and real-time online detection of the solution density at a certain limited temperature (such as 25°C) can be achieved.

[0025] In some embodiments, according to the first aspect, a seventh example of the first aspect is proposed, and the modeling band is 12492cm -1 ~4524cm -1 Alternatively, the modeling band is 9400cm -1 ~7500cm -1 and 6100cm -1 ~5448cm-1 .

[0026] The selection of the modeling band is very important for the prediction effect of the mathematical model. By optimizing the modeling band, it is beneficial to further improve the prediction effect of the mathematical model. Since the full band contains a lot of information, useless information or baseline noise will interfere with the prediction effect of the model. In the case where the object of measurement is the density of the electrolyte solution, in order to improve the prediction effect of the mathematical model, the present application selects the spectral band in which the solvent has effective information in the near-infrared region, that is, the above-mentioned modeling band for modeling, thereby reducing the intake of useless information and improving the prediction effect of the model.

[0027] In some embodiments, according to the first aspect, an eighth example of the first aspect is provided, the calibration set sample contains water, and the modeling band is 7500cm -1 ~6100cm -1 and 5028cm -1 ~4600cm -1 .

[0028] By optimizing the modeling band, it is helpful to further improve the prediction effect of the model.

[0029] In some embodiments, according to the first aspect, a ninth example of the first aspect is proposed, wherein the spectral preprocessing method includes one or more of first-order derivative, second-order derivative, multivariate scattering correction, and standard normalization method.

[0030] Since some physical states of electrolyte solution samples, such as color and turbidity, can cause interferences such as baseline drift or translation and scattering, making the effective information of the band unclear, this application selects a spectral preprocessing method that can effectively reduce interference to improve the model prediction accuracy.

[0031] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. DETAILED DESCRIPTION

[0032] The exemplary embodiments of the present disclosure will be described in more detail below. However, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusions.

[0034] For simplicity, only some numerical ranges are explicitly disclosed herein. However, any lower limit can be combined with any upper limit to form an unambiguous range; and any lower limit can be combined with other lower limits to form an unambiguous range, and any upper limit can be combined with any other upper limit to form an unambiguous range. In addition, although not explicitly stated, each point or single value between the range endpoints is included in the range. Thus, each point or single value can be combined with any other point or single value as its own lower limit or upper limit or with other lower limits or upper limits to form an unambiguous range.

[0035] In the description of this article, it should be noted that, unless otherwise specified, "above" and "below" are inclusive of the number itself, and the "multiple" in "one or more" means more than two (including two).

[0036] The above-mentioned summary of the invention of the present application is not intended to describe each disclosed embodiment or each implementation in the present application. The following description more specifically illustrates exemplary embodiments. In many places throughout the application, guidance is provided by a series of examples, which can be used in various combinations. In each example, enumeration is only used as a representative group and should not be interpreted as exhaustive.

[0037] Electrolyte is an important component of secondary batteries. Commercially available electrolytes include solid form and solution form. In the process of producing electrolyte solution, it is necessary to monitor the solution parameters of the electrolyte solution in a timely manner, such as electrolyte content and solution density, to determine whether the electrolyte solution meets the use standard to improve production efficiency. At present, the method for determining the solution parameters of the electrolyte solution, such as the ion chromatography method for determining the electrolyte content, cannot achieve real-time online detection, and the densimeter method for determining density, for example, cannot achieve real-time online detection of the solution density at a limited temperature.

[0038] Therefore, there is an urgent need to develop a method for determining solution parameters of an electrolyte solution that can achieve real-time online detection.

[0039] After research, the present application provides a method for determining the solution parameters of an electrolyte solution based on near-infrared spectroscopy. The method uses at least part of the near-infrared absorption band of the solvent as the modeling band, and indirectly reflects the solution parameters of the electrolyte solution through the characteristic absorption peak of the solvent, thereby realizing real-time online detection of solution parameters during the production process of the electrolyte solution. The present application does not directly reflect the solution parameters of the electrolyte solution through the characteristic absorption peak of the electrolyte. Therefore, the method of the present application is independent of whether the electrolyte has a characteristic absorption peak in the near-infrared region, and the temperature has little effect on the near-infrared spectrum of the solvent, so the test has high accuracy and strong universality.

[0040] Specifically, the method for measuring solution parameters of an electrolyte solution based on near infrared spectroscopy provided in the present application comprises the following steps:

[0041] Selecting a solution parameter to be measured of the electrolyte solution sample to be measured;

[0042] Determine solution parameters of calibration set samples to obtain measured values, where the calibration set samples include electrolytes and solvents;

[0043] Scan the near infrared spectra of the samples in the calibration set;

[0044] Based on the partial least squares method, a modeling band and a spectrum preprocessing method are selected to establish a mathematical model, wherein the modeling band covers at least part of the near-infrared absorption band of the solvent;

[0045] The spectral data of the electrolyte solution sample to be tested is collected, and the solution parameters of the electrolyte solution sample to be tested are calculated in combination with a mathematical model.

[0046] The determination method of the present application also has the advantages of high efficiency, rapidity, non-destructiveness, environmental protection, low cost, etc. In addition, the determination method of the present application has good accuracy and repeatability.

[0047] “The modeling band covers at least part of the near-infrared absorption band of the solvent” means that at least part of the near-infrared absorption band of the solvent is selected as the modeling band. In some embodiments, the modeling band may also include near-infrared absorption bands of other solution components besides the solvent.

[0048] The "electrolyte solution" refers to an electrolyte in the form of a solution, which is formed by dissolving an electrolyte in a solvent and can be used to prepare an electrolyte for a secondary battery.

[0049] In some embodiments, the solution parameter is electrolyte content.

[0050] At present, the solute content in the solution is determined by near-infrared spectroscopy, and most of them directly reflect the solute content through the characteristic absorption peak of the solute in the near-infrared region, so the solute needs to have a characteristic absorption peak in the near-infrared region. However, almost all electrolytes used in secondary batteries do not have characteristic absorption peaks in the near-infrared region, which makes it impossible to directly determine the electrolyte content using near-infrared spectroscopy. The present application indirectly reflects the electrolyte content of the electrolyte solution through the characteristic absorption peak of the solvent, solving the problem of being unable to detect the electrolyte content in the electrolyte solution production process in real time online.

[0051] The method of the present application is suitable for determining the electrolyte content of an electrolyte solution. The mathematical model has a good prediction effect and can realize real-time online detection of the electrolyte content. The test has high accuracy and strong universality.

[0052] In some embodiments, the electrolyte is LiFSI.

[0053] Lithium bis(fluorosulfonyl)imide, abbreviated as LiFSI in English, is an excellent electrolyte lithium salt with good electrochemical stability and thermal stability. Most of the commercially available LiFSI are in the form of solutions, which are formed by dissolving LiFSI in a solvent. LiFSI has no obvious characteristic absorption peak in the near-infrared band, so it is impossible to directly measure the LiFSI content in the LiFSI solution by near-infrared spectroscopy. The present application uses near-infrared spectroscopy, takes at least part of the near-infrared absorption band of the solvent as the modeling band, and indirectly reflects the content of LiFSI through the characteristic absorption peak of the solvent, which solves the problem that the LiFSI content in the LiFSI solution cannot be directly measured by near-infrared spectroscopy.

[0054] In some embodiments, the solution parameter is the electrolyte content. The modeling band is 12492 cm -1 ~4524cm -1 Alternatively, the modeling band is 9400cm -1 ~7500cm -1 and 6100cm -1 ~5448cm -1 .

[0055] The selection of the modeling band is very important for the prediction effect of the mathematical model. By optimizing the modeling band, it is beneficial to further improve the prediction effect of the mathematical model. Since the full band contains a lot of information, useless information or baseline noise will interfere with the prediction effect of the model. When the object of measurement is the electrolyte content, in order to improve the prediction effect of the mathematical model, the present application selects the spectral band with effective information of the solvent in the near-infrared region, that is, the above-mentioned modeling band for modeling, thereby reducing the intake of useless information and improving the prediction effect of the model.

[0056] In some embodiments, the solution parameter is electrolyte content. The calibration set samples contain water, and the modeling band is 6052cm -1 ~4600cm -1 .

[0057] During the production process, water may be introduced into the electrolyte solution. -1 ~4600cm -1 The band range includes both solvent absorption peaks and water absorption peaks. Selecting this band range for modeling is conducive to further improving the prediction effect of the model.

[0058] In some embodiments, the solution parameter is electrolyte content. The spectrum preprocessing method includes one or more of first-order derivative, second-order derivative, multivariate scattering correction, and standard normalization method.

[0059] Since some physical states of electrolyte solution samples, such as color and turbidity, can cause interferences such as baseline drift or translation and scattering, making the effective information of the band unclear, this application selects a spectral preprocessing method that can effectively reduce interference to improve the model prediction accuracy.

[0060] In some embodiments, the solution parameter is electrolyte content. The solvent includes a carbonate solvent.

[0061] For electrolyte solutions using carbonate as solvent, the determination method of the present application can better predict the electrolyte content.

[0062] In some specific embodiments, the carbonate solvent includes one or more of ethyl methyl carbonate, dimethyl carbonate, diethyl carbonate, and propylene carbonate.

[0063] In some embodiments, the solution parameter is density.

[0064] Commercially available electrolyte solutions need to meet the density standards at a certain limited temperature (such as 25°C). The density of the electrolyte solution is greatly affected by the solution temperature. The temperature of the electrolyte solution will change during the production process, so how to monitor the density of the electrolyte solution in real time during the production process becomes a problem. At present, most methods for determining the density of electrolyte solutions use a density meter method for measurement, which can only detect the density of the electrolyte solution at the current temperature during the production process in real time. If you want to detect the density of the electrolyte solution at a certain limited temperature (such as 25°C), you need to take a sample to the laboratory to adjust the solution temperature and then use a density meter to measure it. Since the solution temperature needs to be adjusted, the density meter method cannot achieve real-time online detection of the density of the electrolyte solution at a certain limited temperature (such as 25°C) during the production process. This application applies near-infrared spectroscopy to the density determination of electrolyte solutions, using at least part of the near-infrared absorption band of the solvent as the modeling band, and reflects the solution density through the characteristic absorption peak of the solvent. Since the temperature has little effect on the near-infrared spectrum of the solvent, there is no need to adjust the temperature of the electrolyte solution, and real-time online detection of the solution density at a certain limited temperature (such as 25°C) can be achieved.

[0065] In some embodiments, the solution parameter is density. The modeling band is 12492 cm -1 ~4524cm -1 Alternatively, the modeling band is 9400cm -1 ~7500cm -1 and 6100cm -1 ~5448cm -1 .

[0066] The selection of the modeling band is very important for the prediction effect of the mathematical model. By optimizing the modeling band, it is beneficial to further improve the prediction effect of the mathematical model. Since the full band contains a lot of information, useless information or baseline noise will interfere with the prediction effect of the model. In the case where the object of measurement is the density of the electrolyte solution, in order to improve the prediction effect of the mathematical model, the present application selects the spectral band in which the solvent has effective information in the near-infrared region, that is, the above-mentioned modeling band for modeling, thereby reducing the intake of useless information and improving the prediction effect of the model.

[0067] In some embodiments, the solution parameter is density. The calibration set samples contain water and the modeling band is 7500 cm -1 ~6100cm -1 and 5028cm -1 ~4600cm -1 .

[0068] During the production process, water may be introduced into the electrolyte solution. Studies have found that 7500cm -1 ~6100cm -1The band range includes the water absorption peak, 5028cm -1 ~4600cm -1 The band range includes the solvent absorption peak, and selecting these two band ranges as modeling bands can improve the predictive ability of the model.

[0069] In some embodiments, the solution parameter is density. The spectral preprocessing method includes one or more of first-order derivative, second-order derivative, multivariate scattering correction, and standard normalization.

[0070] Since some physical states of electrolyte solution samples, such as color and turbidity, can cause interferences such as baseline drift or translation and scattering, making the effective information of the band unclear, this application selects a spectral preprocessing method that can effectively reduce interference to improve the model prediction accuracy.

[0071] In some embodiments, the solution parameter is density. The solvent comprises a carbonate solvent.

[0072] For electrolyte solutions using carbonate as solvent, the determination method of the present application can better predict the solution density.

[0073] In some specific embodiments, the carbonate solvent includes one or more of ethyl methyl carbonate, dimethyl carbonate, diethyl carbonate, and propylene carbonate.

[0074] Since temperature has little effect on the near-infrared spectrum of the solvent, especially on the near-infrared spectrum of carbonate solvents, the selection of carbonate solvents can achieve the solution at different temperatures without constant temperature to a certain limited temperature (such as 25°C), and the density of the solution at the limited temperature (such as 25°C) can be obtained by near-infrared spectroscopy, thereby reducing the impact of temperature changes on the density of the solution.

[0075] In some embodiments, a near-infrared spectrometer may be used to scan the near-infrared spectra of the calibration set samples. Optionally, the near-infrared spectrometer may be a Fourier transform near-infrared spectrometer. Collection method of the near-infrared spectra: colorimetric tube transmission liquid sampling. Collection parameters: resolution 2-8 cm -1 , scanning times 16-64 times, scanning spectrum range 12500cm -1 ~4000cm -1 , ambient temperature 20-30°C. For example, the resolution can be 2cm -1 、4cm -1 or 8cm -1 For example, the number of scans can be 16, 32 or 64. The ambient temperature has little effect on the test, and the ambient temperature only needs to meet the use requirements of the near-infrared spectrometer.

[0076] In some embodiments, the number of calibration set samples may be greater than 30. For example, the number of calibration set samples may be 30-200, optionally 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, or 200.

[0077] In some embodiments, the electrolyte content of the calibration set samples may be determined using ion chromatography. The present application does not specifically limit the method for determining the actual electrolyte content, as long as it can be accurately determined.

[0078] In some embodiments, the density of the calibration set samples may be measured using a densitometer. The present application has no particular limitation on the method for measuring the true density, as long as it can be measured accurately.

[0079] In some embodiments, the electrolyte includes an electrolyte for a secondary battery. Secondary batteries include lithium-ion batteries and sodium-ion batteries.

[0080] In some embodiments, after the mathematical model is established, the method further includes:

[0081] Model evaluation: The determination coefficient of the mathematical model and the internal cross-check root mean square error are used to evaluate the model;

[0082] Model verification: Collect multiple electrolyte solution samples as prediction set samples; accurately measure the solution parameters of the prediction set samples to obtain measured values; scan the near-infrared spectra of the prediction set samples to obtain spectral data of the prediction set samples; calculate the solution parameters of the prediction set samples based on the mathematical model and the spectral data of the prediction set samples to obtain predicted values, and compare the predicted values ​​with the corresponding measured values.

[0083] Before applying the mathematical model in practice, it is necessary to evaluate and verify the mathematical model. This application uses the coefficient of determination of the mathematical model and the root mean square error of the internal cross-check to evaluate the model. 2 ) is closer to 1, indicating that the model linearity is better. The smaller the root mean square error of the internal cross-validation of the calibration set (RMSECV), the smaller the model prediction deviation. 2 Above 0.99, very close to 1, RMSECV is very small, indicating that the linearity of the model is good and the model prediction bias is small.

[0084] In some embodiments, the solution parameter is electrolyte content. Model validation includes: collecting multiple electrolyte solution samples as prediction set samples; determining the electrolyte content of the prediction set samples by ion chromatography to obtain measured values; scanning near infrared spectra of the prediction set samples to obtain spectral data of the prediction set samples; calculating the electrolyte content of the prediction set samples based on the mathematical model and the spectral data of the prediction set samples to obtain predicted values, and comparing the predicted values ​​with the corresponding measured values.

[0085] In some embodiments, the solution parameter is the density of the electrolyte solution. The model validation includes: collecting multiple electrolyte solution samples as prediction set samples; measuring the density of the prediction set samples using a densitometer to obtain measured values; scanning the near infrared spectra of the prediction set samples to obtain spectral data of the prediction set samples; calculating the density of the prediction set samples based on the mathematical model and the spectral data of the prediction set samples to obtain predicted values, and comparing the predicted values ​​with the corresponding measured values.

[0086] In some specific embodiments, the number of prediction set samples may be 3-50, for example, 3, 5, 10, 15, 20, 25, 30, 35, 40, 45 or 50.

[0087] In some embodiments, a near infrared spectrometer may be used to scan the near infrared spectra of the prediction set samples. Optionally, the near infrared spectrometer may be a Fourier transform near infrared spectrometer. Collection method of near infrared spectra: colorimetric tube transmission liquid sampling. Collection parameters: resolution 2-8 cm -1 , scanning times 16-64 times, scanning spectrum range 12500cm -1 ~4000cm -1 , ambient temperature 20-30°C. For example, the resolution can be 2cm -1 、4cm -1 or 8cm -1 For example, the number of scans can be 16, 32 or 64. The ambient temperature has little effect on the test, and the ambient temperature only needs to meet the use requirements of the near-infrared spectrometer.

[0088] The LiFSI content prediction range of the mathematical model of the present application is related to the measured value range of the calibration set samples selected for modeling. In some embodiments, the LiFSI content prediction range of the mathematical model can be 20.41% to 50.58%. The density prediction range of the mathematical model of the present application is related to the measured value range of the calibration set samples selected for modeling. In some embodiments, the density prediction range of the mathematical model can be 1.1497 g / cm 3 ~1.3032g / cm 3 .

[0089] In some embodiments, the calibration set samples, the prediction set samples and the electrolyte solution samples to be tested are all samples collected during the production process. The material composition of the electrolyte solution samples to be tested is the same as the material composition of the calibration set samples, the LiFSI content of the electrolyte solution samples to be tested is within the LiFSI content prediction range of the mathematical model, and its density is within the density prediction range of the mathematical model.

[0090] In some embodiments, the modeling software used to establish the mathematical model includes OPUS software from Bruker Corporation.

[0091] The present application is further described below in conjunction with the examples. The following examples describe the disclosure of the present application in more detail, and these examples are merely illustrative, because it is obvious to those skilled in the art that various modifications and variations are made within the scope of the disclosure of the present application. Unless otherwise stated, all reagents used in the examples are commercially available or synthesized according to conventional methods, and the instruments used in the examples are commercially available.

[0092] Determination of electrolyte content of electrolyte solutions

[0093] Example 1

[0094] (1) Collect samples

[0095] 35 batches of LiFSI solution samples were collected, and 30 batches of LiFSI solution samples were randomly selected as calibration set samples, and the remaining 5 batches of LiFSI solution samples were used as prediction set samples. Among them, the LiFSI solution samples contain electrolyte LiFSI and solvent ethyl methyl carbonate.

[0096] (2) Accurate quantification

[0097] The LiFSI content of 30 batches of calibration set samples and 5 batches of prediction set samples was detected by ion chromatography, and the actual value of LiFSI content was measured.

[0098] (3) Near-infrared spectrum acquisition

[0099] The near infrared spectra of 30 batches of calibration set samples and 5 batches of prediction set samples were collected by Fourier transform near infrared spectrometer to obtain the near infrared spectrum data of calibration set samples and prediction set samples. Spectrum collection method: colorimetric tube transmission liquid sampling. Collection parameters: resolution 8cm -1 , scanning times 64 times, scanning range 12500cm -1 ~4000cm -1 , ambient temperature 23℃.

[0100] (4) Modeling

[0101] The OPUS software of Bruker was used to establish a mathematical model based on the partial least squares method, selecting the modeling band, spectral preprocessing method, and the number of principal component factors. The model parameters are as follows: Modeling band range: 6052cm -1 ~4600cm -1 , spectral preprocessing method: first-order derivative, number of principal component factors: 2.

[0102] (5) Model evaluation

[0103] The coefficient of determination R of the mathematical model 2 The internal cross-validation root mean square error RMSECV is 0.288. The model has good linearity and small RMSECV, indicating that the model has good predictive ability.

[0104] (6) Model Verification

[0105] Based on the mathematical model established in step (4) and the spectral data of the prediction set samples obtained in step (3), the LiFSI content of the five batches of prediction set samples is calculated to obtain the predicted values. The predicted values ​​of the five batches of prediction set samples are compared with the corresponding measured values ​​of the LiFSI content obtained in step (2), as shown in Table 1.

[0106] Table 1

[0107]

[0108] Methodological investigation

[0109] 1. Precision test

[0110] A batch of LiFSI solution samples not involved in the modeling were randomly selected, and the mathematical model established in Example 1 was used to repeat the test 10 times, and the model prediction results were recorded. The relative standard deviation RSD value of the 10 results was 0.031%, indicating that the precision of the model was good.

[0111] 2. Repeatability test

[0112] A batch of LiFSI solution samples that did not participate in the modeling were randomly selected, and the samples were loaded into 6 colorimetric tubes. The mathematical model established in Example 1 was used to predict the LiFSI content of the samples in the 6 colorimetric tubes. The RSD value of the 6 prediction results of the same sample was 0.11%, indicating that the repeatability of the method of the present application was good.

[0113] Embodiment 2-5

[0114] The method described in Example 1 was followed, and the calibration set samples and prediction set samples used were the same as those in Example 1. The only difference was that the selected modeling bands were different from those in Example 1, as shown in Table 2 below.

[0115] The coefficient of determination R of the mathematical model 2 The internal cross-validation root mean square error RMSECV is shown in Table 2 below.

[0116] Table 2

[0117]

[0118] Embodiment 6-8

[0119] The method described in Example 1 was followed. The calibration set samples and prediction set samples used were the same as those in Example 1. The only difference was that the spectral preprocessing method was different, as shown in Table 3 below.

[0120] Table 3

[0121]

[0122] Examples 9-11

[0123] The method described in Example 1 was followed, except that the electrolytes contained in the calibration set samples and the prediction set samples were different from those in Example 1, as shown in Table 4 below.

[0124] Table 4

[0125] serial number Electrolytes <![CDATA[R 2 ]]> RMSECV Example 1 LiFSI 0.9938 0.288 Example 9 <![CDATA[C2BF2NaO4]]> 0.9935 0.291 Example 10 <![CDATA[NaClO4]]> 0.9921 0.300 Embodiment 11 <![CDATA[NaPF6]]> 0.9930 0.298

[0126] Determining the density of electrolyte solutions

[0127] Example 12

[0128] (1) Collect samples

[0129] 35 batches of LiFSI solution samples were collected, and 30 batches of LiFSI solution samples were randomly selected as calibration set samples, and the remaining 5 batches of LiFSI solution samples were used as prediction set samples. Among them, the LiFSI solution samples contain electrolyte LiFSI and solvent ethyl methyl carbonate.

[0130] (2) Accurate quantification

[0131] The density of 30 batches of calibration set samples and 5 batches of prediction set samples was tested with a densitometer to obtain the actual density values.

[0132] (3) Near-infrared spectrum acquisition

[0133] The near infrared spectra of 30 batches of calibration set samples and 5 batches of prediction set samples were collected by Fourier transform near infrared spectrometer to obtain the near infrared spectrum data of calibration set samples and prediction set samples. Spectrum collection method: colorimetric tube transmission liquid sampling. Collection parameters: resolution 8cm -1 , scanning times 64 times, scanning range 12500cm-1 ~4000cm -1 , ambient temperature 23℃.

[0134] (4) Modeling

[0135] The OPUS software of Bruker was used to establish a mathematical model based on the partial least squares method, selecting the modeling band, spectral preprocessing method, and the number of principal component factors. The model parameters are as follows: Modeling band range: 7500cm -1 ~6100cm -1 ;5028cm -1 ~4600cm -1 , Spectral preprocessing method: multivariate scattering correction, number of principal component factors: 8.

[0136] (5) Model evaluation

[0137] The coefficient of determination R of the mathematical model 2 The internal cross-validation root mean square error RMSECV is 0.000092, the model has good linearity and small RMSECV, indicating that the model has good predictive ability.

[0138] (6) Model Verification

[0139] Based on the mathematical model established in step (4) and the spectral data of the prediction set samples obtained in step (3), the density of the five batches of prediction set samples is calculated to obtain the predicted values. The predicted values ​​of the five batches of prediction set samples are compared with the corresponding density measured values ​​obtained in step (2), as shown in Table 5.

[0140] Table 5

[0141]

[0142] Methodological investigation

[0143] 1. Precision test

[0144] A batch of LiFSI solution samples not involved in the modeling were randomly selected, and the mathematical model established in Example 12 was used to repeat the test 10 times, and the model prediction results were recorded. The relative standard deviation RSD value of the 10 results was 0.003%, indicating that the precision of the model was good.

[0145] 2. Repeatability test

[0146] A batch of LiFSI solution samples that did not participate in the modeling were randomly selected, and the samples were placed in 6 colorimetric tubes. The mathematical model established in Example 12 was used to predict the density of the samples in the 6 colorimetric tubes. The RSD value of the 6 prediction results of the same sample was 0.01%, indicating that the repeatability of the method of the present application was good.

[0147] Examples 13-16

[0148] The method described in Example 12 was followed, and the calibration set samples and prediction set samples used were the same as those in Example 12. The only difference was that the selected modeling bands were different from those in Example 12, as shown in Table 6 below.

[0149] The coefficient of determination R of the mathematical model 2 The internal cross-validation root mean square error RMSECV is shown in Table 6 below.

[0150] Table 6

[0151]

[0152] Examples 17-19

[0153] The method described in Example 12 was followed. The calibration set samples and prediction set samples used were the same as those in Example 12. The only difference was that the spectral preprocessing method was different, as shown in Table 7 below.

[0154] Table 7

[0155]

[0156] It can be seen from the above embodiments that the present application uses the established mathematical model to predict the solution parameters of the prediction set samples, has a good prediction effect, can detect the solution parameters of the electrolyte solution in the production process in real time online, has high test accuracy and strong universality.

[0157] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for measuring solution parameters of an electrolyte solution based on near infrared spectroscopy, characterized in that: The following steps are involved: Selecting a solution parameter to be measured of the electrolyte solution sample to be measured; Determining the solution parameters of the calibration set samples to obtain measured values, wherein the calibration set samples include electrolytes and solvents; Scanning the near infrared spectra of the calibration set samples; Based on the partial least squares method, a modeling band and a spectrum preprocessing method are selected to establish a mathematical model, wherein the modeling band covers at least part of the near-infrared absorption band of the solvent; The spectral data of the electrolyte solution sample to be tested is collected, and the solution parameters of the electrolyte solution sample to be tested are calculated in combination with the mathematical model.

2. The method according to claim 1, characterized in that: The solution parameter is the electrolyte content.

3. The method according to claim 2, characterized in that The electrolyte is LiFSI, C2BF2NaO4, NaClO4 or NaPF6.

4. The method according to claim 2 or 3, characterized in that: The modeling band is 12492cm -1 ~4524cm -1 ; Alternatively, the modeling band is 9400cm -1 ~7500cm -1 and 6100cm -1 ~5448cm -1 .

5. The method according to claim 2 or 3, characterized in that: The calibration set samples contain water, and the modeling band is 6052cm -1 ~4600cm -1 .

6. The method according to any one of claims 2 to 5, characterized in that: The spectrum preprocessing method includes one or more of the first-order derivative, the second-order derivative, the multivariate scattering correction, and the standard normalization method.

7. The method according to claim 1, characterized in that The solution parameter is density.

8. The method according to claim 7, characterized in that The modeling band is 12492cm -1 ~4524cm -1 ; Alternatively, the modeling band is 9400cm -1 ~7500cm -1 and 6100cm -1 ~5448cm -1 .

9. The method according to claim 7, characterized in that: The calibration set samples contain water, and the modeling band is 7500cm -1 ~6100cm -1 and 5028cm -1 ~4600cm -1 .

10. The method according to any one of claims 7 to 9, characterized in that: The spectrum preprocessing method includes one or more of the first-order derivative, the second-order derivative, the multivariate scattering correction, and the standard normalization method.

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