Rapid detection method for free silicon dioxide on the basis of ftir transmission spectrum of breath

Abstract

This invention relates to a rapid, pretreatment-free detection method for breathable free silica on membranes based on FTIR transmission spectroscopy, belonging to the field of dust monitoring. This method establishes the RCS quality within a 5.78 mm spot diameter and the relationship between the RCS and the 800 cm⁻¹ area. ‑ ¹Standard algorithm for characteristic absorption peaks; targeting kaolinite, the most abundant interfering mineral in coal mine dust, utilizing its 915 cm⁻¹ peak... ‑ ¹The absorption peak was established at 800 cm⁻¹ ‑ ¹ An algorithm for subtracting interference. To address the non-uniform distribution of respirable dust deposited on the filter membrane surface, a piecewise function non-uniform distribution model was constructed. Based on this, a rapid algorithm was proposed that can calculate the total RCS mass of the entire filter membrane using only 10 key measurement points. The entire detection process of this invention eliminates the need for pretreatment steps such as strong acid digestion and high-temperature ashing, shortening the single detection cycle to less than 10 minutes, providing a practical and feasible technical approach for rapid on-site RCS detection in the workplace.

Description

Technical Field

[0001] This invention belongs to the field of dust monitoring and relates to a rapid detection method for breathable free silica on membranes without pretreatment based on Fourier transform infrared (FTIR) transmission spectroscopy. Background Technology

[0002] The direct cause of pneumoconiosis is the long-term inhalation of respirable crystalline silica (RCS) in inhalable dust, leading to irreversible pulmonary fibrosis. Therefore, accurate detection of RCS exposure levels in the workplace is a crucial prerequisite for preventing pneumoconiosis at its source. However, RCS measurement still heavily relies on offline laboratory methods, and the significant lag in data collection has become a prominent weakness in the accurate early warning and monitoring of pneumoconiosis.

[0003] Currently, laboratory testing methods for RCS mainly include pyrophosphate assay, infrared spectrophotometry, and X-ray diffraction. These methods all require collecting dust samples at the dust-generating site and undergoing purification steps that can take several hours, including strong acid digestion, weak alkali neutralization, and high-temperature ashing. Some methods also require preparing KBr tablets and plotting standard curves before quantitative analysis can be performed. This cumbersome pretreatment not only results in single-sample testing cycles lasting days and extremely poor timeliness, but also makes the operational procedures highly dependent on the skill level of the laboratory personnel, making it difficult to guarantee the stability and comparability of the test results.

[0004] Faced with the increasing demand for rapid on-site detection, there is an urgent need to develop a simple, fast-responding, and non-complex pretreatment method for rapid RCS detection. Fourier transform infrared (FTIR) transmission spectroscopy, with its advantages of fast scanning speed (seconds), rich information, and low requirements on sample morphology, combined with respirable dust filter membrane sampling technology, holds promise for overcoming the limitations of existing laboratory methods and achieving rapid on-site quantification of RCS. Its basic principle is based on the Lambert-Beer law, which measures RCS at 800 cm⁻¹. - The content of silica was inverted by the intensity of the characteristic absorption peak at ¹. Qiao Jinlin et al. used FTIR transmission method under laboratory conditions to achieve reliable minute-level determination of standard pure SiO2 samples deposited on filter membranes, preliminarily verifying the feasibility of this technical approach.

[0005] In recent years, Zheng et al. pointed out in a systematic review that rapid on-site interpretation is an important development direction for RCS analysis methods; Osho et al. compared PVC and PTFE filter membranes, confirming that FTIR can be used for direct quantification of RCS on filter membranes, but background correction is required; Greth et al. proposed to re-deposit the recovered dust onto the PVC filter membrane before FTIR analysis to address substrate interference; Parks et al. used ashing treatment before performing FTIR measurement on the membrane. Overall, existing studies still generally involve filter membrane reprocessing after sampling, and have not yet truly achieved rapid on-site RCS quantification that is "sampling and measuring immediately without pretreatment".

[0006] Therefore, there is an urgent need for a rapid on-site RCS detection technology to address the shortcomings of existing technologies. Summary of the Invention

[0007] In view of this, the purpose of this invention is to provide a rapid detection method for respirable free silica on membranes based on FTIR transmission spectroscopy without pretreatment, which solves the problem that existing RCS detection methods rely on laboratory pyrophosphate methods, which involve cumbersome pretreatment steps and take several hours. This method enables direct and rapid determination of RCS, a fundamental pathogenic indicator, and provides technical support and equipment foundation for improving the accuracy and timeliness of pneumoconiosis prevention and control.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A rapid detection method for breathable free silica on membranes based on FTIR transmission spectroscopy without pretreatment is provided, comprising the following steps: S1: Place a 37 mm PVC filter membrane in a respirable dust separator, connect a respirable dust sampler, and collect samples at an appropriate flow rate for a certain period of time at the coal mine dust-generating site to allow respirable dust to adhere to the surface of the filter membrane, thus obtaining a dust-laden filter membrane to be tested.

[0009] S2: Place the filter membrane to be tested in a dedicated support. Use a Fourier transform infrared (FTIR) spectrometer to perform infrared spectral scanning at 10 measurement points (0 mm, ±5 mm, ±11 mm) along two mutually perpendicular directions (A and B) on the filter membrane. Obtain the infrared spectrum at each measurement point at 800 cm⁻¹. - ¹ and 915 cm - The characteristic peak at ¹.

[0010] S3: For the above 10 measuring points, firstly, the 915cm of each measuring point is calculated using the kaolinite interference subtraction algorithm (Equation (5)). - ¹The characteristic peak corresponds to kaolinite at 800 cm⁻¹ - Interference characteristic peak at ¹ From the actual measurement of 800 cm -¹The characteristic peak value is subtracted; then the RCS quality value of each measurement point within the spot range is obtained according to the RCS standard algorithm (Equation (4)). Finally, the mass value Divide by the effective spot area (diameter 5.78 mm, area approximately 26.24 mm²) to obtain the RCS surface density value of each measuring point.

[0011] S4: Using the RCS surface density values ​​of the above 10 measuring points, the piecewise regression method (combining equations (6), (7) and (8)) is adopted to first calculate the RCS surface density values ​​of the remaining measuring points in the range of -11 mm to 11 mm in the A and B directions; on this basis, the calculation range is further extended to -15 mm and +15 mm at both ends, so as to completely cover the effective deposition area of ​​30 mm in diameter on the filter membrane.

[0012] S5: Based on RCS surface density values ​​of all measuring points Using the total RCS mass algorithm based on finite measurement point data (Equations (11) to (14)), the RCS mass in the four quadrants of the circular filter membrane is calculated respectively. , , , .

[0013] S6: Sum the RCS masses in the four quadrants to obtain the total RCS mass of the entire filter membrane. .

[0014] Furthermore, in step S3, the calculation formula for the kaolinite interference subtraction algorithm is as follows:

[0015] in, Kaolinite at 800cm -1 The characteristic absorption peak at that location; Kaolinite at 915cm -1 The characteristic absorption peak at that location; It is kaolinite at 800cm -1 915cm -1 The proportional relationship of characteristic absorption peaks.

[0016] Furthermore, in step S3, the calculation formula of the RCS standard algorithm is as follows:

[0017] in, The mass of the RCS on the filter membrane within an effective spot diameter of 5.78 mm, in μg; These are the calibration coefficients obtained from linear regression of the standard curve. For RCS at 800cm -1 The characteristic absorption peak at that location.

[0018] Furthermore, in step S4, the piecewise regression method specifically includes: when x When ∈[-11,-5], ; when x When ∈(-5,5), ; when x When ∈[5,11], ; in, , , The locations of the three measurement points are respectively. x RCS areal density at [location], μg / mm 2 ; , The magnitude coefficient of the exponential function. , For exponential coefficients; , The value is related to the measured RCS areal density values ​​at key points such as -11mm and -5mm on the filter membrane; , The value of is related to the measured RCS areal density values ​​at key points such as 5mm and 11mm on the filter membrane. The reference measurement point location is in the interval [-11, -5]. The reference measurement point location is in the interval [5,11]. , , These are the magnitude parameter, distribution center location, and distribution width parameter of the Gaussian function, respectively.

[0019] Furthermore, in step S5, the formula for calculating the RCS mass in the four quadrants of the circular filter membrane is as follows:

[0020]

[0021]

[0022]

[0023] in, , , , The masses of the RCS in the first, second, third, and fourth quadrants are respectively. , They are respectivelyx Positive half-axis And the surface density of the negative half axis, , for y Positive half-axis And the surface density of the negative half-axis; Let x be the x-coordinate of any point in each quadrant. The unknown variable in the integral function is the radius.

[0024] Furthermore, the apparatus for preparing filter membrane samples with attached respirable dust includes a respirable dust sampler, a separator, a dust generator, and a dust diffuser.

[0025] The beneficial effects of this invention are as follows: Starting from the Lambert-Beer law, this method derives a theoretical formula for the RCS quality within the light spot range under direct transmission mode of the filter membrane, eliminating the difficulty in measuring optical path parameters. Using α-quartz as an RCS substitute, the RCS quality within a light spot diameter of 5.78 mm and its relationship with 800 cm⁻¹ were established. - ¹Standard algorithm for characteristic absorption peaks; targeting kaolinite, the most abundant interfering mineral in coal mine dust, utilizing its 915 cm⁻¹ peak... - ¹The absorption peak was established at 800 cm⁻¹ - ¹ An algorithm for subtracting interference. Addressing the non-uniform distribution of respirable dust deposited on the filter membrane surface, the distribution patterns of RCS areal density at measurement points in directions A and B were analyzed. A piecewise function non-uniform distribution model was constructed, and based on this, a rapid algorithm was proposed to calculate the total RCS mass of the entire filter membrane using only 10 key measurement points. Experimental verification shows that the average error between the calculated and measured values ​​using the non-uniform distribution algorithm is ≤3.17%, and the maximum error is ≤7.99%. Using the pyrophosphate method as a benchmark, the maximum deviation error of the total RCS mass percentage is 17.87%, and the method achieves a detection resolution of 10 μg for RCS on the membrane. The entire detection process requires no pretreatment steps such as strong acid digestion or high-temperature ashing, and the single detection cycle is shortened to less than 10 minutes, providing a practical and feasible technical approach for rapid on-site RCS detection in the workplace.

[0026] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1This is a flowchart of the rapid detection method for breathable free silica on membranes based on FTIR transmission spectroscopy without pretreatment according to the present invention. Figure 2 For the mass of α-quartz within a spot diameter of 5.78 mm and 800 cm - ¹Characteristic peak absorption curve; Figure 3 For different kaolinite masses within a spot diameter range of 5.78 mm, 800 cm - ¹Absorption peak at 915 cm⁻¹ - ¹Relationship curve of absorption peaks; Figure 4 This is the RCS surface density distribution of a portion of the filter membrane at various measuring points along direction A; Figure 5 The non-uniformity of the RCS algorithm at the measurement point in direction A on the membrane; Figure 6 The non-uniformity of the RCS algorithm at the measurement point along direction B on the membrane; Figure 7 This represents the deviation error of the RCS mass percentage for each filter membrane. Detailed Implementation

[0028] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0029] Please see Figures 1 to 7 This invention is based on FTIR transmission spectroscopy and combined with a respirable dust sampling filter membrane to construct a truly rapid on-site RCS detection technology that requires no pretreatment. This enables direct and rapid determination of RCS, a fundamental pathogenic indicator, and provides technical support and equipment foundation for improving the accuracy and timeliness of pneumoconiosis prevention and control.

[0030] 1. Principle of FTIR direct transmission measurement of RCS on membrane Breathable free silica (RCS) appears in the infrared spectrum at approximately 800 cm⁻¹. - A distinct characteristic absorption band is observed at ¹. The NIOSH 7603 method explicitly identifies this band as the primary analytical region for RCS quantification, while also noting that this region may be affected by interference from some silicate minerals, thus requiring correction based on sample matrix conditions. Existing studies have also shown that RCS is at 800 cm⁻¹.- ¹A quantitative relationship can be established between the absorption signal in the vicinity and its loading, indicating that this band is suitable for the analysis of RCS on the membrane.

[0031] Based on this principle, if respirable dust is directly collected onto a filter membrane and then placed directly into the FTIR optical path for transmission measurement, a reading of 800 cm⁻¹ can be obtained. - The absorption signal near ¹ is used to invert the RCS content on the filter membrane. The basic theoretical basis for this quantitative relationship is the Lambert-Beer law, and its expression is shown in equation (1).

[0032] (1) In the formula, The characteristic absorption peak is located at 800 cm⁻¹ and is dimensionless. RCS is the absorption coefficient in this band, cm2 / μg; The optical path length is the effective thickness of the dust layer through which the infrared beam penetrates, expressed in cm. denoted as RCS mass concentration in the dust layer, in μg / cm³.

[0033] In direct transmission measurements of filter membranes, dust deposits on the surface of the filter membrane or in the interfiber gaps within it affect the effective optical path. It is difficult to measure directly. Therefore, the mass concentration of RCS in the dust layer can be determined. Represented as the total RCS mass on the filter membrane With dust layer volume The ratio, that is Let the effective spot area of ​​the infrared beam be... The volume of the dust layer Substituting into equation (1), we can obtain equation (2).

[0034] (2) As can be seen from equation (2), the absorption peak Total mass of RCS on filter membrane It is directly proportional to the intrinsic absorbance of the RCS, and the proportionality coefficient is only related to the intrinsic absorbance coefficient of the RCS. and instrument spot area Related to, but not directly measurable, optical path length Irrelevant. Therefore, in the known... and Under the given conditions, the RCS mass on the filter membrane can be directly calculated using equation (3).

[0035] (3) In the formula, The mass of RCS on the filter membrane, in μg; The effective area of ​​the light spot for FTIR measurement is measured in cm². The absorbance coefficient of RCS at 800 cm⁻¹ can be obtained from literature or through independent calibration, in cm² / μg.

[0036] Compared to traditional laboratory methods, the key to this direct on-membrane analysis approach lies in reducing sample processing steps, allowing the sampled filter membrane to be used directly for detection. Related studies show that on-membrane FTIR transmission is feasible in practical sample analysis, but the results are still affected by interference from coexisting minerals; therefore, excluding such interference remains necessary.

[0037] 2. Construction of a rapid detection method for RCS on membranes without pretreatment The preceding text, starting from the Lambert-Beer law, derived the theoretical calculation formula for RCS quality in the direct transmission measurement mode of the filter membrane, and demonstrated that through 800 cm... - ¹Feasibility of using characteristic absorption peaks to retrieve RCS quality on the membrane. Based on this, this invention further conducts a systematic study on a rapid RCS detection method for membranes, specifically including: a standard RCS algorithm, a kaolinite interference subtraction algorithm, an algorithm for non-uniform RCS distribution on the membrane, an algorithm for total RCS quality on the membrane, and a rapid detection process, etc.

[0038] 2.1 Establishment of the RCS Standard Algorithm RCS mass and infrared spectrum at 800 cm⁻¹ on the membrane - ¹ The quantitative correspondence between peak height and 800 cm⁻¹ was established, and the RCS quality on the membrane was correlated with the peak height. - ¹The quantitative relationship between absorption peaks, i.e., the RCS standard algorithm, is the foundation for subsequent rapid detection. In this study, commercially available α-quartz was selected as the physical substitute for RCS, with a median particle size of 5 μm, which meets the relevant requirements of GBZ / T 192.4—2007 regarding the particle size of respirable dust.

[0039] To eliminate the influence of the filter membrane itself on the detection results, PVC filter membranes with good consistency were used uniformly in the experiment. To avoid the uneven distribution of α-quartz on the filter membrane surface interfering with the establishment of the standard algorithm, sample preparation was carried out by stirring and low-altitude filtration to ensure that α-quartz particles were uniformly deposited on the filter membrane. After filtration, the filter membrane was allowed to dry completely under natural conditions, and then the α-quartz-coated filter membrane was scanned by infrared spectroscopy using an FTIR instrument (Nicolet Summit Pro), with an effective infrared spot diameter of 5.78 mm. Under different total loading conditions on the membrane, the IR spectra corresponding to the mass of α-quartz in the area covered by the spot were obtained. Based on this, the 800 cm⁻¹ values ​​were read under each mass condition. - The absorption peak value at point ¹ is a characteristic peak. After data processing and regression analysis, the mass of α-quartz within a spot diameter of 5.78 mm and its relationship with the 800 cm⁻¹ region were obtained. -¹The correspondence curves between characteristic absorption peaks and the standard algorithm expression, such as Figure 2 As shown in equation (4).

[0040] (4) In the formula, The mass of α-quartz on the filter membrane within an effective spot diameter of 5.78 mm is expressed in μg. These are the calibration coefficients obtained from linear regression of the standard curve.

[0041] 2.2 Kaolinite Interference Reduction Algorithm Equation (3) above gives the theoretical formula for calculating the RCS mass on the membrane, which is based on the premise that the RCS is 800 cm. - The absorption peak at ¹ is entirely contributed by the RCS. However, actual respirable dust samples often contain various symbiotic silicate minerals, which exhibit absorption peaks at 800 cm⁻¹. - ¹Infrared absorption of varying degrees also exists in the vicinity, thus interfering with the accurate quantification of RCS. Taking coal mine dust as an example, common symbiotic silicate minerals mainly include kaolinite, muscovite, mullite, pyrophyllite, montmorillonite, and feldspar group minerals, among which kaolinite is the most abundant, reaching about 20% in some coal mine respirable dust.

[0042] Kaolinite has the chemical formula Al₂O₃·2SiO₂·2H₂O and belongs to the 1:1 type layered silicate minerals. In the mid-infrared spectral region, kaolinite is most prominent at 915 cm⁻¹. - ¹The location belongs to Al The characteristic absorption peak of the OH bond bending vibration, and also at 800 cm⁻¹ - ¹Nearby, belonging to Si Absorption peaks of O-bond stretching vibrations. Among them, 800 cm⁻¹... - ¹The absorption peak near the RCS of Si O The peak bands of Si symmetric stretching vibration overlap, so the presence of kaolinite will directly interfere with the quantitative analysis of RCS by FTIR.

[0043] Based on the above analysis, this invention establishes a corresponding interference subtraction algorithm to address the interference problem of kaolinite. Kaolinite dust with a purity greater than 99% and a median particle size of less than 5 μm was selected for the experiment. Sample preparation followed the previously described method, using a PVC filter membrane and low-altitude filtration with stirring to ensure uniform deposition of kaolinite particles on the filter membrane surface. After filtration, the filter membrane was allowed to dry naturally, and then infrared spectroscopy was performed using an FTIR instrument, with an effective infrared spot diameter of 5.78 mm. Under different kaolinite loading conditions on the membrane, the IR spectra corresponding to the mass of kaolinite within the spot coverage area were obtained. Based on this, IR spectra were read at 800 cm⁻¹.- ¹ and 915 cm - The absorption peak value at point ¹ is a characteristic peak. After data processing and regression analysis, the absorption peak values ​​at 800 cm⁻¹ were obtained for different kaolinite masses within a spot diameter range of 5.78 mm. - ¹Absorption peak at 915 cm⁻¹ - ¹The correspondence curve between absorption peaks is used to establish the expression for the correction algorithm to remove kaolinite interference, such as... Figure 3 As shown in equation (5).

[0044] (5) In the formula, Kaolinite at 800cm -1 The characteristic absorption peak at that location; Kaolinite at 915cm -1 The characteristic absorption peak at that location; It is kaolinite at 800cm -1 915cm -1 The proportional relationship of characteristic absorption peaks.

[0045] In actual sample testing, it is only necessary to read the light spot diameter within 800 cm. - ¹and 915 cm - ¹The characteristic absorption peaks at the two locations are substituted into equation (5) to calculate the absorption peaks of kaolinite at 800 cm⁻¹. - The interference absorption peak generated at ¹, and then measured at 800 cm⁻¹ - ¹Subtracting this interference value from the absorption peak will eliminate the influence of kaolinite on the quantitative detection of RCS.

[0046] 2.3 Modeling of Non-uniform RCS Distribution on the Membrane The previous text has established the RCS quality within a spot diameter of 5.78 mm and the relationship with 800 cm. - ¹Standard algorithm between absorption peaks, and kaolinite at 800 cm⁻¹ - The algorithm for subtracting the interference absorption peak¹ provides a foundation for accurate quantification of RCS within the area covered by the infrared spot. However, the above algorithms are all based on the assumption of uniform dust distribution within the infrared spot area. In actual sampling, respirable dust is collected by a separator onto a 37 mm diameter PVC filter membrane, with an effective deposition area diameter of 30 mm, much larger than the effective diameter of the infrared spot (5.78 mm). Due to the influence of the sampling airflow distribution characteristics, the dust deposition on the filter membrane surface exhibits a non-uniform distribution characteristic. Therefore, to obtain the total mass of the RCS on the entire filter membrane, it is necessary to first study the non-uniform distribution law of the RCS on the membrane and establish a corresponding non-uniform distribution correction algorithm. Only on this basis can the quantitative results within the spot area be extrapolated to the entire filter membrane.

[0047] This invention takes respirable dust from coal mines as an example and constructs an experimental system consisting of a respirable dust sampler, a separator, a dust generator, and a dust diffuser to prepare filter membrane samples with attached respirable dust from coal mines.

[0048] For the collected dust-laden filter membrane samples, FTIR transmission method was used to perform infrared spectral scanning along two mutually perpendicular directions A and B on the filter membrane, at distances of ±11, ±9, ±7, ±5, ±3, ±2, ±1, and 0 mm from the center point, to obtain the positions of each measuring point at 800 cm. - ¹ and 915 cm - The absorption peak value of ¹ was then calculated. Subsequently, the initial RCS value at each measuring point was calculated using equation (4), and the RCS value of kaolinite at 800 cm⁻¹ was calculated using equation (5). - The interference value at point ¹. Subtracting the kaolinite interference value from the initial RCS value yields the RCS weight values ​​at different locations on the filter membrane.

[0049] After obtaining the RCS weight value at each measuring point on each dust-laden filter membrane, since the RCS mass at each measuring point is within an effective spot diameter of 5.78 mm, dividing it by the corresponding spot area (approximately 26.24 mm²) yields the RCS mass per unit area at each measuring point, i.e., the RCS areal density. Based on this, the non-uniform distribution characteristics of the RCS areal density on the filter membrane surface are further analyzed. Figure 4 The distribution of RCS surface density at various measuring points along direction A of some filter membranes is given. The distribution characteristics along direction B are similar and will not be repeated here.

[0050] Figure 4 The measured distribution of RCS areal density along the A direction of five typical filter membranes is shown. It can be seen that although the total RCS loading of different filter membranes is significantly different, the radial distribution of areal density all exhibits a Gaussian-like characteristic with high center and low edge. This provides a basis for establishing a unified piecewise non-uniform distribution function.

[0051] Taking direction A as an example, if a single Gaussian function is used for fitting throughout the interval [-11, 11], the decay rate at both ends of the function is too fast, which is inconsistent with... Figure 4 The measured data does not show a gradual decline trend at both ends. Therefore, a piecewise regression method is considered, dividing the entire interval into three segments: [-11, -5], (-5, 5) and [5, 11], for fitting. The three-segment function expressions established accordingly are shown in equations (6), (7), and (8).

[0052] when x When ∈[-11,-5], (6) when x When ∈(-5,5), (7) when x When ∈[5,11], (8) In the formula, , , The locations of the three measurement points are respectively. x RCS areal density at [location], μg / mm 2 ; , The value is related to the measured RCS areal density values ​​at key points such as -11mm and -5mm on the filter membrane; , The value of is related to the measured RCS areal density values ​​at key points such as 5mm and 11mm on the filter membrane. The reference measurement point location is in the interval [-11, -5]. The reference measurement point location is in the interval [5,11]. , , These represent the magnitude parameter, distribution center location, and distribution width parameter of the Gaussian function, respectively.

[0053] After completing the non-uniform distribution modeling, since the effective deposition diameter of the filter membrane is 30 mm, while the current measurement point range only covers 22 mm, the distribution function needs to be extended outward to -15 mm and +15 mm in the A and B directions according to equations (6), (7), and (8) to fully cover the effective deposition area of ​​the filter membrane.

[0054] 2.4 Calculation of Total RCS Mass on the Membrane The preceding analysis of the RCS areal density at various measurement points along directions A and B of the filter membrane established a piecewise function for the non-uniform distribution of RCS on the membrane, providing a basis for calculating the total RCS mass of the entire filter membrane. However, in actual testing, scanning all 30 measurement points along directions A and B one by one would significantly increase the time cost of a single measurement and reduce the practical efficiency of rapid testing. Utilizing the established non-uniform distribution pattern, it is hoped that by measuring the RCS mass of a small number of key measurement points (0, ±5, and ±11 along directions A and B), the distribution at other locations can be calculated, thereby estimating the total RCS mass of the entire filter membrane. This section will focus on this problem, establishing an algorithm for the total RCS mass of the membrane based on data from a limited number of measurement points.

[0055] Since directions A and B are perpendicular to each other, they can be used as two-dimensional rectangular coordinate systems. x shaft and y Along the axial direction, the filter membrane is divided into four quadrants. Within each quadrant, any point is... The surface density is obtained by linear interpolation of the radial functions on two adjacent half-axis through angle.

[0056] Taking the first quadrant as an example, from arrive Surface density function As shown in equation (9).

[0057] (9) In the formula, for x Positive half-axis areal density, for y Positive half-axis Surface density.

[0058] in, , All values ​​were obtained by dividing the RCS quality of the 5.78mm infrared spot measurement point by the spot area (26.24mm²).

[0059] Then, by integrating equation (9), the mass of the RCS in the first quadrant is obtained, as shown in equation (10).

[0060] (10) First, consider equation (10) Integrating, we get equation (11).

[0061] (11) Similarly, the RCS quality in the other three quadrants can be obtained, as shown in equations (12), (13) and (14).

[0062] (12) (13) (14) In summary, the total mass of the RCS on the membrane is obtained as shown in equation (15).

[0063] (15) 2.5 Rapid detection process without pretreatment Based on the completed RCS standard algorithm, kaolinite interference subtraction algorithm, non-uniform RCS distribution algorithm on membrane, and total RCS mass algorithm on membrane, these algorithms are integrated into a complete rapid RCS detection workflow on membranes. This workflow requires no pretreatment steps in actual detection, significantly shortening the detection cycle. Taking coal mine dust as an example, the specific operation steps are as follows: (1) Place a 37 mm PVC filter membrane in a respirable dust separator, connect a respirable dust sampler, and collect samples at an appropriate flow rate for a certain period of time at the coal mine dust-generating site to allow respirable dust to adhere to the surface of the filter membrane and obtain a dust-containing filter membrane to be tested. (2) Place the filter membrane to be tested in a special support, and use an FTIR spectrometer to perform infrared spectral scanning at 10 measurement points (0, ±5, ±11 mm) along two mutually perpendicular directions (A and B), and obtain the infrared spectrum at each measurement point at 800 cm⁻¹. - ¹ and 915 cm - The characteristic peak at ¹; (3) For the above 10 measuring points, first use formula (5) to calculate the 915 cm for each measuring point. - ¹The characteristic peak corresponds to kaolinite at 800 cm⁻¹ - Interference characteristic peak at ¹ From the actual measurement of 800 cm - ¹The characteristic peak value is subtracted; then the 800 cm⁻¹ value after removing interference is... - Substituting the characteristic peak value into equation (4), the RCS quality value of each measurement point within the spot area is obtained. Finally, divide the mass value by the effective spot area (diameter 5.78 mm, area approximately 26.24 mm²) to obtain the RCS surface density value of each measuring point. (4) Using the RCS surface density values ​​of the above 10 measuring points, combined with Equations (6), (7) and (8), first calculate the RCS surface density values ​​of the remaining measuring points in the range of -11 to 11 mm in the directions A and B; on this basis, further extend the calculation range to -15 mm and +15 mm at both ends, so as to completely cover the effective deposition area of ​​30 mm in diameter on the filter membrane. (5) Calculate the RCS surface density values ​​of all measuring points. Substitute into equations (11) to (14) to calculate the RCS mass of the circular filter membrane in each of the four quadrants. , , , ; (6) Sum the RCS masses in the four quadrants to obtain the total RCS mass of the entire filter membrane. .

[0064] The entire detection process eliminates the need for RCS purification steps that take several hours, such as strong acid digestion, weak alkali neutralization, and high-temperature ashing. The detection cycle can be shortened to less than 10 minutes, significantly improving the timeliness of RCS detection.

[0065] 3. Method Validation and Error Analysis This invention proposes a rapid RCS detection method for membranes based on FTIR transmission method. Its core lies in the accuracy of the non-uniform RCS distribution algorithm and the total RCS mass algorithm. The errors of these two algorithms are experimentally verified below.

[0066] 3.1 Error Analysis of Non-Uniform Distribution Algorithm Figure 5 The measured RCS areal density values ​​of five typical filter membranes at all 30 measurement points in both directions A and B are presented. Specifically, 15 measurement points are arranged in direction A from -11 mm to +11 mm, and 15 measurement points are arranged in direction B, for a total of 30 measurement points in both directions.

[0067] To verify the accuracy of the non-uniform distribution algorithm, this experiment only selected the measured areal density values ​​of 5 key measuring points (±11 mm, ±5 mm, and 0 mm) in each direction as input (a total of 10 measured values ​​in directions A and B). These values ​​were substituted into the non-uniform distribution algorithm described in equations (6), (7), and (8) to calculate the RCS areal density values ​​of the remaining 10 measuring points in direction A and the remaining 10 measuring points in direction B (a total of 20 measuring points in both directions). The algorithm-calculated values ​​of the above 20 measuring points were compared with the actual measured values ​​at the corresponding locations to obtain the error of the non-uniform RCS distribution algorithm on the membrane. The error distribution of the calculated RCS areal density values ​​of each measuring point in directions A and B is shown below. Figure 5 and Figure 6 As shown.

[0068] Depend on Figure 5 and Figure 6 It can be seen that the calculation error of the non-uniform RCS distribution algorithm on the membrane is within 5% at most measurement points, with an average error of no more than 3.17%; there are slightly larger deviations at individual measurement points in directions A and B, with the maximum error not exceeding 7.99%.

[0069] 3.2 Comparison and Verification of the Total Mass Calculation and the Pyrophosphate Method Based on the verification of the non-uniform RCS distribution algorithm on the membrane, further error experiments were conducted on the total RCS mass algorithm on the membrane. The comparison method adopted was the laboratory standard method specified in GBZ / T 192.4—2007—pyrophosphate method.

[0070] To ensure that the respirable dust collected on the membrane and the dust used in the pyrophosphate method chemical analysis originated from the same batch of samples, the same model of respirable dust sampler and separator were used for sampling. The pyrophosphate method requires a dust sample mass greater than 100 mg, while the maximum adsorption amount on a single 37 mm PVC filter membrane is approximately 10 mg, which is insufficient. Therefore, 30 dust-laden filter membranes were collected simultaneously. The respirable dust collected on the membranes was eluted with anhydrous ethanol solution into a beaker. After the solvent slowly evaporated and the samples dried, the dust samples were combined for pyrophosphate analysis.

[0071] The RCS (Respirable Solids) percentage of this batch of coal mine respirable dust was determined to be 2.58% by the pyrophosphate method.

[0072] Taking a portion of dust-laden filter membranes as examples, the total mass of respirable dust on each membrane was obtained by weighing. Then, the RCS mass calculated using the total RCS mass algorithm was divided by the corresponding respirable dust mass to obtain the RCS mass percentage for each filter membrane. Table 1 lists the RCS mass, respirable dust mass, and RCS mass percentage data for a portion of the filter membranes.

[0073] Table 1. Data on respirable dust, RCS mass, and RCS mass percentage on the membrane.

[0074] As shown in Table 1, this method achieves a resolution of less than 10 μg for detecting the RCS mass on the membrane. The RCS mass percentage measured for each filter membrane was compared with the baseline value of 2.58% determined by the pyrophosphate method. The deviation error between the two values ​​was calculated and the absolute value was taken. The results are as follows: Figure 7 As shown. Figure 7 As shown, the maximum deviation error between the RCS mass percentage measured by the rapid RCS detection method on the membrane and the standard method is 17.87%.

[0075] 3.3 Discussion of Verification Results The algorithms for non-uniform RCS distribution and total RCS mass distribution on the membrane were experimentally verified, and the main results are as follows: (1) Algorithm error due to non-uniform distribution of RCS on membrane. Taking five typical filter membranes as examples, the RCS values ​​calculated by the algorithm at each measuring point were compared with the measured values. The results show that the calculation errors at each measuring point in both directions A and B are mostly small, with an average error of no more than 3.17%; however, some measuring points have larger deviations, with the maximum error not exceeding 7.99%.

[0076] (2) Algorithm error of total RCS mass on membrane. The algorithm calculation results of 19 filter membranes were compared and verified based on the RCS mass percentage determined by the pyrophosphate method (2.58%). The results showed that the maximum deviation error of the RCS mass percentage of each filter membrane was 17.87%. The deviation error of the total mass percentage was relatively large in the low-load sample, which may be related to the decrease in signal-to-noise ratio and the change in the relative proportion of interfering minerals at low concentrations. It can be further improved by optimizing the algorithm weights or increasing the number of calibration points.

[0077] In summary, the non-uniform RCS distribution algorithm on the membrane has good fitting accuracy at most measurement points and can effectively describe the distribution characteristics of RCS on the filter membrane surface. The consistency between the total RCS mass algorithm on the membrane and the standard method is generally acceptable, and it has reliable resolution at the 10 μg level, which preliminarily verifies the feasibility of the proposed rapid detection method.

[0078] 4. Conclusion This invention addresses the need for rapid on-site detection of respirable free silica (RCS). Based on FTIR transmission spectroscopy and combined with direct sampling and measurement of filter membranes, a rapid RCS detection method on membranes without pretreatment is constructed. The main conclusions are as follows: (1) Starting from Lambert-Beer's law, the theoretical calculation formula for the RCS quality within the spot range under the direct transmission measurement mode of the filter membrane was derived, eliminating the optical path parameter of the dust layer that is difficult to measure directly, and providing a theoretical basis for the quantitative analysis of RCS on the membrane.

[0079] (2) Using α-quartz as a physical substitute for RCS, the mass of α-quartz within a spot diameter of 5.78 mm and its relationship with 800 cm⁻¹ were established. - ¹A standard algorithm for the characteristic peak absorption peaks; a algorithm based on 915 cm⁻¹ was established for kaolinite, the most significant symbiotic interfering mineral in coal mine dust. - ¹Absorption peak subtracted by 800 cm⁻¹ - ¹An algorithm for correcting interference.

[0080] (3) To address the problem of non-uniform distribution of respirable dust deposited on the filter membrane surface, the distribution law of RCS surface density at each measuring point on the test lines in directions A and B was analyzed, and a piecewise function expression for the non-uniform distribution of RCS on the membrane was established. Based on this, an algorithm was proposed that can quickly calculate the total mass of the RCS of the entire filter membrane by measuring only 10 key measuring points.

[0081] (4) Experimental results show that the average error between the calculated and measured values ​​of the non-uniform RCS distribution algorithm on the membrane is no more than 3.17%, and the maximum error is no more than 7.99%. Based on the pyrophosphate method, the maximum deviation error of the total mass percentage of RCS on the membrane is 17.87%. The resolution of the RCS mass on the membrane by this method can reach 10 μg. The proposed algorithm can effectively describe the distribution characteristics of RCS on the filter membrane surface, and the total mass calculation results have acceptable consistency with the standard method, thus preliminarily verifying the feasibility of this rapid detection method.

[0082] This invention provides a technical approach for rapid on-site detection of RCS exposure levels in the workplace without pretreatment, and has certain reference value for improving the monitoring efficiency of essential pathogenic indicators in the prevention and control of pneumoconiosis.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A rapid detection method for breathable free silica on membranes without pretreatment based on FTIR transmission spectroscopy, characterized in that, The method includes the following steps: S1: Place the filter membrane in the respirable dust separator, connect the respirable dust sampler, and collect the dust at the dust-generating site to allow the respirable dust to adhere to the surface of the filter membrane, thus obtaining the dust-laden filter membrane to be tested. S2: Place the filter membrane to be tested in a dedicated support. Use a Fourier transform infrared spectrometer to perform infrared spectral scanning at 10 measurement points (0 mm, ±5 mm, ±11 mm) along two mutually perpendicular directions (A and B) on the filter membrane. Obtain the infrared spectrum at each measurement point at 800 cm⁻¹. - ¹ and 915 cm - The characteristic peak at ¹; S3: Calculate the 915 cm depth at each measuring point using the kaolinite interference subtraction algorithm. - ¹The characteristic peak corresponds to kaolinite at 800 cm⁻¹ - Interference characteristic peak at ¹ From the actual measurement of 800 cm - ¹The characteristic peak value is subtracted; then the RCS quality value of each measurement point within the spot area is obtained according to the RCS standard algorithm. Finally, the quality value Divide by the effective spot area to obtain the RCS surface density value of each measuring point; where RCS is free silica. S4: Using the RCS surface density values ​​of the above 10 measuring points, the piecewise regression method is adopted to first calculate the RCS surface density values ​​of the remaining measuring points in the range of -11 mm to 11 mm in the directions A and B; on this basis, the calculation range is further extended to -15 mm and +15 mm at both ends respectively. S5: Based on the RCS areal density values ​​of all measuring points, the RCS mass of the circular filter membrane in the four quadrants is calculated using the total RCS mass algorithm based on finite measuring point data. S6: Sum the RCS masses in the four quadrants to obtain the total RCS mass of the entire filter membrane.

2. The rapid detection method for breathable free silica on membranes without pretreatment according to claim 1, characterized in that, In step S3, the calculation formula for the kaolinite interference subtraction algorithm is as follows: in, Kaolinite at 800cm -1 The characteristic absorption peak at that location; Kaolinite at 915cm -1 The characteristic absorption peak at that location; It is kaolinite at 800cm -1 915cm -1 The proportional relationship of characteristic absorption peaks.

3. The rapid detection method for breathable free silica on membranes without pretreatment according to claim 1, characterized in that, In step S3, the calculation formula of the RCS standard algorithm is as follows: in, The quality of the RCS on the filter membrane within the effective spot diameter range; These are the calibration coefficients obtained from linear regression of the standard curve. For RCS at 800cm -1 The characteristic absorption peak at that location.

4. The rapid detection method for breathable free silica on membranes without pretreatment according to claim 1, characterized in that, In step S4, the piecewise regression method specifically includes: when x When ∈[-11,-5], ; when x When ∈(-5,5), ; when x When ∈[5,11], ; in, , , The locations of the three measurement points are respectively. x RCS areal density at the location; , The magnitude coefficient of the exponential function. , For exponential coefficients; , The value is related to the measured RCS areal density values ​​at the -11mm and -5mm points on the filter membrane; , The value is related to the measured RCS areal density values ​​at 5mm and 11mm points on the filter membrane; The reference measurement point location is in the interval [-11, -5]. The reference measurement point location is in the interval [5,11]. , , These are the magnitude parameter, distribution center location, and distribution width parameter of the Gaussian function, respectively.

5. The rapid detection method for breathable free silica on a membrane without pretreatment according to claim 1, characterized in that, In step S5, the formula for calculating the RCS mass in the four quadrants of the circular filter membrane is as follows: in, , , , The masses of the RCS in the first, second, third, and fourth quadrants are respectively. , They are respectively x The surface density of the positive and negative semi-axis, , for y The surface density of the positive and negative semi-axis; Let x be the x-coordinate of any point in each quadrant. The radius is [0, 1].

6. The rapid detection method for breathable free silica on membranes without pretreatment according to claim 1, characterized in that, In step S2, directions A and B are perpendicular to each other along the filter membrane.

7. The rapid detection method for breathable free silica on a membrane without pretreatment according to claim 1, characterized in that, The apparatus for preparing filter membrane samples with attached respirable dust includes a respirable dust sampler, a separator, a dust generator, and a dust diffuser.

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