2.1 Instrument
The instrument used in the present study was Niton XL2–960 GOLDD XRF Analyzer equipped with an Ag anode tube operating at a maximum of 45 kV and 100 mA and a Geometrically Optimized Large Area Drift Detector (GOLDD). The standard analytical range was up to 30 elements from Mg to U. There are several instrumental modes (Soils, Mining: Cu/Zn, Minging:Ta/Hf) for particular analytes. There are two filters for each irradiation session, including the main range and light range. The filters are set to include the following elements: Main: Ba, Sb, Sn, Cd, Pd, Ag, Sb, Sn, Cd, Pd, Ag, Mo, Nb, Zr, Sr, Rb, Bi, As, Se, Pb, W, Zn, Cu, Re, Ta, Hf, Ni, Co, Fe, Mn, Cr, V, Ti. Light: Ca, K. Al, P, Si, Cl, S, and Mg. The irradiation area is circular, with 8 mm in diameter.
The Soils mode is more suitable to measure the elements lower than 0.5%, while the Mining mode is more suitable to measure the elements whose contents are greater than 0.5% for the Mining mode was corrected using the fundamental parameters (FP), which can eliminate the interference between the various elements to a large extent. In the X-ray fluorescence spectrum, the peaks of Cu/Zn and Ta/Hf overlap, and a hand-held instrument cannot distinguish them. Therefore, different modes (Mining: Cu/Zn, Minging:Ta/Hf) should be selected according to whether the sample contains Ta/Hf. In the present analysis, the contents of the main elements were higher than 1%, and there was no Ta/Hf in the brick. Therefore, Mining: Cu/Zn was selected as the mode. The total contents of elements were calculated through the built-in algorithm under Mining: Cu/Zn Mode in the HH-XRF. Two filters were used. The instrument was set to the irradiation times of 30 s for each of the main and light filters with the measurement units set to weight percent (%).
A representative brick spectrum obtained from HH-XRF was shown in Fig.S1; ‘Counts/Sec’ represent the emitted spectrum intensity at each photon energy, so they are the basis for quantitative analysis as well as the built-in algorithm.
2.2 Samples and elements studied
Twenty bricks were used as samples (Table S1).
In experiment 1 and experiment 2, only brick 4 was used. In experiment 3 and cross-validation, all bricks were used.
The main elements studied in the present research were Fe, Ca, K, Al, and Si, whose contents were higher than 1%. In Chinese history, brick and tile have always been mentioned at the same time. Similar to bricks, glazed tiles are also made of mainly clay. Except for the firing temperature, other processes of glazed tiles are similar to bricks. In China, many scholars have studied the characteristics of the main element content of glazed tiles in different regions [25–27]. The elements used include the main elements with the highest content for tile, such as Fe, Ca, Mg, Al, and Si. Yang Guimei et al. distinguished the glazed tiles from Mingzhongdu Site in Fengyang, Minggugong Site in Nanjing and Forbidden City in Bejing by PCA analysis of the main elements [25]. Duan Hongying et al. conducted the main component analysis on 398 glazed tiles from 13 provinces and determined the main element characteristics of glazed tiles from different regions (North China: high silicon and low aluminum; the Central of China: low silicon and high aluminum; the Ningxia province: low silicon and low aluminum; the Liaoning province: high magnesium properties) [27]. According to the research related to the glazed tiles, combined with the element contents of the Linqing brick itself, five elements with the highest content (Fe, Ca, K, Al, and Si) were selected as representatives.
However, in order to determine whether HH-XRF could measure more elements in a good precision, in experiment 1, the elements with a content higher than 0.1% were studied (Fe, Ca, K, Al, Si, Ba, Zr, Mn, Mg, Ti, and Cl). The elements studied in experiment 2, experiment 3, and cross-validation (Fe, Ca, K, Al, and Si) were the main elements considered in the present research.
2.3 Experiment 1: Evaluating the effect of measurement time
The measurement time mainly depends on the HH-XRF Analyzer used, the investigated element, and the content of the investigated element [18]. All studied objects were bricks with a similar composition of elements. Thus, in experiment 1, only 1 brick was used.
In order to evaluate the effect of the measurement time on precision, brick 4 was measured five times at a single point for 60, 90, 120, 150, 180, 210, and 240 s. The point was used for measurement after polishing with a sickle. The relative standard deviation (RSD) value for each element at each measurement time was used to determine the effect of the measurement time on the measurement precision. The US EPA criteria were used to evaluate data quality (Table 1) [28].
2.4 Experiment 2: Evaluating the effect of rain on the day of measurement
Humidity can attenuate the signal of HH-XRF, which depends on the humidity level in the air and the composition of the objects being investigated [29]. In the research, rainfall, temperature, and humidity were constant. So, in experiment 2, only 1 brick was use.
In order to evaluate the effect of rain, HH-XRF measurements were taken before and after washing brick 4 in the rain. The point of measurement was polished with a sickle prior to the measurements (prior to the rain and immediately after rain).
The measurements were performed at 1 point under 10 conditions: prior to the rain, and at 0, 1, 2, 3, 5, 7, 9, and 12 h after rain. The measurement conditions were as follows: rainfall 11.2 mm (1 day), the average temperature of 24.3 °C (1 day), and average humidity of 69.2% (1 day).
2.5 Experiment 3: Evaluating the effect of surface contamination and homogeneity
In order to evaluate the effect of surface contamination, determining the homogeneity, prescribing criteria for point selection, treating surface, and getting final data, the following two experiments were performed.
2.5.1 Experiment 3–1: Uniformity of element distribution on the brick surface: Direct measurement vs. measurement after polishing with a sickle
This experiment evaluated the effect of surface conditions on measurement accuracy. Ten points were selected on each brick surface for the measurement. The measurements were performed under two surface conditions: a direct measurement (DM) and a measurement after polishing with a sickle (MPS). The uniformity of the element distribution on the brick surface was determined through the RSD of 10 points and (The ratio of the maximum value to the minimum value of 10 points) [30, 31].
2.5.2 Experiment 3–2: The difference in contents between the brick surface and the brick interior
This experiment demonstrated the effect of surface conditions on measurement accuracy and verified the reliability of MPS. Three fresh sections were cut for each brick that was subjected to measurements. The mean elemental contents of these three sections were compared with the mean elemental contents of the previous surface points polished with sickles obtained in experiment 3–1. The Max{Cp,Cs}/Min{Cp,Cs} was used to evaluate the difference between the points and sections (The ratio of the larger content of the point (mean of the 10 points) and section (mean of the 3 sections) to the smaller value of them).
2.6 Cross-validation using inductively coupled plasma optical emission spectrometry
2.6.1 ICP-OES
The bricks were cut into small pieces, and each brick sample was subsequently ground into a fine powder with particle diameter less than 150 µm. Next, total microwave digestion was used to digest the sample. The digested sample (500 mg) was placed in a PTFE reactor with 4 mL (70%), 1 mL (20%), and 2 mL HF (40%). When the foam caused by the decomposition of organic matter disappeared, the container was capped and heated using a microwave digestion instrument, namely, Solutions MD (Beijing Ying’an Meicheng Scientific Instrument company). The heating process was in accordance with a three-stage digestion procedure, which included 3 min to reach a temperature of 150 °C, 5 min at 180 °C, and 5 min to reach 200 °C. After the microwave digestion, the sample was heated in the acid-driven processor. Subsequently, the digest was transferred into a 50-mL flask and brought to volume with MilliQ water. Finally, the diluted digest was analyzed using a device: PerkinElmer ICP-OES Optima 8300. The protocol are all from Sinopharm Chemical Reagent Co.,Ltd.
The instrument equips with two charge-coupled device (SCD) detectors covering the spectral range from 163-782 nm and features a 40 MHz, free-running solid-state RF generator. The Plasma and shear gas are argon. The way of plasma viewing is radial. The RF power is 1200W. The plasma, auxiliary and nebulizing gas flow are 12L/min,0.5L/min,0.5L/min, respectively. The pump rate is 50r/min. The integration time for low WL range and high WL range are 15s and 5s, respectively. The number of replicates per sample is three. The flush time is 30s. There are multiple spectral lines for each element to be tested. The present experiment selected the spectral lines based on the principle of non-interference of spectral line, intensity, and high sensitivity. The selected spectral lines are 259.940 nm for Fe, 371.933 nm for Ca, 766.490 nm for K, 308.215 nm for Mg, 251.612 nm for Si. MilliQ water was used for calibration solutions. Calibration solutions were prepared by serial dilution of monoelement stock solutions of 1000 mg L-1. They included Fe, Ca ,K, Al (0.5 to 2.0mg L-1) and Si (1.0 to 10.0 mg L-1). All solutions were prepared in 1% (v/v) HNO3.
2.6.2 Cross-validation
Deming regression was used in the comparison between HH-XRF and ICP-OES values with HH-XRF placed on the x-axis and ICP-OES on the y-axis. The RSD, coefficient of determination (R²), and inferential statistics were used to assess a data quality level and compare the relationship between HH-XRF and ICP-OES. The US EPA criteria were used to evaluate data quality (Table 1). If the data quality meeted the requirement of the US EPA criteria, the model was used to data correction for HH-XRF. Rotational (slope, m) and translational (intercept, b) bias were corrected for HH-XRF data by solving for ‘y’ in ‘y = mx+b’ for each regression model.
2.7 Quality Assurance
The RSD used in experiment 1 and experiment 3-1 was calculated by dividing the standard deviation by the arithmetic mean of the data from the same measurement time or the data from different points on the same bricks. RSD was used to evaluate the precision of measurement time and the uniformity of the brick surface.
The data generated using HH-XRF were assessed using established criteria through cross-validation (Table 1). The RSD, R², and inferential statistics were used to assess a data quality level and compare the relationship between HH-XRF and ICP-OES.
For the linearity level, if the Q3 quality level would to be achieved, the R² obtained in the linear regression analysis between HH-XRF and the validation method must be greater than 0.85. For Q2 quality level, R² must be greater than 0.7 (Table 1).
In regard to precision requirements, if the Q3 quality level would to be achieved, RSD must be lower than 10%. For Q2 quality level, RSD must be lower than 20% (Table 1).
In regard to inferential statistics, if the Q3 quality level would to be achieved, the slope (m) = 1 (at 95% confidence level), and the y-intercept (b) = 0 (at 95% confidence level) (Table 1). For Q2 quality level, there is no requirement for inferential statistics.
Table 1 Criteria for characterizing data quality (US EPA)
Data quality level
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Statistical requirement
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Definitive Q3
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R² = 0.85–1. Relative standard deviation (RSD) ≤ 10%. Inferential statistics (test for gradient of line = 1 and y-intercept = 0) must indicate the two datasets are statistically similar (at the 95% confidence level), i.e., relationship y = x accepted.
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Quantitative screening Q2
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R² = 0.70–1. Relative standard deviation (RSD) <20%. Inferential statistics indicate the two datasets are statistically different; i.e., relationship y = mx or y = mx + c accepted.
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Qualitative screening Q1
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R² = less than 0.70. Relative standard deviation (RSD) > 20%. Inferential statistics indicate that two datasets are statistically different.
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