Jianfeng Cui

To understand the firing technology and the level of production of glazed tiles in the Ming Dynasty, an ultraviolet-visible-near-infrared (UV-Vis-NIR) spectrophotometer and an energy-dispersive X-ray fluorescence (XRF) spectrometer were used to systematically analyze the glaze color and chemical composition of glazed tiles unearthed from the southern area of the Bao’ensi site. The results revealed that little difference exists in the main wavelengths of the same glazed tile samples, but that the colored elements of glaze with different colors are different. Iron (Fe) is the main element that affects the color of yellow glaze; copper (Cu) is the main element that affects the color of green glaze; and the main colorants that affect the color of black glaze are Fe, Cu, and manganese (Mn). The yellow and black glaze unearthed from the southern area of the Bao’ensi site were relatively mature in terms of raw material composition and technological formula, whereas the raw material and technological formula of green glaze were not stable enough. To meet temple construction needs, workers were skilled in firing glazed tiles with different glaze color requirements, indicating that the glaze color of the glazed tiles was relatively mature at that time.

Architectural glazed tile, a type of low-temperature lead-glazed pottery, is a common building material used in traditional Chinese architecture with good decorative and waterproof functions. The process of fabricating glass components in Chinese architecture has a long history. The earliest historical records can be found in the Book of Wei, compiled by the Wei Dynasty during the Northern Qi Dynasty. Since that time, with the development of glazed-tile firing technology, many fine architectural glass components have been developed.

In recent years, ample research of the manufacturing technology of ancient glazed tile in China has been conducted (1–3). The study of ancient glazed tile in Nanjing and Beijing has been a particular area of focus, given that Nanjing and Beijing were the capital cities of the Ming and Qing Dynasties, respectively. The ancient glazed tile has been examined for the chemical composition of yellow glaze, the mechanism of peeling glaze, and the degradation of green glaze (4–7). The Ming Dynasty was a key stage in the transition from the mature period of the Song and Yuan Dynasties to the heyday of Ming and Qing Dynasties on the basis of inheriting the technology of the Song and Yuan Dynasties. Glazed-tile firing technology during this period developed a variety of glaze colors, such as yellow, green, red, purple, blue, black, and white, which met the needs of all types of buildings and laid a foundation for the appearance of glazed tiles in the Ming and Qing Dynasties’ official buildings. However, there is relatively little research on the technology of glaze making at this stage, and even less research on the world-famous buildings of the Ming Dynasty, such as the great Bao’ensi site and the Ming Palace. Yinzhong and others studied the chemical composition of glazes, the melting temperature range of yellow glazes, and the matching of the thermal expansion coefficients of tire glazes of six architectural glazed components unearthed from the northern part of the Bao’ensi site in Nanjing (8).

In the Ming Dynasty, the Nanjing Bao’ensi was built in the tenth year of Yongle (AD 1412). Its temple was built in accordance with the standards of the imperial palace, and it was resplendent and magnificent in scale (9). From its completion to its decay, the pagoda has always been the highest building in China. It ranked among the Seven Wonders of the World in the Middle Ages, and it was regarded by Westerners as a landmark building representing China (9). The Ming Dynasty Bao’ensi site is generally divided into two areas: the south and the north. To cooperate with the construction of the “Bao’ensi Temple Site Park,” the Nanjing Institute of Archaeology conducted a comprehensive and systematic archaeological excavation in the northern area from 2007 to 2010 and the southern area in 2017.

Glazed tiles unearthed from this site represent the highest level of technology of glazed tile-making in the Ming Dynasty. For the purposes of studying the firing technology and production level of glazed tiles in the Ming Dynasty, 31 colored glaze samples were selected at the southern district and excavated archaeologically in the gratitude temple site, using an ultraviolet-visible-near-infrared (UV-Vis-NIR) spectrophotometer and energy-dispersive X-ray fluorescence (XRF) spectrometer. The glaze color and chemical composition of glazed tile unearthed from the great gratitude temple were measured, and the results were analyzed by using a multivariate statistical analysis method.

A total of 31 samples were selected from the southern area of the Bao’ensi site, including bobbin and slab tiles. Some samples were well preserved, with yellow, black, green, and peeling glazes. The body color was mainly gray and white with delicate quality. The samples are shown in Figure 1.

FIGURE 1: Images of several glazed tile examples from Bao’ensi in Nanjing.

For chromaticity analysis, a UV-Vis-NIR spectrometer was used to measure the reflection spectrum of the sample, with a 10° field of view during the spectral range of 380–800 nm and a slit width of 5 nm.

A D65 light source was used for testing, and daylight illumination with a color temperature of 6500 K was simulated.

The energy-dispersive XRF (EDXRF) experiments were carried out at the Laboratory of Science and Technology Archaeology at Peking University. An XGT-7000 X-ray fluorescence microscope was used for the experiments. The diameter of the X-ray incident spot was 1.2 mm, the voltage of the X-ray tube was 30 kV, the current of the tube was 0.029 mA, and the data acquisition time was 120 s.

Chromaticity Analysis of Glazed Tiles of Bao’ensi Site

Glaze color is an important expression of glazed tile appearance, and the brightness, saturation, and other related information on glaze color also reflects the level of glazed-tile firing technology. Human eyes have limitations in color discrimination, whereas a UV-Vis-NIR spectrometer is used to measure the reflection spectrum of glaze, and can distinguish the differences and changes in glaze color. To express the glaze color of glazed tiles scientifically and objectively, a color test of the glazed tiles of Bao’ensi was carried out.

The main wavelength of the glaze color corresponds to the peak of the reflectivity curve. Color saturation and brightness are important optical properties of glazes. The main wavelength determines the hue of the glaze, whereas the saturation depends on the selectivity of the glaze to light reflection, and brightness depends on the relative lightness of the glaze (10). The reflectivity of glaze is one of the factors influencing glaze gloss, which affects the specular reflection ability of glaze to visible light. When the reflectivity is high, light is mainly reflected by the mirror, and the glossiness is good. When the reflectivity is low, light is given priority over diffuse reflection, and the glossiness is poor. L* represents brightness or lightness; the larger the value, the lighter the color. The a* represents the red and green phase axes, with positive and negative directions indicated in red and green, respectively; the b* represents the yellow and blue phase axes, with positive direction indicated in yellow and negative direction indicated in blue. The c* represents the chroma, purity, or saturation; the higher the value, the higher the spectral color content; finally, the h* represents the tone angle value (11). The CIE L*a*b* color space values of the green, yellow, and black glaze colors of the glazed tiles of Bao’ensi that were tested and analyzed are shown in Table I.

Comparing the spectral curves of the yellow and green glaze from Figure 2, the main wavelength of the yellow glaze is greater than 800 nm and is located in the NIR region, while the spectral curve rises gently in the shortwave region (12). The wavelength of the green glaze is approximately 540–570 nm, rising sharply in the shortwave within the wavelength range of green light, and the reflection peak is narrow. Black glaze has low reflectivity and no main wavelength.

FIGURE 2: The Vis-NIR reflection spectral curve of glazed tiles of Bao’ensi.

Table I and Figure 3 show the chromaticity values of the samples, and the corresponding scatter plots of L*, c*, and h*. In Figure 3a, the L* value of the green glaze is between 40 and 70. In Figure 3b, the glaze color of the sample is distributed in the yellow–green region, close to the yellow axis, and there is little difference between the hue angle h* and saturation c*. The L* value of yellow glaze is approximately between 40 and 80, and there are samples with similar brightness of glaze color. The glaze color is close to the yellow axis, hue angle h* is not the same, glaze saturation c* is quite different, and some samples are on the high side while others are on the low side. However, there are also samples with the same glaze saturation, among the three glaze colors, with the yellow glaze having the highest saturation. The black glaze L* value (Figure 3a, y-axis) was between 30 to 50. There are two black samples of higher lightness (L*) than other black samples (be70, be81). Note in Figure 3b, that the black sample with higher brightness (be70) also has higher saturation.

FIGURE 3: (a) Scatter plot of L* of sample glaze; (b) Scatter plot of c* and h* of sample glaze.

The chromaticity analysis of glazed tiles at the Bao’ensi site shows that the wavelengths of glazed tile and the samples of the same glazed color were not different. The brightness and saturation of the glazed color samples were high or low, which may be because of the different application positions of the buildings. Therefore, glazed tiles with different glaze color requirements are needed. It can be inferred that in order to meet the needs of the construction of the temple, the workers were skilled in firing glazed tiles with different glaze color requirements, indicating that the glaze color of glazed tiles used was well-established (mature).

Chemical Composition Analysis of Glazed Tile Layer in Bao’ensi Site

EDXRF was used to determine the chemical composition of Al

, CuO, and PbO in the sample glaze. The partial test results are shown in Table II.

The main components of the glazed tile glaze in the Nanjing Bao’ensi were PbO, SiO

O, and CaO, and the main coloring elements were Ti, Mn, Fe, and Cu. Al

 and alkaline oxide to prevent glaze cracking.

To reveal the characteristics and differences in the chemical composition of the glazed tiles at the Bao’ensi site, a distribution analysis of the chemical composition of the experimental results was carried out. Using the content of the chemical components of one element as the abscissa and the content of the chemical components of another element as the ordinate, a two-dimensional (2D) dispersion analysis diagram can be constructed.

Figure 4a shows that the yellow glaze sample has a high content of Fe

, indicating that Fe is the main element affecting the color of the yellow glaze. The Fe

 content in most of the green glaze sample was low, but the CuO content was high, and the Fe

 content in a few samples (be37, be29) was high. Clearly, Cu is the main element affecting the color of the green glaze. The contents of Fe

 and CuO in the black glaze samples were relatively high, so Fe and Cu jointly affect the color of the black glaze. According to the content of Fe

 in Figure 4a, the glaze of the Bao’ensi glazed tile can be divided into two parts: one is green glaze, in which the content of Fe

 is less than 1%, and the other is yellow glaze and black glaze, in which the content of Fe

 is more than 2%. Therefore, it can also be shown that Fe is the coloring element of yellow, black, and green glaze. However, there are certain differences in the chemical composition of the three, which may indicate that the color of the glaze is related to the coloring elements and their content.

FIGURE 4: Distribution analysis diagram of glazed tile glaze of Bao’ensi: (a) is the dispersion analysis figure of CuO-Fe

, (b) is the dispersion analysis figure of MnO-TiO

, and (c) is the dispersion analysis figure of SiO

In Figure 4b, the MnO content of the black glaze sample is relatively high. Based on the analysis of the Fe

 and CuO contents of the black glaze sample in Figure 3a, the main colorants that affect the color of the black glaze are Fe, Cu, and Mn.

In Figure 4c, the PbO content in the green, yellow, and black glaze increases gradually with a decrease in the SiO

 content, and there is a negative correlation overall. SiO

 can improve the melting temperature and viscosity of the glaze; provide a glaze with high mechanical properties (such as hardness and wear resistance); improve the whiteness, transparency, and chemical stability; and reduce the thermal expansion coefficient of the glaze. As a flux, PbO can reduce the thermal expansion coefficient of the glaze, improve the thermal stability, and reduce the melt viscosity, such that the glaze has good fluidity. This can increase the melting temperature range of the glaze, improve the glaze elasticity and gloss, and increase the tensile strength. PbO has the strongest flux and can easily react with SiO

 to form lead silicate with a low melting point. Owing to the high refractive index of lead silicate, it can form a high-gloss glaze (13).

 is 2.15:1 for the green glaze, 2.72:1 for the yellow glaze, and 2.81:1 for the black glaze. In Figure 4c, the ratio (5,9) of PbO to SiO

 in the yellow-glazed tile is slightly higher than that in the Ming glazed tile of the Beijing Imperial Palace; there are also differences in chemical composition, which may be related to the glaze materials used and the production system at that time (5,9).

The Song Dynasty work (14) by Li Jie “Ying Zao Fa Shi (State Building Standards)” has a detailed record. It was also recorded during the Wanli period of the Ming Dynasty: “306 Jin of Huangdan, 102 Jin of Maya stone, 8 Jin of ochre” (15). Huangdan is rich in lead, whereas Luohe stone and Maya stone are rich in silicon. The ratio of Huangdan (PbO) to Luohe stone:Maya stone (SiO

) in the two books is approximately 3:1, indicating that the glaze formula from the Song dynasty to the Ming Dynasty tended to be stable (16).

 of the yellow glaze with the highest grade of glazed tiles in the Bao’ensi site is relatively close. However, it is similar to that recorded in “Ying Zao Fa Shi” and “Gong Bu Chang Ku Xu Zhi.” The 3:1 ratio is somewhat different, which is the main reason for this phenomenon (5). On the one hand, as the initial formula recorded in the literature, Pb volatilizes to a certain extent in the actual firing process, resulting in a decrease in the content of PbO in the ingredients, so that the ratio cannot reach 3:1. On the other hand, as the glazed tiles of the Bao’ensi site had been buried underground for a long time, the Pb content would be continuously dissolved owing to the humidity and abundance of bacteria and microorganisms, resulting in a decrease in Pb content.

FIGURE 5: Lead:silicon ratio of the three glazes.

The comprehensive analysis shows that the firing technology of glazed tiles at the Bao’ensi site has matured, and artisans can flexibly make glazed tiles of different glaze colors by adjusting the proportion of raw materials and adding appropriate flux to meet the needs of the Bao’ensi site buildings.

Factor analysis is a statistical technique used in the analysis of EDXRF data for tile analysis. It allows for the identification of underlying factors (components) that contribute to the observed variations in the elemental composition of the glazed tiles. By analyzing the covariance matrix of the EDXRF data, factor analysis can extract the common sources of variation and determine the relationships between the measured elements in the different glaze types (17).

To directly observe whether there is any difference in the chemical composition of glazes of different colors, 31 samples from Bao’ensi site were analyzed using the factor analysis method.

Figure 6 shows the load diagram of the yellow glaze, green glaze, and black glaze of the Bao’ensi site. The seven colored elements were selected, such as Al

, and CuO. The primary and secondary quantitative element composition factors extracts two factors, F1 and F2, with a cumulative contribution rate of 77%, as shown in equations [1] and [2].

FIGURE 6: The factor analysis diagram of glaze.

As can be seen from the factor analysis diagram, the distribution of most data points of the green, black, and yellow glazes was concentrated and separated from each other, indicating that the glaze formulas of the three glazes were significantly different according to the coloring elements. In terms of the dispersion of data points, the yellow and black glaze were relatively stable in terms of raw material composition and process formula, whereas the data points of the green glaze were relatively dispersed, indicating that the glaze formula may be different.

The be54 sample in the yellow glaze has a distant relationship with other yellow glaze samples, but it is close to the green glaze samples. According to EDXRF and Figure 4a, it can be found that not only are the contents of Fe, Cu, and other elements in be54 glaze not very different from those of other yellow glazes, but that the contents of PbO are lower than those of other yellow glazes. It was also observed that the contents of SiO

 were higher than those of other yellow glazes. The sum of the SiO

 and PbO contents in the glaze accounted for more than 90% of the glaze formula. It was speculated that the be54 yellow glaze sample may have been caused by a lack of strict formula control.

The be30 and be43 samples in the green glaze are far from the other green glaze samples. According to EDXRF and Figure 4c, it can be found that the PbO content of be30 and be43 samples is low, and the SiO

 content was high, but the total was over 90%. Therefore, it was inferred that glaze color modulation technology has been relatively mature to meet the different requirements of glazed tiles.

The chroma and chemical composition of the glazed tile glaze of the Bao’ensi site were analyzed, and the following conclusions were drawn:

The firing technology of glazed tiles in the great Bao’ensi is relatively mature, and craftsmen could already skillfully fire glazed tiles with different glaze color requirements according to their needs. There are no differences in the main components and flux content of various glaze formulations. Different glaze colors are caused by different types and contents of coloring elements. Cu and Fe are the main elements that affect the color of yellow glaze, Cu is the main element that affects the color of green glaze, and the main coloring elements that affect the color of black glaze are Fe, Cu, and Mn. Yellow and black glaze are relatively mature in terms of raw material composition and process formula, whereas green glaze is not stable enough in terms of raw materials and process. The glaze formulations of the green glaze of the great Bao’ensi have both similarities and certain differences, indicating that the glazed tiles of the great Bao’ensi may have come from the same kiln, using different glaze formulations, or they may have been the result of later repairs.

This work was supported by National Natural Science Foundation of China (11975210), and the National Social Science Fund of China (17CKG016).

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(9) Leng, M. Analysis on the glaze pagoda of the Temple of Bao’ensi in Ming Dynasty. 

Identification and Appreciation to Cultural Relics

(11) Wang, C. W.; Zhao, W. J.; Wu, B.; Zhang, B. Scientific study on the celadon glaze from the Donggou kiln, China. 

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(12) Li, Y.; Zhang, B.; Cheng, H. S.; Zheng, J. M. Revealing the coloration mechanism in the earliest Chinese celadon glaze. 

; Bibliographic Literature Publishing House, 2000.

(16) Hou, J. Y.; Li, H.; Kang, B. Q.; et al. The origins of imperial yellow glazed porcelain in the Ming Dynasty (1,368 to 1,644) China: Technical comparison to low-fired tile with yellow glaze. 

SPSS Statistical Analysis Method and Application

; Publishing House of Electronics Industry, 2013.

 are with the School of Physics and Microelectronics at Zhengzhou University, in Zhengzhou, China. 

 is with the Nanjing Research Institute of Archaeology, in Nanjing, China. 

 is with the School of Archaecology and Museology at Peking University, in Beijing, China. 

 is with the School of History, Zhengzhou University, Zhengzhou, China. Direct correspondence to: 

Articles

UV-Vis-NIR can be used to understand how ancient buildings were constructed. Here, a UV-Vis-NIR and EDXRF spectrophotometer were used to analyze glazed tiles that comprised a historical site built in Ancient China.

Technological Analysis of Glazed Tiles Unearthed from Bao’ensi in Nanjing

Inductively coupled plasma (ICP) is a well-known source for atomic spectroscopy, either using optical emission spectroscopy (OES) or mass spectrometry (MS) as a detection technique. The high energy and temperatures in an ICP (typically between 6,000 to 8,000 °C) lead to an efficient and complete decomposition of the sample as well as the vaporization, excitation, and possibly ionization of atoms in their gas phase. The high temperature of the plasma makes this source inherently robust and tolerant to a wide variety of sample types (such as solutions containing an elevated percentage of dissolved solids, different acids, and organic solvents). However, inadequate sample preparation, or inappropriate configuration of the sample introduction system, can have negative consequences such as drift of the analytical signal, increased backgrounds, inadequate detection limits, or unexpected interferences. This tutorial highlights the most important steps necessary for creating a trouble-free sample preparation workflow for elemental analysis.

Inductively coupled plasma–optical emission spectroscopy (ICP-OES) and ICP-mass spectrometry (MS) are designed primarily to analyze samples in liquid form. In most cases, a sample must be dissolved (if possible) in a suitable diluent. Whereas aqueous diluents, like ultrapure water or dilute acids, are generally easier to handle, organic solvents are commonly applied, for example, to determine elemental impurities in certain active pharmaceutical ingredients. In the event a sample is not immediately soluble, a digestion method using a hot block, fusions (for example, lithium metaborate), or a microwave-assisted digestion are often the only routes to analysis. However, all the aforementioned approaches have common drawbacks, such as the time required for the digestion, the cost of reagents, and the requirement to invest in digestion hardware. In addition, contamination could become a problem from the reagents used for digestion, or the insufficient cleaning of crucibles or digestion vessels between samples.

Irrespective of the digestion method used, a clear and particle-free solution is the ideal outcome of the digestion process. However, before introducing this solution to the analytical instrument of choice, it is important to consider a few parameters. The amount of total dissolved solids (TDS) is calculated by dividing the amount of sample used for digestion by the final volume of the solution after digestion. The maximum working range for TDS is different between ICP-OES and ICP-MS and is highlighted later in this tutorial. The remaining acid concentration and the acid mixture can have an influence on not only the performance of the instrument, but also the configuration of the sample introduction system. If hydrofluoric acid (HF) is used, it is important to exchange all components made from quartz for their inert replacements. Ideally, the remaining acid concentration in a digested sample should be below 5% (v/v).

The direct analysis of solid samples can be an alternative in some cases, such as conducting samples (metals and semiconductors), nonconducting samples (mineral grains, paper, and plastics), and biological materials (tissue sections). Using a laser system focused directly on the surface of the sample, short pulses of high-intensity light can convert a solid sample directly into an aerosol, which is transported to the ICP ion source for analysis. Laser ablation (LA) enables direct sampling of a range of solid materials without the need for hazardous chemicals and minimizes the potential for contamination by sample handling. Because of the typical small spot size of approximately 200 μm, laser ablation is considered quasi nondestructive and can therefore be an alternative approach for analyzing valuable samples.

However, sample homogeneity can be an issue when LA-ICP-MS is employed for concentration determinations in a bulk material, so careful selection of the sampled area is required. On the other hand, LA-ICP-MS also allows information to be obtained on the lateral distribution of an analyte across a sample, which is beneficial for characterizing geological or biological samples.

ICP-OES and ICP-MS are technologies for analyzing elemental impurities at trace and even ultratrace levels. As a result, backgrounds for the analytes of interest must be as low as possible. Otherwise, false positive results and poor detection limits may cause significant follow-on work in a routine operating laboratory. For many elements, such as the rare earth elements, natural abundance in the environment is low and so there is likely no background contamination found in the water and acids or the vials used during sample preparation. For other elements, especially alkali, alkaline earth, and transition metals, such as sodium, potassium, iron, copper, or zinc, there may be significant amounts leaching out from plasticware such as vials and vial caps. When changing from batch to batch or between different brands, a preliminary leach test for purity of vials is highly recommended.

Lower purity acids may also add to the backgrounds observed in an analysis. Acids and other reagents typically used in elemental analysis, such as nitric and hydrochloric acid or hydrogen peroxide, are available in different purities, but especially for ultratrace analysis using ICP-MS, only the highest purity should be used.

As an alternative, lower-purity acids can be purified using sub-boiling distillation, which is a cost-effective way to increase the purity of a less expensive acid. Another potential contribution to contamination can be the water used for the preparation of diluent solutions. A regular check for trace elements is highly recommended, as well as regular maintenance of the water purification system (as per the manufacturer recommendations). For trace element analysis, it is recommended to use water with a resistivity of 18.2 MΩ cm wherever possible.

Many sample types only dissolve well using microwave-assisted digestion, which aids the decomposition of difficult matrix materials by enabling digestions to be run at higher temperatures and pressures. However, despite the conditions, the selection of the right acid is still crucial, and in many cases, an effective digestion will often require a combination of different acids or oxidationents. The use of an acid mixture also allows for immediate stabilization of some elements in solution. Whereas nitric acid (HNO

) is often used as a diluent for elemental analysis, it is mostly ineffective for sample digestion alone. In combination with hydrogen peroxide (H

), effective decomposition of mostly organic matrices (such as food and feed), can be achieved. As an alternative, perchloric acid can be used to increase the oxidation potential of the mixture, but extreme care is recommended as perchloric acid can react strongly and rapidly with organic matrices. The addition of a suitable amount of hydrochloric acid (HCl) is beneficial to unlock and stabilize certain inorganic materials. In many cases, aqua regia (a combination of hydrochloric acid with nitric acid in a 3:1 mixture) is effective for dissolving metallic materials. Hydrochloric acid can also help stabilize critical contaminants, such as mercury and platinum group metals, in the digested sample. For this purpose, it is important to ensure the concentration of HCl is high enough (2% or even higher), so that initially formed precipitations (HgCl or HgCl

) are converted into soluble chloro complexes ([(HgCl

). All acids typically added in digestion methods are summarized in Figure 1. Sulfuric acid can be beneficial for digesting some sample types on a hotplate and in some types of high pressure digestion systems, but this acid should never be used with Teflon vessels in a microwave because Teflon melts at a temperature lower than the boiling point of sulfuric acid at atmospheric pressure. In addition, sulfuric acid generates several additional sulfur-based interferences in ICP-MS, so it should only be used when there is no alternative.

FIGURE 1: Common acids used for sample digestion for the analysis of trace elements.

For any of the above, it is of utmost importance that appropriate personal protective equipment is always worn when handling acids and other hazardous chemicals. Detailed guidance on the right acid or acid mixture, sample and acid amount as well as temperature program can be obtained from the manufacturer of the microwave digestion system available in the laboratory.

Samples with an elevated content of dissolved solids can cause signal suppression and drift with ICP-MS. The typical upper limit for the TDS content in ICP-MS is between 0.2 and 0.5% (m/v). This means that many samples, which can be run without any prior dilution on an ICP-OES, need to be diluted first for ICP-MS analysis. Special sampling accessories on the front end of the ICP-MS, such as dilution with argon gas, can enable the direct aspiration of solutions containing TDS well beyond 3 to 4%, but dilution is also taking place here, ultimately reducing the detection sensitivity of the system. The biggest advantage of such accessories is that a manual dilution step in the sample preparation can be omitted, which liberates operator time, reduces cost and waste created by diluent solutions, prevents contamination, and avoids human error. However, as the dilution gas affects the plasma conditions, the resulting dilution factor needs to be fixed across all standards and samples in a batch. Because of the changing plasma conditions during the measurement, long stabilization times are required after a change in the plasma conditions to avoid instrumental drift. For analyses containing different sample types, this limited flexibility becomes a drawback because with the dilution factor set to enable the measurement of higher matrix samples, lower matrix samples will be diluted more than is necessary, leading to poorer method detection limits (MDLs) and degraded accuracy for those samples.

Flexible dilution for every sample in a batch is possible using automated and intelligent liquid dilution. Automated liquid dilution is possible in conjunction with valve systems, which are available as an extension to the standard configuration of the autosampler. Using integrated control in the ICP-MS software, either a fixed liquid dilution factor for all samples or different factors (up to several hundred) for each sample can be predefined before starting the analysis. In addition, the system can also be set up to intelligently dilute further and automatically re-analyze samples that exhibit internal standard suppression higher than the acceptable limits or have concentrations outside of the calibrated range on their initial measurement. The repeat analysis data are accurately documented in the software together with the new dilution factor, so that no issues with regulatory compliance occur during data acquisition. Intelligent autodilution can also help to free up operator time by eliminating the need to prepare individual standard solutions for calibrating the system. With online autodilution, a typical calibration curve (consisting of 3 to 5 points and covering three orders of magnitude) can be created from a single, higher concentration stock solution.

As mentioned previously, it is important to be conscious of potential backgrounds derived from laboratory equipment and chemicals (contamination) or other samples (carry-over or cross contamination). Whereas the former can only be identified by rigorously testing all equipment and different batches of the chemicals involved, the latter requires an effective cleaning strategy in between runs to be put in place. For samples that need to be digested before analysis, a blank digestion should be performed with every batch of samples, including all steps and reagents used in the process, but no sample. This way, any potential contamination from the digestion step can be identified, traced back to its origin and rectified as far as possible, before potentially biased results need to be troubleshooted or are even reported. Table I gives an overview of common reasons why an analysis may be biased through contamination or carry-over cross-contamination.

Oftentimes, a challenge is not immediately recognized upfront as only limited information is available on the sample. Sometimes, the presence of other elements not subject to quantitation can cause a range of difficulties, which are only recognized upon analyzing the sample. In such cases, the analysis often delivers confusing results for the sample itself, but also the subsequent samples may suffer from the effects of residual contamination. To identify such potential challenges caused by previously unknown samples before they cause a disruption, it is recommended to follow a few simple steps. To discover unexpected elements or challenging interferents, running a full mass scan can provide a good overview about highly concentrated matrix elements or indicate the presence of unexpected interferences. A slightly higher dilution than usual safeguards the instrument from persistent contamination. Typical interferences causing puzzling results could be of different nature to those that are normally encountered. For example, doubly charged ions can be formed if an element has a second ionization potential lower than the ionization potential of argon. In this case, ions with two positive charges are formed, which appear at half mass in the spectrum (as all mass spectrometers detect ions based on their mass to charge ratio rather than their mass). The presence of rare earth elements, predominantly neodymium (Nd), samarium (Sm), or gadolinium (Gd), create false positive results on key contaminants such as arsenic (

). The impact of doubly charged interferences can, in some cases, be reduced by mathematical corrections (usually within a narrow concentration range), but only triple quadrupole ICP-MS systems are capable of reliably eliminating these interferences. Several doubly charged interferences may also fall on common internal standards. For example, in presence of higher amounts of lead in the sample, 

, a common internal standard. In this case, the interference would not create a bias on the sample itself, but instead lead to potential failure of the internal standard response, or at least an incorrect estimation of the concentration of all analytes that use 

Rh for internal standardization. Although kinetic energy discrimination (KED) is considered an effective tool for suppressing polyatomic interferences in the low and middle mass range of an ICP-MS, it becomes gradually less effective in the higher mass range because the relative size difference between an analyte and the polyatomic interference decreases. This means that low concentrations of an interfering element can create an unexpected bias. An example is the analysis of mercury, which can be interfered by tungsten through the formation of tungsten oxide, even at single digit μg/L concentrations.

It is worthwhile to take a critical look at all steps in the sample preparation protocol to assure successful, accurate, and reliable determination of elements (trace and major). Through evaluating all consumables and reagents, as well as preparing appropriate blanks and quality control (QC) checks, help to identify and troubleshoot potentially biased results.

 are with Thermo Fisher Scientific in Bremen, Germany. Direct correspondence to: 

How to create trouble-free sample preparation workflow for elemental analysis.

Jianfeng Cui

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