Nitrophenol is one of the most highly toxic heavy pollutants in the environment. It has a slow degradation rate, and is harmful to humans even at low concentrations. Therefore, it is crucial to detect nitrophenol quickly, easily, and accurately. In this study, the nitrophenol isomers (o-nitrophenol, m-nitrophenol, and p-nitrophenol), in solid and liquid phases with methanol or acetone as the solvent, were investigated using Raman spectroscopy. The characteristic Raman peaks of nitrophenol isomers were observed. In addition, the intensities of the corresponding Raman peaks for nitrophenol isomers in the liquid phase with methanol or acetone as solvent at different concentrations (0.3%, 0.6%, 1.2%, 1.8%, 2.4%, 3.0%, and 3.6%) were analyzed. It revealed that the intensities increased when the concentrations increased, and the four corresponding linear correlation coefficients between intensities and concentrations were greater than 0.99. Furthermore, the fitted linear relationships were verified by nitrophenol isomers in the liquid phase with methanol or acetone as the solvent at known concentrations (0.9%, 1.5%, 2.1%, 2.7%, and 3.3%), and the average deviations between the measured data and the known values were 4.94%, 2.69%, 4.37%, and 1.86%, respectively.

Nitrophenol isomers (o-nitrophenol, m-nitrophenol, p-nitrophenol) are widely used as analytical reagents or intermediates for a variety of chemicals (for example, in pesticides, medicines, and dyes). Hence, a large amount of nitrophenol isomers in wastewater and gas is discharged into rivers and the atmosphere. Because nitrophenol isomers are highly toxic and have a slow degradation rate (1,2), they are listed as priority control pollutants by the United States Environmental Protection Agency (EPA), and their concentrations in natural water bodies should be controlled (3). Therefore, developing simple and reliable determination methods for nitrophenol isomers is crucial to protecting people and the environment.

A lot of research has been carried out to investigate nitrophenol isomers. Hofmann and others (4) qualitatively determined p- nitrophenol and 2,4-dinitrophenol in cloud water at low concentrations by high performance liquid chromatography–mass spectrometry (HPLC–MS). Dou and others (5) measured p-nitrophenol and other nitrobenzene compounds by combining dispersive solid-phase extraction (SPE) and HPLC, but these techniques are both complex and expensive. Rahman and others (6) detected p-nitrophenol in a phosphate buffer phase with an electrochemical sensor. Li and others (7) measured o- and p-nitrophenol in aqueous samples by preparing reduced graphene oxide-cyclodextrin-chitosan (RGO-CD-CS) sensors, but the fabrication requirements of these sensors are very strict. Xu and others (8) developed a fluorescence sensor with hybrid magnetic cadmium telluride to detect p-nitrophenol, and a linear relationship between relative fluorescence intensities and concentrations of p-nitrophenol was obtained. However, the measurement accuracy of the sensor was influenced by fluorescence quenching. Manuvesh and others (9) determined that the wide absorption band of nitrophenol was at 345 nm, and the absorption cross sections of nitrophenol were obtained within the range of 295~400 nm by cavity attenuation spectroscopy, but qualitative analysis of nitrophenol cannot be achieved.

Because each molecule has an unique Raman spectrum, Raman spectroscopy in recent years has been widely applied in the fields of medicine, food safety, and environmental pollution monitoring (10–12) owing to the advantages of its noninvasive, real-time, label-free, and fast response. Chi (13) investigated the molecular vibration modes of 2,4-dinitrophenol in polycrystalline phase by Fourier transform (FT)-Raman spectroscopy. Zhang and others (14) compared the Raman scattering enhancement effects of trinitrophenol by synthesizing different types of nanoparticles. In this study, the nitrophenol isomers in both the solid and liquid phase with methanol or acetone as the solvents were investigated using Raman spectroscopy. In addition, the relationships between the intensities and concentrations of the corresponding Raman peaks for nitrophenol isomers in liquid phase were explored.

Experimental Setup and Sample Preparation

The self-built Raman spectrometer (15) (Figure 1) developed in our previous work was employed here. A diode laser at 785 nm excitation was employed as the light source, and its output power was 50 mW. The spectral resolution of spectrometer (iHR320) was 0.8 cm

. The detected spectra were recorded with a 1200 g/mm grating. The operating temperature of the charged-coupled detector (CCD) detector (Horiba) was 203 K. To improve the signal-to-noise ratio (S/N) of the spectrometer, the integration time was set at 10 s, with the number of accumulations set to four times. Each sample was measured for five replicate times, and the average spectra were processed for subsequent analysis. The spectrometer was calibrated with anhydrous ethanol before each experiment.

In the work, o-nitrophenol (purity: 99%), m-nitrophenol (purity: 99%), and p-nitrophenol (purity: 99.5%) were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. Methanol (purity: 99.9%) and acetone (purity: 99.5%) were from Sinopharm Chemical Reagent Co., Ltd. and Shanghai Lingfeng Chemical Reagent Co., Ltd., respectively. Because of the limitation of current experimental conditions in the laboratory, nitrophenol isomers in the gas phase were not investigated in this study. They will be examined in a future study by using near-infrared (IR) laser absorption spectroscopy (16).

The self-built Raman spectrometer was calibrated daily with anhydrous ethanol (purity: 99.6%). The Raman peak at 873 cm

 was attributed to the stretching vibration of C-C-O. Figure 2 shows the Raman spectra of anhydrous ethanol for six continuous days (D1–D6). The overlayed spectra of anhydrous ethanol reveals that the spectrometer employed here possessed good stability.

O-, m-, and p-nitrophenol in the solid phase were first detected by the Raman spectrometer. The Raman spectrum of the nitrophenol isomers in the solid phase in the range of 1120~1500 cm

 are shown in Figure 3. Previous research (14,17) showed that the Raman peaks at 1134, 1167, 1232, 1268, and 1279 cm

 were attributed to the deformation of C-H. The Raman peaks at 1333 and 1343 cm

 were caused by the asymmetric stretching vibration of the nitro group, and the Raman peak at 1430 cm

 was attributed to the symmetric stretching vibration of the nitro group. The study revealed that the Raman peaks at 1134 and 1232 cm

 were found in the spectrum of o-nitrophenol. The Raman peaks at 1268 and 1343 cm

 were obtained in the spectrum of m-nitrophenol, and the Raman peaks at 1167, 1279, 1333, and 1430 cm

 were determined in the spectrum of p-nitrophenol. Based on these characteristic Raman peaks, nitrophenol isomers in the solid phase can be identified.

Nitrophenol Isomers in Liquid Phase with Methanol as the Solvent 

O-, m-, and p-nitrophenol in the liquid phase with methanol as the solvent at a mass fraction of 0.3% were separately investigated. As shown in Figure 4a, a Raman peak at 1343 cm

 was observed in the Raman spectrum of m-nitrophenol in the liquid phase, and it was consistent with the peak shown in Figure 3. The Raman peak at 1333 cm

 was also found in the Raman spectrum of p-nitrophenol in the liquid phase, and it also matched well with the Raman peak in the solid phase. Furthermore, the mixture of o-nitrophenol, m-nitrophenol, and p-nitrophenol with methanol at a mass fraction of 0.9% was measured. As shown in Figure 4b, the Raman peaks at 1343 and 1333 cm attributed to m-nitrophenol and p-nitrophenol were also observed and aligned with the results shown in Figure 4a.

Nitrophenol Isomers in Liquid Phase with Acetone as Solvent

O-, m- and p-nitrophenol in the liquid phase with acetone as the solvent at a mass fraction of 0.3% were separately investigated. As shown in Figure 5a, the Raman peak at 1134 cm

 was observed in the Raman spectrum of o-nitrophenol in the liquid phase, and it was consistent with the Raman peak in the solid phase shown in Figure 3. Raman peak at 1333 cm

 was observed in the Raman spectrum of p-nitrophenol in the liquid phase, and it matched well with the Raman peak shown in Figure 3. Meanwhile, the mixture of o-nitrophenol, m-nitrophenol, and p-nitrophenol with acetone at a mass fraction of 0.9% was measured. As shown in Figure 5b, the Raman peaks at 1134 cm

attributed to o-nitrophenol and p-nitrophenol were observed, and it matched well with the results shown in Figure 5a.

To summarize the findings, it was found that corresponding Raman peaks can be observed for m-nitrophenol and p-nitrophenol in the liquid phase with methanol as the solvent, and o-nitrophenol and p-nitrophenol in the liquid phase with acetone as the solvent, respectively. These four characteristic Raman peaks (1343 cm

 of m-nitrophenol with methanol as solvent, 1333 cm

 of p-nitrophenol with methanol as solvent, 1134 cm

 of o-nitrophenol with acetone as solvent, and 1333 cm

 of p-nitrophenol with acetone as solvent at 1333 cm

Relationship Between Intensities of Corresponding Raman Peaks and Concentrations for Nitrophenol Isomers in Liquid Phase

 is the optical efficiency of the measurement system; 

Ω is the differential scattering cross sections; 

 is the volume. Here, when the external environment remains stable and the volume is fixed, the intensity of Raman scattering is linearly dependent on the concentration.

M- and p-nitrophenol in liquid phase with methanol as the solvent at different concentrations (0.3%, 0.6%, 1.2%, 1.8%, 2.4%, 3.0%, and 3.6%), and o- and p-nitrophenol in the liquid phase with acetone as the solvent at different concentrations (0.3%, 0.6%, 1.2%, 1.8%, 2.4%, 3.0%, and 3.6%) were measured. As shown in Figure 6a–h, the intensities of corresponding Raman peaks (1343, 1333, 1134, and 1333 cm

) increased along with the increasing concentrations. The linear relationships between the intensities and concentrations were found, the corresponding linear correlation coefficients were 0.9965, 0.9971, 0.9933, and 0.9971, respectively. The detection limits of concentrations for m-nitrophenol and p-nitrophenol in the liquid phase with methanol as the solvent, and o-nitrophenol and p-nitro- phenol in the liquid phase with acetone as the solvent were 0.10%, 0.03%, 0.07%, and 0.20%, respectively.

To verify the accuracy of four fitted linear relationships between Raman intensities and concentrations, nitrophenol isomers in the liquid phase with methanol or acetone as the solvent at known concentrations (0.9%, 1.5%, 2.1%, 2.7%, and 3.3%) were investigated, and the measured concentrations calculated by the fitted linear relationships were compared with the known concentrations. As observed in Figure 7a–d, the circle symbol represents the known values of concentrations, and the triangle symbol represents the measured values of concentrations. It revealed that these measured values of concentration were in good agreement with the known values, and average deviations were 4.94%, 2.69%, 4.37%, and 1.86%, respectively.

In this paper, the nitrophenol isomers in solid phase and in liquid phase with methanol or acetone as solvent were investigated by Raman spectroscopy. It showed that several Raman peaks of nitrophenol isomers, except o-nitrophenol with methanol as the solvent and m-nitrophenol with acetone as the solvent, can be obtained. Meanwhile, m-nitrophenol and p-nitrophenol in the liquid phase with methanol as the solvent, and o-nitrophenol and p-nitrophenolin in liquid phase with acetone as the solvent at different concentrations (0.3%, 0.6%, 1.2%, 1.8%, 2.4%, 3.0%, and 3.6%) were investigated. It revealed that the intensities of four corresponding Raman peaks increased with the increasing concentrations linearly, and the four linear correlation coefficients between intensities and concentrations were greater than 0.99. In addition, the fitted linear relationships were verified by nitrophenol isomers in liquid phase with methanol and acetone as the solvent at known concentrations (0.9%, 1.5%, 2.1%, 2.7%, and 3.3%), and it was found that the measured concentrations aligned with the known values, and average deviations between the measured data and the known values were 4.94%, 2.69%, 4.37%, and 1.86%, respectively. This study provides a foundation for determining nitrophenol isomers in environmental monitoring.

This work was supported by National Nature Science Foundation of China (NSFC) (Grant Nos. 51676130, 51776129, 51306123) and State Key Laboratory of Applied Optics Open Fund (SKLAO-201909).

(1) A. Chatzimarkou, T.G. Chatzimitakos, A. Kasouni, L. Sygellou, A. Avgeropoulos, and C.D. Stalikas, 

(2) B. Thirumalraj, C. Rajkumar, S.M. Chen, and K.Y. Lin, 

(4) D. Hofmann, F. Hartmann, and H. Herrmann, 

(5) Y.N. Dou, L. Guo, G.L. Li, X.X. Lv, L. Xia, and J.M. You, 

(6) M.M. Rahman, H.M. Marwani, F.K. Algethami, A.M. Asiri, S.A. Hameed, and B. Alhogbi, 

(7) C.K. Li, Z.L. Wu, H. Yang, L. Deng, and X.Q. Chen, 

(11) C.J. Strachan, T. Rades, K.C. Gordon, and J. Rantanen, 

(12) D.V. Petrov, I.I. Matrosov, and A.R. Zaripov, 

(14) Z. Zhang, X.H. Liang, C.Z. He, C. Zhang, R. Xiao, and H. Cheng, 

(15) C. Zhao, R. Li, H.N. Yang, C.X. Shen, X.S. Cai, and C.F. Luo, 

(16) X.Q. Guo, F. Zheng, C.L. Li, X.F. Yang, S.P. Liu, J.L. Wei, X.B. Qiu, and Q.S. He, 

(17) P. Srinivasan, M. Gunasekaran, T. Kanagasekaran, R. Gopalakrishnan, and P. Ramasamy, 

 are with the School of Energy and Power Engineering at the University of Shanghai for Science and Technology, in Shanghai, China. 

 is with the State Key Laboratory of Applied Optics at the Chinese Academy of Sciences in Changchun, China. 

 is with the Shanghai Customs District P.R. China in Shanghai, China. Direct correspondence to Huinan Yang at: 

Articles

Peer-Reviewed Research

In this study, the nitrophenol isomers, in solid and liquid phases, were analyzed using Raman spectroscopy, laying the groundwork for determining nitrophenol isomers in environmental monitoring with this technique.

Determining Nitrophenol Isomers Using Raman Spectroscopy

This article is the first of a 3-part series that gives a procedure to avoid common errors in spectrofluorometry using an example case. Part 2 will focus on throughput and polarization correction of spectrofluorometers for the purpose of inter-laboratory comparisons, while Part 3 will focus on non-ideal sample behavior.

In this first of a three-part series, we address the challenge of collecting good quality data by recommending procedures designed to “collect data right the first time." We focus our attention on the two most common fluorescence spectroscopies. The first is fluorescence emission spectroscopy, in which a single excitation wavelength is used and fluorescence is measured as a function of emission wavelength. The second is fluorescence excitation spectroscopy, in which a single emission wavelength is used to measure how well fluorescence is excited as a function of excitation wavelength. We assume the case in which the user plans to record both types of spectra for a compound in a solution.

All spectrofluorometers have a light source with a wavelength selector, optics that transfer the light to a sample, optics that pick up light from the sample, a wavelength selector for detection, and one or multiple light detectors. A common instrument layout for the optical bench of a spectrofluorometer is shown in Figure 1.

In a classic instrument, the light source might be a broadband high-pressure xenon arc lamp. For this type of light source, a housing with mirror optics and a condenser lens are used to transfer the brightest part of the emission from the arc to the entrance slit of an excitation monochromator. The excitation monochromator selects mostly a narrow band of wavelengths that exit toward the sample. Many instruments include a pick-off mirror in this region that directs light to a reference detector (often a photomultiplier tube) to compensate for the varying intensity of light at each wavelength emerging from the excitation monochromator. Most spectrofluorometers have a provision for a bandpass filter (BPF) to clean up the excitation light, after which the excitation then encounters a sample. The sample is often a solution of an analyte in a 1-cm pathlength fluorescence cuvette.

Fluorescence from the sample emerging at right angles to the excitation is collected by more optics. A longpass filter (LPF) is frequently used here to remove any scattered excitation light from the emission before it focuses on the entrance slit of an emission monochromator. Wavelengths are again selected and a narrow band of emission wavelengths is focused to a detector, which is often a red-enhanced photomultiplier tube.

The most common substantial errors we observe in spectrofluorometry are as follows: a) poor choices of excitation and emission wavelengths leading to noisy spectra; b) a poor choice of the sample concentration leading to inner-filter effects; c) a poor choice of the spectral bandwidth (SBW) and step size leading to an unfaithful reproduction of the true spectrum, or long, noisy measurements; d) a poor choice of sample cell or solvent that obstructs the analyte spectrum; and e) detecting scattered light that leads to artifacts in the spectra.

Procedure: Absorption → Emission → Excitation

You can avoid all of these problems by beginning studies with a good quality absorption spectrum of the analyte recorded on a spectrophotometer using the same sample cuvette you intend to use for spectrofluorometry, ideally with a SBW of 2 nm or better. This measurement helps you select the best choices for excitation wavelengths, the likely range for emission wavelengths, an appropriate concentration, and an appropriate SBW and step size. It confirms that the sample cell and solvent are suitable. Finally, it allows you to select conditions that avoid most problems with scattered light.

If you are planning to record fluorescence emission and excitation spectra for a compound in solution, the best procedure for getting your data right the first time is to do the following in order:

use a spectrophotometer to collect an absorption spectrum of the sample

record a fluorescence emission spectrum of the sample based on information in the absorption spectrum

record a fluorescence excitation spectrum based on the information in the absorption and fluorescence emission spectra.   

As an example, let us consider the following absorption and fluorescence spectra (Figure 2) for tetrakis(4-carboxyphenyl)porphyrin (TCPP). Consider the absorption spectrum recorded in our intended fluorescence cuvette and solvent first. The fact that we can see the absorption spectrum tells us that ethanol and the quartz sample cell are appropriate for the measurement through this range of wavelengths. If the solvent or cuvette absorb light, the absorption spectrum will either go off-scale or will be nothing but random noise.

The absorption spectrum itself tells us that we could potentially excite TCPP at most wavelengths below about 660 nm because it has absorption throughout the region, although the best initial choice of excitation wavelength is likely near 417 nm because that is where the strongest absorption is.

Our next step would be to record a fluorescence emission spectrum using our selected excitation wavelength. The absorption spectrum does not give us direct information on the wavelength range where the strongest emission will occur in our fluorescence or phosphorescence spectrum. But even this is guided by our absorption spectrum, because it tells us what wavelength range to consider.

The longest wavelength absorption we see for TCPP has a maximum near 645 nm; our fluorescence will likely not be at a significantly shorter wavelength (in fact this molecule has its apparent emission maximum at nearly the same wavelength, which is unusual). Because our selected excitation wavelength of 417 nm is pretty far below 645 nm, we might suggest recording our first fluorescence emission spectrum beginning at 600 nm to start.

The absorption spectrum also tells us what concentration of analyte to use. The peak absorbance at 417 nm in Figure 2 is near 

 = 0.42. That is far too high, and as a result, we should dilute the sample so that its peak absorbance is in the range 

 = 0.05–0.1 before spectrofluorometry, which avoids a phenomenon known as an 

The absorption spectrum further helps us choose the SBW for our fluorescence emission spectrum. In Figure 2, we observe that the sharpest spectral feature has a full width at half maximum (fwhm) near 10 nm. This is much broader than the SBW of the spectrophotometer we used for the absorption measurement, so we can feel confident this represents something inherent to our analyte. We should not use any SBW broader than that, and probably should start a little better—for instance, a SBW of 5 nm. As a first guess, we can use a SBW of 5 nm for both the emission monochromator and the excitation monochromator. The maximum step size for the measurement follows directly from our SBW: We should record a minimum of two points in each SBW. If the SBW is 5 nm, it is convenient to choose a step size of 1 or 2 nm.

The absorption spectrum also helps us address the issue of scattered light. There are two types of scattered light problems that we face, but the first and most obvious is because of the second-order transmission of light in the emission monochromator. For example, if your emission monochromator is set at a wavelength of 834 nm, this means it transmits light at a wavelength of 834 nm to the detector in the first order; however, it can also transmit light at 417 nm in the second order. Running our fluorescence emission spectrum through 834 nm could produce a giant spike in our spectrum because of scattered excitation that could easily saturate our detector. The second, less obvious scattered light problem is stray light in our excitation and emission monochromators. This stray light can cause a baseline artifact, which could be important if the spectrum is weak.

Both of these stray light problems can be avoided with optical filters that clean up out-of-band light from your excitation source and prevent the excitation from reaching your emission monochromator. A BPF that transmits 417 nm light, but blocks shorter and longer wavelengths, and a LPF that blocks 417 nm light, but transmits 600 nm wavelength light and beyond, will prevent scattered light from impacting the measurement at all. If you only have a longpass filter, that is still helpful; if you have no filters at all—and many spectrofluorometers do not come with them automatically—then avoid running the emission monochromator to a wavelength that is an integer multiple of the excitation wavelength.

We’re now ready to record our fluorescence emission spectrum, shown in Figure 2 as a red curve.

Our fluorescence emission and absorption spectra can then help us decide how to record a fluorescence excitation spectrum. For example, in our fluorescence emission spectrum of TCPP above, there are two large fluorescence maxima, one at 646 nm and another at 713 nm. But the absorption spectrum tells us that our fluorescence excitation spectrum will likely continue to ~660 nm. It makes sense then to choose 713 nm as our detection wavelength and record our excitation spectrum from 360 nm (below that could potentially produce a second-order peak) to 670 nm. A LPF that transmits 713 nm but blocks below 670 nm will help us avoid most light scattering problems, and we have already selected a SBW and step size for our measurement, so a good quality fluorescence excitation spectrum can then be acquired.

If you decide you want better data than this procedure provides, you can return and adjust the SBW separately for excitation and emission spectra. For example, the SBW for the excitation source can usually be increased when you are recording a fluorescence emission spectrum, and the SBW for the emission detector can be increased when you are recording a fluorescence excitation spectrum. In addition, the integration time or dwell time at each measurement point can be increased from the default offered in commercial software. Each of these changes will increase the measurement signal-to-noise ratio.

The procedure we provided here will allow you to record good quality spectra in most cases in your first measurement. Part 2 of this three-part series will focus on generating data that can be reproduced on different instruments. Part 3 will discuss how some samples can be responsible for making data irreproducible, even when everything else was "done right the first time".

 (Springer, Boston, Massachusetts, 3rd Ed., 2006).

 (Prentice-Hall Inc., Englewood Cliffs, New Jersey, 1988).

 are with the University of South Carolina, in Columbia, South Carolina. Direct correspondence to: 

Tutorial

Analysts using fluorescence emission and fluorescence excitation spectroscopy may encounter several common problems in their measurements. This tutorial, the first of a three-part series, provides a procedure to help avoid them.

Challenges of Spectrofluorometry, Part 1: Collect Data Right the First Time

“Ru-type ware” and unglazed firing bodies were unearthed from the Qingliang Temple Kiln in Baofeng County in the Henan province of China in 2014; the excavation site was close to the central firing area of the ancient Ru Kuan Kiln. Ru-type ware has several similar characteristics to Ru Kuan ware. Here, four samples of unglazed firing bodies, 10 samples of sky green Ru-type wares, and seven samples of sky green Ru Kuan wares were selected. The chemical compositions of 16 elements in body and glaze were measured by energy-dispersive X-ray fluorescence (EDXRF), and multivariate statistical analysis was conducted to determine the classification relations between Ru-type and Ru Kuan wares. Results indicate that the source of raw material and glaze formula of sky green Ru-type are different from Ru Kuan wares. For the sky green Ru-type ware, the raw materials for body is concentrated, and the glaze formula is stable, which conforms to the characteristics of official celadon.

The Ru Kuan kiln is one of the top five famous kilns of the Song Dynasty, and it was reserved for the Imperial Court. The Ru kiln produced an extremely rare type of pottery and porcelain, called Ru Kuan ware, that was only created for the Imperial Court in the Northern Song Dynasty (1). Ru Kuan ware pottery was famous for its delicate body quality, its exquisite workmanship, and its bright and smooth glaze color. The Ru Kuan kiln existed only approximately 20 years, prospering as a result of the continuous war between the Song and Jin Dynasty at the end of the Northern Song Dynasty. The result of this war led to the rarity of products handed down from ancient times and made this material even more precious.

Qingliang Temple, located in Baofeng County in the Henan Province of China, was identified as the historical site of the official Ru kiln of the Song Dynasty in 2000 (2). From March 2014 to September 2014, the Henan Provincial Institute of Cultural Heritage and Archaeology conducted archaeological excavations in the central firing area and surrounding areas of the Ru Kuan kiln. A kiln furnace for unglazed firing and a small amount of unglazed firing shards were unearthed in the stratum of the nearest exploration pit (Figure 1) to the central firing area of Ru Kuan kiln (3). The stratigraphic relationship showed that the kiln furnace was built later than the mature period of the Ru Kuan kiln, which belonged to the late Northern Song or Jin Dynasty. In the same layer, another celadon-glazed porcelain was also unearthed; its body had fine and solid quality, with a greyish-white color and a little bit of the incense ash color normally associated with Ru Kuan ware. The celadon-glazed porcelain also contained the similar glaze color of sky green and grass green that was historically associated with Ru Kuan ware porcelain. Although the glaze color was bright and the glass texture is obvious, its jade texture was not as good as that of Ru glaze. This material was temporarily named “Ru- type ware” to distinguish it from Ru porcelain and also to acknowledge the several similarities that the two types of pottery possess (4). Ru-type ware is concentrated in the pit under the Yuan Dynasty layer (Layer 3) and the accumulation of the Jin Dynasty layer (Layer 4), and the stratigraphic age is also later than the mature period of the Ru Kuan kiln. Moreover, the Ru-type ware is large in size, with a regular shape, solid body quality, and a pure glaze color. Ru-type ware is also produced by full-glaze and pad firing. According to the standards for judging official and civil use, Ru-type ware should be for official use (5).

Amongst the celadon glaze color of Ru Kuan kiln, the sky green color is the most precious, as it represents the highest firing level of Ru Kuan ware. In this paper, the energy-dispersive X-ray fluorescence (EDXRF) technique is used to measure the chemical compositions of body and glaze samples of unglazed firing, sky green Ru-type ware, and Ru Kuan ware (Figure 2). The experimental results were subjected to multivariate statistical analysis to obtain the relationship between the raw material sources and glaze formulas of unglazed firing, sky green Ru-type ware and sky green Ru Kuan ware. Both Ru Kuan ware and Ru-type ware are very precious archaeological samples and there was no relevant research on Ru-type ware until now. This study lays the foundation for the preservation of samples and subsequent research studies. Furthermore, it provides a scientific basis for the firing age of Ru-type ware, identification of Ru Kuan and Ru-type wares, and research on the Ru kiln as well as the Northern and Southern Song Dynasties.

This experiment was conducted with the X-ray fluorescence spectroscopy elemental analysis system in the super-large sample room (Eagle III XLL μ-Probe) of the Institute of High Energy Physics Chinese Academy of Sciences. The spectrometer is equipped with a molybdenum tube and a 125 μm beryllium window that has an incident beam angle of 65° and an emergence angle of 60°. A silicon–lithium X-ray detector with a beam spot diameter of Ф = 1 mm, operating voltage of 40 kV, operating current of 250 mA, and the energy resolution of 160.3 eV, was used in this study because it can analyze the elements of 

U. To reduce the absorption of the low-energy secondary X-rays produced by the low-Z elements, the spectrometer was equipped with a vacuum chamber.

The relative position of the sample surface, the detector, and the light outlet were fixed, and the samples were positioned to the fixed focus of the analysis system accurately with the three-dimensional (3D) stepping motor, and high- and low- magnification charged-coupled device (CCD) cameras. The fundamental parameter (FP) method was used for nondestructive quantitative analysis of unglazed firing, sky green Ru-type and Ru Kuan ware body and glaze samples for 16 elements, such as sodium oxide (Na

O), magnesium oxide (MgO), aluminium oxide (Al

O), calcium oxide (CaO), titanium dioxide (TiO

), manganese(II) oxide (MnO), and iron(III) oxide (Fe

). To calibrate the matrix effects and improve the accuracy of the data, we used a set of ceramic reference materials for EDXRF by the Institute of High Energy Physics. The data were processed using the program version 32, which was provided with the equipment. Through discriminant analysis and hierarchical cluster analysis (HCA) in multivariate statistical analysis, the relationship between the chemical components was studied (6). The classification of unglazed firing, sky green Ru-type, and Ru Kuan ware body and glaze samples were obtained.

Four samples of unglazed firing, 10 samples of sky green Ru-type ware, and seven samples of sky green Ru Kuan ware shards were selected for this experiment. The shards were provided by the Henan Provincial Institute of Cultural Heritage and Archaeology, and those samples were from accurate and reliable sources. The selection of samples is shown in Table I. The glaze color of Ru-type and Ru Kuan wares mentioned in this paper is sky green and will not be repeated hereinafter.

The chemical composition of 16 elements of body and glaze samples of unglazed firing, Ru-type, and Ru Kuan ware was measured by EDXRF. The contents of seven elements, including Al

Body Raw Materials by Discriminant Analysis

The main raw material of celadon body in northern China is clay, which is comprised of many minerals. The oxide contents in clay minerals would vary in different geological formation conditions and include a small amount of alkaline-earth metal oxides, such as Fe

, in addition to hydrous aluminosilicate (7). Fe

 are usually dispersed in clay as fine grains, and their combinations combine and form compounds, such as FeO·TiO

 at high temperatures; thus, making the bodies show different shades of gray. The Fe

 content was taken as the horizontal coordinate and the TiO

 content as the vertical coordinate. A 2D dispersion analysis chart was made, as shown in Figure 3. Table II and Figure 3 show that the contents of Fe

: 1.2%~1.38 wt%), except for RG14B, were higher than those of Ru-type ware (Fe

: 1.11~1.16 wt%) bodies. This finding indicates that the source of bodies of Ru Kuan ware is different from that of Ru-type ware and unglazed firing, which may have used clay materials with higher hematite, ilmenite, or rutile minerals. The RG14B sample has a higher TiO

 content than unglazed firing and Ru-type ware, and the Fe

 content is only slightly lower than that of one Ru-type ware sample (QL15B). RG14B shows that the contents of Fe

 of Ru Kuan ware bodies are higher than that of the Ru-type ware and unglazed firing bodies. Ru-type ware and Ru Kuan ware can be classified by the iron and titanium contents of bodies.

 content range of Ru-type ware and unglazed firing body overlaps to some extent, the overall content of Fe

 of Ru-type ware was higher than that of unglazed firing. This result shows that the two may have differences in the choice of raw materials, but they may have the same sources of raw materials. However, the content of iron minerals is reduced in the raw material treatment processes, such as elutriation, in the preparation process of unglazed firing. The differences in Fe

 content imply that the level of raw material composition and origin result in that the body of Ru Kuan ware is incense ash color, Ru-type ware is greyish white, and unglazed firing is white.

Discriminant analysis can be used to train the discriminant function based on the different eigenvalues and the category to which the research object is known to belong; when a sample with an unknown category is entered, the sample is classified according to the similarity level between it and the discriminant function (8). In this experiment, Fisher discriminant method was used to analyze the content of all chemical components of 25 body samples. Figure 4 shows a two-dimensional (2D) visual classification scatter diagram. The body samples of unglazed firing, Ru-type ware and Ru Kuan ware are concentrated in three areas of A, B, and C, respectively. This condition is ideal and indicates that the raw material sources of the three kinds of body samples are completely different. Although unglazed firing and Ru-type ware belong to the same excavation layer, their sources of body materials differ, which is caused by differences in the origin of raw materials or firing batches. The two categories differ from those of Ru Kuan ware, which shows that the two differ in terms of body material selection for the porcelain made in the Ru Kiln. The Ru-type ware data points in area B and the Ru Kuan ware data points in area C are concentrated, indicating that sky green Ru-type ware body is strictly controlled in raw material selection and craftsmanship, which proves it was used for official occasions.

Glaze Formula by Dispersion Analysis and Hierarchical Cluster Analysis

The content of oxides of Ru-type and Ru Kuan ware glazes is shown in Table III. After the molar coefficient of alkaline oxide (RO+RO

) is adjusted to 1, the Seger formulas of Ru-type and Ru Kuan ware glazes are 1.1 and 1.2, respectively.

 are the colorants of ceramic glaze, with iron being the main coloring element of celadon glaze in northern China (9). Figure 5a shows the 1D dispersion analysis diagram of the Fe

 content of Ru-type and Ru Kuan ware glazes is at the range of 1.85~2.18 wt%, which shows that the iron mineral content in the glaze formula of the two is close. Manganese and titanium are not the main coloring elements, but they have certain influence on the hue of celadon. MnO and TiO

. Figure 5b shows the 1D dispersion analysis diagram of MnO and TiO

. The MnO contents of Ru-type and Ru Kuan ware glazes fluctuate around 0.11 wt%, and the content of each glaze sample is close to that of others. On the whole, the TiO

 content of Ru-type ware is higher than that of Ru Kuan ware glazes, which indicates that the mineral content of titanium of Ru-type ware glaze is higher, or some ilmenite is involved in the glaze ratio.

 accounts for 81~85.7 wt% and 78.9~80.1 wt% of the total oxides of Ru-type and Ru Kuan ware glaze samples, respectively. The two oxides with a great deal of fluxing agent chemically react to form glaze at high temperature (9), and the molar ratio of the two contents can intuitively characterize the changes in glaze formula and process technology (10). Figure 6 shows the dispersion analysis of the SiO

 contents and the silica-alumina ratio for Ru-type and Ru Kuan ware glazes. The molar silica-alumina ratios are listed in Table III. Except RG20G, Ru Kuan ware is concentrated in area C, and Ru-type ware is concentrated in area D. The comparison shows that Ru Kuan ware glaze has high aluminum and low silicon content, whereas Ru-type ware glaze has high silicon and low aluminum element content, indicating that Ru-type ware is different from Ru Kuan ware regarding its glaze formula. Ru Kuan ware is glazed with agate. A rich agate ore is observed near the site of the Qingliang Temple Kiln (11). Agate is a cryptocrystalline mineral of SiO

, which is added into the glaze to achieve the similar function to quartz. The SiO

 content of Ru-type ware glaze is higher than that of Ru Kuan ware glaze, which might result from the higher quartz content in the glaze formula, or the similar glaze technology with agate in Ru Kuan ware to some extent, but the amount used is higher than that of Ru Kuan ware glaze. Al

 is mainly brought by clay and feldspar, and the content of Al

 of Ru Kuan ware glaze is higher than that of Ru-type ware. Therefore, the Ru Kuan ware used high aluminous clay in the formula, and a larger amount of feldspar is found in the formula. Although RG20G is not in area C, its SiO

 contents are consistent with the characteristics of high aluminium and low silicon contents of Ru Kuan ware.

Table III and Figure 6 show that the molar radio n(SiO

) of Ru-type ware glaze ranges from 8.29 to 8.99, higher than that of Ru Kuan ware glaze with the range from 6.26 to 7.18, which also shows that the glaze formulas of the two differ. Moreover, the silicon:aluminium ratio fluctuation range of the two is relatively small, which implies that the glaze formula is relatively stable and also proves that Ru-type ware has been used for official occasions.

The traditional high-temperature glaze in China has always been glazed by limestone and plant ash, amongst which the fluxing agents, such as CaO, MgO, and K

O, can reduce the melting temperature of glaze. Their contents and types also have a decisive influence on the melting temperature and appearance characteristic of glaze. CaO in glaze mainly comes from limestone and calcite (main ingredient is CaCO

), whereas MgO mainly comes from dolomite (CaCO

). Figure 7a shows a 2D dispersion analysis of calcium and magnesium oxides. A positive correlation is observed between the CaO and MgO contents of Ru-type and Ru Kuan ware glazes, which may be associated with the dolomite composition of the initial mixed glazes. In addition, the contents of CaO and MgO of Ru Kuan ware glaze are higher than those of Ru-type ware glaze, which may be caused by higher content of anorthite, limestone, and dolomite in glaze.

O as a fluxing agent is mainly from potassium feldspar, and ice cracks can be produced on the glaze when the content is high because of the larger coefficient of expansion. P

 as a kind of opacifier that can result in varying degrees of opacified texture on the glaze surface. P

 basically comes from plant ash, and the content of varies considerably from species to species. Figure 7b shows the box plot of K

 of Ru Kuan ware glaze are higher than those of Ru-type ware glaze on the whole. Therefore, Ru Kuan ware adopts the glaze formula with higher potassium feldspar ratio, while Ru-type ware is glazed with plant ash with less P

Hongjie Luo (12) proposed a reference standard for classifying Chinese ancient porcelain glaze based on the coefficient b of wood ash glaze formula, which is the molar ratio of alkali-earth metal oxide (RO) to the sum of alkali metal oxide (R

O) and alkali-earth metal oxide. This classification consists of three groups: calcium glaze (

 ≥ 0.76), calcium–alkali glaze (0.76 > 

 ≥ 0.5) and alkali–calcium glaze (b < 0.5). The calculated 

 values of each sample are listed in Table III, and the box plot is shown in Figure 8. The results show that the 

 values of Ru-type and Ru Kuan ware glazes are all above the dividing line 

 = 0.76, which indicates that both are high calcium glazes. The 

 values of the two are relatively close (the range of 

 values is 0.76~0.82 for Ru-type ware and 0.78~0.81 for Ru Kuan ware), which indicates that Ru-type ware may follow the glaze-making technology of Ru Kuan ware to some extent. With the small fluctuation range of the relatively stable glaze formula, Ru-type ware contains the characteristics of official celadon.

HCA classifies the samples into groups and subgroups according to the similarity and correlation of the samples (13). The HCA of Ru-type and Ru Kuan ware glazes is shown in Figure 9, and can be divided into two categories.

The first category includes all Ru Kuan ware glaze samples from RG14G to RG20G, indicating that the formula of Ru Kuan ware glaze in this category is relatively stable.

The second category includes all Ru-type ware glaze samples from QL11G to QL19G. The formula of Ru-type ware glaze in this category has also become mature and relatively stable, which conforms to the properties of the official glaze formula. Although sample QL11G is slightly far from the nine other samples of Ru-type ware, it can still be classified into one category. On the whole, the formulas of Ru-type and Ru Kuan ware glazes are completely different.

The raw material sources of unglazed firing, sky green Ru-type ware and sky green Ru Kuan ware bodies differ. Although unglazed firing and sky green Ru-type ware belong to the same excavation layer, their sources of body materials differ. The body of sky green Ru Kuan ware is made of clay raw materials with minerals, such as high hematite, ilmenite, or rutile.

Sky green Ru-type ware is different from sky green Ru Kuan ware in terms of glaze formula. The high content of quartz in sky green Ru-type ware glaze and the high content of feldspar in sky green Ru Kuan ware glaze show that the element composition characteristics of the former are a high ratio of silicon and low aluminium, while those of the latter are high ratio of aluminium and low silicon. Moreover, the sky green Ru Kuan ware glaze is glazed of plant ash with high P

 content, and the glaze contains higher limestone, dolomite and potassium feldspar. The content of coloring elements iron and manganese of the two glazes are close, whilst the content of TiO

 of sky green Ru Kuan ware is higher. Ilmenite is involved in the glaze ratio.

The glaze and body of sky green Ru-type ware are strictly controlled in raw material selection and craftsmanship, which proves that it has been used for official occasions. The sky green Ru-type and Ru Kuan ware glazes are high calcium glazes with the relatively close b values and small fluctuation. The glaze formulas of the two are stable and concentrated, which belong to the characteristics of official celadons.

The sky green Ru-type and Ru Kuan wares are classified by the Fe

This work was supported by National Natural Science Foundation of China 11275173 and 11975210.

(1) W. Li, J. Li, Z. Deng, J. Wu, and J. Guo, 

(2) W.J. Zhao, Z.J. Wu, G.X. Li, M. Guo, J.Z. Xie, X.K. Lu, X.M. Sun, S.L. Feng, and M.S. Guo, 

Archaeological Discoveries of Ru Kiln in Baofeng Qingliang Temple, Chinese Ru Kiln academic proceeding

 (Henan People’s Publishing House, Zhengzhou, China, 2017). 

(4) B. Wu, W.J. Zhao, S.L. Feng, H. Zhao, X.M. Sun, and M.S. Guo, 

(5) X.M. Sun, H. Zhao, J.L. Zhao, T.J. Wang, Z.J. Niu, W.B. Wang, and H.W. Lu, 

 (Materials Information, ASM International, Novelty, Ohio, 1996).

 (Shanghai People’s Fine Arts Publishing House, Shanghai, China, 2000).

(10) Y.Z. Ding, J.Y. Hou, H. Li, J.M. Wu, and X.M. Sun, 

History of Science and Technology of China (Ceramic Volume)

Chinese Ancient Ceramics and Mutivariate Statistical Analysis

 (China Light Industry Press, Beijing, China, 1997).

(13) R.A. Ikeoka, C.R. Appoloni, M.A. Rizzutto, and A.M. Bandeira, 

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

 are with the Institute of High Energy Physics Chinese Academy of Sciences in Beijing, China. 

 is with the Henan Provincial Institute of Cultural Heritage and Archaeology in Zhengzhou, China. Direct correspondence to Weijuan Zhao: 

As this study demonstrates, energy-dispersive X-ray fluorescence (EDXRF) and multivariate statistical analysis can be used to distinguish different classes of historical artifacts, such as ancient pottery—revealing insights about theirs origin and uses.

Studying the Source of Raw Material and Glaze Formula of Sky Green “Ru-type Ware” and Ru Kuan Ware by EDXRF

Quadrupole-based inductively coupled plasma–mass spectrometry (ICP-MS) is undoubtedly the fastest growing trace element technique today. Since its commercialization in 1983, approximately 20,000 systems have been installed worldwide to carry out a diverse range of applications, including environmental, geological, semiconductor, biomedical, food, pharmaceuticals, cannabis-hemp, petrochemical, and nuclear applications. To meet such varied application needs, modern ICP-MS instrumentation has to be very flexible if it is to keep up with the increasing demands of its users. Nowhere is this more important than in the area of peak integration and measurement protocol. The way the analytical signal is managed in ICP-MS has a direct impact on its multielement characteristics, isotopic capability, detection limits, dynamic range, precision, signal stability and sample throughput—the major strengths that attracted the trace element community to the technique almost 40 years ago. This month’s column focuses on the fundamentals of quadrupole-based ICP-MS measurement regimes—in particular, the balance between maximizing productivity and optimizing performance.

To understand signal management in greater detail and its implications on data quality objectives, it is important to discuss how measurement protocol is optimized based on the analytical requirements and, in particular, its impact on both continuous signals generated by traditional pneumatic nebulization devices and transient signals produced by sampling techniques such as laser ablation, chromatographic separation, and particle and size studies.

This month’s column will be split into two parts. The first part will focus on the fundamental principles of a scanning quadrupole and, in particular, how measurement protocols can be optimized based on the data quality objectives of the analysis. The second part, which will be published early next year, will look at different ways of evaluating detection capability with conventional continuous signals and those generated by sampling methods involving transient peaks.

There are many variables that affect the quality of the analytical signal in inductively coupled plasma–mass spectrometry (ICP-MS). The analytical requirements of the application will often dictate this, but there is no question that instrumental detection and measurement parameters can have a significant impact on the quality of data in ICP-MS. Some of the variables that can potentially impact the quality of the data, particularly when carrying out multielement analysis, are as follows:

whether the signal is continuous or transient

the temporal length of the sampling event 

Before we go on to discuss these in greater detail and how these parameters affect the data, it is important to remind ourselves how a scanning quadrupole operates, and the fundamental principles of measurement protocol used for multielement peak quantitation.

A quadrupole operates by placing both a direct current (DC) field and a time-dependent alternating current (AC) of radio frequency on opposite pairs of the four rods. By selecting the optimum AC/ DC ratio on each pair of rods, ions of a selected mass-to-charge (

) are allowed to pass through the rods to the detector, whereas the others are unstable and ejected from the quadrupole. Figure 1 shows this in greater detail.

In this simplified example, the analyte ion (black) and four other ions (gray) have arrived at the entrance to the four rods of the quadrupole. When a particular AC/DC potential is applied to the rods, the positive or negative bias on the rods will electrostatically steer the analyte ion of interest down the middle of the four rods to the end, where it will emerge and be converted to an electrical pulse by the detector. The other ions of different 

 values will be unstable, pass through the spaces between the rods, and be ejected from the quadrupole. This scanning process is then repeated for another analyte with a completely different 

 ratio until all the analytes in a multielement analysis have been measured.

The process for the detection of one particular mass in a multielement run is represented in Figure 2, which shows a 

 ion emerging from the quadrupole and being converted to an electrical pulse by the detector. As the AC/DC voltage of the quadrupole—corresponding to 

—is repeatedly scanned, the ions are stored and counted by a multichannel analyzer as electrical pulses. This multichannel data acquisition system typically has 20 channels per mass. As the electrical pulses are counted in each channel, a profile of the mass is built up over the 20 channels, corresponding to the spectral peak of 

In a multielement run, repeated scans are made over the entire suite of analyte masses, as opposed to just one mass represented in this example. The principles of multielement peak acquisition are shown in Figure 3. In this example, signal pulses for two masses are continually collected as the quadrupole is swept across the mass spectrum, shown by sweeps 1–3. After a fixed number of sweeps (determined by the user), the total number of signal pulses in each channel is obtained, resulting in the final spectral peak (1).

When it comes to quantifying an isotopic signal in ICP-MS, there are basically two approaches to consider. One is the multichannel ramp scanning approach, which uses a continuous smooth ramp of 1–

 is typically 20) per mass across the peak profile. This approach is shown in Figure 4 (2). In addition, there is the peak hopping approach, where the quadrupole power supply is driven to a discrete position on the peak (normally the maximum point), allowed to settle, and a measurement is taken for a fixed amount of time. This approach is represented in Figure 5.

The way the analytical signal is managed in ICP-MS has a direct impact on the results generated. In this first of a two-part series, we explain the fundamental principles of a scanning quadrupole and how measurement protocols can be optimized based on data quality objectives.

October 2021

Determining Nitrophenol Isomers Using Raman Spectroscopy

Challenges of Spectrofluorometry, Part 1: Collect Data Right the First Time

Studying the Source of Raw Material and Glaze Formula of Sky Green “Ru-type Ware” and Ru Kuan Ware by EDXRF

Impact of Measurement Protocol on ICP-MS Data Quality Objectives: Part I

Survey of Key Descriptive References for Chemometric Methods Used for Spectroscopy: Part II

Geochemical Analysis Using Laser-Induced Breakdown Spectroscopy