Open access peer-reviewed chapter
Abstract
Sequential analysis of metal speciation in geological samples has been demonstrated using mass spectrometry (MS). Various speciation for metal occurrences, such as water-soluble, exchangeable, oxidable, reducible, and crystalline, have been sequentially extracted from geological samples using a homemade device, followed by online ionized for MS analysis. The metal speciation in geological samples was not only useful for revealing the formation mechanism of geological samples but also beneficial for guiding the separation and exploitation of metals. Compared with the conventional methodology, the present approach takes the advantages of short analysis time (1 h), low sample consumption (1.0 mg), and high recovery (>95%), providing a useful platform for the efficient quantitative speciation analysis of metals in geological samples. This chapter introduces the mechanism and application of the MS approach for the analysis of rare earth minerals, dinosaur fossils, soil, etc.
Keywords
- sequential analysis
- direct analysis
- metal speciation
- mass spectrometry
- geological samples
1. Introduction
Geological samples include a wide range of substances, such as rocks, minerals, sediments, soil, coal, oil, fossil, atmosphere, and organisms. Chemical analysis of geological samples is one of the primary techniques in earth science, which generally involved to get information on major and trace elements, stable and radioactive isotopes, biomarkers, etc. The distribution coefficients of trace elements have been applied to deduce the diagenetic process [1], while the contents and associations of trace elements are usually employed to discriminate the tectonic environment [2]. Moreover, some trace elements can be regarded as geothermometers and geobarometers to estimate the physical-chemical conditions of diagenesis and mineralization [3]. The fractionation mechanisms of stable isotopes and the decay effects of radioactive isotopes have played an irreplaceable role in solving fundamental problems of earth science [4], by providing valuable information about geological time, terrestrial heat sources, atmosphere-ocean interaction, the evolution of crust and mantle, diagenesis, mineralization, tectonism, paleoclimate, and paleoenvironment. Besides, the speciation of metals also shows great implications for guiding the exploitation of minerals [5]. Overall, chemical analysis of geological samples benefits the understanding of the earth from a microscopic perspective, with diversified application scenarios and broad application prospects.
To date, a great number of approaches have been proposed for the analysis of metal speciation in geological samples, including spectrometry, energy spectrometry, and mass spectrometry. For example, X-ray absorption spectroscopy (XAS) [6], X-ray diffraction (XRD) [7], and X-ray photoelectron spectroscopy (XPS) [8] were both applied to analyze the metal species. The advantages of these methods were that the sample pretreatment was simple and the analytical speed was fast. However, these methods always required a high concentration of metal species because of the relatively low detection sensitivity, and the quantitative performance of these methods was also deficient. On the other side, complex sample pretreatment, such as grinding, digestion, centrifugation, and filtration were used to extract the metal compounds from geological samples. Each process is time-consuming and labor-consuming, and maybe suffer from analytes losses [9]. After the sample pretreatment, the extracted compounds were then analyzed by chromatography methods (e.g., high-performance liquid chromatography, capillary electrophoresis chromatography, etc.) to identify the speciation by retention time, followed by coupling to inductively coupled plasma mass spectrometry (ICP-MS) [10] for quantitative analysis and electrospray ionization (ESI) MS for molecular structure analysis [11], which required expensive instruments and complex operation. Otherwise, a multistep offline sample pretreatment procedure was required for the sequential extraction of different heavy metal fractions according to the association form of metals in the samples [12, 13]. For example, in the Community Bureau of Reference protocol (BCR) method [12], 0.1 mol/L acetic acid (HAC), 0.1 mol/L NH2OH·HCl, 8.8 mol/L H2O2 and 1.0 mol/L NH4AC were used sequentially for extraction of different metal speciation from the soil, respectively. Each extraction step took more than 16 h. In the Tessier method [13], 1 mol/L NaAc solution (pH = 8.2), 1 mol/L NaAc/HAc (pH = 5.0), 0.3 mol/L Na2S2O4 + 0.2 mol/L Na-citrate +0.03 mol/L NH2OH·HCl in 25% Hac, 0.02 mol/L HNO3 + 30% H2O2 (pH = 2), HF-HClO4 were used to extract exchangeable, bound to carbonates, bound to Fe-Mn oxides, bound to organic matter, and residual fraction or speciation, respectively. The whole procedure would take more than 40 h. The extracted fraction or speciation was then analyzed by ICP-MS [14] or by inductively coupled plasma optical emission spectrometry (ICP-OES) [15]. Thus, a method that has the advantages of without tedious sample pretreatment, high analytical speed, high detection sensitivity, and high accuracy is necessitous for the analysis of the speciation of the metal.
Herein, a new strategy for sequential analysis of metals speciation in geological samples is proposed by regulating the interaction between reagents/energy and geological samples. Assisted by multiple energy forms such as ultrasound and heat, various element species in geological samples were sequentially extracted by chemical reagents with different physicochemical properties of polarity, acidity, and redox using a small amount of samples. The extracted analytes were then ionized online by different ionization techniques (e.g., ESI, ICP, etc.) for MS analysis. This strategy takes the advantages of avoiding sample pretreatment, high analysis speed, low sample consumption, and low labor and energy consumption, which has been used in various areas, such as environment monitoring [16, 17], material science [18, 19], food science [20, 21], life science [22, 23], and geology [24, 25]. In this chapter, we will focus on the application of this sequential speciation analytical strategy in geological samples, such as mineral samples, fossils samples, and soil samples.
2. Sequential speciation analysis of metals in geological samples
2.1 Construction of the sequential ionization device
A sequential ionization device was home-built (Figure 1), which consists of eight systems: (1) quantitative addition and mixing system of the chemical reagents, which was used to prepare the corresponding reagents for extraction of the defined speciation from geological samples; (2) microscale liquid sample transportation system, which was used for precision transfer of the reagents and the extracted speciation; (3) microstructure morphology imaging system, which was used to monitor the morphology change in real time; (4) micro-electrolytic cell and interface system, which was used to load the sample; (5) field energy (temperature, ultrasonic, microwave) coupling and regulation system, which was used to promote the extraction of speciation from samples; (6) high-efficiency ionization system, which was used to ionize the extracted speciation; (7) timing trigger and intelligent control system, which was used to control the device; and (8) power supply and support connection system. This device can be directly coupled to the commercial mass spectrometer for direct MS analysis of geological samples.
Figure 1.
The picture of a home-built sequential ionization device.
2.2 Sequential analysis of rare earth elements (REEs) speciation in mineral samples
REEs contained 15 elements of the lanthanide group and yttrium and scandium, which are widely applied in multiple areas, including materials science, energy sources, biomedical science, chemo-catalysis, etc. Rare earth mineral is a kind of non-renewable resource and their reserves are decreasing rapidly. Thus, it is urgent to increase the exploration and exploitation efficiency of REEs which is closely related to the elemental speciation of REEs in minerals [5]. For example, ionic rare earth mines are typically exploited by the ion exchange methods [26], while the fluorocarbon cerium earth mines are typically exploited by oxidizing roast chemical methods [27]. Moreover, the information on REEs speciation in mineral samples is useful for the study of metallogenic mechanisms. Therefore, comprehensive analysis of the REEs composition and speciation in mineral samples has great significance for the rare earth industry.
Xu et al. developed a method for rapid characterization of REEs speciation in ore samples by coupling sequential speciation extraction with ICP-MS analysis [24]. As shown in Figure 2, six extraction solutions including ① H2O, ② NH4AC, ③ NH2OH-HCl, ④ mixture of HNO3 and H2O2, ⑤ HF, and ⑥ HNO3 sequentially flowed through the sample chamber at 0.3 mL/min to extract the ① water-soluble REEs speciation, ② exchangeable REEs speciation, ③ reducible REEs speciation, ④ oxidable REEs speciation, and ⑤-⑥crystalline REEs speciation from the REEs ore sample. The extracted REEs species were mixed online with 4% HNO3 (0.3 mL/min) at the outlet of the sample chamber for online ICP-MS analysis. The extracted ion chromatography (EIC) signals of Y (
Figure 2.
The procedure for speciation analysis of REEs in ore samples.
As shown in Figure 3, REEs and associated metals (e.g., Mn, Fe, etc.) with five types of speciation, were sequentially detected within 1 h. Standard curves of REEs (Figure 4a) and the EIC area of the corresponding REE MS signal (Figure 3a-o) were used to quantify the REEs content. The amount of each REE was La 391.7 μg/g, Ce 203.1 μg/g, Pr 74.1 μg/g, Nd 243.9 μg/g, Sm 41.0 μg/g, Eu 5.2 μg/g, Gd 45.9 μg/g, Tb 6.3 μg/g, Dy 28.6 μg/g, Ho 5.5 μg/g, Er 14.9 μg/g, Tm 2.1 μg/g, Yb 10.8 μg/g, Lu 2.0 μg/g, and Y 148.4 μg/g. Figure 4b showed the normalized ratio of each REE with the order of La (32.01%) > Nd (19.94%) > Ce (16.60%) > Y (12.13%) > Pr (6.05%) > Gd (3.76%) > Sm (3.35%) > Dy (2.33%) > Er (1.22%) > Yb (0.88%) > Tb (0.52%) > Ho (0.45%) > Eu (0.42%) > Tm (0.17%) > Lu (0.16%). The ratio of different REEs speciation was shown in Figure 4c. For most of the REEs, the exchangeable speciation (>50%) was dominant. However, for Ce, the dominant speciation was the crystalline (≥80%). These results provided essential information for guiding REEs exploration and exploitation.
Figure 3.
Analytical results of REEs in an ore sample. a)-o) EIC of 15 kinds of REEs and p) 2 kinds of associated metal elements.
Figure 4.
Quantitative information of REEs speciation in an ore sample. (a) The standard curves of REEs (n = 3); (b) The normalized ratio of REEs; (c) The ratio of REEs speciation; (d) The accuracy of the present method varied by standard methods.
The quantitative performance of this method was verified by standard methods (Figure 4d). The recoveries of most REEs (e.g., La, Ce, Pr, and Nd) were in the range of 95%–110%. However, higher recovery (>120–228%) was observed for those low abundance REEs, such as Er (14.9 μg/g), Yb (10.8 μg/g), Tb (6.3 μg/g), Eu (5.2 μg/g), Tm (2.1 μg/g), and Lu (2.01 μg/g), which could be due to the fact that this method required no sample pretreatment, thus reduced the potential analytes losses which may occur in the conventional methods.
In summary, the sequential speciation analytical method developed by Xu et al. takes the advantage of high analysis speed, high detection throughput, high efficiency, low energy consumption, and low sample consumption. For instance, the sequential speciation analytical method can obtain quantitative results of 15 REEs with five speciations (15*6 = 90 data sets) using 1.0 mg ore sample within an analytical procedure (1 h), which took about 40 s for one data set. In general, the BCR method required about 80 h to get 90 data sets using 1.0 g sample [12], and the Tessier method required about 40 h to get 90 data sets using 1.0 g sample [13]. Besides, no sample pretreatment could greatly lower labor and energy consumption.
2.3 Sequential analysis of REEs speciation in fossil samples
REEs pattern in fossil samples is useful for taphonomy, provenance, and paleoenvironment [28]. It has been applied to solve some significant problems such as to identify reworked fossil samples as well as a useful indicator of bone provenance within mixed deposits [29]. Besides, the REEs pattern of fossils was also applied for the determination of stratigraphy, such as the dating formation [30]. Basically, REEs enriched in fossils were attributed to a post-mortem process [28]. Two processes were proposed during the enrichment process of REEs, which are substitution and adsorption. These two main processes corporately determined the concentration of REEs in biogenic apatite. On one side, the adsorption process can rapidly reach an equilibrium. However, because of the weak physical molecular force, such as the van der Waals force, these REEs ions can be desorbed easily. On the other side, the substitution process is up to the properties of bulk crystal-chemical, which accounts for the major contribution of concentrated REEs. Therefore, REEs speciation is vitally essential for the research of fossil bones.
Thus, Wang et al. carried out the sequential ionization MS analysis (Figure 2) for the quantification of REEs in dinosaur bone fossils samples [25]. Fossil particle samples were sampled in different spots on the horizontal part of the bone fossil. Drilled fossils were ground and filtered to obtain powders using a 200-mesh sieve, followed by a sequential ionization MS procedure as shown in Figure 2. As an example, Figure 5 showed the results of La in a powder sample and particle sample. H2O was first selected as an eluent to extract the water-soluble REEs. After the MS signal of water-soluble REEs (inset enlarged peaks 1) decreased to zero, 0.3 mol/L NH4Ac, 0.1 mol/L NH2OH·HCl, and a mixture of 0.1 mol/L HNO3 + 1% H2O2, were sequentially flowed through the fossil sample to extract the exchangeable REEs, reducible REEs, and oxidable REEs until the MS signals of the previous speciation disappeared. Finally, the crystalline REEs was extracted by 0.1 mol/L HF and 1 mol/L HNO3 solutions (inset enlarged peaks 3). The contents of REEs speciation were obtained by integrating the EIC area of the REEs speciation peaks and the corresponding standard curve. Compared with traditional standard digestion methods, this method gave similar results of REEs concentration in a dinosaur bone, but with the advantages of a higher analysis speed (1 h), lower sample consumption (1 mg), and more information on spatial distribution. Furthermore, the REEs in both powder samples and particle samples of dinosaur bone could be sequentially detected with high sensitivity in this work. This approach may offer us a wonderful tool for the investigation of REEs distribution in the microstructure of the fossil sample in the future.
Figure 5.
Sequential analysis of REEs speciation in a dinosaur bone fossil sample. (a) EIC of La in fossil powder; (b) EIC of La in fossil particle. The insets in (a) and (b) were the enlarged REEs peaks.
2.4 Sequential speciation analysis of heavy metals in soil samples
Heavy metal is one of the most toxic species which exists widely in soil and is closely related to human health [31]. The heavy metals in soil can further enter drink water and plants, and then enter the human body by diet. In particular, the biotoxicity of heavy metals is closely related to their speciation [32]. For example, soluble Pb speciation are easily metastasis and assimilated by the plants, resulting in strong toxicity, while insoluble speciation Pb has weak toxicity. Thus, chemical profiling of heavy metal speciation in soil samples is of great significance for the evaluation of health risks. In addition, the information regarding heavy metal speciation is useful for tracing the pollution source.
Song et al. developed a sequential ionization MS method for analysis of the speciation of the metal in soil samples without sample pretreatment by a combination of a flow electrolytic cell to ESI-MS [17]. Four speciations of heavy metals, including water-soluble speciation, organic speciation, insoluble speciation, and elemental speciation, were extracted sequentially online by H2O, CH3OH, EDTA-2Na, and electrolysis for ESI-MS detection. Figure 6a displays the high-resolution MS results of Pb(NO3)2 which was employed as a representative of water-soluble metal compounds. EDTA-2Na was used to chelate the extracted Pb2+ online for ESI-MS analysis. The related signals were assigned to [EDTA-2H]2− (
Figure 6.
Qualitative analysis of different speciation Pb by EC-MS. (a) water-soluble speciation Pb(NO3)2; (b) organic speciation trimethyl lead chloride; (c) insoluble speciation Pb(CO3)2; (d) elemental Pb.
Figure 7 showed the results of the heavy metals in the faucet sediment obtained by sequential speciation analysis. The whole analytical procedure was completed within 20 min without complex sample pretreatment. The signals of water-soluble speciation, organic speciation, insoluble speciation, and elemental speciation were sequentially detected at 0–4 min, 4–6 min, 6–11 min, and 12–18 min, respectively. The results indicated that the sediment contained water-soluble, insoluble, and elemental Pb, Ni, Zn, and Fe, insoluble and elemental Cu, water-soluble and insoluble Ca, and insoluble Mn. No organic speciation metal was detected in the sediment.
Figure 7.
Application of EC-MS in the analysis of heavy metals in the faucet sediment. (a) EIC of the different speciation of heavy metals; (b) mass spectrum of the corresponding speciation.
2.5 Sequential speciation analysis of arsenic in sediments and organisms
Despite great efforts have been made to study redox-sensitive elements in the environment such as arsenic (As) and antimony (Sb), there are still some challenges and difficulties for the speciation analysis. As is one of the most toxic elements and exists widely in the natural environment [33]. Furthermore, the chemical behavior of As, such as adsorption, toxicity, transformation, and bioavailability, was not only related to its speciation but also related to the co-existing elements (e.g., Fe, Mn) [34]. Therefore, a robust analytical method is required that can provide information on the concentration, spatial distribution, redox states, and the inter-influence with coexisting elements. However, conventional speciation analysis methods normally required tedious sample pretreatment [12, 13].
Thus, Wu et al. proposed an online sequential fractionation analysis method for As in ferrihydrite samples [16]. As adsorption experiment was first conducted in solution with synthesized ferrihydrites as adsorbents. The collected As-adsorbed ferrihydrite was then used as a raw sample for direct MS analysis. The online fractionation analysis method consisted of eluents, an online extraction device, online acidification, and detection (Figure 8). Based on the association between As and iron oxides, various eluents were selected, such as H2O, 5% NH4NO3, 0.2 mol/L NH4H2PO4, 0.1 mol/L (NH4)2C2O4 (pH = 3), and a mixture of 0.5% HF and 5% HNO3, respectively. Online extraction of As, which was assisted by an ultrasonic bath, was then performed and acidified by 2% HNO3. Finally, the analytes were efficiently detected by ICP-MS.
Figure 8.
Sequential analysis of As speciation in ferrihydrite sample. (a) Schematic diagram of online sequential analysis of As species on ferrihydrite. The eluents (1)–(5) are H2O, 5% NH4NO3, 0.2 mol/L NH4H2PO4, 0.1 mol/L (NH4)2C2O4 and a mixture of 0.5% HF and 5% HNO3, respectively; (b) online elution component with detailed parameter; (c) the picture of the sample before and after analysis.
The results showed that water-soluble fraction, non-specifically adsorbed fraction, specifically adsorbed fraction, iron oxide bonded fraction, and residual fraction were sequentially determined with different proportions (Figure 9). For instance, iron oxide bonded As species (83%) and specifically adsorbed As species (13%) were the dominant As(III) fractions on ferrihydrite. The method had the advantages of no sample pretreatment, low sample consumption, high detection sensitivity, short analysis time, and simple experimental manipulation, providing a useful tool for understanding As behavior in the environment. Besides, compared to conventional methods, the present method could provide more information on the As association with iron oxides on the surface of solid samples.
Figure 9.
Sequential analysis of 75As(III) and 54Fe in ferrihydrite.
In the second work, an extraction electrospray ionization-hydride generation-microwave plasma torch-mass spectrometry (EESI-HG-MPT-MS) method was proposed by Wu et al. for online speciation analysis of As from raw bio-matrices [20]. In this work, the MPT was coupled to an organic mass spectrometer for sensitive detection of inorganic arsenic species. The overall setup was mainly composed of five parts, which are detailed in Figure 10. A mixture of CH3OH/H2O/HAc (V:V:V = 50:49:1) was used as an eluent reagent. Raw samples were extracted online by a syringe pump and an applied high voltage of 4 kV. The As-containing effluent was then either directly ionized for organic As detection or for the detection of inorganic As converted into volatile arsenic hydrides under certain pH conditions. It is worth noting that, in this work, a novel sample introduction device that can be used for both gas and solution samples was prepared by inserting a glass pipette into a larger quartz tube (i.d. 0.25 cm). When the analytes reached the tip of the inner glass pipette, a stable electrospray plume was formed, and therefore organic As could be analyzed directly. On the other hand, inorganic As was determined via ionization by MPT in the form of m/z 204 (H3AsO4 + NO3−). This method enabled direct obtaining of As speciation in raw biological samples, which avoided multistep extraction and sample preservation, therefore potential redox processes, interconversion, or mass loss could be effectively alleviated. Ambient ionization by MPT allows sensitive detection of inorganic As on a common organic mass spectrometer. The developed method may play a role in different fields such as analytical chemistry, environmental chemistry, and bioanalysis.
Figure 10.
Sequential analysis of organic and inorganic arsenic species. (a) Schematic diagram of the EESI-HG-MPT-MS, which consists of a (1) syringe pump, (2) sample chamber, (3) generator of hydride As, (4) sample introduction device, (5) MPT and mass spectrometer; (b) schematic diagram of the generator of hydride As; (c) picture of the home-made ion source.
3. Conclusions and Future Directions
3.1 Conclusions
In summary, a novel strategy for direct MS analysis of metal speciation in geological samples was proposed by regulating the interaction process between reagents/energy and geological samples, and a sequential ionization device was home-built which can be directly coupled to the commercial mass spectrometer (e.g., ICP-MS, ESI-MS) for the direct MS analysis of geological samples. Rapid sequential characterization of metal speciation in multiple geological samples, such as minerals, fossils, soil, and bio-tissue has been quantitatively achieved based on the sequential analytical strategy and ionization device, without complex sample pretreatment. Compared with traditional methods (e.g., BCR method, Tessier method), the present method offers significant advantages such as high analysis speed, low sample consumption, high recovery, simple operation, low energy and labor consumption, and rich chemical information. These results demonstrate the power of the present method for obtaining information on element abundance, occurrence, and paragenetic association, which is vitally essential for geochemistry.
3.2 Future directions
3.2.1 Sequential analysis of the distribution of metallic constituents in geological samples
Micro-area analysis of geological samples, such as minerals, can provide information on the concentration and distribution of the elements, which is essential for studying the metallogenic mechanism and understanding the structure. In general, laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) [35], second ion mass spectroscopy (SIMS) [36], proton-induced X-ray emission spectroscopy (PIXRES) [37], etc. were widely used in the micro-area analysis of minerals with point-by-point scanning, which has the advantage of high spatial resolution and disadvantage of time-consuming. Thus, a novel analytical strategy for rapid micro-area analysis is needed. For instance, using a sequential ionization device (Figure 1) coupled with MS, the speciation (S) and concentration (C) of the metal in the geological samples can be obtained sequentially with time (T) by MS to establish the relationship Y(S, C) = F(T). Meanwhile, the Microstructure morphology imaging system in the sequential ionization device can record the change of morphology (M) in real time to establish the relationship Y(M) = F(T). By combining the relations the Y(S, C) = F(T) and Y(M) = F(T), a relationship between (S, C) and (M) could be established as Y(S, C) = F(M), which would be the distribution of metal speciation.
3.2.2 Sequential analysis of metallic and organic constituents in geological samples
Besides metallic constituents, organic constituents are also important for geological samples [38]. For example, biomarkers, such as alkanes, aromatic hydrocarbons, terpenoids, steroids, fatty acids, porphyrins, and so on, are helpful in identifying the source of organic matters, inferring the redox conditions of depositional and early diagenetic stages, and in reflecting thermal maturity of sediments. Thus, a method is urgent that could simultaneously analyze the metallic and organic constituents, which would not only provide information on metallic and organic constituents but could also establish the relationship between the metallic and organic constituents. Recently, we have started research to simultaneously analyze the metallic and organic constituents in fossils, soil, PM2.5, tissue, alloy, etc. by sequential ionization MS.
Acknowledgments
This work was financially supported by the National Natural Science Foundation of China (No. 21864001, 21727812, 21705016).
References
- 1.
Knut B. Relationships between depositional environments, burial history and rock properties. Some principal aspects of diagenetic process in sedimentary basins. Sedimentary Geology. 2014; 301 :1-14. DOI: 10.1016/j.sedgeo.2013.12.002 - 2.
Fisher RV, Schmincke HU. Pyroclastic rocks and tectonic environment. In: Fisher RV, Schmincke HU, editors. Pyroclastic Rocks. Berlin: Springer; 1984. pp. 383-409. DOI: 10.1007/978-3-642-74864-6_14 - 3.
Du Y, Ma T, Xiao C, Liu YJ, Chen LZ, Yu HT. Water-rock interaction during the diagenesis of mud and its prospect in hydrogeology. International Biodeterioration & Biodegradation. 2018; 128 :141-147. DOI: 10.1016/j.ibiod.2017.03.020 - 4.
Clayton RN. Isotopes: From earth to the solar system. Annual Review of Earth and Planetary Sciences. 2007; 35 :1-19. DOI: 10.1146/annurev.earth.35.092006.145059 - 5.
Belova VV. Development of solvent extraction methods for recovering rare earth metals. Theoretical Foundations of Chemical Engineering. 2017; 51 :599-609. DOI: 10.1134/S004057951605002X - 6.
Voegelin A, Weber FA, Kretzschmar R. Distribution and speciation of arsenic around roots in a contaminated riparian floodplain soil: Micro-XRF element mapping and EXAFS spectroscopy. Geochimica et Cosmochimica Acta. 2007; 71 :5804-5820. DOI: 10.1016/j.gca.2007.05.030 - 7.
Javed MB, Kachanoski G, Siddique T. A modified sequential extraction method for arsenic fractionation in sediments. Analytica Chimica Acta. 2013; 787 :102-110. DOI: 10.1016/j.aca.2013.05.050 - 8.
Wu D, Sun S, He M, Wu Z, Xiao J, Chen XD, et al. As (V) and Sb (V) Co-adsorption onto ferrihydrite: Synergistic effect of Sb (V) on As (V) under competitive conditions. Environmental Science And Pollution Research. 2018; 25 :14585. DOI: 10.1007/s11356-018-1488-2 - 9.
Wu D, Pichler T. Preservation of co-occurring As, Sb and Se species in water samples with EDTA and acidification. Geochemistry: Exploration, Environment, Analysis. 2016; 16 :117-125. DOI: 10.1144/geochem2015-369 - 10.
Popp M, Hann S, Koellensperger G. Environmental application of elemental speciation analysis based on liquid or gas chromatography hyphenated to inductively coupled plasma mass spectrometry-a review. Analytica Chimica Acta. 2010; 668 :114-129. DOI: 10.1016/j.aca.2010.04.036 - 11.
Chen BB, Peng L, He M, Wang C, Bin H. Identification of cadmium containing metabolites in HepG2 cells after treatment with cadmium-selenium quantum dots. Chinese Chemical Letters. 2023; 34 :107262. DOI: 10.1016/j.cclet.2022.02.067 - 12.
Sahuquillo A, López-Sánchez JF, Rubio R, Rauret G, Thomas RP, Davidson CM, et al. Use of a certified reference material for extractable trace metals to assess sources of uncertainty in the BCR three-stage sequential extraction procedure. Analytica Chimica Acta. 1999; 382 :317-327. DOI: 10.1016/S0003-2670(98)00754-5 - 13.
Tessier A, Campbell PGC, Bisson M. Sequential extraction procedure for the speciation of particulate trace metals. Analytical Chemistry. 1979; 51 :844-851. DOI: 10.1021/ac50043a017 - 14.
Yang JQ , Chen YW, Shi KL, Hu KS, Li R, Gao XQ , et al. Stability of selenium and its speciation analysis in water using automatic system separation and HR-ICP-MS measurement. Chinese Chemical Letters. 2022; 33 :3444-3450. DOI: 10.1016/j.cclet.2022.03.002 - 15.
Bogusz A, Oleszczuk P. Effect of biochar addition to sewage sludge on cadmium, copper and lead speciation in sewage sludge-amended soil. Chemosphere. 2020; 239 :124719. DOI: 10.1016/j.chemosphere.2019.124719 - 16.
Wu DB, Yang SP, Li FL, Zhu TG, Chen HW. Online sequential fractionation analysis of arsenic adsorbed onto ferrihydrite by ICP-MS. Analytical Chemistry. 2020; 92 :14309-14313. DOI: 10.1021/acs.analchem.0c03516 - 17.
Song LL, Li H, Li T, Xu JQ , Chen HW. Sequential speciation analysis of heavy metals in drinking water pipe scales by mass spectrometry. Chemical Research in Chinese Universities. 2022; 38 :774-781. DOI: 10.1007/s40242-022-2131-9 - 18.
Xu JQ , Zhu TG, Chingin K, Liu YH, Zhang H, Chen HW. Sequential formation of analyte ions originated from bulk alloys for ambient mass spectrometry analysis. Analytical Chemistry. 2018; 90 :13832-13836. DOI: 10.1021/acs.analchem.8b04309 - 19.
Xu JQ , Zhong DC, Chingin K, Song LL, Chen HW. Chemical profiling of bulk alloys using micro-electrochemical probe mass spectrometry. Analytical Chemistry. 2019; 91 :8304-8309. DOI: 10.1021/acs.analchem.9b01056 - 20.
Wu DB, Li D, Dong LL, Li GL, Wang L, Tang ZY, et al. Sensitive online speciation analysis of arsenic in biological samples by ambient mass spectrometry. Journal of Analytical Atomic Spectrometry. 2022; 37 :2103-2110. DOI: 10.1039/D2JA00075J - 21.
Xu JQ , Xu SR, Xiao YP, Chingin K, Lu HY, Yan RH, et al. Quantitative determination of bulk molecular concentrations of β-agonists in pork tissue samples by direct internal extractive electrospray ionization-mass spectrometry. Analytical Chemistry. 2017; 89 :11252-11258. DOI: 10.1021/acs.analchem.7b00517 - 22.
Song LL, Chingin K, Wang M, Zhong DC, Chen HW, Xu JQ. Polarity-specific profiling of metabolites in single cell by probe electrophoresis mass spectrometry. Analytical Chemistry. 2022; 94 :4175-4182. DOI: 10.1021/acs.analchem.1c03997 - 23.
Lu HY, Zhang H, Chingin K, Wei YP, Xu JQ , Ke MF, et al. Sequential detection of lipids, metabolites, and proteins in one tissue for improved cancer differentiation accuracy. Analytical Chemistry. 2019; 91 :10532-10540. DOI: 10.1021/acs.analchem.9b01507 - 24.
Xu JQ , Li FL, Xia F, Zhu TG, Wu DB, Chingin K, et al. High throughput online sequential extraction of natural rare earth elements and determination by mass spectrometry. Science China: Chemistry. 2021; 64 :642-649. DOI: 10.1007/s11426-020-9928-6 - 25.
Li FL, Li H, Yang ZQ , Huang TD, Wu DB, Wang SL. Rapid online fractionated analyses of rare earth elements in a dinosaur fossil by mass spectrometry. Journal of Analytical Atomic Spectrometry. 2021; 36 :2612-2616. DOI: 10.1039/d1ja00265a - 26.
Qiu TS, Yan HS, Li JF, Liu QS, Ai GH, Sun YY, et al. Response surface method for optimization of leaching of a low-grade ionic rare earth ore. Powder Technology. 2018; 330 :330-338. DOI: 10.1016/j.powtec.2018.02.044 - 27.
Huang XW, Long ZQ , Wang LS, Feng ZY. Technology development for rare earth cleaner hydrometallurgy in China. Rare Metals. 2015; 34 :215-222. DOI: 10.1007/s12598-015-0473-x - 28.
Anderson PE, Benton MJ, Trueman CN, Paterson BA, Cuny G. Palaeoenvironments of vertebrates on the southern shore of Tethys: The nonmarine Early Cretaceous of Tunisia. Palaeogeography, Palaeoclimatology, Palaeoecology. 2007; 243 :118-131. DOI: 10.1016/j.palaeo.2006.07.015 - 29.
Grandstaff DE, Terry DO. Rare earth element composition of Paleogene vertebrate fossils from Toadstool Geologic Park, Nebraska, USA. Applied Geochemistry. 2009; 24 :733-745. DOI: 10.1016/j.apgeochem.2008.12.027 - 30.
Trueman CN, Behrensmeyer AK, Potts R, Tuross N. High-resolution records of location and stratigraphic provenance from the rare earth element composition of fossil bones. Geochimica et Cosmochimica Acta. 2006; 70 :4343-4355. DOI: 10.1016/j.gca.2006.06.1556 - 31.
Li Y, Dong Z, Feng D, Zhang XM, Jia ZY, Fan QB, et al. Study on the risk of soil heavy metal pollution in typical developed cities in eastern China. Scientific Reports. 2022; 12 :3855. DOI: 10.1038/s41598-022-07864-3 - 32.
Kennou B, El Meray M, Romane A, Arjouni Y. Assessment of heavy metal availability (Pb, Cu, Cr, Cd, Zn) and speciation in contaminated soils and sediment of discharge by sequential extraction. Environmental Earth Sciences. 2015; 74 :5849-5858. DOI: 10.1007/s12665-015-4609-y - 33.
Abernathy CO, Thomas DJ, Calderon RL. Health effects and risk assessment of arsenic. The Journal of Nutrition. 2003; 133 :1536S-1538S. DOI: 10.1093/jn/133.5.1536S - 34.
Okkenhaug G, ZhuYG HJ, Li X, Luo L, Mulder J. Antimony (Sb) and Arsenic (As) in Sb mining impacted paddy soil from Xikuangshan, China: Differences in mechanisms controlling soil sequestration and uptake in rice. Environmental Science & Technology. 2012; 46 :3155-3162. DOI: 10.1021/es2022472 - 35.
Liu YS, Hu ZC, Li M, Gao S. Applications of LA-ICP-MS in the elemental analyses of geological samples. Chinese Science Bulletin. 2013; 58 :3863-3878. DOI: 10.1007/s11434-013-5901-4 - 36.
Reed SJB. Geological Applications of SIMS. In: Benninghoven A, Okano J, Shimizu R, Werner HW, editors. Secondary Ion Mass Spectrometry SIMS IV. Springer Series in Chemical Physics. Vol. 36. Berlin: Springer; 1984. pp. 451-455. DOI: 10.1007/978-3-642-82256-8_117 - 37.
Ahmed M. Analysis of geological samples by the micro-PIXE facility at KFUPM. Journal of Radioanalytical and Nuclear Chemistry. 2005; 265 :39-45. DOI: 10.1007/s10967-005-0786-6 - 38.
Simoneit BRT. Biomarkers (molecular fossils) as geochemical indicators of life. Advances in Space Research. 2004; 33 :1255-1261. DOI: 10.1016/j.asr.2003.04.045