Developments in the application of gas chromatography with atomic emission (plus mass spectrometric) detection

Available online at www.sciencedirect.com

Journal of Chromatography A, 1186 (2008) 109–122

Review

Developments in the application of gas chromatography with atomic emission (plus mass spectrometric) detection L.L.P. van Stee ∗ , U.A.Th. Brinkman Free University, Department of Analytical Chemistry and Applied Spectroscopy, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands Available online 18 October 2007

Abstract Capillary gas chromatography with atomic emission detection is a highly element-selective and sensitive detection technique for many nonmetal as well as metallic elements. A 3–5 order of magnitude element/carbon selectivity, compound-independent calibration and the possibility to calculate (partial) molecular formulae are some of the attractive features of the technique. In the present review, the emphasis is on real-life applications for non-metals such as sulphur, phosphorus, nitrogen and the halogens, and on the potential of combined atomic emission/mass spectrometric detection. © 2007 Elsevier B.V. All rights reserved. Keywords: Atomic emission detection; Gas chromatography; Mass spectrometry; Organic analysis; Applications

Contents 1. 2.

3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Atomic emission detection performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Food and drinks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1. Atomic emission detection/mass spectrometry detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Soil and sediments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4. Air and gaseous samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5. Petrochemical samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6. Synthetic polymers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7. Chemical warfare agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8. Biological samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Atomic emission detection is a sensitive as well as selective detection technique for capillary gas chromatography (GC) which provides very valuable element-selective information. The well-defined and identifiable electron transitions in excited atoms or ions render atomic spectroscopy the best elementselective method available to the analyst. Since its first analytical ∗

Corresponding author. Tel.: +31 20 4447538; fax: +31 20 4447543. E-mail address: [email protected] (L.L.P. van Stee).

0021-9673/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2007.10.031

109 110 110 110 112 112 114 115 116 117 119 119 120 121

use – the visual identification of salts by introducing a sample into a flame – for a long time atomic spectrometry remained in the domain of the inorganic chemist. The situation changed dramatically in the early 1990s, after the introduction of an atomic emission spectrometer (AES) which was compatible with capillary GC, and so became the tool of many organic, environmental and analytical chemists. The first use of GC–AES was reported in 1965 [1,2]. With an argon microwave-induced plasma (MIP), limits of detection (LoDs) in the pg/s range were achieved for several elements, but the selectivities against carbon were very poor. The introduction

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of the Beenakker TM010 cavity [3,4] was a major breakthrough. Because of the better energy transfer to the discharge tube, it allowed the operation of a stable helium plasma at atmospheric pressures. Helium has the advantage over argon that there are fewer emission lines of diatomic species formed by recombination of analyte atoms with contaminants present in the gas or by incomplete degradation of the analyte molecules. The cavity was first used in conjunction with GC in 1978 [5]; LoDs were in the 2–60 pg/s range and selectivities were much better than observed with other types of plasma. A modified version of the Beenakker cavity was included in the Hewlett–Packard system – usually called an atomic emission detector (AED) rather than a spectrometer – launched in 1989. Today, many hundreds of papers have been published on GC–AED with the HP5921A (and its successors, the Agilent G2350A, and the 2370AA, marketed by JAS). It is not the intention of this paper to present a complete overview of the many technical developments introduced since 1965 and/or of all real-life applications. For this, the reader should consult reviews on, e.g. element-selective detection in chromatography [6], speciation of, especially, mercury-, tin- and lead-containing compounds [7–9] and environmental and other applications [10–12]. The main goal of the present review is to show the versatility and practicability of GC–AED to solve a wide variety of problems in trace-level organic analysis. Because of the detailed information available in the literature on (speciation studies of) organo-metal and organo-metalloid compounds, the focus will be on the monitoring of non-metallic elements. For the convenience of readers not familiar with the technique, the main characteristics of AED are briefly discussed below. 1.1. Atomic emission detection performance GC–AED provides simultaneous multi-channel detection for up to four elements with excellent LoDs of 1–30 pg/s for many important elements, response linearities of typically 3–5 orders of magnitude and element versus carbon selectivities of the same order of magnitude. The high selectivity helps to maintain analyte detectability at its standard level with even complex samples. Selected data are shown in Table 1. As regards multi-element detection, depending on the application – i.e. on the elements of interest – several runs are often required to cover the complete set of elements which, of course, increases the time of analysis. This is due to the fact that the optics of the AED are designed to realize the high resolution required to distinguish certain elements, e.g. chlorine (479.5 nm) and bromine (478.6 nm). Because of this, during one run, detection is possible within a window of only 20–25 nm out of the total 160–800 nm that the AED can cover. In addition, elements such as, e.g. phosphorus require special make-up gases and/or flow conditions (see Table 1). Throughout the text, the wavelength used to measure an element refers to the wavelengths in Table 1. In case more wavelengths are given in Table 1 or when the wavelength is not included in the table, the wavelength will be indicated by subscript (e.g. C193 ). A most rewarding aspect of AED is the so-called universal or compound-independent calibration (CIC). The high temperatures of the MIP-type plasma cause an essentially complete

Table 1 Analytical characteristics of AED detection for selected elementsa Element

Wavelength (nm)

Setb

LoD (pg/s)

N S C P C H Cl Br F O Si Hg Pb Sn N

174.2 180.7 193.1 178.1 495.8 486.1 479.5 478.6 685.6 777.2 251.6 253.7 261.4 270.7 388.3

1 1 1 2 3 3 3 3 4 5 6 6 6 6 7

15–50 1–2 0.2–1 1–3 15 1–4 25–40 30–60 60–80 50–120 1–7 0.1–0.5 0.2–1 1 10

a b

Selectivity over carbon (×10−3 ) 2–5 5–20 – 5–8 – – 3–10 2–6 20–50 10–30 30 250 300 300 >5

Data collected from various sources. Arbitrary order.

breakdown of all analyte molecules into their constituent atoms. Consequently, the response per mass unit of an element is more or less independent of the structure of the analyte of interest. As a result, quantification for a whole series of compounds can be based upon data recorded for a single analyte containing the common hetero-atom; if reference compounds are not available, a related compound can be used. In addition, elemental ratios and, thus (partial) molecular formulae can be calculated. In experimental practice, these tools are frequently used (see [13] for a review), and with marked success. In many studies, the use of GC–AED and GC–MS (mass spectrometry) is combined. Usually, the data are obtained using two separate GC systems and are manually correlated during data processing. However, since the AED operates slightly above atmospheric pressure and MS at vacuum, correlation is confounded by persistent differences in retention times, even when the same type of GC column and properly adjusted inlet pressures are used. Similar problems are encountered when two GC columns are used which are installed in the same oven, and experience has shown that using a single column, with eluent splitting at the GC column outlet, i.e. GC–AED/MS, is the most rewarding technique. Illustrative results are presented below. 2. Applications 2.1. Food and drinks The commercially available HP 5921A AED was first used for pesticide analysis by Wylie and Oguchi [14]. They developed a method to detect 27 pesticides in apple extracts. By using the traces of nine elements (LoDs, 0.1–75 pg/s) to derive molecular formulae and combining these with retention data, proper identification of 20 pesticides was achieved. Three pesticides were correctly identified in an apple spiked much below the maximum residue limit of the US Environmental Protection Agency (EPA). A literature overview of pesticide analysis in food is

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Table 2 GC–AED analysis of pesticides in food products Sample(s)

Techniques

Comments

References

Apple Vegetables, fruits Fruits, vegetables Rice grains Onion, radish, potato Fruits, vegetables Fruits Strawberry Honey Fruits, vegetables Honey

LLE LLE LLE SFEa S19 methodb S19 S19, LVIc S19 LLE LLE SPME

Organo-P (OPP) and -halogen (OHP) pesticides; empirical formula calculation; 0.3–0.7 mg/kg spikes Comparison with FPD, NPD and ECD; 0.2 mg/kg spikes OPPs, OHPs, carbamates, metal-containing pesticides OPPs at 10 ␮g/kg, used over a two-week period with up to 70 samples/day Snapshots used to prevent false positives due to matrix compounds 385 pesticides; LoD, 10 ␮g/kg AED vs. ECD and NPD; AED LoDs of same order as ECD or NPD (0.02–0.9 mg/kg) 9-Week monitoring of decomposition of two fungicides applied to strawberry in the field Acaracides such as amitraz and decomposition products; plus ECD, NPD and MS. Validation with large set of pesticides and fruits. GC–AED and –MS; partial formulae calculation 16 organochlorines, OPPs and pyrethrins; LoDs 0.02–10 ␮g/kg

[14] [17] [23] [24] [25] [16] [26] [15] [19–21] [18] [22]

a b c

Supercritical fluid extraction. LLE, GPC and fractionation on silica column. Large-volume injection.

given in Table 2; some selected studies are discussed in more detail below. In an early study, the decomposition of two fungicides applied to strawberries was monitored for several weeks by means of the Cl, N and S traces [15]. Next, the same group published an extensive study on the detection of 385 pesticides in fruits and vegetables, giving LoDs for each of the main hetero-atom channels for each individual pesticide. Generally speaking, the high selectivity of AED was found to outweigh its lower sensitivity (e.g. halogen atoms), compared with electron-capture detection (ECD). The element traces of the most interesting hetero-atoms, N, Cl, S, P, Br and F, were found to be almost free of disturbances caused by co-eluting matrix constituents. Only in the case of rather notorious vegetables such as onion, leek and garlic, did interferences occur in the S trace. The high selectivity allowed the sample preparation procedure to be simplified, and the feasibility to screen for pesticide residues in plant foodstuffs down to the 10 ng/g level (ca. 10 g samples; 6% of final extract injected) was demonstrated [16]. The results of this extensive study were in full agreement with those of an earlier study by Lee et al. [17], who compared AED with flame photometric detection (FPD), nitrogen-phosphorus detection (NPD) and ECD for the analysis of pesticides in 12 types of fruits and vegetables, and emphasized that the high selectivity of AED detection could obviate the need for clean-up in most cases. In a more recent study [18], fruit and vegetable extracts were screened for over 400 pesticides using an experimental database. Retention-time locking (RTL) was used to match GC–AED and GC–MS retention times. Samples were analyzed for S, N, P and Cl; possible pesticides were suggested by database search and identified by GC–MS. For blind spikes of extracts, the database suggested 22 out of 26 pesticides as matches; 19 were identified by GC–MS. Jim´enez et al. [19–21] devoted several papers to the determination of acaricides (which are used to treat beehives against mites). In early work on four acaricides, the authors found that GC–AED is more sensitive (C193 LoDs, 0.05–0.5 ng/g), but less selective, than GC–ECD/NPD [19]. In another study [21], combined AED and MS detection was used to detect degradation products—a challenging task because for such products stan-

dards often are not available. By using the Cl and N traces, the degradation of chlordimeform in spiked honey was followed over a period of 28 weeks. Two degradation products were shown to be present which were, next, identified by means of GC–MS. Very recently, direct sampling of honey with a solidphase microextraction (SPME) fiber [22] was shown to be a simple method for residue analysis (16 organochlorines, organoPs and pyrethrins) in honey. Element-selective detection by means of the Cl, Br and S channels gave LoDs of 0.02–10 ng/g. The method can be used for routine analysis. However, for reliable quantification standard addition had to be applied to correct for variation in recovery caused by the complexity of the matrix. AED has also been employed for the determination of compounds other than pesticides in food products (Table 3). In this context, the high selectivity and sensitivity of the S channel is a major advantage because of the low odour thresholds and flavour characteristics of many S-containing compounds. Headspace SPME was used to study the release of 4-mercapto-4methylpentan-2-one, a volatile thiol which is a potent contributor to wine aroma, from its non-volatile l-cysteine precursor. The fermentation temperature as well as the yeast strain selected were found to provide important tools to enhance or modulate the final wine aroma [27]. In another paper [28], the high selectivity of the Cl channel was used to detect trichloroanisole (TCA) in wine and cork stoppers. TCA is a common off-flavour in many food products and is formed by microbial degradation of chlorophenols. GC–MS in the selected ion monitoring mode and GC–ECD can both be used for trace-level TCA studies, but GC–AED enables easier quantification. With purge-and-trap (P&T) for sample preparation, LoDs of 25 pg/g and 5 ng/L were obtained for corks and wine, respectively. Trace amounts of TCA were detected in 2 out of 15 corks analyzed. Several other studies [29–31] also illustrate the extremely good performance of S-based GC–AED, for example in the analysis of various volatile (di)sulphides and related compounds in wine, beer and coffee powder down to less than 1 ng/L. In one such study [31], S-based GC–AED was used to locate 15 volatile S-containing compounds in various types of garlic samples. The

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Table 3 GC–AED analysis of non-pesticides in food and beverages Sample(s)

Techniques

Comments

References

Wine Wine, corks Garlic headspace Water, beer, coffee Wine Cured ham Onion, garlic

Headspace SPME P&T Static headspace P&T P&T SDEa P&T

Aroma compound 4-mercapto-4-methylpentan-2-one 2,4,6-Trichloroanisole 15 S-containing compounds; plus MS 13 S-containing targets with potential for off-flavour Dimethyl sulphide in red wines Profiling of volatiles in complex flavour isolate; plus GC–MS, –NPD, –FPD and –FIDb Analysis of onion and garlic headspace

[27] [28] [31] [29] [30] [32] [33]

a b

Likens–Nickerson, simultaneous steam distillation/solvent extraction. Flame ionisation detection.

C/H/S ratios and the subsequently recorded mass spectra were successfully used to identify all but two of them. 2.2. Water On the basis of many national and international directives, threshold values for the concentration of organic microcontaminants in raw water used for the production of drinking water, and the final product itself, typically are in the sub-␮g/L range. Trace enrichment, possibly combined with largevolume injection, is therefore a key issue. Today, non-selective solid-phase extraction (SPE) on a copolymer sorbent and, occasionally, SPME have largely superseded classical liquid–liquid extraction (LLE) procedures. There is no need to go into details regarding the various procedural improvements introduced in the early 1990s. These can be summarized as follows: classical 1-␮L injections out of a 1-mL extract representing a 1-L sample (e.g. [34]) were increasingly replaced by large-volume injections of 10–100 ␮L (with proper venting ensuring there are no adverse effects such as flame-outs [35]) and, subsequently, by off-line and, finally, on-line SPE–GC–AED procedures. With the last-named approach, when the entire amount of analytes contained in the sample is injected, LoDs (P channel) for a series

of organo-P pesticides were found to be 30–150 ng/L (off-line SPE; 10-mL samples), 10–30 ng/L (on-line SPE; 10 mL) and as low as 1–2 ng/L (on-line; 100 mL) [36]. For obvious reasons, it is the SPE-based options which are used in most modern ultra-trace-level GC–AED studies. The studies quoted above mainly deal with semi-volatile target analytes (Table 4), but the determination of volatile compounds by means of P&T or static headspace has also attracted attention. In one paper [45], 16 volatile organic compounds (VOCs) were analyzed by means of P&T–GC–AED (using a dryer to prevent plasma destabilization caused by water vapour carried over from the P&T unit by the purge gas). Elementselective detection (C, H, Cl, Br) was in most instances fully successful and empirical formulae calculated generally reliable. LoDs were in the 30–400 pg/L range for 5-mL samples. 2.2.1. Atomic emission detection/mass spectrometry detection With several of the examples discussed earlier, it was indicated that the potential of GC–AED can be considerably enhanced if complementary structural information is provided by MS detection. This approach is used in close to half of the research papers cited in this review.

Table 4 Target analysis of various compounds in water using GC–AED Sample(s)

Techniques

Comments

References

Water, beverages, soil Surface water Surface water, suspended matter

P&T SPME, SPE LLE

[37–39] [34] [40]

Surface water Surface and wastewater Surface water Surface and reagent water

SPE, LLE, LVI On-line SPE, LVI SBSEa LLE

Drinking water Distilled water

Derivatization, SPE P&T

Aqueous solution

P&T

Water

LLE

Snow, glacial ice Wastewater

SPE, LLE SPME

10 volatile organohalogens and chlorophenols; LoDs, 0.05–0.5 ␮g/L Small set of pesticides; LoDs, 0.5–5 ␮g/L (1-L sample) 145 targets (volatiles, haloethers, chlorobenzenes, nitro-compounds, anilines) with GC–MS and complementary AED, NPD and ECD LoDs for OPPs: 0.1–0.5 ␮g/L (10-mL sample) On-line SPE–GC–AED with 100 ␮L LVI; P186 LoD for OPPs: 1–30 ng/L 8 OPPs (including polar fenamiphos); P186 LoDs: 0.8–15 ng/L Large set of N-containing herbicides; compound-independent calibration (CIC); N LoDs: 30–200 ng/L (3-L sample) 8 chlorophenols; LoDs: 0.05–0.2 ␮g/L 16 volatiles; CIC and empirical formula determination; LoDs: 0.03–0.4 ␮g/L Photocatalysis of methyl-t-butyl ether; six transformation products identified 14 pesticides; LoDs: 0.04–0.2 ␮g/L (25-mL samples with 5-␮L splitless injections) Chloroacetates Metazochlor

a

Stir bar sorptive extraction.

[35,41] [36] [42] [43] [44] [45] [46] [47] [48] [49]

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Table 5 Analysis of non-target compounds in water by combined GC–AED and GC–MS Sample(s)

Techniques

Comments

References

Surface water Wastewater Rain, snow Surface water Wastewater Raw water Coal wastewater Wastewater Seawater

SPE On-line SPE, LVI Various SPE, LVI Continuous LLE LLE LLE and SFE SPE LLE

Trace pollutants and pesticides; selection rules and formula calculation On-line SPE; partial formula calculation; LoDs: 0.05 (tapwater) − 0.5 ␮g/L (wastewater) Identification of chlorinated compounds by (Py)–GC–MS and –AED Parallel AED/MS detection; LoDs: 0.02–0.5 ␮g/L Chlorinated sulphur compounds in pulp-mill effluent with GC–MS and –AED Cl- and Br semi-volatile compounds after chlorination procedure; plus MS Analysis of highly contaminated waste water and sediment by (Py)–GC–MS and –AED Parallel AED/MS detection; comparison of treatment plant influent and effluent water Products of benzothiophene photooxidation

[55,56] [51] [57–59] [52] [60] [61] [62,63] [53] [64]

A particularly rewarding strategy – especially for non-target compounds (Table 5) – is firstly to screen for certain elements of interest and, next, use MS for identification. The elemental composition data then help to reduce the length of the hit list and, hence, simplify the MS identification process. Careful matching of the two sets of chromatographic data is extremely important when using this approach. If two different GC systems are used, this can be achieved by retention index (RI) based data correlation or by matching retention times with RTL (cf. [18]). Even so, with very complex samples, the proper matching of element-selective peaks with those in the crowded full-scan mass chromatogram easily creates problems. The best method to obtain the required high AED/MS data correlation, is to use a single GC and split the eluent for parallel AED/MS detection. After an early report by Hooker et al. [50], this approach was re-introduced in 1999. First, a protocol for the (non-target) screening of hetero-atom-containing microcontaminants was designed by still using two separate GC systems, and applied to tap and waste water. Because of the use of on-line SPE–GC, 10–50 mL samples sufficed to reach LoDs as low as 20–500 ng/L [51]. Next, fully integrated SPE–GC with parallel AED/MS detection was presented. This reduced the earlier observed retention time differences of up to 9 s, to 0.5 s or less. The practicability of the approach was demonstrated for river water and vegetable extracts and compound-independent calibration showed good agreement with MS quantification [52]. An improved setup was used to analyze influents and effluents from a sewage treatment plant. In such complex samples many peaks of interest will go unnoticed in full-scan GC–MS because they are obscured by much larger co-eluting interferences and/or background signals. This is demonstrated in Fig. 1 which shows blow-ups of parts of the elemental traces, and the total ion chromatogram (TIC) as well as relevant mass traces of an influent sample. It is clear that even the small peak of chlorpyriphos (spiked at 1 ␮g/L) would probably have gone unnoticed during non-target analysis and the same is true for alachlor. From among several non-target compounds which were identified, two are included in the figure. Peak 11 was identified as 5-chloro2-(2,4-dichlorophenoxy) phenol or triclosan, a disinfectant, and peak 5 as tris(2-chloroethyl) phosphate. In both instances, no peak was visible in the TIC [53]. In another study both SPE and SPMD (semi-permeable membrane devices) were used in order to extend the compound polarity range usually associated with the former technique, in

an automated GC–MS-based screening study of river water for over 400 organic micro-contaminants [54]. The SPMD essentially consisted of a thin-walled polyethylene tube filled with a fatty substance, and mimics the absorption in fat-containing aquatic organisms such as fish, which is the basis of so-called bioconcentration. When the various AED traces were carefully searched for relevant – i.e. mainly chlorinated and brominated – ‘unknowns’, several Br-containing compounds were detected and tentatively identified in an estuarine water sample (Fig. 2). Some of these are reported to be naturally formed by marine organisms, which explains their absence in river water more upstream.

Fig. 1. Blow-ups of parts of GC–AED/MS chromatograms showing obscured peaks of chlorinated compounds in the TIC trace and the corresponding extracted ion chromatograms and AED elemental traces. Peak assignment: (5) tris(2-chloroethyl)phosphate; (8) alachlor; (9) chlorpyriphos; (11) 5-chloro-2(2,4-dichlorophenoxy)phenol [53].

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Fig. 2. Full-scan GC–MS chromatogram (bottom) and GC–AED bromine trace (top) of an SPMD extract of estuarine river water. For compound 213, two isomers were identified [54].

2.3. Soil and sediments The isolation of micro-contaminants from soils and sediments is more difficult than from water because of stronger interaction with the matrix. In addition, background interferences due to humic substances are much higher. Therefore, after solid–liquid extraction (SLE), clean-up with gel permeation chromatography (GPC) is usually necessary to make the final method more reliable and robust. An overview of studies using GC–AED for this type of analyses is given in Table 6; some illustrative examples are briefly discussed below. In a study [65] of a highly polluted harbour sediment sample, some 120 ‘major’ peaks were observed by GC–MS screening in the TIC mode. For all but ten compounds high-match-quality provisional identification was found to agree with the elemental information obtained by GC–AED. In 2 (out of these 10) cases, AED enabled positive identification; in all others, additional information was provided but full characterization was not possible. In addition, by comparing the Cl-trace AED results and a reconstructed PCB-targeted ion chromatogram, the presence of several ‘non-PCB’ compounds was revealed—one of these, nonachlorodiphenyl ether, being reported for the first time in a marine sediment [65]. In a related paper [66], the same group of authors showed that the selectivity of the AED was much higher than that of ECD and even high-resolution MS (matrix problems) detection. For reliable analysis of the highly polluted sample, the raw extract had to be subjected to GPC to remove elemental sulphur. The relative insensitivity of AED-based Cl detection was overcome by using 15 g of dry sample; individual CBs could then be detected down to the 5–20 ␮g/kg level [66].

As regards pesticides, Bernal et al. [67] determined LoDs and retention indices on GC–AED and GC–MS for 181 phytochemicals in standard solutions. Using liquid extraction and SPE clean-up, 90 samples were analyzed revealing the presence of about 30 different pesticides in total. In another study [68] soil samples were first cleaned-up with an acidic solution before extraction, and various pesticides could be detected with LoDs in the low ␮g/kg range. One research group used the combined AED/MS strategy to study naturally occurring halogenated compounds. The presence of several chlorinated aromatic substructures in macromolecular organic matter derived from various types of decaying plant material and soil was established after chemical [69] and thermal [70] degradation. Among the compounds detected in both studies were several chlorophenols and substituted chlorobenzoic acid methyl esters. In a related study [71], low-molecular-weight halogenated compounds were shown to be present as such – i.e. not as part of macromolecules – in forest soil collected in the vicinity of a fungus. Fourteen compounds were detected, and identified as halogenated and methoxylated benzaldehydes, benzoic acids and benzenes. The chlorinated compounds were found to be present in concentrations of up to 20 mg/kg of soil; the three brominated compounds in the set were present in the soil at 0.1–2 mg/kg levels. For both classes, additional GC–MS data were used to achieve unambiguous identification. Some selected structures are given in Fig. 3 which shows the three relevant AED traces. According to the authors, this is the first report on the natural occurrence of low-molecular-weight brominated compounds in terrestrial soil [71].

Table 6 GC–AED analysis of soil and sediments Sample(s)

Techniques

Commentsa

References

Spiked soil Marine sediment Marine sediment Soil Sediments, sludges Soil, decaying plant matter Soil Marine sediment

SLE SLE SLE SLE Pyrolysis Chemical degradation, pyrolysis SLE SLE

Retention data of 181 pesticides; validated recoveries for 11 spiked pesticides Non-target organic micropollutants Comparison of GC–MS, –ECD and –AED for detection of PCBs 10 pesticides in soil; LoDs: 2–5 ␮g/kg soil Differentiation of sludges; measurement of adsorbed/bound chlorine ratio Natural chlorinated matter in plant material and soil Natural small halogenated compounds in forest soil Identification of sulphur compounds in marine sediments

[67] [65] [66,72] [68] [73,74] [69,70] [71] [75]

a

In all studies except [68], GC–MS was used additionally.

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115

2.4. Air and gaseous samples

Fig. 3. GC–AED of neutral components in a sample collected at the arc of a Lepista nuda fairy ring. The carbon (496 nm), chlorine (479 nm) and bromine (478 nm) traces are shown. The internal standard (IS) was 1-chlorotetradecane (100 ng/␮L) [71].

Several studies in which GC–AED has contributed to the detection of contaminants in air and gases are included in Table 7. Becker et al. [76] used GC–AED to detect S-containing (thiaarenes or PASHs) polyaromatic hydrocarbons (PAHs) in the workplace air of an aluminium melting facility. The S trace revealed the presence of over 130 S-containing compounds. Some 50 of these were identified as thiaarenes by utilizing complementary information derived from GC–MS. Three benzonaphthothiophene isomers were found to account for 35–40% of the total PASH concentration. The main advantages of AED detection were that (i) compound-independent response allowed reliable quantification, even in the absence of many standards, and (ii) PAHs did not interfere in the detection. The LoDs were as low as a few ng/m3 based on a sampling volume of less than 1 m3 . The merits of SPME-based air sampling with subsequent GC–AED were evaluated for organoleptic volatile sulphur compounds such as methanethiol and dimethyl sulphide. Although SPME is an attractive means of sampling since the fiber itself acts as a passive sampler and no equipment such as pumps and flow controllers are required, and LoDs in the 5–50 nL/m3 range could be achieved, the authors emphasize that the technique is suitable only for qualitative analysis: several rather serious experimental problems (humidity, fiber quality, loss of analytes) made accurate quantification impossible [77]. AED-based detection of fluorine is poorer than that of most other elements (cf. Table 1). However, GC–AED has been shown to be the preferred choice for the detection of alternative fluorocarbons (AFCs) in air. AFCs were developed as refrigerant coolant fluids which can replace the ozon-depleting chlorofluorocarbons (CFCs). The latter compounds can be detected sensitively by means of GC–ECD. However, with the AFCs, especially those not containing chlorine, the detectability is 100–1000-fold poorer. As is illustrated in Table 8, the detection problem can be overcome by using GC–AED instead. The LoDs using the F-channel are only twice as high as those found by means of GC–MS; however, with the latter technique the high element selectivity is lost. Based on predicted atmospheric concentrations, one can estimate that 1–10 L samples are required for proper monitoring [82].

Table 7 Analysis of air and other gaseous samples by GC–AED Sample(s)

Techniques

Commentsa

References

Spiked air Workplace air Landfill and sewage gas

SPME Glass fiber filter in personal sampler Canister sampling

[77] [76,78,79] [80]

Landfill gas Gas standards

Sorbent trap Loop injection

Air

Cryogenic trapping

Organo-S volatiles; LoDs 4–50 ppt; storage stability found to be low Detection of PAHs and PASHs in air of aluminum melting plant VOCs and siloxanes in landfill and sewage gas with parallel GC–AED and GC–MS (split after the injector to two analytical columns) S-containing volatiles Fluorocarbons; additional ECD and MS; AED superior to ECD, especially for Cl-free compounds (Table 8) Field application of GC–AED for analysis of atmospheric sulphur gases; LoDs: 0.3 nmol/m3

a

In all studies except [83], GC–MS was used additionally.

[81] [82] [83]

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Table 8 LoDs (ppt, v/v) of CFC and selected AFCs with GC and various detectorsa Compound

ECD

AED

MS

CFC CCl2 F2

0.2

0.5

0.2

Cl-containing AFCs CHClF2 CHCl2 CF3 CHClFCF3

10 2 6

0.3 0.2 0.3

0.2 0.1 0.2

Cl-free AFCs CHF2 CF3 CH2 FCF3 CH3 CHF2

1100 4100 nd

0.4 0.4 0.5

0.3 0.2 0.2

a

Data for 1-L samples; nd, not detected [82].

2.5. Petrochemical samples Most applications of GC–AED in the field of petrochemical samples focus on the detection of S- and N-containing compounds. For three reasons they cause concern: the presence of polluting SO2 en NO in exhaust gases, the reduction of catalyst life-time due to poisoning and an adverse effect on product stability [84]. Catalytic hydrotreating is the subject matter of many AED-based studies although other methods for desulphurization or denitrogenation such as chemical, photochemical and bacterial processes have also been studied (Table 9). A typical example obtained by split injection of an untreated medium cycle oil (MCO) is shown in Fig. 4. The major S-containing species found were dibenzothiophene (DBT), monomethylated (MDBT) and dimethylated (DMDBT) DBT. Small amounts of C3- and C4-alkylbenzothiophenes were also detected. N-containing species found included carbazole, monoand dimethylcarbazoles, and di- and trimethylindoles [85]. The effects of various types of catalytic hydrotreatment are shown in Fig. 5. Single-stage hydrotreatment removed 75% of sulphur, but an additional 30-min treatment only resulted in slight improvement, probably due to strong inhibition by the products and/or catalyst deactivation. After renewal of the

hydrogen atmosphere to remove H2 S and NH3 (the products of the process) and addition of fresh catalyst (stage B, Fig. 5), a 97% reduction was achieved. Even in the latter stage, nitrogen could only be reduced by 66%, which illustrates the relative difficulty of hydrodenitrogenation. Wiwel et al. [86] used the 388-nm nitrogen line to assess compositional changes of N-containing compounds in diesel-range gas oils during hydrotreating. Some 60 N-containing compounds were identified. By applying pure-silica-based SPE trace enrichment, the authors could quantify individual nitrogen compounds down to the 50 ␮g N/L level. In many studies, a fractionation step is included to improve the analytical performance. On-line fractionation by LC–GC–AED and LC–GC–MS was used by Lewis et al. [87] to detect and quantify polycyclic aromatic compounds in, e.g. fuels and their combustion products. The use of multidimensional chromatographic techniques proved effective in producing online fractions of compounds of a particular chemical class. Moreover, detection of trace-level species was much improved. Among the compounds identified were several carbazoles, benzothiophenes and substituted dibenzothiophenes. Another interesting application is the rapid determination of alkylphenols in non-polar samples such as crude oils. Derivatization with ferrocenecarboxylic acid chloride and subsequent GC–AED at 302 nm for Fe-selective detection was found to yield a LoD of 0.2 pg Fe. Consequently, sample amounts of less than 1 mg suffice for the ng/g detection of the target analytes. The total sample preparation takes 45 min, which is much less than with conventional procedures. As an application, 20 C0 –C3 -alkylphenols were quantified in shale oil and crude oil [88]. Another study that combines the selectivity of AED detection and derivatization used bromination to determine alkenes in complex mixtures of aromatic and saturated hydrocarbons. Detection of the formed dibromoalkanes using the Br channel was found to be essentially free of interferences, with sub-ng LoDs [89]. AED was used as a detector for comprehensive twodimensional gas chromatography (GC × GC) by van Stee et

Table 9 GC–AED analysis of petrochemical samples Sample(s)

Techniques

Comments

References

Coal Shale oil Light cycle oil Light cycle oil Crude oil, tar balls Gasoline Coal Various Crude oil, coal tar Crude oil Naphtha FCC product Various Vacuum gas oil Vacuum gas oil Various

Pyrolysis SPE, LC None Dilution SPE Derivatization SFE On-line LC SPE, LC Derivatization Oxidation None Various SPE SPE Various

Thermal desorption followed by pyrolysis in one system PASHs; RIs of 93 thiophenes on three GC columns Large group of thiophenes; plus GC–MS Identification of 90 organo-S; structure vs. retention time PASH distribution; relative abundance used to distinguish sample types; plus MS Alkenes after bromination and selective detection (Br); plus MS Elemental sulphur (S8 ) Heterocyclic polyaromatics by LC–GC–AED and –MS Identification of PASHs; three GC columns, including liquid crystalline phase; plus MS Phenols and alcohols as ferrocenecarboxylic acid esters; Fe detection Selective oxidation to differentiate thiophenes and other sulphides Identification of sulphur compounds with GC × GC–AED and GC × GC–MS Effects of catalytic hydrotreating; plus MS Photochemical desulphurization and denitrogenation; plus MS Desulphurization and denitrogenation by methylation and precipitation as tetrafluoroborates; plus MS Bacterial desulphurization; plus MS

[91] [92,93] [94] [95] [96,97] [89] [98] [87] [99] [88,100] [101] [90] [84–86,102–108] [109] [110] [111–113]

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117

Fig. 4. C-, S- and N-trace chromatograms of an MCO recorded by GC–AED. Cn: normal paraffin with n number of C; Cz, carbazole; C1–Cz, monomethylcarbozole; C2–Cz, dimethylcarbozole [85].

al. [90]. The performance of GC × GC–AED was evaluated with a pesticide standard, and as a real-life sample a fluidized catalytic cracking (FCC) product was analyzed. An overlay of the S and C traces with some well-known classes of Scontaining compounds is shown in Fig. 6. Analyte identification was possible by combining GC × GC–AED and GC × GC–MS data, and demonstrated for a compound not part of the quoted classes which was tentatively identified as phenathro[4,5bcd]thiophene.

2.6. Synthetic polymers Pyrolysis–GC (Py–GC) is often used to unravel polymer composition and there are several papers in which AED detection is shown to be of significant help in this regard (Table 10). To quote an example, polyvinyl alcohol (PVA) is a watersoluble synthetic resin used amongst others in various types of adhesives to which a small amount of polyacrylamide is often added. Analysis is difficult because separation cannot easily

Fig. 5. Hydrotreatment configuration (left) and comparison of sulphur species in an MCO as a function of reaction method (right) [85].

118

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Fig. 6. GC × GC–AED chromatogram of an FCC product. The S and C channels are represented as orange and blue, respectively. The boxes indicate some known classes of S-containing compounds: BT, benzothiophenes; DBT, dibenzothiophenes; BNT, benzonaphthothiophenes [90]. Table 10 Analysis of synthetic polymers by pyrolyis GC–AEDa Sample(s)

Techniques

Comments

References

PVA Latex Latex Various Various Epoxy resins Adhesive tape Chitin/PVC blend

Py–GC Off-line pyrolysis Derivatization, Py–GC Py–GC Off-line pyrolysis Py–GC Py–GC Py–GC

Polyacrylamide in PVA by Py–GC Copolymerized acrylamide in latex Copolymerized fumaric and itaconic acids by derivatization with amines Brominated polymeric flame retardants in various thermoplastic resins Effect of brominated flame retardants and PVC on thermal degradation of ABS Cured epoxy resins; plus GC–FT-IR Characterization of adhesives Thermal degradation products of chitin/PVC blend

[114] [115] [116] [117] [118] [119] [120] [121]

a

In all studies GC–MS was used in addition to GC–AED.

be achieved, and most non-destructive spectrometric techniques suffer from a lack of sensitivity or from interference. Wang [114] showed that Py–GC–AED and Py–GC–MS enable the detection of 1% polyacrylamide by monitoring the fragments by means of N trace AED (Fig. 7). The same author also used Py–GC–AED to detect copolymerized acrylamide [115] and the widely used co-monomers of fumaric and itaconic acid [116], with analysis of the acids involving derivatization with a primary amine to form a cyclic imide. In another study, Wang [117] used the same

analytical approach to identify the type of brominated polymeric flame retardants used in thermoplastics by means of peak-pattern recognition through a halogen-element AED trace. Today, more and more plastic waste is accumulating, which poses serious problems to the environment. An efficient way to recover the material is pyrolysis, since only 10% of the energy content of the waste is used to convert the scrap into valuable hydrocarbon products. Brebu et al. [118] used GC–MS and GC–AED (C, O, N, Cl and Br traces) to study the products

Fig. 7. N trace AED pyrograms (plus blow-ups of relevant ranges) of polyvinylalcohol with 1% polyacrylamide (left) and without polyacrylamide (right) [114].

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Table 11 GC–AED analysis of CWAs and related compoundsa Sample(s)

Techniques

Comments

References

Munition shell Water, wipes, soils Water, soil, toxic waste Yperite waste CWAs Spiked extracts Toxic waste Standard atmospheres

Various SPE, derivatization Various SLE LLE SLE, derivatization LLE, derivatization Derivatization on sampling tube

Characterization of a yellow liquid from shell using many analytical techniques Quantitation of alkyl methylphosphonic acids by derivatization Various CWAs and degradation products; plus GC–IR Sulphur mustard and transformation products Products of sulphur mustard with decontamination fluids Development of derivatization method for Adamsite Lewisite-1 and related compounds; empirical formula calculation Acylating gases and vapours

[123] [126] [127,128] [124] [129,130] [131] [122] [125]

a

In all studies except [129,130] GC–MS was used in addition to GC–AED.

formed during off-line pyrolysis of acrylonitrile-butadienestyrene (ABS) and brominated ABS as individual polymers or mixed with polyvinyl chloride (PVC). Thermal degradation at 450 ◦ C led to pyrolysis oils rich in valuable benzene derivatives but, also, significant amounts of halogenated phenols and benzenes, and N-containing organics. Catalyst development to remove these compounds from liquid products is therefore indicated. 2.7. Chemical warfare agents One application area in which GC–AED is increasingly being used, is that of the detection and identification of chemical warfare agents (CWAs) and/or related compounds (see Table 11). In one recent study [122], a sludge sample from an old so-called ton container used to store chemical warfare material, was extracted and derivatized with 1,3-propanedithiol to look for the presence of lewisite-1. This compound was indeed detected by GC–AED (Cl and As traces) with subsequent identity confirmation by means of MS. Three isomers of lewisite-3 and two isomers of a dimer were also detected. The same strategy was used to study ozone-based surface decontamination of equipment exposed to CWAs; the persistent nerve gas VX was selected as CWA material. Combined AED/MS allowed screening of the VX removal, detection and provisional identification of three reaction products, and the proposal of a reaction scheme. In another study [123], the same group identified a major and several minor Pcontaining compounds in a munition shell by the combined use of GC–AED, GC–IR–MS, LC–MS and stand-alone NMR.

Fifty compounds were detected in a block of yperite (or sulphur mustard) fished up from the Baltic in 1997. Thirty compounds were identified using AED and MS data. Not too surprisingly, most of these contained sulphur and/or chlorine, and a few, arsenic. The final product of hydrolysis, thiodiglycol, was not detected: presumably, as a water-soluble compound, it was leached into the water phase [124]. Amongst the chemicals that can be found in CWA-related waste are very reactive acylating species such as cyanogen chloride, phosgene and chloroformates. Because they are hydrolyzed very easily, it is extremely difficult to perform sampling under conditions which do not cause loss of analytes. Schoene et al. [125] demonstrated the use of in situ sampling/derivatization and subsequent analysis by GC–AED and GC–MS. The compounds were derivatized with dibutylamine that was applied as a coating on the solid-phase material inside the sorbent tube. Standard atmospheres were sampled and empirical formula calculation together with MS data was used to identify the compounds. For ten compounds a 25–90% recovery was found, but it was as low as 8% for one compound after 14-day storage of the tube. As indicated by the authors, optimization of sampling and storage conditions may be required for several individual compounds. 2.8. Biological samples GC–AED has also been used for a wide variety of biological samples and compounds of interest (Table 12). Some examples are highlighted below.

Table 12 GC–AED analysis of biological samples Sample(s)

Techniques

Comments

References

Fish oil, cow fat

SLE

[138]

Gray seal tissues Harbour porpoise Mouse liver Duck gizzard Fish tissues

SLE, GPC SLE, dialysis, GPC LLE SLE SLE

Human urine Bacteria Plant leaves Essential oil

LLE, derivatization None SLE, derivatization Steam distillation

PCB-contaminated and non-contaminated samples; linearity and CIC evaluated; LoD 0.15 mg/kg; plus ECD 24 methyl sulphonyl PCBs using S and Cl channels; plus ECD PCBs, DDTs and methyl sulphone metabolites in various tissues; plus MS Method development for detection of low-molecular-weight silicones in tissue; plus MS Quantification of elemental P in duck gizzard PBDEs; extremes found: 1.4 and 1250 ␮g/kg, in fish caught in remote and urbanized area, respectively (Fig. 8) 13 C-labelled caffeine metabolites in human urine Differentiation of bacteria using Py–GC–AED and statistical pattern recognition Long-chain halowaxes in halophytes; plus MS S-containing compounds in essential oil of Tagetes; plus GC–MS and FT-IR

[133] [132] [139,140] [137] [134] [136,141,142] [143,144] [145] [146]

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Fig. 8. Cl (top) and Br (bottom) GC–AED traces of fish tissue showing CBs and BDEs, respectively. Surrogate standards (SS), tetrachloro-m-xylene (TMX), decachlorobiphenyl (DCB), and dibromooctafluorobiphenyl (DBOB) were added to sample prior to extraction. Total CB and BDE concentrations were 130 and 150 ␮g/kg, respectively [134].

GC–AED was used to detect the methylsulphonyl metabolites of CBs and DDT in porpoise [132] and gray seal tissue [133]. The LoDs of 0.5 ng/g were adequate for these samples; because of the high selectivity introduced by monitoring both the S and Cl channels, sample clean-up could be simplified. Complementary MS data were used to confirm the identification of the target compounds. The highest methylsulphonyl-CB concentrations were found in liver (0.15–0.5 ␮g/g lipid weight); they represented about 2% of the total CB concentration. Polybrominated diphenyl ethers (PBDEs) are one of several classes of brominated compounds which are extensively used as flame retardants. Johnson and Olson [134] used GC–AED to detect and quantify BDEs and CBs in fish tissue. The high Br/Cl selectivity is clearly demonstrated in Fig. 8. The results also show that tetra and penta isomers were the major BDEs present. It is interesting to note that total BDE concentrations detected in a fish from a remote spring-fed river were 1000-fold lower than in a fish from a river in an urbanized area, i.e. 1.4 and 1250 ␮g/kg wet weight, respectively. Another interesting application of GC–AED is in isotope analysis. Small differences in the properties of isotopes can be exploited to detect stable isotope-labelled compounds. One practical example is the molecular band of CO, which is formed inside a plasma when using O2 and H2 as reactant gases. The second-order lines of 12 CO and 13 CO are 342.574 and 341.712 nm, respectively, whence a 0.86 nm difference. An algorithm, ‘SUPPRESS’, developed by Quimby et al. [135] calculates the real-time contributions from 12 C and 13 C, and is able to produce chromatograms only showing peaks that have 13 C enrichment higher than the natural abundance (1.1%). Boukraa et al. [136] used this method to detect metabolites of caffeine in human urine (Fig. 9). When using standards and complementary MS detection, at least ten metabolites could be detected and identified.

Fig. 9. GC–AED profiles obtained from a urine extract: (a) before caffeine intake, (b) from the urine of a subject taking [13 C]caffeine. Profiles are the results of ‘SUPPRESS’ data processing [136]. Identities of some relevant compounds: (1) caffeine; (3) 1,7-dimethylxanthine; (3 ) 3,7-dimethylxanthine; (7) 1-methylxanthine.

In a completely different type of application, elemental white phosphorus was determined in ducks found in an estuarine salt marsh located in an artillery range. Because the phosphorus was submerged, it remained as elemental P in the sediment and was subsequently taken in by the sediment-feeding ducks. The advantage of using AED is that a stable organo-P compound such as triethyl phosphate can be used as a standard, instead of elemental phosphorus which is more difficult to prepare. The highest content of phosphorus was ca. 6 mg in the gizzard of one duck [137]. 3. Conclusions Combining a GC separation on-line with AED provides a powerful means to screen for the presence of hetero-atom-

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containing organic micro-contaminants in a wide variety of complex samples. Selectivity is excellent and LoDs are in the low pg/s range for many elements of interest. The detection technique enables the calculation of (partial) molecular formulae and (semi-) quantification can be achieved even for non-target analytes by means of compound-independent calibration. If simultaneous AED-plus-MS detection is used, unambiguous identification is possible in many instances. The selected applications discussed above, and many others of a similar nature published in the scientific literature, as well as those dealing with the detection – and, frequently, speciation – of organometals and organometalloids [7–9] convincingly demonstrate that GC–AED is a powerful hyphenated technique that is to be recommended especially for the analysis of complex and/or highly contaminated samples. In this context, a distinct advantage of AED over MS is that non-target screening can be carried out for individual elements. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

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