Pyrolysis gas chromatography-mass spectrometry in environmental analysis: focus on organic matter and microplastics

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Pyrolysis gas chromatography-mass spectrometry in environmental analysis: focus on

organic matter and microplastics

Yolanda Picó, Damià Barceló

PII: S0165-9936(20)30193-X

DOI: https://doi.org/10.1016/j.trac.2020.115964

Reference: TRAC 115964

To appear in:

Trends in Analytical Chemistry

Received Date: 22 April 2020

Revised Date: 22 June 2020

Accepted Date: 23 June 2020

Please cite this article as: Y. Picó, D. Barceló, Pyrolysis gas chromatography-mass spectrometry in

environmental analysis: focus on organic matter and microplastics, Trends in Analytical Chemistry,

https://doi.org/10.1016/j.trac.2020.115964.

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PYROLYSIS

SEPARATION

IDENTIFICATION

Pyrolysis gas chromatography-mass

spectrometry in environmental analysis: focus on

organic matter and microplastics

Yolanda Picó

and Damià Barceló

2,3

Environmental and Food Safety Research Group-University of Valencia (SAMA-UV),

Desertification Research Centre (CIDE), Joint Center CSIC-University of Valencia-

Generalitat Valenciana, Moncada Naquera Road km 4.3, 46113 Moncada, Valencia

(Spain).

Water and Soil Quality Research Group, Department of Environmental Chemistry,

IDAEA-CSIC, C/Jordi Girona 18–26, 08034 Barcelona, Spain

Catalan Institute for Water Research (ICRA), C/Emili Grahit 101, 17003 Girona,

Spain

Highlights

 Recent innovations in Py-GC-MS for environmental analysis are presented

 Advances in the design of currently used pyrolyzers are discussed

 Recently developed working modes within Py-GC-MS are described

 Py-GC-MS applications to organic matter and microplastics are highlighted

 Progresses and promising trends in Py-GC-MS analysis are pointed out

ABSTRACT

Pyrolysis gas chromatography-mass spectrometry (Py-GC-MS) has great potential

within environmental analysis. This technique is mainly used for the chemical

identification of macromolecules that cannot be characterized by either liquid or gas

chromatography due to their big size. Through pyrolysis (controlled thermal

degradation), these macromolecules are broken down into simpler molecules that can be

separated by gas chromatography and detected by mass spectrometry. This technique

has been traditionally used in environmental samples for the characterization of organic

matter and humic substances, contaminants, lignins, etc. It attains the identification of

the different types of chemical units that integrate the macromolecules. In addition,

recently, this technique has experienced an important boom in the chemical

characterization of microplastics present in environmental samples. This has triggered

its use in this type of matrix. We describe the fundamentals and modalities of Py-GC-

MS and outline some of the recent applications for the environmental analysis with

special emphasis on humic substances and/or other types of organic matter components

as well as microplastics, but reporting also other interesting application of

environmental relevance.

Keywords: microfurnaces; filaments; Curie-point; Evolved gas analysis; Reactive

pyrolysis.

1. Introduction

Pyrolysis gas chromatography-mass spectrometry (Py-GC-MS) is one of the essential

and dynamic analytical tools available today to be able to analyze and characterize

chemicals occurring in the environment. The International Union of Pure and Applied

Chemistry (IUPAC) has classified Py-GC-MS as a type of reaction chromatography, in

other words “a technique in which the identities of the sample components are

intentionally changed between sample introduction and detection” [1]. In GC, these

changes can occur before the column (the chemical compounds separated in the column

differs of those of the original sample) or between the column and the detector (the

original chemicals are separated in the column but their identity changes before

reaching the detector). The IUPAC has also defined Pyrolysis (if used for analytical

purposes) as “the characterization in an inert atmosphere, of a material or a chemical

process by a chemical degradation reaction(s) induced by thermal energy”, and Py-GC

as “a version of reaction chromatography in which a sample is thermally decomposed to

simpler fragments before entering the column” [1]. This set of definitions gives a clear

idea of the technique fundamentals also summarized in Figure 1. Nowadays, pyrolysis

involves the thermal fragmentation of the analytical sample at a temperature between

500 -1400 º C. Under these conditions and in the presence of an inert gas, the original

macromolecule is decomposed to characteristic units further separated

chromatographically using fused silica capillary columns and, subsequently, identified

by mass spectrometry mostly with the aid of mass spectral libraries or by the selection

of characteristic ions of indicator compounds. This analytical technique can eliminate

the need to pre-treat the sample as it is capable of performing the analysis directly on

the macromolecules and/or polymers whether they are in a liquid or solid state but it is

compatible with most of the pre-treatments applied to extract (macro)molecules from

environmental samples.

Applications to identify substances of low volatility were first reported in 1961 for Py-

GC [2-4] and in 1967 for Py-GC-MS [5, 6]. Since then, Py-GC-MS increases the range

of tools applicable to the analysis of macromolecules including polymers, copolymers

and additives, wheels, packaging materials, textile fibres, coatings, electronic

intermediates, coals, paints, varnishes and lacquers, leather, paper or wood derivatives,

bio-oils and biomass, fire retardants, food, drugs, surfactants, and fragrances.

Surprisingly, a retrospective of the literature published since its origins shows that this

technique has only been used when all other conventional methods of gas or liquid

chromatography coupled to mass spectrometry have been exhausted. According to Web

of Science (WoS) all databases (accessed by March 22, 2020), 6,849 peer-reviewed

research articles based on the keyword “Py-GC-MS” as topic have been published since

the technique’s begins, with a rate over the last ten years of near 500 publications yearly

[7]. The applications of Py-GC-MS range from research, quality control and

characterization of materials to forensic environmental analysis including the

conservation and restoration of cultural heritage, as well as medicine, biology,

biotechnology, geology, aeronautics, agriculture, energy fuels, etc. Reviews on Py-GC-

MS are mostly focus on its applications, such as polymer characterization [8-10],

lacquer [11], lignin [12], humic substances [13], soil organic matter [14], etc. Clearly,

these reviews cover individual issues and none addresses global fields such as

environmental analysis, a field in which it is recently becoming an indispensable tool.

To evidence this, it is sufficient to mention latest reviews on the identification of

microplastics in the environment [15-19] or the study of environmental organic matter

[14, 20]. Parallel, to find a review on the recent trends and developments within the

technique is needed to back 12 years ago to 2008 [21]. This contrasts with the rapid

technological evolution within Py-GC-MS in terms of devices design, inertness of their

components, versatility of the working modes and reproducibility of the obtained data.

Within this review, key trends and recent evolutions in the area of Py-GC-MS are

covered. In the past years, several important new developments have been made in the

pyrolysis devices (microfurnace chambers, quartz liners for the sample, etc.) as well as

in the GC-MS process itself (time-of-flight MS in the low- or high-resolution mode,

comprehensive GCxGC, etc.). This review also presents some specific environmental

applications within the environmental field that highlights the prospects achieved thanks

to the implementation of these advances, based on the literature published in the last 5

years (2016-2020). Mostly microplastics and organic matter characterization but also

other environmental applications of interest are included.

2. Fundamentals and instrumental advances of pyrolysis gas chromatography-

mass spectrometry

As stated in the introduction, Py-GC-MS is a technique capable of analyzing

macromolecules by gas chromatography breaking them into volatile fragments

(schematized in Figure 2) [9, 10]. This hyphenated technique benefits from coupling as

pyrolysis extends the scope of GC-MS to the analysis of non-volatile macromolecules

in matrices such as, various types of new and ancient materials, biological and

environmental samples. In turn, pyrolysis exploits the resolving power of gas

chromatography and the identification capability of mass spectrometry. It involves an

interesting symbiosis that provides a complex mixture of volatile fragments (pyrogram)

of the original non-GC amenable sample (or macromolecule) that may be a very

effective fingerprint [12, 13]. This is its most significant advantage.

In the pyrolyzer the sample is heated with GC carrier gas, typically an inert gas (helium

is most commonly used) to a relatively low pressure. The high temperatures induce

chemical changes in the macromolecules by a progressive bond breakage that goes from

the weakest to the strongest according to the temperature. The main reactions involved

are depolymerisation (resulting in decomposition to the basic units or, occasionally,

oligomers), random excision (providing randomly smaller fragments as all C-C bonds

are of equal energy) and the removal of side groups (side groups cleaved of the polymer

bond). Other reactions such as, chard formation, cross-linking, cyclization,

hydrogenation, isomerization, and oxidation are possible but minor. The molecular

fragmentation caused by pyrolysis (and the formed products) depends on the relative

strengths of the molecular bonds and the ability of the free radicals formed to give

stable products [22, 23].

Nowadays, different working modes of the Py-GC-MS have been described depending

on the purpose of the analysis [7, 23, 24]:

Single-shot analysis (Py-GC-MS), in which, pyrolysis is perform at a single

temperature, normally >500°C (dependent on the material being examined). The sample

temperature goes as rapid as possible from ambient to the pyrolysis temperature (in

current instruments <20 ms). The macromolecules are almost instantly fragmented in

the pyrolyzer and then, their pyrolyzate of their degradation products is separated in the

chromatographic column.

Double-Shot analysis (TD/Py-GC-MS) provides information about both types of

compounds, volatile (low molecular weight compounds analyzed at low temperature

through a thermal desorption step) and non-volatile (analyzed at high pyrolysis

temperatures that allow for the fragmentation of the macromolecules). Therefore, the

analysis of the sample involves two stages: in the first one there is the thermal

desorption of the volatile compounds then, analyzed by GC-MS and in the second one,

the residual sample left after desorption (in which the non-volatile macromolecules

remain) is pyrolyzed and the pyrolyzate is also analyzed by GC-MS.

Evolved gas analysis (EGA-MS) involves the separation of degradation products from

macromolecules according to the temperature at which they are formed rather than

according to their volatilization temperature. This is achieved through a sequential

macromolecules degradation that takes place in the pyrolyzer using a slow temperature

ramp (instead of reaching the pyrolyzation temperature as quickly as possible) and the

replacement of the chromatographic column by a short and narrow (2.5 m, 0.15 mm

i.d.) deactivated capillary tube without a stationary phase to connect directly GC-

injector and MS-detector. This is very similar to the thermogravimetric analysis (TGA).

The weight loss of the sample cannot be measured but the result shows the thermogram

such as a differential thermogravimetric curve for a given sample. EGA-MS is done as

previous step in order to identify the temperature range for the components and set-up

Py-GC-MS for a more in-depth study of the identified compound.

Heart-cut analysis (Heart-cut EGA-GCMS), is the two-dimensional way of working

in the combination of Py-GC. In this technique, EGA is used to obtain a thermogram

and each temperature zone of interest is analyzed separately by heart cutting evaporated

components and selectively introduce them to a GC column where they are temporarily

trapped at the beginning of the column prior to analyze them by GC-MS. This technique

allows examination of regions that may contain multiple components under a peak but

are not obvious during Py-GC-MS analysis. This method could be incredibly useful to

search both, for specific components in a highly complex matrix or the whole

composition of a complex system.

Reactive Pyrolysis-GC-MS. The macromolecule undergoes a chemical derivatization

reaction in the pyrolysis chamber. This may be instead of, or in addition to, heat-

induced pyrolysis of the large polymer into smaller fragments. The most used

derivatized agent is tetramethylammonium hydroxide (TMAH).

In addition to these modes, modern pyrolyzers can be also used only for thermal

desorption of analytes (as in double-shot analysis).

Py-GC-MS can provide quantitative results with high accuracy and precision. As in any

other analytical method compound quantification in the sample requires standards and

even isotopically labeled internal standard as well as the selection of proper ions[13, 25,

26].

Another advantage of this technique is the small sample amount (5–200 μg) without

pre-treatment. However, samples must be dried and, as already mentioned, in the case

of environmental samples, it is sometimes necessary to perform a previous extraction of

the macromolecules. Py-GC-MS is compatible with most established method[11, 12,

27].

The most important disadvantage attributed to Py-GC-MS was poor reproducibility due

to factors such as sample inhomogeneity, slow transfer of the pyrolyzate to the

chromatographic column and catalytic events in the pyrolyzer, which may alter the

chemical nature of the sample. Other disadvantages of the technique are its destructive

character and the difficulty of interpreting the pyrograms obtained due to the large

number of reactions that can take place and the density of peaks in the pyrograms [21].

Hence, developments have been focused on areas such as developing new approaches to

improve the reproducibility of the pyrolyzer, to enhance separation of the compounds

and to better identify the reactions products. Major instrumental developments in

pyrolysis, chromatography and mass spectrometry are described in the following.

2.1. Pyrolyzer

The critical prerequisites for the apparatus in analytical pyrolysis affect pyrolysis

temperature (its reproducibility, rapid rise, and accurate control are crucial to ensure

proper results) and the connection of Py-GC that must be as direct as possible. Pyrolysis

process as fast as possible is desirable to prevent pyrolyzates back into the injector. The

pyrolyzer devices must be small and with little internal volume while the flow of carrier

gas through them should be fast to ensure that all volatiles are swept into the column

and do not remain in the pyrolyzer where they can undergo secondary pyrolysis. Small

sample amount is needed to ensure that it is rapidly degraded and that the capacity of

the column is not exceeded [5, 12].

Pyrolysis systems were classified according to the heating mechanisms into:

continuous-mode pyrolyzers (furnace or microfurnace pyrolyzer) and pulse-mode

(sample is introduced cold and receives a thermic pulse) pyrolyzers (heated filament and

Curie-point) (schematized in Figure 3). The three types of pyrolyzers are able to carry

out flash pyrolysis involving a very rapid temperature rate up to reach the pyrolysis

temperature Their ability to work in the different modes described in the previous

section is summarized in Table 1.

Nowadays, filament pyrolysis (Figure 3a) uses as heating element a coil made a very

thin wire of resistive metal (commonly platinum). A small quartz tube holds the sample

inside is inserted into the coil. Recently, progress has been made in developing better

and more effective sample holder designs that shorten the analyte diffusion path and

reduce peak broadening. Poor temperature accuracy and reproducibility have been

reported because of non-uniform heating (heat-transfer variations affect the temperature

of the sample that is not directly measured). Considerable effort has been made in the

implementation of the technology ensuring accurate monitoring of the filament

resistance and accurate temperature control. In addition to pulsed pyrolysis, recent

instruments can also perform programmed heating to obtain sequential pyrolysis

(multiple pyrolysis steps performed on a single sample at increasing temperatures) with

temperature ramps that can be rapid or slow depending on the purpose. In the filament

pyrolyzer, the sample preheating before analysis is essential to avoid condensation of

high boiling point pyrolyzates. This preheating step causes evaporation of the volatile

and semi-volatile compounds and denaturation, degradation or thermofixation of the

samples. However, the last instruments can perform thermal desorption but using

modular systems or complicated trapping sequences [23, 28, 29].

The Curie-point pyrolyzer (Figure 3b) is similar to the filament pyrolyzer but the

sample is inserted on an appropriate ferromagnetic alloy wire having the desired Curie-

point; that is the exact reproducible temperature at which the ferromagnetic material

loses its magnetism. The temperature ceases to rise when the Curie-point of the metal

has been reached. The most important disadvantage of these pyrolyzers is that different

temperature steps cannot be programmed, then, the evolved gas analysis (EGA) is

impossible. The pre-heating of the sample is necessary, similar to the filament pyrolyzer

[23, 28, 29].

The heating-furnace pyrolyzer mostly used in recent years is a vertical microfurnace-

type (Figure 3c). This microfurnace is simply a hollow quartz tube covered by a heater

device for rapid heating and cooling. State-of-art microfurnaces are equipped with

hollow ceramic heater and powerful cooling fans achieve rapid heating and cooling

across a wide temperature range as well as temperature sensors that measure the

temperature in the sample. The sample is placed in an inert deactivated cup held at the

ambient temperature in helium even through at the same time that the microfurnace is

preheated to the pyrolysis temperature. Special thermal insulation achieves excellent

thermal stability maintaining the sample at ambient temperature at the stand-by position

when the furnace temperature is at a 600 ºC and reaching 60 º C if the furnace is at 1000

ºC. In the recent years, also evolutions in the sample cup materials such as chemically-

bonded quartz thin layer on cup surface for inertness or directly quartz cups has been

reported. In the moment of the analysis, the cup is dropped by free fall, due to gravity,

into the center of the vertical furnace. Thus, in this case, the problems caused by the

preheating for the filament and the Curie-point pyrolyzers of evaporation, degradation,

or thermosetting before the analysis can be avoided. In addition, the sample is purged of

oxygen before heated so that there are no oxidation reactions [22, 30, 31].

The pyrolysis unit (pyrolyzer) is connected directly to the injector port of the GC.

Heavy and polar compounds are directly placed on-column and light compounds are

never lost. The main concern is to transfer the analytes to the GC in a tight ‘plug’ so that

the peaks are not broad and the column can separate them. At its most basic, this means

heating the sample as quickly as possible so that the resulting volatiles are produced in a

very short time and the resulting peaks are narrow and well separated. Cryotrapping

capabilities are available for the Pyrolysis GC-MS in both pulse mode and continuous

pyrolyzers. Prior to introduction into the GC-MS, the pyrolyzates are cryotrapped using

liquid nitrogen. This is done to narrow the chromatographic band and improve the

detection limit. This also allow to use the microfurnace as a pulse mode pyrolyzer [32,

33].

2.2. Gas chromatography

Once the polymer/copolymer sample has been pyrolyzed, volatile fragments are swept

from the heated pyrolysis unit by the carrier gas (helium) into the gas chromatograph.

The volatile pyrolysis products (pyrolyzate) are chromatographically separated by using

a fused silica capillary column, according to the boiling points and the affinity of

analytes to the stationary phase (internal capillary column wall coating) [34].

Although GC-MS Pyrolysis has mostly been combined with conventional

chromatographic separation, i.e. using a capillary column with a 5 % phenylmethyl

siloxane phase 30 x 0.25 mm and 0.25 um film thickness, there is no limitation in terms

of possible GC separation modes. There are undoubtedly two major trends within

separations in gas chromatography —fast chromatography and comprehensive two-

dimensional GC (GCxGC) [3].

Fast GC involves reducing analysis time by using multiple combinations of narrower

and shorter columns, with less phase thickness and/or higher carrier gas flow. There is

no reason why this type of chromatography cannot be combined with Py-GC-MS.

However and probably because pyrolyzates are very complex samples, to our

knowledge, this combination has not been reported yet [26, 35].

GCxGC provides higher peak capacity and better resolution power by adding a second

GC column of a different polarity, connected through a modulator (commonly a

cryomodulator) that traps the eluting fractions of the first column and injects them into

the second column to achieve true two-dimensional separation. Since one of the

premises of Py-GC-MS is the separation of complex mixtures of molecules (the

pyrolyzates), GCxGC has been combined with pyrolysis in several studies showing its

great capacity to facilitate identification [36, 37]. In the future, this technique could gain

a third dimension of separation if it is combined with Heart-cut EGA. However, it

should be noted that the unquestionable improvement in separation implied by these

techniques is sometimes slowed down by the great amount of information obtained and

time required to interpret it.

2.3. Mass spectrometry

The detection technique of the separated compounds is typically mass spectrometry

(MS). The analysis of the samples fingerprint pattern is often accomplished by single

quadrupole mass analyzer, the most used system, due to its robustness and capacity to

identify the molecules. The substances detected by the mass spectrometry are

subsequently identified by the interpretation of the obtained mass spectra, by using mass

spectra libraries (e.g., NIST/EPA/NIH, Wiley, MPW, Norman Mass Bank, m/z Cloud),

or by using reference substances [23].

Triple quadrupole instruments (QqQ) were introduced more recently and today are the

most recommended systems for the analysis of target compounds, mainly due to the

higher sensitivity and specificity of the application of tandem MS (MS/MS). However,

this instrument is little used in combination with Py-GC-MS, since in this case the

compounds that are formed in the pyrolysis are mostly unknown a priori, so the QqQ

has less application. [27]

Contrarily, high resolution mass spectrometry (HRMS), mostly (quadrupole)time-of-

flight (TOF or QqTOF), has been became essential tools in Py-GC-MS. The use of

these detectors provide information on the most probable empirical formula of both, the

analyzed molecule and its characteristic fragment. This is a very structural useful

information especially in the case of pyrolysis were the ultimate objective is to identify

all the compounds responsible of the fingerprint pattern of the pyrogram. Although the

application focus on using selected ion monitoring (SIM), the analysis of the scan

spectra in the method development is essential. [13, 26]

3. Environmental applications of Py-GC-MS

Two of the most interesting applications of Py-GC-MS in environmental samples are

(i) the evaluation of natural organic matter (NOM) and (ii) the identification and

characterization of microplastics and nanoplastics.

3.1. Characterization of organic matter

The complex and variable composition of natural organic matter (NOM)

(polysaccharides, amino sugars, proteins, polyhydroxy aromatics, lipids, lignin, etc)

makes characterization of its components very challenging within environmental

studies. Py-GC-MS has been applied to analyze the changes in the dissolved organic

matter (DOM) in water [38, 39]. This technique has also demonstrated to be a potent

tool for studying the molecular fingerprints of the natural organic matter (NOM) in soils

and sediments, evaluating the changes in soil organic matter (SOM), identifying soil

and sediment carbon storage [39]. Furthermore, other applications focus on

characterization of humic acids because there is an urgent need to understand the

structure and to improve definition of the function of humic substances in nature.

In water, separation and purification of DOM is important and often linked to further

analysis. The first step is filtration through a glass filter to retain any other organic

matter fraction. After filtration, the DOM in water is isolated and enriched by sorbent

extraction methods, such as solid phase extraction SPE [39] and pre-HPLC [38]. In both

techniques, DOM fraction is concentrated by reverse phase mechanisms. The

characterization of soil and sediment organic matter does not requires extraction, just to

dry, sieve and pulverize the soil [30, 31, 40, 41]. However, there are many types of

organic matter fractions, one of them commonly determined also in soils is DOM that

can be extracted using a lysimeter-pump or by shaking soil with water [42]. Other

fraction involves the humic substances, probably the most important one because soil

fertility and stability depends on it. The extraction and purification humic acids (HA)

and/or fulvic acids (FA) according to standard methods is compulsory to study them.

These standard methods involved as a first step elimination of the free organic matter by

flotation (using H

) and demineralization (with Na

needed to remove Al

associated with SOM and release the humic substances that are complexed with hydrous

amorphous oxides). Then, HA and FA are extracted from soil using a basic solutions

(NaOH), and separated by precipitation of the humic acids in the solution by

acidification (pH < 2). HA are further purified by washing with several acidic solutions

[43] and/or dialyzed into cellophane bags [44, 45]. The acidified supernatant including

FA was passed through a column of DAX-8, a nonfunctionalised

polymethylmethacrylate resin with strong hydrophobic organic matter endorsed to

humic and fulvic acids and through a strong cation-exchange resin column [46].

The three types of described pyrolyzers —micro furnace [30, 31, 34, 43, 47], platinum

heated filament pyrolyzer [22, 41, 42, 44, 48, 49] and Curie point [38, 45] has been

applied to characterize NOM in the environment. However, most of the studies only test

the single shot [22, 30, 31, 34, 38, 41-45, 47, 49] at temperatures > 500 º C and <800

ºC. The reactive pyrolysis has also been applied in some studies [42, 46] because it

provides less fragments simplifying the pyrogram. However, a study comparing the

ability of both techniques to characterize soil-derived DOM across the Three Gorges

Reservoir areas [48], concluded that Py-GC–MS fingerprinting is a more helpful tool

able to quantify microbial DOM than reactive pyrolysis. Reactive Py-GC-MS is instead

useful to determine the molecular features of the polyphenolic (cinnamic acids, lignin

and tannin compositions) and aliphatic (identification of cutin and suberin) fractions. As

an interesting alternative to palliate this problem, branched-chain fatty acids in HAs

analyzed by Reactive py–GC-MS has been proposed as biomarkers (BMs) for

determining the history of microbial activities that occurred during composting

processes [46].

The double-shot pyrolysis that makes both, free volatiles and high molecular weight

structures, accessible has been scarcely used in the studies involving NOM

characterization, probably because the organic matter is formed by non-volatile

compounds. In fact, Figure 4 illustrates that free evaporable molecular structures are

barely present in terrestrial DOM. In contrast, a lot of components are accessible only in

the pyrolysis step [39]. Hence, DOM mainly consists of large high molecular complex

structures. Many of these are aromatic, especially phenolic compounds that are lignin

degradation products.

In most of the covered studied, GC-MS is performed in practically all cases using the

most conventional system, which involves 30 m capillary columns and a simple

quadrupole working in scan mode. The detected pyrolysate compounds were assigned

by comparing the obtained mass spectra with the NIST or any other of the mentioned

mass spectral database. The identified compounds can be grouped according to their

probable origin as: (i) aliphatic hydrocarbons (n-alkanes, n-alkenes and n-methyl

ketones), (ii) aromatics and alkylbenzenes, (iii) polyaromatic hydrocarbons (PAHs) and

benzofurans, (iv) methyl esters, (v) lignin phenols, (vi) N-containing compounds, (vii)

phenols and catechols, and (viii) polysaccharides. Commonly number of compounds

identified in a pyrogram ranged from 100 to 400 compounds. The relative abundance of

the pyrolysis products can be calculated by normalizing the peak areas of each

individual compound to the total area for all the peaks of the detected products.

Although powerful advances of GC and MS in last years, only one Py-GC-MS method

reports the use two ionization systems EI and photon-ionization (PI) with different mass

selective analyzers [quadrupole and a time-of-fight MS (ToFMS)] for the analysis of

terrestrial DOM in water [39]. The PI –a soft ionization method- was performed by

resonance-enhanced-multi-photon-ionization (REMPI) providing information on the

molecular weight and a high selectivity and sensitivity for aromatic hydrocarbons.

Furthermore, the accurate mass of TOF-MS attains an additional confirmation. It

enables the characterization of natural samples by a universal (electron ionization

quadrupole MS) as well as an aromatic fingerprint (REMPI-TOF-MS). However, it is

clear that new developments in the field of GC-MS have not been incorporated yet,

perhaps because analysis using routine systems is enough complex and, especially,

time-consuming and requires highly specialized personnel.

One of the main problems of the NOM characterization is, as stated in the previous

paragraph, the complexity of the obtained pyrograms, all of them with a high number of

compounds. If, in addition, the number of samples is very high, the amount of

information to be processed increases exponentially making it very difficult for the

analyst. Some studies have tried to find solutions to this problem, mostly using two

different ways. The simplest way is to focus the study on a few good biomarkers of the

processes that the organic matter undergoes. A study proposing the use of branched

chain fatty acids as bioindicators has already been discussed [46]. The methoxyphenols

(descriptors of organic matter composition) have also been proposed as indicators of the

capacity of the soil C storage [34]. Methoxyphenols (12 major guaiacyl- and syringyl-

type compounds) are released by Py-GC-MS from topsoil samples and are distinctive

molecules supposedly advising on the occurrence and degree of alteration of lignin in

soils as different statistical analysis (simple regression, partial least scuares (PLS),

statistical molecular design (SMD)) coincide in showing proper correlation between C

storage in soil and the methoxyphenol complexity.

The other methods are based on the application of statistical tests that help to

systematize or improve the visualization of the information obtained. Effort is devoted

to develop automated identification and quantification software that helps to process

mass chromatographs from Py-GC-MS and visualize the results [44, 50]. Moreover,

pyrolyzates fluctuate significantly among samples in their presence and intensity.

Statistical techniques can overcome the challenges of data interpretation. For example,

analysis of variance (ANOVA) has be used to assess differences in functional groups

among samples, whereas factor analysis has been reduce the dimension of pyrolyzates

and separate the samples using the reduced variables, or factors. Fig. 5 exemplifies one

systematic approach that has been proposed to obtain as much sample information as

and overcome these limitations [50]. There are many more reported in the literature.

3.2. Determination of micro and nanoplastics

Environmental contamination by micro and nanoplastics is a globally recognized

problem that has worldwide dimension. The large number of reviews on nano or

microplastics that have been published in recent years gives a proper picture of the

raised concerns [15-19, 51-55]. Py-GC-MS has become one of the most promising

techniques to identify micro and nanoplastics in environmental samples because it

achieves detection of lower microplastics dimensions, is more sensitive than other

methods and less affected by impurities and interferences of the samples. “Less” does

not mean “absence”. Some samples present serious interferences due mainly to the

organic matter. This is alleviated by the broad compatibility of Py-GC-MS with all

types of extraction and purification processes. Furthermore, this technique has open an

interesting horizon to determine nanoplastics. The most difficult ones due to their low

size.

Py-GC-MS has already been applied to determine microplastics in soil [56], soil

amended with municipal solid waste compost [57], biosolids [58], salt [59, 60], river

sediments, [30, 61], beach sediments [62], coastal sediments [63], tidal flat sediment

[60], suspended particulate matter [30], wastewater, sea water [64], surface water [65],

bivalves [66], fish and other types of biota. Furthermore, Py-GC-MS has been used to

determine nanoplastics in water [67-69].

The determination of microplastics can be carried out directly in matrices, such as soil

[57] but is usually performed by separating and concentrate them from the matrix by

wet digestion (with acids, oxidants or enzymes [63, 70]) or density differences (using

solutions of NaCl [62], NaI, ZnCl

, NaBr [59], sodium polytungstate [61]) and

filtration. In the case, of wastewater analysis and due to its high organic matter content

sequential filtration can help to isolate the microplastic without clogging filters [71]. As

alternative technique, very recently pressurized liquid extraction (PLE) has been

proposed since high temperature and pressure helps to solubilize microplastics in

solvents, such as dichloromethane and tetrahydrofuran [58, 72]. In these extracts, not

only microplastics but also NOM was extracted when analyzed sediment and soils, a

pre-extraction with methanol, eliminates NOM without affecting the polymers.

Nanoplastics can be isolated by using filtration through common PTFE membranes

(pore sizes 0.45 and 0.1 µm) to deposit MPs and NPs from aqueous samples working in

the limit range of nanoscale (24 nm–52 nm) [68]. Ultrafiltration also retains altogether

micro and nanoplastics from water but retains lower nanoplastics sizes than filter

(

5–50

nm) [67] Triton X-45 (TX-45)-based cloud-point extraction (CPE) has also been tested

to preconcentrate nanoplastics [69]. This method provides enrichment factor of 500

without disturbing their original morphology and sizes. At present, nanoplastics can be

analyzed using optical and spectroscopic techniques [73]. The optical methods can

analyzed particles < 200 nm but cannot provide chemical identification, thus is prone of

false positive [74]. By comparison, spectroscopy, such as Raman or Py-GC-MS can

provide specific information about the polymer via its fingerprint spectra, but the small

size of nanoplastics provides weak responses being prone of false negatives [73-75].

Imaging version of spectroscopies points as the solution to the determination of

nanoplastics [73].

The determination by Py-GC-MS is also used in a very conventional format. The most

commonly used pyrolyzer in the case of micro and nanoplastics is the micro furnace

[58-63, 69]. Filament [56, 57, 68] and Curie point [56, 57, 68] pyrolyzers have also

been reported in a much lesser extend. The advantage of the microfurnace is its higher

sample capacity. In this case, the double-shot pyrolysis is the preferred mode because

NOM compounds are a potential source of interfering during pyrolysis [30, 56, 58, 63,

69]. None of the natural materials compounds interfering with the selected indicators for

polypropylene (PP), polyethylene terephthalate (PET), polycarbonate (PC), poly-

(methyl methacrylate) (PMMA) and polystyrene (PS) when using thermal desorption

(first-shot of the double-shot method) as a clean-up step. This is difficult to explain if

we take into account that in the previous section one of the conclusions drawn was that

the NOM produced few compounds when thermo-desorbed. However, the great

variability of composition of NOM and the wide range of different conditions used may

well justify these apparently contradictory results. Residual organic matrix leads to a

variety of non-volatile pyrolysis products, enhances the risk of interferences with

specific indicator compounds, boosts the maintenance frequency and hampers reliable

quantification. Regarding the preferred pyrolyzer, a comparison of Curie-Point and

micro furnace pyrolyzers to determine microplastics in the environment [60]. showed

that microfurnace Py-GC-MS can process more sample quantity because of the larger

sample cups allowing the use of glass fibre filters for direct transfer of pre-concentrated

microplastics. Besides simplification, this minimized sample losses during the transfer

from the filter to the pyrolyzer.

The GC-MS is also very conventional with a quadrupole mass analyzer, even with a

ToF (pulsed) analyzer was also reported with advantages over more common scanning

mass spectrometers (quadrupole analyzers). In this study, ToF operated in nominal mass

units (not accurate mass) similar to the quadrupole but offers substantial sensitivity

because in a ToF, all the ions accumulated are transmitted to the detector [68].

Sensitivity is crucial to work in full scan mode because using quadrupolar detector. In

most cases and unlike the analysis of NOM, selected ion monitoring (SIM) mode is the

only working mode that attains sufficient sensitivity to detect microplastics still at much

lower concentrations than organic matter.

Residual organic matrix present in the extract/sample leads to a variety of non-volatile

pyrolysis products, enhances the risk of interferences with specific indicator

compounds, boosts the maintenance frequency and hampers reliable quantification.

Compounds in the pyrograms can be identified querying against any of the previous

mentioned mass spectral database, custom database containing pre-acquired pyrograms

with reference plastic samples or manually compared with the available literature [62,

76]. However, to identify and quantify plastics in environmental samples, specific

indicator compounds are usually selected for each type of plastic. These indicator

compounds, specific to each type of plastic, are selected either by using analytical

standards of each type of plastic or by comparing the results of the pyrograms with the

existing literature. Table 2 reports the most used indicator compounds for most reported

type of plastics. These indicators are selected assessing their specificity against a

number of natural materials: chitin, wood, pine needles, humic acid, cellulose, etc. [58].

Reactive pyrolysis [59, 61, 70] with TMAH as derivatization reagent has been used to

reduce the organic matrix interferences and because this technique improve the

detection sensitivity for PET and PC. If pyrolysis is performed after TMAH addition the

pyrolytic behavior stayed unaffected for polyethylene (PE), PP, PS, and

polyvinylchloride (PVC) while that of PET, PMMA, PC, and polyamide (PA6)

changed. The determination PS is complicated, even though the microplastics

derivatization because chitin, widely present in the natural environment, releases styrene

(m/z 104) during pyrolysis, styrene is not indicative for PS identification and

quantification in environmental samples, although it is an abundant PS pyrolysis

product. In contrast, most of the studies used styrene trimer (m/z 312) because it is

specific for PS [69].

The first international comparative study of commonly applied analytical methods for

microplastic analysis served as a first attempt to assess the suitability of frequently used

methods in microplastic research that also shows obstacles when conducting a

comparative study for microplastics [77]. In this study, microscopy, Fourier-transform

infrared microspectroscopy (μ-FTIR), Raman microspectroscopy (μ-Raman), thermal

extraction and desorption or pyrolysis- combined with GC/MS, scanning electron

microscopy and particle counter were compared regarding results on total particle

number, polymer type, number of particles and/or particle mass for each polymer type.

The quantification of polymer mass for identified polymer types was questionable for

Py-GC-MS, whereas other methods failed to determine the correct polymer mass. For

the identification of polymer type μ-Raman and Py-GC/MS performed best [77]. The

other thermodegradation technique applied to microplastics, is thermogravimetric

analysis (TGA), a thermal analysis technique that measures weight loss of a sample as it

is heated at a programmed rate in a controlled gaseous environment and is coupled to

several detectors, such as FTIR, MS or GC-MS [78, 79]. TGA has evolved to a new

combination, thermo-extraction desorption gas chromatography mass spectrometry

(TED-GC-MS) [80]. This approach is based on trapping the decomposition products

from a TGA onto a solid-phase adsorber and then, analyzing the adsorber by thermal

desorption gas chromatography mass spectrometry. Thus, the instrument remains

cleaner and is more stable for extended time. Recently, an interlaboratory comparison

of thermal procedures for the identification and quantification of polymers in freshwater

suspended organic matter revealed an acceptable analyte recovery and reproducibility

among participants performing Py-GC-MS, TGA-FTIR, and thermos extraction

desorption gas chromatography mass spectrometry (TED-GC-MS) given the still

relative novel character of this complex analyte/matrix combination [78].

In other studies, Py-GC-MS performance has been compared to that obtained by μ-

FTIR and µ-Raman, as most successful complementary approaches in the identification

of microplastics. The optimized Py-GC/MS method identified 40 particles already

identified by µ-Raman. Py-GC-MS identified copolymer like PE-PP or PE-PP-PA6

which could be difficult to identify with μ-Raman without chemometrics approach

leading to results with a finer identification [66]. In the case of µ-FTIR differentiated

between plastic vs .non-plastic in the same way in 26 cases, with 19 particles and fibers

(22 after re-evaluation) identified as the same polymer type. To illustrate the different

information obtained by these different approaches and emphasize the complementarity

of their information content, Fig. 5 shows the identification of the particles using µFTIR

and py-GC-MS [61].

3.3. Others

The other application of Py-GC-MS are mostly focused on the determination of

contaminants. The unintentional poisoning of off-target animals by bromadiolone, a

second generation anticoagulant rodenticide has been determined through its analysis in

liver and blood plasma by means of in-injector Py-GC-MS (heating the normal GC

injector at 400 ºC using with ion trap tandem mass analyzer with electron ionization

[81]. The pyrolysis products provided a very selective analysis without interferences of

other rodenticides.

The analytical capabilities of double shot Py-GC–MS were applied to evaluate

environmental samples of petroleum hydrocarbons from the Deepwater Horizon oil

spill. EGA Py-GC–MS can quantify the overall degree of petroleum hydrocarbon

weathering. Furthermore, Heart-cut Py-GC–MS can quantify specific compounds in the

‘‘thermal desorption zone” (50–370 °C), as well as characterize pyrolyzed fragments

from non GC-amenable petroleum hydrocarbons (including oxygenated hydrocarbons)

in the ‘‘cracking zone” (370–650 °C). This analysis not only elucidates weathering

trends in Deepwater Horizon oil over several years, but also illustrates the analytical

capacity of this method for future research on petroleum hydrocarbon, filling a void in

research connecting Py-GC–MS and environmentally weathered oil samples [82]. As

can be seen from the previously mentioned examples, the Py-GC-MS, although little

exploited, can still have multiple applications in other environmental fields, especially

in the determination of complex mixtures of contaminants.

4. Conclusions

Py-GC-MS has proven to be a valuable technique for environmental analysis, not

widespread use but covering crucial environmental aspects. Characterization of NOM is

essential for carbon sequestration and the maintenance of soil stability and fertility,

evaluation of terrestrial inputs on oceans, etc. The evaluation of contamination by micro

and nanoplastics has become an essential pillar. Py-GC-MS has demonstrated important

advantages over other techniques such as µRaman and µFTIR to determine

microplastics, and has been the only one able to detect nanoplastics. Something that

until very recently was considered almost impossible.

However, either because of the difficulty in interpreting the results, or because the

studies are more focused on the robustness of the pyrolyzer, the applications have not

yet introduced the latest innovations in GC-MS.

Hopefully, in near future, Py-GC-MS will not only be applied in its conventional format

but will also take advantage of the great separation power of GCxGC or the speed of

rapid GC and will more frequently incorporate the incredible identification capability

that HRMS and HRMS in tandem can provide. Furthermore, environmental analysis

could also benefit from the application of other pyrolysis’ working modes, such as EGA

or shot-cut-EGA, not applied yet in environmental analysis and could add a further

dimension to multidimensional techniques. Therefore, there are still challenges to

overcome in this aspect though we can be confident in that several of these new trends

within Py-GC-MS will be applied soon to environmental analysis.

ACKNOWLEDGEMENTS

This work has been supported by the Spanish Ministry of Science, Innovation and

Universities and the ERDF (European Regional Development Fund) through the project

CICLIC -subproject WETANPACK (RTI2018-097158-B-C31), by the Generalitat

Valenciana through the project ANTROPOCEN@ (PROMETEO/ 2018/ 155) and the

Generalitat de Catalunya (Consolidated Research Groups 2017 SGR 1404 - Water and

Soil Quality Unit).

LIST OF FIGURES

Figure 1. Fundamentals of Py-GC-MS

Figure 2. Scheme of the instrumentation in Py-GC-MS

Figure 3. Scheme of the different types of pyrolyzers currently in used.

Figure 4. Image plot (GC retention time vs. mass-to-charge-ratio vs. signal intensity)

of At4 surface water (rich in tDOM) under TD (top) and Py (bottom) conditions

resulting from the use of REMPI-ToFMS detection. Reprinted from ref. [39] Copyright

(2016) royal society of chemistry.

Figure 5. Flowchart of the proposed systematic approach for unveiling the changes of

chemical composition on pyrograms. ANOVA: analysis of variance; Tukey’s HSD:

Tukey’s honest significant difference; and PCA: principal components analysis.

Appropriate techniques can be used depending on whether or not the assumptions (such

as independence of cases, normality, and homogeneity of variances for ANOVA) can be

met. Reprinted from ref [50]. Copyright (2018) Elsevier.

Figure 6. ATR-FTIR spectra (left) and Py-GC-MS ion chromatograms (right) of the MP

particles. a–d (black) each with a reference (red); C10–C38—alkadienes, alkenes, and

alkanes with chain lengths; DiOP—diisooctyl phthalate; DMP—dimethyl phthalate;

FAME—fatty acid methyl ester; FADME—at 740 and 705 cm−1 (out-of-plane

deformation of the aromatic ring). They originate presumably from phthalic acid fatty

acid dimethyl ester; MB—methyl benzoate; P4ME—pentaerythritol tetramethyl ether,

P3ME—pentaerythritol trimethyl ether, P2ME—pentaerythritol dimethyl ether,

asterisk—thermochemolysis artifacts. Reprinted from ref. [61] Copyright (2018)

Springer.

Table 1. Ability of working in different modes of the three types of pyrolyzers currently

in use.

Microfurnace Filament Curie-point

Single-shot analysis (Py-GCMS)

Yes Yes Yes

Double-Shot analysis (TD/Py-GCMS)

Yes Difficult No

Evolved gas analysis (EGA-MS)

Yes Yes No

Heart-cut analysis (Heart-cut EGA-

GCMS)

Yes Yes No

Reactive Pyrolysis-GC-MS:

Yes Yes Yes

Table 2. Plastic indicator compounds and ions monitored by MS in SIM

Plastic Pyrolysis product Indicator ions

(m/z)

Reference

Dimethyl alkenes (2,4-Dimethyl-1-heptene, 2,4-

Dimethyl-1-decene)

70, 83, 126

111, 97

Okoffo et al.,[58] Dierkes et al.,[68] Fischer & Scholz-

Böttcher,[70] Steinmetz et al.,[56] Dierkes et al.,[68]

Hermabessiere et al.[66]

Tertramethyl alkenes (2,4,6,8-tetramethyl-1-decene,

2,4,6,8-Tetramethyl-1-undecenes)

100, 69,111, 97

Fischer & Scholz-Böttcher, [70] Steinmetz et al.,[56]

Dierkes et al.,[68]

methyl alkenes (7-methyl-decene, 7-methyl 1-undecene,

5 methyl undecene, 7 methyl-2-decene)

111,97 Dierkes et al.,[68]

3-Dodecene 111,97 Dierkes et al.,[68]

Trimethyl alkenes (2,4,8-trimethyl octane, 2,4,6-

trimethyl nonene)

111,97 Dierkes et al.,[68]

PS 5-Hexene-1,3,5-triyltribenzene (styrene trimer)

91, 117, 194, 312,

207

Okoffo et al.[58], Zhou et al.,[69] Steinmetz et al. [56]

Mintening et al.,[67]

Styrene 104, 78

Fischer & Scholz-Böttcher, [70] Steinmetz et al.,[56]

Mintening et al.,[67], Dierkes et al.,[68] }

Hermabessiere et al.[66]

2,4-Diphenyl-1-butene or 3-butene-1,3-diyldibenzene

(styrene dimer)

91, 208

Fischer & Scholz-Böttcher, [70], Hermabessiere et

al.[66]

α-Methylstyrene 103, 118 Steinmetz et al.,[56]

Toluene 91 Dierkes et al.,[68]

Plastic Pyrolysis product Indicator ions

(m/z)

Reference

1-Ethyl-2-methylbenzene 117,

105

Dierkes et al.,[68]

1,2,3-trimethylbenzene 120,

105

Dierkes et al.,[68]

2-Propenyl-benzene 117, 91 Dierkes et al.,[68]

3-Butenyl-benzene 132, 91 Dierkes et al.,[68]

3-Phenyl-1-propyne 115 Dierkes et al.,[68]

1-Methylenepropyl-benzene 132, 117,91 Dierkes et al.,[68]

Bibenzyl 182, 104, 91 Dierkes et al.,[68]

1,1’-(1-Methyl-1,2-ethanediyl)bis-Benzene 105, 91 Dierkes et al.,[68]

Stilbene 180, 165 Dierkes et al.,[68]

1,2-Dihydro-3-phenylnaphthalene 191, 128, 91 Dierkes et al.,[68]

2,5-Diphenyl-1,5-hexadiene 143, 130, 104, 91 Dierkes et al.,[68]

PMMA Methyl methacrylate 69, 100, 89

Okoffo et al.,[58] Zhou et al.,[69] Hermabessiere et

al.[66]

Methylacrylate 55 Fischer & Scholz-Böttcher, [70]

PET Vinyl benzoate 105, 77, 148, 51 Okoffo et al.[58] Hermabessiere et al.[66]

Dimethyl terephthalate*

163

Fischer & Scholz-Böttcher, [70] Hermabessiere et

al.[66]

Benzene 52, 78 Hermabessiere et al.[66]

Plastic Pyrolysis product Indicator ions

(m/z)

Reference

Acetophenone 51, 77, 105 Hermabessiere et al.[66]

Benzoic acid 77, 105, 122 Hermabessiere et al.[66]

PC Bisphenol A

213, 119, 91, 165,

288

Okoffo et al.[58] Hermabessiere et al.[66]

2,2-Bis(4'-methoxy-phenyl)propane

256, 241 Fischer & Scholz-Böttcher, [70]

p-Methoxy-tert-butylbenzene* 149, 164 Fischer & Scholz-Böttcher, [70]

Phenol 66, 94 Hermabessiere et al.[66]

p-Cresol 77, 107 Hermabessiere et al.[66]

p-Ethylphenol 107, 122 Hermabessiere et al.[66]

p-Vinylphenol 91, 120 Hermabessiere et al.[66]

p-Isopropenylphenol 119, 134 Hermabessiere et al.[66]

1-Decene (C10:1), 1-Pentadecene (C15:1) Alkanes (e.g.

C14, C15, C16, C17, C18), Alkenes (C9:1, C11:1,

C12:1, C13:1 C14:1, C16:1, C17:1, C18:1),

83, 97, 111, 140,

55, 69

Okoffo et al., [58] Fischer & Scholz-Böttcher [70]

Steinmetz et al.,[56] Hermabessiere et al.[66] Dierkes et

al.,[68]

Alkanes (e.g. C20)

Fischer & Scholz-Böttcher [70]

-Alkenes (e.g. C20:2, C14:2, C16:2, C18:2)

82, 95

Fischer & Scholz-Böttcher [83] Steinmetz et al.,[56]

1,14-Pentadecadiene 81, 82, 95 Dierkes et al.,[68] Steinmetz et al.,[56]

Styrene

104

Dierkes et al.,[68]

PVC

Benzene

78, 74, 52, Okoffo et al.,[58] Fischer & Scholz-Böttcher [70]

Plastic Pyrolysis product Indicator ions

(m/z)

Reference

Hermabessiere et al.[66]

Toluene

Sullivan et al. [68] Hermabessiere et al.[66]

Ethylbenzene

Sullivan et al. [68]

Indane

116, 117

Sullivan et al. [68] Hermabessiere et al.[66]

Indene 116 Sullivan et al. [68]

1,4-dihydronaphthalene 130 Sullivan et al. [68]

Naphtalene

128

Sullivan et al. [68] Hermabessiere et al.[66]

1-Methylnaphtalene or 2-methylnaphthalene 115, 142 Sullivan et al. [68] Hermabessiere et al.[66]

Biphenyl 154 Sullivan et al. [68]

Chlorobenzene

112

Fischer & Scholz-Böttcher [60]

Styrene

78, 104

Sullivan et al. [68] Hermabessiere et al.[66]

PA6 ε-Caprolactam

113

Fischer & Scholz-Böttcher [60] Hermabessiere et

al.[66]

N-methyl caprolactam

127

Fischer & Scholz-Böttcher [60]

MDI-

PUR

N,N-Dimethyl-4-(4-methylamino)benzylanilin* 240

Fischer & Scholz-Böttcher [60]

4,4’-Methylenbis(N,N-dimethylaniline)* 253, 254 Fischer & Scholz-Böttcher [60]

Italics and bold values used for calibration and quantification. *Only after TMAH treatment

MDI-PUR= MDIPolyurethane, PA6=Polyamide, PC= Polycarbonate, PE=polyethylene, PET= Polyethylene terephthalate, PMMA= Poly-

Plastic Pyrolysis product Indicator ions

(m/z)

Reference

(methyl methacrylate), PP= polypropylene, PS= polystyrene, PVC= polyvinylchloride

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Highlights

 In the environmental sciences, Py-GC-MS is becoming more and more relevant

 Advances in the design of currently used pyrolyzers are discussed

 Recently developed working modes within Py-GC-MS are described

 Py-GC-MS application to organic matter and microplastics is highlighted.

 Progresses and promising trends in Py-GC;S analysis are pointed out.

Declaration of interests

☒

The authors declare that they have no known competing financial interests or personal relationships

that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered

as potential competing interests: