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Chemical Geology
journal homepage: www.elsevier.com/locate/chemgeo
Effect of iron and nanolites on Raman spectra of volcanic glasses: A
reassessment of existing strategies to estimate the water content
Danilo Di Genovaa,⁎, Stefania Sicolab, Claudia Romanob, Alessandro Vonab, Sara Fanarac,
Laura Spinad
a School of Earth Sciences, University of Bristol, Wills Memorial Building, Queens Road, BS8 1RJ Bristol, UK
bDipartimento di Scienze, Università degli Studi Roma Tre, L.go San Leonardo Murialdo 1, 00146 Rome, Italy
c Institut für Mineralogie, GZG, Universität Göttingen, Goldschmidtstr. 1, Göttingen, Germany
d Department of Physics and Geology, University of Perugia, via Alessandro Pascoli, 06123 Perugia, Italy
A R T I C L E I N F O
Editor: D.B. Dingwell
Keywords:
Raman spectroscopy
Glasses
Water
Iron
Nanolites
A B S T R A C T
The effect of iron content and iron nanolites on Raman spectra of hydrous geologically-relevant glasses is pre-
sented. Current procedures to estimate the water content using Raman spectra were tested to explore potential
effects of iron content, its oxidation state, and nanolites on models' reliability. A chemical interval spanning from
basalt to rhyolite, including alkali- and iron-rich compositions, with water content up to 5.6 wt% was in-
vestigated using two spectrometers. When considering nanolite-free samples, the area of the band at 3550 cm−1
linearly correlates with the sample water content regardless of chemical composition. Using this approach, data
were reproduced with a root-mean-square error (RMSE) of ~0.15 wt%. Depending on the sample chemistry,
water content, and acquisition conditions the laser-induced sample oxidation led to underestimating the water
content up to ~90% with a long acquisition time (26 min). Normalising the water band region to the silicate
band region minimises such a limitation. The area ratio between these bands linearly correlates with the water
content and the use of different baseline procedures does not remove the dependence of such a correlation by the
iron content and its oxidation state. With this procedure, data were reproduced with a RMSE of ~0.16 wt%. For
both approaches, the presence of iron nanolites may result in underestimating the water content.
1. Introduction
Water is the most abundant volatile species dissolved in natural
melts and greatly affects, even at low concentration, a variety of ther-
modynamic and physical properties, from phase equilibria, to reaction
kinetics, element diffusivities, electrical conductivity, heat capacity,
and partial melting (Behrens and Zhang, 2009; Giordano et al., 2015;
Lange and Carmichael, 1990; Poe et al., 2012; Scaillet and Macdonald,
2001; Stebbins et al., 1995). Moreover, bulk properties such as viscosity
and density of the melt can vary by several orders of magnitude de-
pending on the dissolved water content (Bouhifd et al., 2015; Dingwell
et al., 1996; Lange and Carmichael, 1990; Whittington et al., 2000).
Such properties control the entirety of magmatic and volcanic processes
occurring from the melt generation, magma rise, decompression and,
ultimately, the fate and style of volcanic eruptions.
Volcanic glasses, from glass shards to melt inclusions trapped in
crystals, represent the products of most of volcanic eruptions.
Analytical studies of water distribution in natural glasses are crucial for
understanding the plethora of physical and chemical processes, and
their feedbacks, occurring before, during, and after the eruption
(Bachmann et al., 2009; Berry et al., 2008; Blundy and Cashman, 2005;
Dingwell, 2006; Hartley et al., 2014; Kennedy et al., 2005; Métrich
et al., 2010). Moreover, investigations of run products from solubility,
diffusion, decompression, crystallisation, and bubble nucleation ex-
periments help to constrain the timescale of physical and chemical
processes in hydrous systems (Blundy and Cashman, 2005; Fanara
et al., 2015; Gardner et al., 2000; Gonnermann and Gardner, 2013;
Hammer et al., 2000; Le Gall and Pichavant, 2016; Martel and Iacono-
Marziano, 2015; Shishkina et al., 2010).
Raman spectroscopy is a non-destructive technique used to chemi-
cally discriminate glasses, study and estimate the oxygen fugacity and
volatile content (e.g., Di Genova et al., 2016a, 2016b; Di Muro et al.,
2006a, 2009; Le Losq et al., 2012; Morizet et al., 2013; Thomas, 2000).
The minor sample preparation and high-spatial resolution of few μm2
represent the main advantages of using Raman spectroscopy over other
standard techniques typically used for water content determination
https://doi.org/10.1016/j.chemgeo.2017.10.035
Received 25 June 2017; Received in revised form 26 October 2017; Accepted 30 October 2017
⁎ Corresponding author.
E-mail address: danilo.digenova@bristol.ac.uk (D. Di Genova).
Chemical Geology 475 (2017) 76–86
Available online 17 November 2017
0009-2541/ © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
MARK
[Fourier transform infrared spectroscopy (FTIR), Karl Fischer titration
(KFT), and thermogravimetric analysis (TGA)]. The potential of Raman
spectroscopy, together with progress in the performance of spectro-
meters, now opens future opportunities for producing high-resolution
maps of water distribution in volcanic and experimental products ne-
cessary to constrain processes involved in volcanic eruptions and their
equilibrium versus disequilibrium timescales.
Over the past few decades, several authors have adopted different
protocols for the quantification of water content by Raman spectro-
scopy based on internal and external calibrations (Behrens et al., 2006;
Chabiron et al., 2004; Di Muro et al., 2006b; Mercier et al., 2009;
Thomas, 2000; Thomas et al., 2008; Zajacz et al., 2005). The external
calibration requires a set of standards where the water content is in-
dependently determined (Behrens et al., 2006; Thomas et al., 2008;
Mercier et al., 2009). Moreover, each spectrometer needs to be cali-
brated due to the different performance of detectors and instrumental
settings (e.g., grating, excitation source, objective, acquisition time,
focus depth) which affect the spectra intensity and the band area. Dif-
ferently, the internal calibration is based on spectra normalisation be-
tween the water and silicate regions. This approach is expected to re-
move most of instrumental effects on Raman spectra. Therefore, so far,
the internal calibration has been considered to allow different labora-
tories to use a common calibration.
In order to provide a single calibration valid over a large compo-
sitional interval, Le Losq et al. (2012) embedded the chemical-depen-
dence of Raman spectra into a background procedure which depends on
the sample SiO2 content (more details are provided in the following
sections). After background subtraction, their calibration relied on the
ratio (HW/LW) between the water (HW, 2700–3900 cm−1) and silicate
(LW, 200–1300 cm−1) area bands to estimate the dissolved water
content H2O (wt%) as follows:
− =
H O
H O
A HW
LW(100 )
·2
2 (1)
The left member of the equation represents the water/glass pro-
portion and the A coefficient is equal to 7.609 · 10−3. While A might
change with the used spectrometer, the relationship between HW/LW
and the water/glass proportion was found to be unique and linear re-
gardless of the sample composition (Le Losq et al., 2012).
However, the starting materials used in their study were mainly
iron-free glasses (9 out of 12 glasses). Natural glasses contain iron
which is present in both reduced (Fe2+) and oxidised (Fe3+) forms
depending on temperature, oxygen fugacity, and chemical composition.
The dual behaviour of iron affects the Raman spectra of natural glasses
(Di Muro et al., 2009; Di Genova et al., 2016a). Moreover, Di Muro
et al. (2006a, 2006b) and Di Genova et al. (in press) found that iron-
bearing crystals at the micro- and nanoscale dramatically alter the
Raman features of glasses. These particles nucleate and grow during
cooling or thermal annealing above the glass transition temperature
and have been recently recognised to be pervasive in experimental
specimens and natural products (Di Genova et al. in press). Based on
these observations, it is evident that any Raman model used to estimate
the water content of natural products should consider such effects.
These considerations led us to reassess the relationship between
Raman spectra and the water content of volcanic glasses. Here, we in-
vestigate a series of hydrous glasses with FeOtot. up to 14.1 wt% char-
acterised by chemical composition spanning from basalt to iron-poor
and iron-rich phonolite and rhyolite. We used two Raman instruments
to investigate possible effects of diverse instrumental characteristics.
This contribution aims to test current strategies and provide reliable
procedures to estimate the water content of naturally-occurring glasses
by Raman spectrometry.
2. Materials and analytical methods
2.1. Samples from previous studies and starting materials
To explore chemical effects on Raman spectra of glasses, we in-
vestigated hydrous samples with variable water content over a wide
range of chemical composition. Sample set includes 20 glasses from
previous studies and 9 glasses synthesised specifically for this study. In
term of silica, iron, and alkali content, the used compositions span al-
most the entire chemical spectrum of magmas erupted on Earth.
2.2. Samples and starting material from previous studies
The chemical composition of samples synthesised in previous stu-
dies is reported in Table 1 and shown in a TAS (total alkali versus silica)
diagram in Fig. 1A. The samples include:
– Trachybasalt (ETN, Di Genova et al., 2014a) from Etna (1991–1993
lava flow field in Val Calanna, Italy);
– Latite (FR, Di Genova et al., 2014b) from Fondo Riccio eruption
(9.5 ka Campi Flegrei, Italy);
Table 1
Dry composition (wt%) of the starting materials. The associated errors are reported in Section 2.3 of this work and in the mentioned studies.
Sample KRa HOa ETNb FRc AMSb PS-GMb 472ADd 79ADd V_1631_We RHf
Composition Fe+ basalt Dacite Trachy-basalt Latite Trachyte Fe+ rhyolite Fe+ phonolite Fe− phonolite Tephri-phonolite Fe− rhyolite
H2Og TGA TGA KFT + NIR KFT + NIR KFT + NIR KFT + NIR KFT KFT NIR TGA
SiO2 50.24 66.17 48.06 56.55 57.72 69.21 51.36 56.09 53.52 78.87
TiO2 1.99 0.77 1.67 0.81 0.39 0.5 0.48 0.19 0.6 0.10
Al2O3 13.55 15.96 16.72 17.92 18.4 9.18 21.63 22.02 19.84 12.52
FeOtot. 14.08 5.02 9.92 6.59 4.51 7.94 4.54 2.26 4.8 1.55
MnO 0.25 0.12 0.24 0.17 0.1 0.32 − − 0.14 0.04
MgO 5.86 1.70 5.46 2.36 1.46 0.08 0.74 0.18 1.76 0.04
CaO 10.09 4.65 10.03 5.52 4.23 0.6 5.90 2.8 6.76 0.84
Na2O 2.46 3.70 3.68 4.55 3.72 6.52 5.92 6.22 4.66 1.01
K2O 0.32 2.23 1.83 4.53 7.9 4.35 9.42 10.25 7.91 5.28
P2O5 0.55 0.01 0.48 0.01 0.19 0.04 − − − 0.02
a This study.
b Di Genova et al. (2014a).
c Di Genova et al. (2014b).
d Scaillet and Pichavant (2004).
e Romano et al. (2003).
f Di Genova et al. (in press).
g The dissolved water content in glass was determined by thermogravimetric (TGA), Karl–Fischer titration (KFT), and near-infrared spectroscopy (NIR) analyses.
D. Di Genova et al. Chemical Geology 475 (2017) 76–86
77
– Trachyte (AMS-B1, Di Genova et al., 2014a) from Agnano Monte
Spina eruption (4400 BP Campi Flegrei, Italy);
– Phono-tephrite (V_1631_W, Romano et al., 2003) from AD 1631
Vesuvius eruption;
– Tephri-phonolite (472AD) and phonolite (79AD) from Scaillet and
Pichavant (2004) belonging to the main explosive phases of Pollena
(472AD) and Pompei (79AD) Vesuvius eruptions;
– Iron-rich rhyolite (PS-GM, Di Genova et al., 2013) from the Khaggiar
lava flow (8.2 to 5.5 ka Pantelleria, Italy);
– An anhydrous iron poor-rhyolite (RH, Di Genova et al. in press) with
chemistry similar to rhyolitic melts of the Yellowstone Plateau
Volcanic Field (USA).
Details of the experimental procedures employed to synthesise hy-
drous glasses are reported in the mentioned studies. Detailed anhydrous
composition and water contents (determined by FTIR, TGA, and/or
KFT) are reported below.
2.3. Samples synthesised in this study
In addition to the samples from literature, two anhydrous glasses
belonging to the calcalkaline magma series were produced starting
from: i) a basalt (KR) from the 1984 lava flow at Krafla volcano
(Iceland; Tryggvason, 1986); ii) a dacite (HO) from AD 1707 Hoei
eruption at Mt. Fuji (Japan; Miyaji et al., 2011).
The rocks were melted in a thin-walled Pt crucible using a
Nabertherm MoSi2 box furnace at 1400 °C for 5 h and rapidly quenched
in air. Glass chips from KR and HO samples were separately loaded into
a Pt80Rh20 crucible. A concentric cylinder assembly was used to che-
mically homogenise the samples and remove bubbles from the melts.
Samples were continuously stirred at 1 atm from 4 h to 1 day at 1400 °C
until the melt was free of bubbles and completely homogenised.
Afterwards, the sample was rapidly quenched by immerging the cru-
cible in water. The obtained glasses were chemically characterised and
prepared for hydrous synthesis.
Chemical compositions were measured with a Cameca SX100 elec-
tron micro probe analyser (EMPA) using a defocused beam (10-μm) to
minimise alkali loss. Analyses were carried out at 15 kV acceleration
voltage and 5 nA beam current. Wollastonite (Ca, Si), periclase (Mg),
hematite (Fe), corundum (Al), natural orthoclase (K), and albite (Na)
were used as standards. Additionally, a matrix correction was per-
formed according to Pouchou and Pichoir (1991). The precision was
better than 2.5% for all analysed elements. The chemical homogeneity
of glasses was verified by performing ~25 chemical analyses for each
sample.
In order to obtain water-bearing glasses, anhydrous glasses from KR
and HO, together with the iron-poor rhyolite RH (Di Genova et al. in
press), were powdered and sieved to obtain two powder fractions with
grain sizes of 200–500 μm and< 200 μm. Afterwards, powders were
loaded into AuPd capsules (3 mm outer diameter, 10 mm lengths,
0.2 mm wall thickness) with a weight ratio of 1:1 to minimise the pore
volume together with the appropriate amount of doubly distilled water.
Syntheses were performed at 260 MPa and at 1250 °C for 3 days in an
internally heated gas pressure vessel (IHPV) at the Institute of Miner-
alogy at the University of Göttingen using the drop-fast quench tech-
nique. Samples were placed in the furnace hot zone through a platinum
wire. At the end of the experiment, the wire was melted with a sudden
D.C. current and samples were quenched rapidly in the cold part of the
vessel. Each capsule was weighted before and after the experiment to
test for possible leakage. The obtained glasses were free of bubbles and
crystals at the microscale. Each composition was nominally hydrated
with three different water contents of ~1, ~3, and ~4.5 wt%.
Dissolved water content in glasses was measured using the Fourier
Transform Infrared (FTIR) spectroscopy technique and thermogravi-
metric analysis (TGA). We used a Bruker Vertex 70 spectrometer cou-
pled with an IR Microscope Hyperion 3000 at the Institute of
Mineralogy at the University of Göttingen. For NIR analyses, each
sample was double-polished below ~400 μm thickness measured using
a Mitutoyo instrument (error of 2 μm). The densities of the water-
bearing glasses were calculated after Lange and Carmichael (1987). The
Lambert-Beer law was used to determine the sample water content
(Stolper, 1982) using the NIR and MIR spectra (see the Supplementary
materials section and Table S1 for measurements details).
Thermogravimetric analyses were performed using a Setaram™ TGA
92 instrument at the Institute of Mineralogy at the University of
Göttingen. Between 10 and 20 mg of coarsely powdered glass was
loaded into a Pt crucible (4 mm diameter, 10 mm height) covered with
a Pt lid. The sample was heated from ambient temperature at 10 °C/min
A B
Fig. 1. A) TAS (total alkali versus silica) diagram showing the composition of samples used in this work. KR and HO glasses were synthesised in this study, while all other glasses were
previously synthesised (see Table 1 for samples reference). Abbreviations in the plot mean: PB - picrobasalt, B - basalt, BA - basaltic andesite, A - andesite, D - dacite, R - rhyolite, TB -
trachybasalt, BTA - basaltic trachyandesite, TA - trachyandesite, TD - trachydacite, T - trachyte, Ba - basanite, Te - tephrite, PT - phonotephrite, TP - tephriphonolite, P - phonolite, F -
foidite. B) Samples water content (wt%) as a function of FeOtot. (wt%). The shaded area shows the occurrence of iron-bearing nanolites in some of the investigated samples (see text for a
detailed discussion). The sample water content is reported in Table 2.
D. Di Genova et al. Chemical Geology 475 (2017) 76–86
78
up to 1200 °C. After a dwell time of 30 min, the sample was cooled at
30 °C/min to room temperature. During the entire analysis, the sample
weight loss was continuously recorded. To account for buoyancy
changes with temperature of the sample and therefore correct the
measured sample weight loss, a subsequent heating and cooling cycle
was performed with the degassed sample (Schmidt and Behrens, 2008).
For each sample, three thermogravimetric analyses were performed.
2.4. Raman spectroscopy
Raman spectra were acquired with two different Raman instruments
available at the Mineralogical State Collection of Munich (SNSB, Horiba
XploRa-Raman-System) and Department of Science at Roma Tre
University (Horiba LabRam HR 800) hereafter termed M and R spec-
trometers, respectively. For each sample, 10 spectra were acquired to
investigate the experimental reproducibly of the results.
The instruments are equipped with an attenuated doubled Nd3:YAG
laser having a wavelength of 532 nm and a microscope. The laser power
on the sample surface was measured to be 7.15 mW (M spectrometer)
and 11 mW (R spectrometer) through a 100× objective and ~5 μm2
spot size. The instruments were calibrated using a silicon standard.
Instrumental settings consisted of 1800 grooves/mm grating density,
confocal hole of 300 μm and slit of 200 μm with an exposure time of
60 s (3 times). The backscattered Raman radiation was collected on a
polished sample surface over a range from 100 to 1500 cm−1 and from
2700 to 4000 cm−1 hereafter defined as the low-wavenumber (LW) and
high-wavenumber (HW) regions respectively. In total, the M spectro-
meter required 6 min to acquire both LW and HW regions, while for the
R spectrometer 26 min were necessary. Raman signal was found to be
maximised at 6 μm of depth using a motor on the Z axis. Therefore,
spectra were collected at the same depth for all samples. Prior the
Raman spectra acquisition, the samples were stored at 100 °C in an
oven to avoid water absorption on the surface.
3. Raman spectra treatment
Raman spectra intensity were corrected for the frequency-depen-
dence scattering intensity and temperature (Long, 1977; Neuville and
Mysen, 1996) as it follows:
= ⎧⎨⎩
− −
−
⎫
⎬⎭
I I ν ν hcν kT
ν ν
· · [1 exp( / )]
( )obs 0
3
0
4 (2)
where Iobs is the Raman spectra intensity, ν0 is the wavenumber of the
incident laser light (107/532 cm−1 for the green laser), ν is the mea-
sured wavenumber in cm−1, h is the Planck constant
(6.62607 × 10−34 J s), c is the speed of light (2.9979 × 1010 cm s−1),
k is the Boltzmann constant (1.38065 × 10−23 J K−1) and T the abso-
lute temperature.
Several procedures have been proposed to remove the spectra
background (e.g., Behrens et al., 2006; Le Losq et al., 2012) with the
aim to provide a general and chemically independent model to estimate
the water content. Here, Matlab© and R codes were developed to fully
automatize the background subtraction procedure (see Supplementary
materials for the Matlab© code). We followed two different approaches
to determine the spectra background in the LW region: the SiO2-de-
pendent procedure provided by Le Losq et al. (2012) and a composi-
tionally independent strategy (this study).
The background subtraction procedure from Le Losq et al. (2012)
relies on the definition of a set of zones devoid of peaks (Background
Interpolation Regions, BIRs) to constrain the baseline. Specifically, in
the silicate region (100–1500 cm−1, LW), the number and wavelength
interval of BIRs depend on the SiO2 content, while in the water region
(2700–4000 cm−1, HW) two BIRs are maintained constant regardless of
the composition. The results showed that the independently measured
water content correlates linearly with the ratio between the LW and the
HW band areas. In this work, we explored the possibility to extend this
strategy to a wide range of naturally-occurring iron-bearing glasses
with different iron oxidation state. Furthermore, we tested a baseline
subtraction procedure based on a single cubic spline fitting independent
on the chemical composition (Di Genova et al., 2015). With this, we aim
to provide a simple and reproducible procedure to estimate the baseline
of iron-bearing glasses which are characterised by a substantial spectral
variability and fluorescence (see Results section) due to the effect of the
iron content, its oxidation state, and the presence of iron-bearing na-
nolites on Raman spectra. This would help when analysing samples
with unknown chemical composition, namely small melt inclusions
trapped in crystals. By using Raman spectra, this study provides best
procedures to both estimate water content of natural glasses and re-
cognize iron nanolites.
4. Results
The measured chemical compositions of anhydrous glasses from this
study (KR, HO) and samples from previous studies are listed in Table 1.
Samples are also shown in a TAS diagram (Fig. 1A). Overall, SiO2
content spans from ~48 to ~79 wt%, with Na2O + K2O between ~2.8
and ~16.5 wt%, FeOtot. and H2O up to ~14 and 4.53 wt%, respectively
(Fig. 1B).
The Table S1 shows the water content measured by FTIR for samples
synthesised in this study, while in Table 2 the TGA results obtained
from the same samples. Furthermore, estimations from previous studies
are reported for the other samples are listed.
For the iron-poor rhyolite (RH) and dacite (HO), the water content
was estimated using the peak intensity of the 4500 and 5200 cm−1
(Table S1, Fig. S1A, B) attributed to the combination of stretching and
bending of OH groups bonded to tetrahedral cations and to the com-
bination of stretching and bending modes of H2O molecules, respec-
tively. A linear baseline was applied to the RH and OH spectra (Fig.
S1A). For the RH and HO samples, we used the linear molar adsorption
coefficients given by Ohlhorst et al. (2001) and Withers and Behrens
(1999), respectively (details are reported in the Supplementary mate-
rials section).
The KR basalt was opaque at thickness compatible with the analyses
in the NIR range due to its high iron content (12.79 wt%) which hin-
dered the water quantification. The KR spectra displayed a broad band
at ~5700 cm−1 which is attributed to crystal field transitions of iron
(Ohlhorst et al., 2001) and, possibly, to the presence of iron-bearing
nanolites. Therefore, for this composition, we used the band at
3550 cm−1 (MIR) to determine the amount of dissolved water in
glasses. A linear baseline was applied to evaluate the peak height to-
gether with the adsorption coefficient given by Stolper (1982) (Sup-
plementary materials and Fig. S1B).
Concerning the TGA analysis of hydrous samples, it must be noted
that for the iron-rich samples such as the KR series, the H2O content
may be underestimated due to the iron oxidation during the high-
temperature extraction of water by releasing H2 instead of H2O.
Assuming initial extremely reduced conditions and, therefore, the iron
only existing in a reduced state (Fe2+), we calculated that the water
concentration would be underestimated to a maximum of 0.8 wt% for
the KR3 sample (H2O = 4.67 wt%). However, the IHPV used for the
sample hydration is about 3 log units above the Ni-NiO buffer and a
significant fraction of Fe3+ is expected. Therefore, we estimated that
the water content may be underestimated to a maximum of ~0.25 wt%
(see Schmidt and Behrens, 2008 for a detailed discussion).
The estimated water content for RH, HO, and KR samples using FTIR
and TGA agrees within ~10% (Tables 2 and S1 for TGA and FTIR re-
sults, respectively). In the following, we consider the water content
estimated via TGA.
D. Di Genova et al. Chemical Geology 475 (2017) 76–86
79
5. Discussion
5.1. Effect of the chemical composition, water content, and iron nanolites
on Raman spectra
Fig. 2 shows the LW region of a selection of corrected (Eq. (2))
spectra from anhydrous glasses to highlight differences in Raman fea-
tures due to the composition. Spectra with a different SiO2 content were
vertically superimposed and listed by increasing iron content from the
bottom. The most striking difference between spectra can be observed
in the region around 1000 cm−1. The iron-poor rhyolite (RH) exhibits
the lowest signal to background ratio. At higher iron content, the signal
to background ratio substantially increases. Moreover, excluding the
iron-poor rhyolite (RH), all samples show a clear contribution at
~970 cm−1 in accordance with previous studies performed on iron-
bearing multicomponent systems (Di Genova et al., 2015; Di Muro
et al., 2009).
The region below 700 cm−1 (Fig. 2) is attributed to TeOeT angle
bending/rocking vibrations and tetrahedral OeSieO bending vibra-
tions (McMillan, 1984; Mysen et al., 1982). The broad band centred at
~500 cm−1 represents vibrational motions involving bridging oxygens
associated with a wide range of TeOeT environments. This region
originates from rings of tetrahedra connected in three-, four-, five-, six-
or higher-members (Bell et al., 1968; Galeener, 1982; McMillan, 1984;
Mysen et al., 1982; Poe et al., 2001; Seifert et al., 1982; Sharma et al.,
1981). The region between 800 and 1300 cm−1 results from TeO
stretching vibrations and provides information on the distribution of
bridging oxygens and therewith on the degree of polymerization of the
structure (Bell and Dean, 1972; Furukawa et al., 1981; McMillan, 1984;
Mysen, 2003). Moreover, recent studies (Di Genova et al., 2017; Di
Muro et al., 2009) drew the attention to the effect of iron redox state on
this region for anhydrous rhyolitic glasses. These studies reported direct
Table 2
Measured and estimated samples water content of nanolite-free samples (chemical composition in Table 1). For both spectrometers (M and R), the calculated HW/LW ratio, m coefficient
(Eq. (4)) and estimated water contents are reported.
Sample Composition H2O (wt%) M spectrometer R spectrometer
HW/LWa std. dev. H2Ob mc H2Od H2Oe HW/LWa std. dev. mc H2Od H2Oe
KR1 Fe+ basalt 0.6 ± 0.04 0.33 0.02 0.68 2.04 0.68 0.67 0.38 0.05 1.83 0.69 0.76
KR2g Fe+ basalt 2.44 ± 0.06 1.19 0.02 – 2.04 2.42 2.40 1.32 0.06 1.83 2.42 2.66
KR3f Fe+ basalt 4.67 ± 0.07 1.88 0.02 – – – – 1.43 0.09 – – –
HO1g Dacite 1.36 ± 0.06 0.97 0.04 – 1.40 1.36 1.12 0.95 0.04 1.44 1.36 1.08
HO2f Dacite 3.54 ± 0.06 2.79 0.11 – – – – 2.24 0.05 – – –
HO3f Dacite 4.58 ± 0.08 3.82 0.14 – – – – 3.10 0.11 – – –
RH1 Fe− rhyolite 1.97 ± 0.03 3.20 0.13 1.76 0.66 2.11 2.60 2.54 0.07 0.72 1.84 2.07
RH2 Fe− rhyolite 3.45 ± 0.06 5.04 0.12 3.21 0.66 3.32 4.09 4.67 0.05 0.72 3.38 3.79
RH3 Fe− rhyolite 4.53 ± 0.05 6.91 0.20 4.73 0.66 4.56 5.61 6.40 0.04 0.72 4.63 5.20
ETN1 Trachy-basalt 1.37 ± 0.05 0.88 0.02 1.28 1.78 1.57 1.43 0.88 0.02 1.63 1.44 1.42
ETN2g Trachy-basalt 2.61 ± 0.04 1.39 0.06 – 1.78 2.48 2.25 1.57 0.07 1.63 2.57 2.55
LAT1g Latite 1.59 ± 0.05 1.05 0.03 – 1.51 1.59 1.37 – – – – –
LAT2f Latite 2.69 ± 0.05 1.43 0.04 – – – – – – – – –
LAT3f Latite 3.76 ± 0.04 2.46 0.10 – – – – – – – – –
LAT4f Latite 6.32 ± 0.08 7.34 0.95 – – – – – – – – –
AMS1 Trachyte 1.29 ± 0.08 1.41 0.09 1.40 0.90 1.27 1.55 1.23 0.02 1.02 1.26 1.35
AMS2 Trachyte 2.57 ± 0.10 2.85 0.10 2.46 0.90 2.58 3.13 2.52 0.06 1.02 2.58 2.77
AMS3f Trachyte 4.78 ± 0.09 3.63 0.04 – – – – – – – – –
PS-GM0.5 Fe+ rhyolite 0.72 ± 0.04 0.74 0.02 0.77 1.32 0.98 1.06 0.62 0.03 1.38 0.85 0.88
PS-GM1 Fe+ rhyolite 1.16 ± 0.02 1.08 0.01 1.18 1.32 1.43 1.55 0.92 0.01 1.38 1.27 1.31
PS-GM2 Fe+ rhyolite 2.11 ± 0.05 1.70 0.02 1.96 1.32 2.24 2.43 1.48 0.02 1.38 2.03 2.11
PS-GM3.5g Fe+ rhyolite 3.55 ± 0.04 2.48 0.06 – 1.32 3.27 3.54 2.56 0.05 1.38 3.53 3.65
V_1631_1 Tephri-fonolite 1.17 ± 0.05 1.58 0.02 1.50 1.00 1.59 1.78 1.29 0.07 1.22 1.57 1.45
V_1631_2 Tephri-fonolite 3.07 ± 0.12 2.90 0.04 3.10 1.00 2.91 3.26 2.41 0.16 1.22 2.94 2.71
V_1631_3g Tephri-fonolite 3.32 ± 0.13 3.24 0.04 – 1.00 3.26 3.58 2.63 0.10 1.22 3.21 2.96
79AD1 Fe− phonolite 2.39 – – – – – – 2.11 0.07 0.96 2.03 1.85
79AD2 Fe− phonolite 4.41 – – – – – – 4.74 0.06 0.96 4.57 4.18
472AD1 Fe+ phonolite 2.36 – – – – – – 2.47 0.04 0.96 2.36 2.72
External sample Trachyte 5.57 6.08 0.18 5.58 0.90 5.49 6.42 – – – – –
a Calculated area ratio between the water (HW) and silicate (LW) bands.
b Calculated water content using the HW band area and Eq. (3).
c Calculated linear coefficient m (Eq. (4)).
d Calculated water content using the HW/LW ratio and Eq. (4).
e Calculated water content using the FeOtot. (wt%) and Eqs. (4) and (5).
f Nanolite-bearing samples.
g Sample alteration due to laser-induced heating (M spectrometer conditions, see Discussion section for details).
Fig. 2. Raman spectra (LW region) of anhydrous glasses corrected for the excitation line
and temperature (Eq. (2)) and normalised to the total area. Spectra were listed with in-
creasing iron content from the bottom. The signal-background ratio is extremely low for
the iron-poor rhyolite (RH). All the other spectra show higher signal-background ratio
and a contribution at ~970 cm−1 (Fe3+ band). Fe+ and Fe− in the legend indicate iron-
rich and iron-poor compositions in Table 1, while numbers show the SiO2 and iron
content in wt%, respectively.
D. Di Genova et al. Chemical Geology 475 (2017) 76–86
80
correlations between the intensity of the band at 970 cm−1 with the
Fe3+/Fetot. ratio of the glass. Di Genova et al. (2017) showed that such
a contribution 1) increased with the structure polymerization of the
anhydrous glass and 2) disappeared in Raman spectra of iron-free
rhyolites. Moreover, based on XANES spectra, Stabile et al. (2017)
suggested that the Fe3+ is four-fold coordinated in these systems. Ac-
cording to these recent results, Di Genova et al. (2017) named the
~970 cm−1 contribution of Raman spectra of rhyolites as “Fe3+ band”.
Here, looking at the hydrous iron-rich rhyolite spectra (Fe+ rhyo-
lite, PS-GM series, FeOtot. = 7.94 wt%, Fig. 3A), for which the Fe3+/
Fetot. ratio is known (Di Genova et al., 2013), we observe that the Fe3+
band increases with increasing Fe3+/Fetot. ratio from 0.36 (PS-GM0.5,
H2O = 0.72 wt%) to 0.56 (PS-GM3.5, H2O = 3.55 wt%). This agrees
with previous observations from anhydrous samples (Di Genova et al.,
2017; Di Muro et al., 2009).
Furthermore, with increasing water content, the spectral contribu-
tion at 1060 cm−1 clearly decreases. This band may be assigned to
symmetric stretching of tetrahedra with 3 bridging oxygens (Q3 species,
Mysen et al., 1980; Zotov and Keppler, 1998). Therefore, for these
samples, we suggest that the decrease of this band is directly related to
the increase of water content depolymerising the silicate structure
(Zotov and Keppler, 1998). This would explain the measured decrease
in viscosity (Di Genova et al., 2013) and glass transition temperature
(Di Genova et al., 2014a) upon hydration for these samples although
the oxidation state of the system increases with water and, therefore, an
opposite behaviour would be expected (Di Genova et al., 2017). How-
ever, in a multicomponent glass, the spectral contributions are not well-
resolved, and a systematic study based on spectra deconvolution is re-
quired to carefully address this aspect.
Raman spectra of the iron-poor rhyolite (Fe− rhyolite, RH series,
FeOtot. = 1.55 wt%, Fig. 3B) show a different behaviour. Spectra are
characterised by a high fluorescence which decreases with increasing
water content.
To obtain a clear picture of the combined effect of iron and water on
Raman features of glasses with different chemical compositions, a
comparison between samples with lower SiO2 content (~57 wt%) is
reported in Fig. 3C. Spectra from the phonolite (79AD) and trachyte
(AMS-B1) with iron respectively equal to 2.26 and 4.51 wt% were used.
As observed for the Fe− rhyolite (Fig. 3B), the iron-poor samples (Fe−
phonolite, 79AD series) display a background which changes with
water content. Conversely, the iron-rich spectra (trachyte, AMS-B1
series) are characterised by a constant background as showed before for
the iron Fe+ rhyolite (Fig. 3A). Therefore, the amount of iron and its
oxidation state (modulated by the water content) play a role in defining
the variability of the spectra fluorescence of volcanic glasses.
With increasing iron content (> 4.5 wt%) we observed a substantial
variation in the Raman features. In Fig. 4A, B the LW and HW regions of
dacitic spectra (HO series) are reported, respectively. With increasing
water content (≥3.54 wt%), the spectra show three main features: 1)
the lowering of the silicate (LW) region area, 2) the development of a
peak at ~690 cm−1 (Fig. 4A) and 3) no changes of the water band
(HW) with water content (Fig. 4B).
Analogous spectral signature was acknowledged by Di Muro et al.
(2006a, 2006b) and Di Genova et al. (in press) for dacitic, trachytic and
rhyolitic samples. Di Genova et al. (in press) experimentally demon-
strated that both the quench from liquidus condition and the heating
above the glass transition temperature induced the crystallisation of
iron oxides at the nanoscale. Here, the nanolite occurrence seems to be
related to the high element diffusivity due to the extremely low-visc-
osity of hydrous melts at liquidus condition. Indeed, excluding the re-
latively viscous iron-rich rhyolite (PS-GM, see Di Genova et al., 2013 for
viscosity measurements and comparisons with other compositions),
samples with FeOtot. and H2O respectively higher than ~4.5 and ~3 wt
% crystallised nanolites.
Importantly, nanolites were present in our samples before the
Raman measurements and were not detected during microprobe ana-
lyses. Therefore, we can exclude their occurrence induced by the laser
heating of glasses. To confirm that, we performed high resolution SEM
analyses on pristine samples that revealed the presence of whitish
particles at the nanoscale (Fig. S2). This is in line with results reported
by Di Genova et al. (in press). There, magnetic-hysteresis analyses were
performed on nanolite-bearing samples. Magnetite nanolites with dia-
meters between ~5 nm to ~30 nm were detected. Therefore, our
A
B
C
Fig. 3. A) Corrected Raman spectra (LW region) of iron-rich rhyolitic series (PS-GM,
SiO2 = 69.21 wt% and FeOtot. = 7.94 wt%). With increasing water content, the Fe3+
band increases due to the increase of Fe3+/Fetot. ratio measured by Di Genova et al.
(2013). Simultaneously, the band at 1060 cm−1 decreases possibly due to the structure
depolymerisation (see text for discussion). Numbers in the legend show the dissolved
water content in wt%. B) Corrected Raman spectra (LW region) of iron-poor rhyolitic
series (RH, SiO2 = 78.87 wt% and FeOtot. = 1.55 wt%). The spectra background de-
creases with increasing water content. Numbers in the legend show the dissolved water
content in wt%. C) Corrected Raman spectra (LW region) of samples with ~57 wt% SiO2
content. The iron-poor phonolite (79AD, FeOtot. = 2.26 wt%) shows a different spectra
background depending on the dissolved water content (as observed in Fig. 5B). The
trachyte, higher in FeOtot. content (4.51 wt%), shows a constant spectra background (as
observed in panel A for the Fe+ rhyolite). Numbers in the legend show the dissolved
water content in wt%.
D. Di Genova et al. Chemical Geology 475 (2017) 76–86
81
results demonstrate that Raman spectroscopy can also be used to show
the presence of iron-bearing nanolites in volcanic glasses. This will help
to reveal overlooked features in the glass matrix of experimental
syntheses and natural rocks. Moreover, the presence of nuclei at the
nanoscale can affect the onset of (heterogeneous) crystallisation of
other phases and degassing of volatiles during cooling and/or decom-
pression and, ultimately, the magma rheology (Di Genova et al. in
press). Based on the documented effect of the chemical composition,
oxygen fugacity and iron nanolites on Raman spectra, we reassessed the
current methodologies to estimate the water content dissolved in vol-
canic glasses.
5.2. Quantification of the dissolved water content
5.2.1. Water content versus HW band area
The effect of increasing H2O content on the HW band for nanolite-
free and nanolite-bearing samples is shown in Fig. 5A, B for both
spectrometers. For nanolite-free samples (green symbols), the HW area
band linearly increases with increasing water content. Therefore, for a
single spectrometer, a (set of) glass standard(s) is sufficient to estimate
the water content of unknown samples with different chemical com-
position. Importantly, spectra must be collected using the same ex-
perimental settings (see Materials and analytical methods section).
However, for some nanolite-free samples, a deviation from linearity
was observed (Fig. 5A, B). Looking at results from the M spectrometer
(blue symbols, bright-grey area in Fig. 5A), where spectra were ac-
quired in 6 min (LW+ HW region), the iron-rich samples (KR and ETN
basalts, FR latite and the PS-GM iron-rich rhyolite sample with
maximum water content) exhibit a HW area lower than expected.
Concerning the data from the R spectrometer (Fig. 5B), and except for
the iron-poor rhyolitic series (RH), all the nanolite-free samples deviate
from the linearity. The time necessary to acquire a single spectrum (LW
+ HW region) using the R spectrometer was 26 min, four times longer
than the time needed when using the M spectrometer. For these spectra,
we clearly observed a change in the LW region. In fact, when comparing
the spectra normalised to the region related to the TeOeT environment
(~500 cm−1), we observed that the spectra intensity increased at
~970 cm−1 (i.e. the Fe3+ band increased, Fig. S3). Furthermore, the
intensity increased with the acquisition time suggesting that the laser
heating induced the oxidation of the glass. We noted that this was
particularly relevant for alkali-rich composition such as the tephri-
phonolite (Fig. S3). We explain the observed decrease of the HW area
(Fig. 5A and B) with the degassing of dissolved H2O through iron oxi-
dation according to the following reaction: H2O + 2FeO = H2 + Fe2O3
(Burgisser and Scaillet, 2007; Humphreys et al., 2015).
In order to avoid the sample oxidation and provide the best ex-
perimental conditions to estimate the water content, we performed
A
B
Fig. 4. A) Corrected Raman spectra (LW region) of the dacitic series (HO,
FeOtot. = 5.02 wt%). With increasing water content, spectra show a peak at ~690 cm−1
due to the iron-bearing nanolites occurrence. Numbers in the legend show the dissolved
water content in wt%. B) Corrected Raman spectra (HW region) of the dacitic series (HO,
FeOtot. = 5.02 wt%). Nanolite-bearing samples, characterised by a different water con-
tent (H2O = 3.54 and 4.58 wt%), exhibit a similar band area. Numbers in the legend
show the dissolved water content in wt%.
A
B
Fig. 5. Area of the HW band for samples investigated in this study. Samples distribute in 3
different regions identified by sample colours: green symbols indicate nanolite-free
samples, blue symbols represent nanolite-free samples that experienced surface oxidation
during the analysis (light-grey area), while black symbols represent nanolite-bearing
samples (dark-grey area). A linear relationship between the sample water content and HW
area can be observed for nanolite-free samples which did not experience surface oxidation
due to the acquisition timescale (see text for a detailed discussion). All nanolite-bearing
samples deviate from the linear relationship. Band area has been divided by 105. A) Data
from M spectrometer, total acquisition time 6 min (3 min for the LW region and 3 min for
the HW region). The trachyte (* symbol, H2O = 5.57 wt% external sample in Table 2)
was used to validate the calibration. B) Data from R spectrometer (total acquisition time
26 min). Some samples were not measured with both spectrometers (see Table 2). (For
interpretation of the references to colour in this figure legend, the reader is referred to the
web version of this article.)
D. Di Genova et al. Chemical Geology 475 (2017) 76–86
82
several tests varying the laser power and/or the spectra acquisition
time. By combining the results obtained from both spectrometers, we
recommend a laser power of ≤5 mW on the sample surface and ac-
quisition time of 5 min maximum (using the 100× objective). For si-
lica-poor and iron-rich hydrous (nanolite-free) systems (basalt, latite
and, at lesser extent, Fe-rich rhyolite), the sample surface may oxidise
within 6 min using a laser power of 7.15 mW on the sample surface.
The sample oxidation results in the underestimation of the sample
water content using the calibration based on the HW band area (i.e.
external calibration). Increasing the acquisition time and laser power
(up to 26 min and 11 mW) this effect occurs regardless of the glass
composition with the exception of the iron-poor rhyolite (RH).
Nanolite-bearing samples differently deviate from the linear re-
lationship observed in Fig. 5A, B depending on the sample composition.
The HW area of nanolites-bearing samples is lower than expected for a
given water content (Fig. 4B). This might be related to a decrease in the
analysed hydrous glass volume due to the presence of nanolites. How-
ever, since the effect of nanolites on the water estimation using stan-
dard techniques is unknown, more studies are required to investigate
their effect on KFT, TGA and NIR analyses.
Therefore, the estimation of the sample water content based on the
comparison of the HW band area (Fig. 5A, B) must be used only for
nanolite-free samples where sample oxidation did not occur. For the M
spectrometer, we derived the linear relationship between the HW and
the water content as it follows:
= −H O wt HW( %) 1. 092·10 ·2 5 (3)
where HW represents the water band area of Long-corrected spectra
(Eq. (2)). It must be noted that this relationship is valid for the M
spectrometer using the experimental conditions reported in Section 2.4.
We recommend that laboratories develop a specific calibration applic-
able to their instrument, measuring condition and spectra treatment
procedure.
A spectrum from a trachyte with H2O = 5.57 wt% estimated by KFT
(“external sample” Table 2) was acquired. Using the Eq. (3), we esti-
mated the water content equal to 5.58 wt%. Therefore, over a large
interval of composition, a linear relationship between the HW band
area and the water content can be used to estimate the water content
provided that the sample surface does not oxidise during the spectra
acquisition.
5.2.2. Water content versus HW/LW band area ratio
For a given sample, the HW/LW band ratio depends on the proce-
dure employed for background subtraction in the LW region. Fig. 6A, B,
C show the background subtraction procedure following Le Losq et al.
(2012) and the protocol used in this study. Nanolite-bearing samples
were not compared as the intermediate Raman region (640–740 cm−1)
is heavily affected by the spectral signature of nanolites (Fig. 4A) and,
therefore, the background could not be assessed following the Le Losq
et al. (2012) model.
While for SiO2-rich systems (Fig. 6A, B) both procedures returned
similar results, for SiO2-poor systems (Fig. 6C) the baseline strategies
resulted in different spectra and, therefore, different LW areas for the
same sample. This agrees with that reported by Le Losq et al. (2012).
We estimated the dissolved water content of our glasses using the Le
Losq et al. (2012) model. The measured water contents versus model
predictions are given in Fig. 7. The RMSE of the model prediction is
0.68 wt% which decreases to 0.32 wt% if only samples with water
content below 2 wt% are considered. At H2O > 2 wt%, the model
overestimates and underestimate the measured data for iron-poor and
iron-rich samples, respectively.
These results suggest that the A coefficient from Eq. (1) depends on
the composition, especially on iron content. Our findings suggest that,
although removing some of the chemical dependence (from SiO2), the
background procedure proposed by Le Losq et al. (2012) does not
completely account for the composition of glasses with variable iron
content and oxidation state.
Fig. 8 shows the calculated A coefficient (Eq. (1)) for each nanolite-
free sample series starting from spectra acquired using the M spectro-
meter. Iron-poor glasses (FeOtot. < 4.5 wt%) exhibit a value slightly
lower than the one provided by Le Losq et al. (2012). However, with
increasing iron content, the coefficient increases up to 1.6 ∗ ⋅ 10−2
(iron-rich rhyolite PS-GM, FeOtot. = 9.55 wt%). We interpret the in-
crease of A with iron content as the result of the effect of iron, and its
oxidation state, on Raman spectra which is known to be particularly
important in SiO2-rich systems (Di Genova et al., 2016a).
Since the modulation of the baseline based on the SiO2 content
cannot remove completely the chemical dependence of the water esti-
mation procedure, we adopted a single criterion for the baseline
A
B
C
Fig. 6. Corrected Raman spectra of A) Fe− rhyolite (RH3), B) Fe+ rhyolite (PS-GM3) and
C) Fe+ basalt (KR2). Blue and red dashed lines represent the baseline according to the
procedure reported in Le Losq et al. (2012) and this study, respectively. Baselines sub-
stantially diverge for SiO2-poor samples resulting in a different LW band area. (For in-
terpretation of the references to colour in this figure legend, the reader is referred to the
web version of this article.)
D. Di Genova et al. Chemical Geology 475 (2017) 76–86
83
assessment regardless of the sample composition. A cubic spline was
applied between 50–200 and 1240–1500 cm−1 for the LW region and
between 2750–3100 and 3750–3900 cm−1 for the HW region. With this
protocol, the nanolite-bearing glasses could be included in the analysis
as the wavelength intervals chosen to assign the baseline in the LW
region are not affected by their spectral contribution (Fig. 4A). Fig. 9A
shows the water content as a function of the HW/LW area ratio using
the M spectrometer. For each sample series, a linear relationship pas-
sing through the origin (Eq. (4)) was adopted for nanolite-free samples
(green symbols, Fig. 9A):
=H O wt HW
LW
m( %) ·2 (4)
where m is the linear fit coefficient (Table 2). Samples that experienced
surface oxidation (Fig. 5A, B) during measurements using both spec-
trometers follow the observed relationship meaning that the internal
calibration removes, or minimises, such an effect. Conversely, the na-
nolite-bearing samples (black symbols in Fig. 9A) deviate from linear
trends. However, for the dacitic series (HO) we observed the lowest
deviation, while the latitic series highly deviates from the linearity.
Therefore, these samples were excluded from the calculation of the fit
coefficient (m, Eq. (4)). The sample water content was estimated using
the Eq. (4) and parameters in Table 2 with a RMSE of 0.17 and 0.15 wt
% for the M and R spectrometer, respectively (Fig. 9B).
The m coefficients reported in Table 2 can be used to retrieve the
water content from Raman analyses whenever the chemical composi-
tion of the investigated sample is close to one of the samples used in this
work.
We also investigated the possible chemical dependence of the fit
parameter (m, Eq. (4)) in order to generalize our results. The Fig. 9A
reveals that the fit parameter (m, Eq. (4)) shows a simple linear re-
lationship with the iron content. A linear trend was observed for all the
samples and both spectrometers (Fig. 10) suggesting that instrumental
effects are removed (or minimised) with this procedure. We para-
meterised the iron dependence of the m parameter in Eq. (4) as it fol-
lows:
= ∗ +m 0.096 FeO 0.663 (5)
where the FeO is the iron content in wt%. We estimated the water
content of nanolite-free samples with a RMSE of 0.47 wt% for samples
investigated with the M spectrometer and 0.29 wt% for the R spectro-
meter. This procedure is intended to be used when samples have dif-
ferent iron content from the standard and a different m coefficient is
Fig. 7. Calculated (after Le Losq et al., 2012) versus measured water content. Numbers
show the sample FeOtot. content in wt%. The model overestimates and underestimates the
water content of iron-poor and iron-rich samples, respectively. All samples are nanolite-
free.
A
Fig. 8. Calculated A coefficient (Eq. (1)) as a function of the sample iron content. The
dashed line represents the value provided by the authors (7.609 ∗ ⋅ 10−3), considered
independent of sample composition. Our results show that the coefficient increases with
increasing iron content of the sample.
A
B
Fig. 9. A) Data from M spectrometer. Sample water content versus the HW/LW band area
ratio calculated following the baseline protocol reported in this study. For each sample
series, a linear relationship is observed when nanolite-free samples (green symbols) are
considered. Nanolite-bearing samples (black symbols) do not follow such a trend. B) Data
from both spectrometers. Comparison between measured and calculated water contents
using Eq. (4) and data from Table 2. (For interpretation of the references to colour in this
figure legend, the reader is referred to the web version of this article.)
D. Di Genova et al. Chemical Geology 475 (2017) 76–86
84
expected. Moreover, in case the sample chemistry is unknown (i.e.
small melt inclusions or in situ investigations), one may approximate
the composition using the Raman spectrum (Di Genova et al., 2015,
2016b).
6. Conclusions
Here, we have shown how iron content, its oxidation state, and iron
nanolites greatly affect Raman spectra of hydrous multicomponent
glasses. Samples with composition spanning from basalt to rhyolite,
including alkali- and iron-rich compositions, were used to explore and
test procedures to estimate the water content of glasses through Raman
spectroscopy. We recommend the following:
1) The sole water band area (HW, Fig. 5A, B) can be only used with
nanolite-free samples regardless of the iron oxidation state of the
glass. Sample and standard spectra must be acquired at the same
depth (possibly at ~6 μm), instrumental conditions and using the
same spectrometer. Sample oxidation during spectra acquisition can
easily occur, especially when analysing iron- and alkali-rich sam-
ples. We found that 6 min of acquisition time and a laser power
~7 mW oxidised basaltic, dacitic, latitic, and iron-rich rhyolitic
compositions, while ~5 mW are enough to alter alkali-rich (i.e.
phonolite) sample. Increasing the acquisition time to ~26 min
brings forth the oxidation for all samples excluding the calcalkaline
rhyolite. Since the glass oxidation depends on the sample compo-
sition, acquisition time, and employed experimental settings (i.e.
objective and laser power), we suggest repeating Raman measure-
ments on the same spot and look at the evolution, if any, of the peak
at ~970 cm−1 (Fe3+ band) which increases with increasing the
Fe3+ content of the glass.
2) The water to silicate band ratio (HW/LW, Fig. 9A) linearly corre-
lates with the water content of the glass. This procedure works when
using nanolite-free samples and, importantly, is not affected by
sample oxidation. Some nanolite-bearing samples (e.g. dacite) also
follow a quasi linear trend. This procedure reduces instrumental
effects. However, the slope (m, Eq. (4)) of the HW/LW ratio versus
H2O content depends on the iron content. For both spectrometers,
we observed a linear relationship between the m coefficient and the
iron content. Therefore, following the spectra treatment presented
in this study, a unique calibration based on the iron content of the
sample can be used to estimate the water content.
Acknowledgments
This study was supported by the NSFGEO-NERC “Quantifying dis-
equilibrium processes in basaltic volcanism” (reference: NE/N018567/
1). S. Sicola was supported by a MIUR fellowship to Roma Tre
University PhD School. L. Spina wish to thank the ERC Consolidator
“CHRONOS” project (reference: 612776). We thank M. Kaliwoda, R.
Hochleitner, and A. Pisello for Raman measurements at the
Mineralogical State Collection Munich (SNSB), L. Melekhova for some
EMPA analyses in Bristol, S. Lo Mastro and D. De Felicis for SEM ana-
lysis at LIME laboratory (Roma Tre University), and R. Cioni for pro-
viding Vesuvius hydrated glass samples (79AD and 472AD). We also
thank two anonymous reviewers for their contribution to the peer re-
view of this work and helpful comments that helped us to improve this
manuscript.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.chemgeo.2017.10.035.
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