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Multivariate analysis of Ion Beam Induced Luminescence spectra of irradiatedsilver ion-exchanged silicate glasses

Gabrio Valotto a,⇑, Alberto Quaranta b, Elti Cattaruzza c, Francesco Gonella d, Giancarlo Rampazzo a

a Department of Environmental Science, Informatics and Statistics, Università Ca’ Foscari Venezia, Dorsoduro 2137, I-30123 Venezia, Italyb Department of Materials Engineering and Industrial Technologies, University of Trento, via Mesiano 77, I-38123 Povo, Trento, Italyc Department of Molecular Sciences and Nanosystems, Università Ca’ Foscari Venezia, via Torino 155/b, I-30172 Venezia-Mestre, Italyd Department of Molecular Sciences and Nanosystems, Università Ca’ Foscari Venezia, Dorsoduro 2137, I-30123 Venezia, Italy

a r t i c l e i n f o

Article history:Received 12 October 2011Received in revised form 22 March 2012Accepted 8 April 2012Available online 23 April 2012

Keywords:Ion Beam Induced LuminescenceMultivariate analysisGlassIon-exchange

a b s t r a c t

A multivariate analysis is used for the identification of the spectral features in Ion Beam Induced Lumi-nescence (IBIL) spectra of soda-lime silicate glasses doped with silver by Ag+–Na+ ion exchange. BothPrincipal Component Analysis and multivariate analysis were used to characterize time-evolving IBILspectra of Ag-doped glasses, by means of the identification of the number and of the wavelength posi-tions of the main luminescent features and the study of their evolution during irradiation. This methodhelps to identify the spectral features of the samples spectra, even when partially overlapped or lessintense. This analysis procedure does not require additional input such as the number of peaks.

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Introduction

Ion irradiation is extensively used for promoting the growth ofnanostructures in silica glass matrices [1]. In particular, ion irradi-ation of either silver or copper ion-exchanged glasses gives rise tothe growth of metal nanoclusters whose dimension and distribu-tion depends on irradiation parameters such as ion species, energyand electronic energy loss [2,3].

Ion-exchanged glasses are prepared by immersing soda-limeglass slides in molten salt baths at temperatures lower than theglass Tg [4–6]. Owing to the difference between the glass and thebath electrochemical potential, at the glass surface an exchange oc-curs between the salt metal ions and the glass sodium ones. There-after, the incoming ions diffuse in the glass network giving rise todiffusion profiles that modify both the mechanical and the opticalproperties of the glass doped layer. Ion irradiation of Ag+–Na+ ion-exchanged glasses promotes the formation of metal nanoclusterswhich can enhance the non-linear optical properties of the treatedlayer. In spite of the amount of work in this field, a detaileddescription of the kinetics underlying the aggregation process isstill lacking, also due to the difficulty to follow the aggregation pro-cess during irradiation. Indeed, a complete phenomenologicaldescription of the nucleation and growth of metal clusters is verycomplex, mainly because three classes of phenomena take placein giving the final system: diffusion, nucleation and aggregategrowth. These processes have different, somewhat conflicting re-

gimes, thus giving rise to dynamic feedbacks that may stop or en-hance any of them upon small changes in any of the involvedvariables (local silver concentration, temperature, glass composi-tion, local stoichiometry, ion bombardment fluence, and so on).New approaches for studying these mechanisms appear thereforenecessary, since an exhaustive and consistent description of diffu-sion and aggregation phenomena is still far from being assessed.

In the case of silver exchanged glasses, luminescence features ofAg+ arising from the transitions between the 4d10 ? 4d95s levels,have been extensively studied in order to characterize the differention environments in the glass matrix [7–9]. For this reason, IonBeam Induced Luminescence (IBIL) should be a suitable techniqueto follow the evolution of silver states during ion irradiation.

IBIL has been used for the identification of impurities and de-fects in several matrices [10–12]. Quite often, luminescence re-sponse of the samples undergoes important modifications due tothe radiation-induced damaging, which gives rise to a decrease ofthe luminescence intensity during the process, as well as to the for-mation of new emitting centers, like irradiation induced defects.The study of the evolution of the luminescence features with theion irradiation fluence can provide information about the lumines-cent centers, which are characterized by different decrease rates, aswell as about the damage mechanisms of the material [13–17]. Upto now, IBIL measurements have been never performed on Ag+–Na+

ion-exchanged glasses.In addition to the scientific information available by the use of

the IBIL technique, recently Valotto et al. [18] developed a newapproach for the analysis of time-evolving IBIL spectra, based on

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⇑ Corresponding author. Tel.: +39 0412348595; fax: +39 0412348639.E-mail address: valotto@unive.it (G. Valotto).

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normalization procedures and multivariate analysis, able to extractadditional information from the recorded spectra. The method isbased on the study of a matrix of normalized IBIL spectra collectedat fixed time intervals during irradiation. The analysis of the maineigenvectors (EVTs) of the covariance matrix as a function of thewavelength allowed to determine the spectral components forthe observed spectra without assuming neither previous numbernor position of the peaks.

In this paper, the above mentioned method along with ordinaryPrincipal Components Analysis (PCA) are used to study evolvingIBIL spectra of silver ion-exchanged silicate glasses. In particular,we show how this method can identify features which are not wellresolved in the luminescence spectra and, moreover, how the anal-ysis of the behavior of the EVTs can be used as an additional toolfor the discrimination of the luminescence peaks and for the studyof their relative importance in the original spectra. This method isused to identify the IBIL spectral features that are suitable for theanalysis of the evolution of silver oxidation and aggregation statesin glass during irradiation.

Materials and experimental methods

Ag+–Na+ ion-exchanged samples were realized by immersingcommercial soda-lime glass (SLG) in a molten salt bath of AgNO3:-NaNO3, with silver nitrate molar concentration of 1% [19]. The bathtemperature was varied to obtain five different samples, rangingfrom 320 to 400 �C (samples labeled as Ag320, Ag340, Ag360,Ag380, Ag400). Process duration was 1 h. The atomic% compositionfor SLG is 59.6 O, 23.9 Si, 10.1 Na, 2.6 Mg, 2.4 Ca, 0.7 Al, 0.5 K, 0.2 S,plus traces. During the ion exchange process, Ag ions penetrate forseveral microns in the material. The different treatment tempera-tures give rise to penetration depths increasing with the tempera-ture owing to the increase of the diffusion coefficients. On theother hand, for all the samples the surface concentration is con-stant and it is determined by the AgNO3 molar fraction in the bath.

IBIL measurements were performed at the AN2000 Van de Graafaccelerator of the INFN Laboratori Nazionali di Legnaro, Italy. IBILspectra were collected with a FL QE65000 Ocean Optic spectrome-ter connected to the vacuum chamber with a UV grade silica fiber(600 lm diameter). Into the chamber, another silica fiber, con-nected to the former by means of a vacuum connector, is placedin front of the sample. The spectrometer software allows to collectan array of full spectra in the range between 200 and 1000 nm withintegration times ranging from 10 ms to 15,000 s. Samples wereirradiated with a 2.0 MeV H+ beam (40 nA with a spot of1 � 1 mm2). The collected charge was measured with a Faradaycup. Each sample was analyzed by collecting 200 spectra, eachwith an integration time of 3 s, for a total irradiation time 600 s.IBIL spectra were collected also by an untreated glass slide (SLG).

Multivariate analysis method

The statistical analysis method described by Valotto et al. [18]works with spectra evolving with the time and it is based on theanalysis of the EVTs plots as a function of the wavelength. In par-ticular, it allows to identify the wavelength position of lumines-cence peaks whose intensity evolves with the irradiation time atdifferent rates. With this method, less intense or significantly over-lapped peaks – the identification of which is quite unreliable fromthe sole shape of the acquired spectra – can be discriminated. Inshort, the method comprises the following steps:1. Experimental spectra are collected in a matrix hereafter indi-

cated as ‘‘A’’ (Eq. (1)) in which each row reports the intensityvalues over the spectral wavelength range and the columnsare formed by the intensity values taken for different irradiationtimes (i.e. for different fluence values).

2. The intensities of the experimental spectra matrix A are nor-malized by imposing the mean and standard deviation valuesfor each column equal to 0 and 1, respectively, thus obtainingthe new matrix B. In Eq. (2) the normalization procedure per-formed to obtain matrix B is shown (is the mean value of the200 nm column intensities, rI200nm is their standard deviation).

3. The EVTs of the covariance matrix of matrix B characterized bythe highest eigenvalues (EVLs) are plotted as a function of thewavelength: their trend is studied in order to identify the peaksof the IBIL spectrum.

EVTs plots as a function of the wavelength are characterized byplateaus in the regions dominated by a single peak or by peakswith the same decreasing trend. When two peaks with differentdecreasing rate slightly overlap, the presence of two plateaus con-nected by a curve can be observed. In regions where two peaks aresignificantly overlapped the EVTs curve exhibits a sharp peak or adeep valley. Finally, in peak-less regions the EVTs are characterizedby a noisy pattern without significant features.

In the present work, matrix A is composed of six sub-matrices AK

for the respective analyzed samples (with K = SLG, Ag320, Ag340,Ag360, Ag380, Ag400), as shown in Eq. (3) where the intensities Ifi,k,for each sub-matrix are normalized in the range from 0 to 1 (fi is thefluence or time value and k is the wavelength, IminAK , ImaxAK are theminimum and maximum intensity values of matrix AK, respec-tively). This normalization makes comparable the intensities of eachsub-matrix, so preventing the possibility that larger values frommore luminescent samples overcome in the computation of thecovariance matrix and then in the corresponding EVTs. Each sub-matrix AK has dimension 200 � 1044 (number of spectra �wave-lengths), and the whole matrix A⁄ has dimension 1200 � 1044.

A ¼If 1;200nm � � � If 1;1000nm

� � � � � � � � �Ifn;200nm � � � Ifn;1000nm

264

375 ð1Þ

B ¼

If 1;200nm��I200nm

rI200nm� � � If 1;1000nm��I1000nm

rI1000nm

� � � � � � � � �Ifn;200nm��I200nm

rI200nm� � � Ifn;1000nm��I1000nm

rI1000nm

2664

3775 ð2Þ

A� ¼

ASLG

AAg320

AAg340

AAg360

AAg380

AAg400

2666666664

3777777775

AK ¼

If 1;200nm�IminAKImaxAK

�IminAK� � � If 1;1000nm�IminAK

ImaxAK�IminAK

� � � � � � � � �Ifn;200nm�IminAKImaxAK

�IminAK� � � Ifn;1000nm�IminAK

ImaxAK�IminAK

2664

3775 ð3Þ

Some of the normalized spectra of matrix A⁄ and B are shown inFigs. 1 and 2, respectively. After to the normalization procedures,spectra from matrix A⁄ obviously resemble IBIL experimental spec-tra, while spectra from matrix B are actually quite different be-cause the constraints on the mean and on the standard deviationgive rise to different relative heights of the main peaks and to neg-ative values for the intensity.

PCA is a multivariate statistical analysis that is widely used toexamine spectra detected with different techniques [20–27] be-cause it allows to reduce the dimensionality of a data set. Actually,by linearly combining the original variables one can obtain a set ofnew variables (named principal components, PCs), equal innumber to the previous ones but for which only a reduced numberof them are able to describe the main characteristics of the startingdata set.

Briefly, the PCs have the form in Eq. (4), where ‘‘ki‘‘ are the ori-ginal variables and ‘‘n’’ is the number of variables. The coefficients

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values (ci) are the values of EVTs (that are orthogonal by definition)of the covariance matrix of experimental matrix suitably normal-ized: this means that the new variables, the PCs, are not correlatedunlike the original ones.

PCi ¼Xn

1!1

Ciki ð4Þ

The ‘‘amount of information’’ of each PC (usually named ex-plained variance) is proportional to the value of the EVL of the cor-responding EVT, and is also complementary to that of the othersPCs: there are PCs more ‘‘explanatory’’ than others, and usuallythe first three components (PC1, PC2 and PC3) are sufficient forobtaining an almost complete description and discrimination ofthe samples, whit an acceptable loss of information.

The PCA method continues by the analysis of score plots, thatare the representation of the samples in the vectorial space ofthe selected PCs. The coordinates of each sample are obtained byusing Eq. (4) with the experimental values of ki, i.e. multiplyingthe experimental matrix by the matrix made by the selected EVTs

(those having the highest EVLs). This operation allows to discrim-inate the samples whose are distributed in different regions of thescore plot (for further information see Ref. [28]).

In the present work, PCA was used to analyze matrix ‘‘B’’ since itis already normalized as required by PCA algorithms. The statisticalsamples and variables are IBIL spectra and wavelength intensities,respectively. The analysis of EVTs shape is combined with PCAscore plots for better discriminating the luminescence features ofIBIL spectra, for evaluating the contribution of the different lumi-nescence peaks and for finding correlations among probedsamples.

Results and discussion

Silver ions embedded in silicate glasses give rise to differentluminescence features related to electronic transitions betweenthe 4d10 ground state and levels of the 4d95s1, whose positionstrongly depends on the ion concentration [8,29–32]. Specifically,

Fig. 1. IBIL spectra representative of intensity decrease of sample SLG, Ag320, Ag340, Ag360, Ag380, Ag400 as a function of irradiation time. The intensity values arenormalized by imposing the maximum and minimum values for each equal to 1 and 0, respectively. These spectra are extracted from matrix A⁄.

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the observed peaks are: a UV band, at about 330 nm, from isolatedAg+ at low concentrations; a blue band at about 450 nm due to Ag+

in higher concentrated systems; a green band at about 530 nmwhose attribution (even still under debate) can be likely ascribedto Ag+–Ag+ pairs [29,33]; an orange band at about 600–650 nm as-cribed to a stable emitting center (Ag3)2+, formed during silverreduction [32].

By observing Figs. 1 and 3, that compare the normalized IBILspectra collected during the first time interval for SLG and ion-ex-changed samples, one can note that SLG exhibits a UV peak at330 nm, a more intense component at 400 nm and a peak at510 nm. Moreover, at higher wavelengths a less defined broad fea-ture can be observed. In Ag320, only the first two spectra are char-acterized by the more intense component at 400 nm, while, byincreasing the irradiation time, the component at 510 nm becomesthe dominant one, along with a shoulder at about 450 nm. Byincreasing the exchange temperature, the band at 510 nm becomesincreasingly dominant, as observed for Ag340 and Ag360. All spec-tra of Ag380 are characterized by a dominant component at510 nm, and for Ag400 the contribution at 400 nm is almost disap-peared. Finally, the spectra of all Ag-doped samples exhibit a peak

at 330 nm whose relative intensity decreases by increasing theexchange temperature.

Fig. 2. IBIL intensity spectra of sample SLG, Ag320, Ag340, Ag360, Ag380, Ag400 as a function of irradiation time. These spectra are extracted from matrix B obtained frommatrix A⁄ properly normalized by imposing the mean and standard deviation values of each column equal to 1 and 0, respectively.

Fig. 3. Normalized IBIL spectra of SLG and Ag ion-exchanged samples.

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The luminescence mechanism under ion irradiation is quitedifferent from those for ordinary photoluminescence. Impingingions produce scattered electrons and holes which radiativelyrecombine at the emitting centers. For this reason, the yield ofthe different centers can be different from that observed in photo-luminescence measurements, since it depends on both trappingand radiative recombination probabilities. Moreover, the range ofprotons in glass is more than 40 lm, which is a depth higher thanthe thickness of the ion-exchanged glass layer. For these reasons,from the sole analysis of the IBIL spectra it is not trivial to discrim-inate the contribution of the silver ions from that of the glassmatrix.

A deeper investigation of the spectral features from IBIL can beobtained by examining the first three EVTs, showed in Fig. 4 and inthe respective inset. In addition to the luminescent peaks identifiedby simple spectra observation, that are responsible for the maindifferences among samples spectra, by using the method describedin Valotto et al. [18], four further peaks were identified. In Fig. 4 thethree EVTs, corresponding to the highest EVLs (EVL1 = 755.5,EVL2 = 224.2, EVL3 = 47.5 and EVL4 = 3.4) of the covariance matrixof matrix A are shown. In the same figure, the band positions, asderived from the analysis of the EVT plots, are marked by verticaldotted lines.

By observing the shape of the EVT plots, the UV peak at 330 nm,which is well evident in all the IBIL spectra, does not correspond toa well defined plateau in any of the three EVTs. On the other hand,a peak at 280 nm of EVT3 and a change of slope in the other twoEVTs indicate that the observed IBIL peak at 330 is detected butoverlapped with another feature located at lower wavelengths.The position of this feature can be tentatively located around230 nm or less, since a plateau occurs in all the EVTs in this region.

The third band at about 400 nm, which is the most intense peakin the IBIL spectra of samples SLG, Ag320 and Ag340, correspondsto a plateau of EVT2 and to the change of slopes in EVT1 and EVT3.The presence of the fourth band at about 450 nm, which is respon-sible for the photoluminescence peak in highly concentrated Agdoped samples, is evidenced by the valley of EVT3 and by the slopeof EVT1 and EVT2. The fifth IBIL peak at about 510 nm, which iswell evident in all IBIL spectra, corresponds to the little plateauspresent in all the EVTs. Finally, the presence of a sixth broad bandat about 680 nm is indicated by the little valley of EVT1 evidencedin the inset of Fig. 4, while the seventh at about 820 nm is evi-denced by the plateaus of all three EVTs. This analysis underlineshow, in order to correctly interpret an evolving spectrum, the

features of all EVTs have to be taken into account, comparing thenumber and position of the identified components of each one,especially in cases were different broad and highly overlappingcomponents are present. It is worth remarking that our purposehere is not that of proposing a different fitting method but a newapproach useful to find the right number of components (and theirposition) that must be used to fit the recorded spectrum.

In order to discriminate the spectral features of the ion-exchanged samples from that of the glass network and to analyzethe relative intensity decrease of the different IBIL peaks, the PCAscore plots obtained from the EVTs were also analyzed. Figs. 5 and6 show the score plots of the components PC1 vs. PC2 and PC1 vs.PC3, respectively. These first three PCs allow to well characterizethe samples and their spectra evolution since the respective ex-plained variance are 72.4%, 21.5% and 4.6% (the cumulative ex-plained variance of all other PCs is only 1.5%). In both score plots,the spectra of the different samples are represented in well sepa-rated groups (the solid arrow indicates the ion-exchange tempera-ture increase), where each group exhibits an evident evolutionwith the irradiation time (the dotted arrows indicate the ion-flu-ence increase). Indeed, the distribution of the spots in Figs. 5 and6 can be related to the EVTs features and to the role of the lumines-cence band in the different samples.

The score plot study is not straightforward since the coordi-nates of each point (PC1, PC2 for Fig. 5 and PC1, PC3 for Fig. 6)are calculated by multiplying rows of matrix ‘‘B’’ by vector columns

Fig. 4. Eigenvector shape for the covariance matrix of matrix B characterized by thehighest eigenvalues. The inset show a zoom of eigenvector 1 in the range between550 and 850 nm.

Fig. 5. PC1 vs. PC2 score plots of IBIL spectra. The explained variance is 72.4% vs.21.5%, respectively. The solid arrow indicates the increasing temperature ofsamples preparation, the dotted arrow highlights the spectra temporal evolution.

Fig. 6. PC1 vs. PC3 score plots of doped glass IBIL spectra. The explained variance is72.4% vs. 4.6%, respectively. The solid arrow indicates the increasing temperature ofsamples preparation, the dotted arrows highlight the spectra temporal evolution.

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EVT1, EVT2 and EVT1, EVT3, respectively. Actually, to ourknowledge, no attempt has been reported in literature to find acorrelation between the spectral features and the PC distributionin the score plots.

As can be observed in Figs. 5 and 6, by increasing the ion-ex-change temperature the groups of the treated samples shift to low-er PC1 values. This behavior can be explained by observing EVT1,which exhibits low positive values in the 280–400 nm range,while, for wavelengths lower than 270 nm and higher than430 nm, it exhibits a significant negative contribution. In fact, theprogressive increase of the relative intensity of the 510 nm compo-nent in the IBIL spectra from SLG to Ag400 samples of Fig. 2, givesrise to an increasing negative contribution in the PC1 coordinate.Furthermore, for increasing irradiation times, the PC1 coordinatefor each sample increases because the dominant negative contribu-tion for wavelengths higher than 430 decreases. This behavior canbe related to the contribution to the spectra of Ag+ and Ag+–Ag+

pairs features, whose relative intensity increases with the treat-ment temperature.

Since the EVT2 has higher positive values in the whole 260–675 nm range, the decreasing value with irradiation time of thePC2 coordinate for each sample in Fig. 5 can be related to the cor-responding decrease of the whole spectra intensity, as shown inFigs. 1 and 2.

In Fig. 6, it is worth noting that this PC3 marks a strong differ-ence between pure SLG and all the Ag-doped samples, since spectratime evolution have opposite direction, as evidenced by the twodotted arrows. This effect can be explained taking into account thatEVT3 takes positive values in the 200–375 nm range, negative val-ues within 375–675 nm and it is negligible for wavelengths higherthan 675 nm. Hence, the distribution in the PC1–PC3 score plot isdue to the opposite evolution of the relative contributions in thetwo wavelength intervals for the two types of samples, i.e., to ahigher value of the Visible/UV ratio in ion-exchanged samples.

Moreover, the different behavior under irradiation is related tothe different origin of the luminescence features in the two sys-tems. In SLG the UV part of the spectra is due to defects of the silicanetwork, while the visible part, whose origin is still unclear, ismore likely related to impurities. Ion irradiation induces a rapidquenching of the defects and a slow decrease of the impurity emis-sion, owing to the low concentration of the latter. On the otherhand, in ion-exchanged samples the UV emission peak comes fromAg+ in low concentration regions, namely, in the inner part of thediffusion profile, while the visible features are characteristic of sil-ver ions in highly concentrated regions, that is, in the near surfaceglass layers. The energy released by the ion beam gives rise to thereduction of Ag+ and to the formation of metal nanoparticles: theaggregation process takes place due to the so called radiation-en-hanced diffusion [1,34], i.e., a diffusion process without needinghigh local temperature, being favored by the dynamic formationof defects in the structure of the host irradiated matrix. This pro-cess occurs at a higher rate in the exchanged regions where thesilver concentration is higher, so explaining the increase with thefluence of the UV/Visible intensity ratio.

The presented preliminary results evidenced how themultiparametric approach developed for the analysis of IBIL datacan be useful for the fast discrimination of the behavior of differentsamples characterized by broad overlapping luminescence fea-tures. Moreover, it has been shown how a combined analysis ofIBIL spectra, EVTs shape and PCA, allow to obtain a exhaustive eval-uation of the spectral features and of their behavior under ionirradiation even if the contributions from the glass matrix are dif-ficult to be separated from those of Ag-related emitting centers. Inparticular, the evolution under irradiation of the Visible/UV ratio ofspectral features is actually related to the kinetic processes leadingto the reduction and aggregation of silver in glass during ion

irradiation. Further study is in progress to correlate the evolutionof the IBIL features with both the beam fluence and the clusterssize.

Conclusions

In this work, the evolution of IBIL spectra of Ag+–Na+ ion-ex-changed glasses was studied by means of combined PCA and mul-tivariate analysis in order to outline the spectral features suitablefor deepening the study of the kinetic processes leading to theAg+ reduction and aggregation into metal nanoclusters during irra-diation. In particular, PCA and multivariate analysis was used tocharacterize time-evolving IBIL spectra of Ag-doped soda-lime sil-icate glasses, by identifying the number and wavelength positionsof luminescent features evolving during irradiation at differentrates, and by investigating the main spectra differences amongthe probed samples. By detecting partially overlapped as well asless intense components, the method helps to identify the lumines-cent contributions that characterize the spectra of different probedsamples. Moreover, this analysis procedure does not requireadditional inputs such as the number of peaks.

Following this method, it was possible to discriminate thebehavior of the untreated glass from that of the doped samples,thus distinguishing the spectral features due to the sole Ag behav-ior. In fact, in ion-exchanged glasses the luminescence features re-lated to highly doped regions decreases with a higher rate withrespect to UV peaks coming from layers with the lower Ag+ con-centration. This result, obtained by investigating both EVT andscore plots, underline how the multiparametric analysis allows toseparate the spectral contributions of the glass matrix from thesilver doped regions. Further work is needed for exploiting allthe capabilities of this approach.

The evaluation of the spectral contribution as a function of bothtemperature treatment and irradiation time allowed to evidencethat silver doping gives rise to a contribution in the visible partof the spectra whose relative intensity as well as degradation rateincrease with the temperature. The analysis of this feature can beused to study the kinetic processes underlying reduction andaggregation of silver during irradiation.

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