The Charge-Balancing Role of Calcium and Alkali Ions in Per-Alkaline

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The Charge-Balancing Role of Calcium and Alkali Ions in Per-Alkaline Aluminosilicate Glasses René Mossing Thomsen, Jørgen Skibsted, and Yuanzheng Yue J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.7b12437 • Publication Date (Web): 07 Mar 2018 Downloaded from http://pubs.acs.org on March 14, 2018

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The Journal of Physical Chemistry

The Charge-Balancing Role of Calcium and Alkali Ions in Per-Alkaline Aluminosilicate Glasses René M. Thomsen†,‡, Jørgen Skibsted‡, Yuanzheng Yue†,* †

Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark

‡

Department of Chemistry and Interdisciplinary Nanoscience Center (iNANO), Aarhus University, 8000 Aarhus C, Denmark

ABSTRACT: The structural arrangement of alkali-modified calcium aluminosilicate glasses has implications for important properties of these glasses in a wide range of industrial applications. The roles of sodium and potassium and their competition with calcium as network modifiers in peralkaline aluminosilicate glasses have been investigated by 27Al and 29 Si MAS NMR spectroscopy. The 29Si MAS NMR spectra are simulated using two models for distributing Al in the silicate glass network. One model assumes a hierarchical, quasi-heterogeneous aluminosilicate network whereas the other is based on differences in relative lattice energies between Si–O–Si, Al–O–Al, and Si–O–Al linkages. A systematic divergence between these simulations and the experimental 29Si NMR spectra is observed as a function of the sodium content exceeding that required for stoichiometric charge-balancing of the negatively charged AlO4 tetrahedra. Similar correlations between simulations and experimental 29Si NMR spectra cannot be made for the excess calcium content. Moreover, systematic variations in the 27Al isotropic chemical shifts and the second-order quadrupole effect parameters, derived from the 27Al MAS NMR spectra, are reported as a function of the SiO2 content. These observations strongly suggest that alkali ions preferentially charge-balance AlO43- as compared to alkaline earth (calcium) ions. In contrast, calcium dominates over the alkali ions in the formation of non-bridging oxygens associated with the SiO4 tetrahedra.

INTRODUCTION Aluminosilicate glasses are used in a wide range of industrial applications. Understanding the underlying structural parameters, which are responsible for physical properties such as hardness, viscosity, elastic moduli, thermal stability, and for glass formation, are becoming increasingly important for the design of e.g. new types of insulation materials or interactive displays for electronic devices. In the field of cement science, aluminosilicaterich materials, including glasses, are increasingly utilized as supplementary cementitious materials (SCMs) to partially replace the Portland clinkers and thereby reduce the embodied CO2 emissions associated with Portland cement production. In the optimization of this replacement process, it is important to characterize the dissolution behavior as well as the pozzolanic reactivity of such glass materials in a cementitious environment.1-4 Studies of blended Portland cements (PC) containing waste glasses have shown that chemically durable mortars with compressive strengths comparable to those of conventional PC blends can be obtained by replacing up to 30 wt.% of the Portland clinkers with calcium aluminosilicate (CAS) glasses, leading to a significant reduction in CO2 emissions of the binder material.5 In

particular, the combination of CAS glasses with specific CaO–Al2O3–SiO2 compositions and limestone as SCMs may result in a synergetic effect with respect to physical performances and reductions in CO2 emissions for PC blends with a replacement level of at least 30 wt.%.6-8 For such glasses, dissolution studies have shown a congruent dissolution behavior for the Al and Si species in the glasses.9 Hence, the dissolution rate is strongly influenced by the overall network connectivity that is governed by the network modifiers. Thorough insight into the role of various network modifiers may thus allow tailoring of the supplementary cementitious material’s properties with the advantage that these glasses are relatively simple to produce. The aluminosilicate network in peralkaline glasses can be considered to consist of two distinct structural regions. One region of partially depolymerized SiO4 domains, caused by non-bridging oxygens (NBOs) that are associated only with SiO4 tetrahedra as a result of the presence of network modifiers, and a second region of Alrich domains in which fully polymerized, alternating Si– O–Al tetrahedra are stabilized by network modifiers.10-11 By adding alkali oxides to CAS glasses, the melting temperature may be reduced, leading to a decrease both in production costs and in the overall CO2 emission 1/15

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associated with the production of a blended cement containing the glass as SCM. The mixed nature of the network modifiers in such glasses implies that it is interesting to study the relation between the chemical reactivity and the network structure of alkali-modified CAS glasses. As suggested in the literature,12-15 highly concentrated alkali ionic channels penetrate the aluminosilicate network with a preference for charge balancing AlO4 tetrahedra, while the less mobile calcium ions stabilize the NBO environments in the SiO4 network. Generally, the physical and chemical properties of glasses are strongly affected by changes in the local network structure. Thus, the different roles of network modifiers may explain changes in dissolution and reactivity of such glasses in a cementitious environment, as well as of other properties largely governed by both the structural arrangement and the transport properties of network modifiers. In this work we present a simple method for probing the structural role of alkali- and calcium ions in the aluminosilicate network. Following previous studies,1011,16 this includes simulations of the aluminosilicate network and their impact on the 29Si MAS NMR spectra of the glasses, using two models that consider the chemical composition of the glasses.

EXPERIMENTAL Sample Preparation. Four glasses within the CaO-Al2O3SiO2 (CAS) system were prepared with specific compositions similar to those of slags and fly ashes in this ternary system. Two of the compositions were modified with up to 20 mol% Na2O, and/or a mixture of Na2O (N) and K2O (K), by reducing the relative contents of the CAS components in the parent glass, in such a manner that the internal molar ratios of CaO, Al2O3, and SiO2 were retained. The chemical compositions of the glasses are given in Table 1, where the samples are denoted as KiNjCxAyS, x and y specifying the Ca/Si and Al/Si ratios, and i and j the molar percentages of Na2O and K2O, respectively. The first two series (Table 1) are characterized by a constant CaO–Al2O3–SiO2 ratio, whereas the third series employs a fixed Na2O–CaO ratio but different Al2O3–SiO2 ratios. The latter series is denoted as NCASy. Powder mixtures of analytical-grade components of SiO2, Al2O3, CaCO3, Na2CO3, and K2CO3, targeting 200 g of bulk glass for each of the compositions given in Table 1, were homogenized by rotation of the powders in plastic containers around their vertical axis overnight. The mixtures were subsequently melted in a Pt/Rh 90/10 crucible using an electric furnace (ENTECH, Sweden). The crucible with the powder mixture was inserted into the furnace at 1500 °C and kept at this temperature for 2 hours. The crucible was removed from the furnace at 1500°C and the melt was quenched by pouring the melt into demineralized water. The resulting glass was crushed and dried, prior to a repetition of the melting procedure to improve the homogeneity of the glass. After the second heating, the glass melt was casted on a brass plate and immediately transferred as a bulk glass to a muffle furnace for annealing at approximately the glass

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transition temperature (Tg) for 10 minutes. The glass was then cooled to ambient temperature by turning off the furnace. Chemical and Structural Characterization. The chemical compositions of the glasses were analyzed by Xray fluorescence (XRF), and the corresponding molar compositions are listed in Table 1 along with the calculated fractions of non-bridging oxygen per tetrahedra (NBO/T). Note that the Al/Si ratio varies slightly for the mixed alkali series (K8-zNzC0.78A0.43S) whereas the glass compositions are close to the target compositions for the two other series. Prior to the NMR measurements, the glasses were ground to a powder using first a jaw crusher and then a ball mill. Solid-state 29Si MAS NMR spectra were obtained on a Varian INOVA–400 (9.39 T) spectrometer using a homebuilt CP/MAS NMR probe for 7 mm outer-diameter zirconia (PSZ) rotors, a spinning speed of νR = 6.0 kHz, single-pulse excitation with a pulse width of 3.0 μs for an rf field strength of γB1/2π = 40 kHz (~45o pulse), a relaxation delay of 30 s, and typically 2048 scans. Similar spectra with relaxation delays of 60 s and 120 s were acquired for three glass samples (C0.78A0.43S, N8C0.78A0.43S and N20C0.78A0.43S, c.f. Table 1), which showed only a minor increase in the overall intensity and no changes in lineshape, indicating a relaxation mechanism dominated by paramagnetic ions in the samples. Thus, the spectra obtained with a relaxation delay of 30 s is considered quantitative reliable. The 27Al MAS NMR spectra were acquired on a Varian Direct-Drive VNMR–600 spectrometer using a home-built CP/MAS probe for 4 mm outer diameter PSZ rotors, a spinning speed of νR = 13.0 kHz, a pulse width of 0.5 μs for an rf field strength of γB1/2π = 60 kHz, a relaxation delay of 2 s, and typically 6200 scans. The 27Al and 29Si chemical shifts are referenced to a 1.0 M aqueous solution of AlCl3·6H2O and neat tetramethyl silane (TMS), respectively. The isotropic chemical shift (δiso) and second-order quadrupole effect parameter (SOQE = CQ(1 + )½) for the Al(4) resonances were determined from the centers of

gravity for the central transition /, / and inner

satellite transitions ±/,±/ in the 27Al MAS NMR spectra, utilizing their difference in second-order quadrupolar shift,17 i.e.

/, / = −

±/,±/ = +

(1)

(2)

for a spin I = 5/2 nucleus, where νL is the Larmor

frequency. /, / is determined as the center of gravity

of the centerband whereas ±/,±/ is obtained as the average of the centers of gravity for the ±3, ±4, and ±5 spinning sidebands form the satellite transitions.


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The Journal of Physical Chemistry Simulation of 29Si MAS NMR Spectra. The 29Si MAS NMR spectra were simulated using two different approaches to

MODELING OF GLASS STRUCTURE

Table 1. Chemical compositions for the glasses (mol%), as determined by XRF.a Name

Composition

CaO

Al2O3

SiO2

Na2O

K2O

Naexcess (+ Kexcess)

Caexcess

NBO/T

C0.78A0.43S N8C0.78A0.43S K2N6C0.78A0.43S K4N4C0.78A0.43S K6N2C0.78A0.43S K8C0.78A0.43S N20C0.78A0.43S

C0.78A0.43S N0.20C0.78A0.43S K0.04N0.13C0.67A0.38S K0.09N0.19C0.69A0.39S K0.14N0.05C0.77A0.43S K0.19C0.77A0.43S N0.56C0.79A0.43S

35.17 32.32 30.04 30.54 32.09 32.08 28.30

19.51 17.90 16.99 17.30 18.08 18.04 15.56

45.16 41.60 45.11 44.14 41.74 41.81 36.02

0.13 8.11 5.88 4.10 2.14 0.13 20.06

1.93 3.92 5.90 7.90 -

0 0 0 0 0 9.00

15.80 22.55 20.87 21.26 22.06 22.08 28.32

0.38 0.58 0.52 0.53 0.57 0.57 0.98

C0.39A0.19S N4C0.39A0.19S b N8C0.39A0.19S b N12C0.39A0.19S b N16C0.39A0.19S b N20C0.39A0.19S

C0.39A0.19S N0.07C0.39A0.19S b N0.14C0.39A0.19S b N0.21C0.39A0.19S b N0.30C0.39A0.19S b N0.39C0.38A0.19S

24.63 23.04 22.08 21.12 20.16 19.30

11.90 11.71 11.22 10.74 10.25 9.54

63.31 61.25 58.70 56.14 53.59 51.02

0.16 4.00 8.00 12.00 16.00 20.10

-

-

0 0 2.53 11.50 21.13

12.89 15.33 18.86 21.12 20.16 19.31

0.30 0.36 0.46 0.58 0.70 0.85

38.90 39.02 39.16 39.05 38.89

22.46 18.82 15.17 11.35 6.50

27.62 31.27 34.79 38.68 43.29

10.84 10.72 10.68 10.67 11.09

-

0 0 0 0 9.18

27.28 30.92 34.67 38.37 38.89

0.75 0.90 1.06 1.25 1.54

c

c

NCAS0.81 NCAS0.60 c NCAS0.44 c NCAS0.29 c NCAS0.15 c

N0.39C1.41A0.81S N0.34C1.25A0.60S c N0.31C1.13A0.44S c N0.28C1.01A0.29S c N0.26C0.90A0.15S c

a

Quantities below 0.25 mol% are neglected

b

Intended quantities only. These compositions were not studied by XRF.

c

Small amounts of iron oxide were also detected (< 0.25 mol%, thus neglected) due to raw material impurities.

distribute Al in the glass network. These approaches are slightly modified versions of the models utilized by Lee and Stebbins10 and Moesgaard et al.11 For both models, the total assemblages of Si(Qn(kAl)) speciation was predicted by first assuming a statistical distribution of Si(Qn) units. Here, Qn specifies the degree of polymerization of the SiO4 tetrahedron, with n (0 ≤ n ≤ 4) being the number of bridging oxygens, and k denotes the number of AlO4 tetrahedra coordinated with the SiO4 tetrahedron in the second coordination sphere. The molar fractions of x(Si(Qn)) were calculated using the expression !"#$ % =

=

&! "1 − *%$ *"+ $% (! "& − (%! !

$!"

%$"1 − *%$ *"

%$(3)

where Z is the oxygen-coordination number of the network former, Z = 4, and n is the number of bridging oxygen atoms (BO) linked to the network former. This statistical distribution is based on the probability (p), for which a randomly chosen Si–O bond includes a nonbridging oxygen (NBO). The probability is calculated from the ratio between the molar amounts of modifier cations, which are in excess of charge-balancing AlO43- in the role of a network former, and the molar amount of oxygens coordinated to Si atoms in the glass (Z = 4): *=

-./01/22 34./01/22 35/01/22 +∙

(4)

It is assumed that the NBOs only affect Si in the glass network and that Al can be neglected in this probability calculation. According to literature,18-20 it is a fair assumption that there is a preference of NBOs in Si tetrahedra, rather than in Al tetrahedra. Moreover, the strong peralkaline environments for the glasses studied in this work suggest that Al is predominantly present in tetrahedral coordination. Up to 7 mol% five coordinated aluminum (Al(5)) has been found in peralkaline aluminosilicate glasses.21 However, considering that the Al(5) fraction is much lower for higher CaO and alkali contents, the fraction of Al(5) is neglected in this study. The impact of low quantities of Al(5) would in any cases be small on the simulations. The distribution of Al in the glass network is calculated using two different approaches. Model 1 is based on a hierarchical system similar to the quasi-heterogeneous model by Moesgaard et al.11 Model 2 is based on differences in lattice energies between Si–O–Si, Al–O–Al, and Si–O–Al bridges using an approach similar to the Al distribution calculated by Lee and Stebbins.10 In the current study, multiple Si(Qn) units exist in the glasses, as opposed to the glasses studied by Lee and Stebbins, which all had a composition with a molar CaO:Al2O3 ratio of 1:1. The models in the present work both assume complete aluminum avoidance, i.e., Al–O–Al linkages are absent unless Si/Al < 1. The average degree (D) of Al–O–Al avoidance in CAS and NAS glasses has been shown to be 0.8 ≤ D ≤ 0.875 and 0.93 ≤ D ≤ 0.99, respectively,10 which is valid for glasses with molar ratios of CaO:Al2O3 or Na2O:Al2O3 of 1:1. However, the present work involves strongly peralkaline glasses with mixed CaO and alkali oxides, and thus D is not known for these systems. It is


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estimated that D approaches 1, implying that Al–O–Al avoidance is completely fulfilled for the Al distribution. Model 1. In the hierarchical approach to the quasiheterogeneous Al distribution, the quantity of Si(Q4(4Al)) units corresponds to the quantity of Si(Q4) tetrahedra determined by the NBOs in the SiO4 network if a sufficient amount of Al atoms are available for this distribution. Otherwise, all available Al atoms are incorporated into Si(Q4(4Al)) units, and the remaining Si(Qn) units exist as Si(Qn(0Al)) units according to Eqs. (3) and (4). If Si(Q4)/Al(Q4) < 1, then the less polymerized Si(Qn Si(Q2(2Al)) > Si(Q1(1Al)), until all Al has been distributed. This hierarchy is based on Si(Q3) units with neighboring Al being more stable than Si(Q2) units with neighboring Al and so forth. Thus, the remaining Si(Qn) units of equal or lower n values, which are not coordinated with Al, exist as Si(Qn(0Al)), giving rise to a bimodal distribution of Si(Qn(nAl)) and Si(Qn(0Al)) units, similar to the model described by Moesgaard et al.11 The calculated Si(Qn(kAl)) distribution according to model 1 is summarized in Table 2. Model 2. All Si and Al are again assumed to be in tetrahedral coordination. Eqs. (3) and (4) determine the Si(Qn) distribution, while the Al distribution in the Si network is calculated in the following manner.10,16 The Si– O–Si, Al–O–Al, and Si–O–Al bridges have different lattice energies, and thus the quantity of each linkage depends on the difference in lattice energy, W, such that the quantity of Si–O–Al bonds, NSi–O–Al, is determined from energy minimization of the system: 789 : ;< =

+4=> 4?@ 4

∙

A3

(5)

Here, NAl and NSi are the quantities of Al and Si cations and N the total number of cations. The β parameter describes the energy difference, i.e. B = C1 + 4E89 E;< " − 1%

(6)

= F "G/+5H%

(7)

I = & JKL89 : ;< − "L89 : 89 + L;< : ;< %MN

(8)

where XSi and XAl are the molar fractions of Si and Al, K is Boltzmann’s constant, and T is the average glass transition temperature for the glasses. λi–O–j is the lattice energy of the i–O–j linkage. The probability of each Si(Qn(kAl)) unit is then calculated by the binomial distribution function: O"P% = $QR

S "TA3T3A %UVS "TA3T3A %U

(9)

Page 4 of 21

where $QR

=

$!

(10)

R!"$ R%!

These Si(Qn(kAl)) fractions are then normalized to the Si(Qn) quantities determined by Eqs. (3) and (4). To obtain similar conditions for the two models, η = 0 is employed for all compositions in this study, corresponding to a complete Al–O–Al avoidance, i.e., D = 1.0, where D = 1 – η2. The calculated Si(Qn(kAl)) distribution according to model 2 is summarized in Table 3. Fitting Procedure. The models for calculating the Si(Qn(kAl)) distribution have been fitted to the 29Si MAS NMR spectra with a set of Gaussian functions, each representing a specific Si(Qn(kAl)) contribution, that is convoluted under each experimental spectrum. Eq. (11) describes the Gaussian shaped resonances: W"% =

.

X∙CY/

∙ F* Z

"[ [\ % X

]

(11)

where a is the normalized signal intensity and w is the linewidth corresponding to 2σ (σ is the standard deviation), which is related to the full width at half maximum by FWHM = 2σ(2 ln 2)1/2 ≈ 1.18w. δ and δ0 are the chemical shift at any point and the isotropic chemical shift of the Gaussian peak corresponding to a specific Si(Qn(kAl)) unit, respectively. Since the peak shifts to higher frequency by the introduction of a NBO as well as upon coordination with Al in the second coordination sphere, two parameters, A and B, are introduced into the δ0 term through the equation: = ^Si#$ "PAl% c

= ^Si# "0Al% c + "4 − (%e + Pf

(12)

The fitting to the 29Si MAS NMR spectra is performed simultaneously for all spectra within one series for each model, and then repeated for the other series, while qualitatively evaluating the obtained fitting parameters according to those expected from literature.10-11,14,19,24-30 A least-squares error minimization process was adopted through a trust-region algorithm of the curve-fitting software (MATLAB, MathWorks, US). The fitting parameter δ(Si(Q4(0Al))) was restricted to be below -100 ppm for some of the compositional series, in order to obtain realistic values for this chemical shift. In total, only four fitting parameters were employed (δ(Si(Q4(0Al))), w, A, and B), thereby minimizing the occurrence of coincidental local minima during the least-square minimization. As an example, Fig. 1 shows the 29Si MAS


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NMR spectrum of the N8C0.39A0.19S glass and the simulated spectra based on both model 1 and model 2, including the Gaussian sub-peaks for the different calculated Si(Qn(kAl)) sites.


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Table 2. Si(Qn(kAl)) distributions calculated using model 1 for the glasses studied in this work (mol%).a Q0

Q1(0Al)

Q1(1Al)

Q2(0Al)

Q2(2Al)

Q3(0Al)

Q3(3Al)

Q4(0Al)

Q4(4Al)

C0.78A0.43S N8C0.78A0.43S K2N6C0.78A0.43S K4N4C0.78A0.43S K6N2C0.78A0.43S K8C0.78A0.43S N20C0.78A0.43S

0 0 0 0 0 0 4.3

1.8 5.8 3.8 4.2 5.4 5.4 9.3

0 0 0 0 0 0 11.3

11.7 7.6 18.9 17.0 7.4 7.8 0

0 15.8 0 3.0 15.2 14.8 36.9

0 0 1.6 0 0 0 0

39.3 42.0 40.4 42.2 42.1 42.1 29.4

0 0 0 0 0 0 0

46.4 28.3 34.9 33.2 29.3 29.4 8.8

C0.39A0.19S N4C0.39A0.19S N8C0.39A0.19S N12C0.39A0.19S N16C0.39A0.19S N20C0.39A0.19S

0 0 0 0 0 0

0 0 1.4 2.5 4.3 7.1

0 0 0 0 0 0

5.0 7.2 10.9 15.3 20.2 25.7

0 0 0 0 0 0

29.5 33.5 38.0 40.9 37.0 29.1

0 0 0 0 5.2 12.4

27.5 20.4 11.4 2.8 0 0

37.6 38.2 38.2 38.3 33.1 25.0

NCAS0.81 NCAS0.60 NCAS0.44 NCAS0.29 NCAS0.15

6.0 6.0 6.2 6.1 6.4

0 0 6.6 24.6 25.2

24.4 24.4 18.2 0 0

0 0 0 10.7 37.5

37.5 37.5 37.5 26.8 0

0 0 0 0 0

25.6 25.6 25.2 25.4 23.9

0 0 0 0 0

6.6 6.5 6.3 6.5 6.1

a

Calculated by Eqs. 3-4 and based on the hierarchical system described for model 1. Quantities 8 mol% (Fig. 2a) and Al/Si ˂ 0.3 (Fig. 2b). For both of these thresholds, all AlO43- tetrahedra are stoichiometrically charge-balanced by the quantity of alkalis. That is, if Na+ (or K+) takes priority over Ca2+ for charge-balancing AlO43-, and thus gives rise to the presence of Naexcess only if Na/Al ˃ 1 (c.f. Table 1). A

Page 10 of 21

27

Figure 5. Variation of the Al isotropic chemical shifts for the tetrahedral Al sites in the glasses (Table 5) as a function of the SiO2 content. The open circles represent the end members of the mixed alkali series. The inset shows the 27 variation in Al isotropic chemical shifts for the entire mixed alkali series, and the red lines indicate this variation in both graphs.

the network. Whether Ca2+ or alkali ions are given priority for charge-balancing AlO43- in the NBO calculations, the total NBO contents are identical. If alkali ions are given this priority however, then Naexcess (and/or Kexcess) appears when the alkali contents are in surplus, relative to the amount required to sufficiently charge-balance AlO43- (c.f. Table 1). If Ca2+ is given this priority, then Caexcess is present for all compositions and almost constant due to the glasses being fully charge-balanced prior to the introduction of alkali ions. Thus, the models for the Si(Qn(kAl)) distributions can predict identical values for the sub-species independently of alkalis or calcium having priority to charge-balance AlO43- in the NBO calculations. However, no relation between the excess amount of network modifiers and the difference between experimental and simulated 29Si resonance line-shapes are observed unless the alkalis are given priority as chargebalancing modifiers. Thus, the differences between the experimental and simulated 29Si NMR spectra (Figs. 2a and 2b) are direct results of changes in average linewidths and sub-peak positions as a result of the formation of NBOs by Na+. The charge-balancing role is not associated with Ca2+ only, as the sodium ions have a higher preference for this role in the aluminosilicate network, in agreement with findings reported earlier.14 The results obtained by fitting the 29Si MAS NMR spectra for the glass series where potassium substitutes for sodium further support a preference for alkali ions rather than calcium for charge-balancing AlO43-. In the calculated Si(Qn) distribution (Eq. 3), sodium and potassium are assumed to possess identical modifying


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effects, implying that the calculated Si(Qn(kAl)) distributions are not affected by this substitution, but only by the small changes in CaO–Al2O3–SiO2 contents in the mixed alkali series (K8-zNzC0.78A0.43S, Table 1). The fitted 29Si NMR line shapes (Fig. 2c) match the experimental 29Si MAS NMR spectra very well throughout the entire series for both models, suggesting that the linewidths and peak positions of the sub-peaks do not change much with the Na/K ratio. On the contrary, the 27 Al isotropic chemical shift for Al(4) (Table 5) decreases by up to approx. 1.5 ppm upon substitution of K2O for Na2O, relative to the linear correlation between the 27Al chemical shifts and the SiO2 contents observed for the alkali-free and the sodium-containing glasses (Fig. 5). This shift may be caused by a change in mean bond angles as a result of the size difference between the Na and K cations, in accordance with a previous suggestion for observed changes in the chemical shifts of both 29Si and 27Al NMR spectra for aluminosilicates.37 Thus, this observation suggests that the Na/K ratio has a larger impact on the Al environments as compared to the Si environments (Fig. 2c). This also supports the interpretation of alkalis having a preference for charge balancing AlO43- rather than generating NBOs in the Alfree, depolymerized SiO2 domain. According to Uchino et al.,29 the sodium ions that charge-balance the aluminum tetrahedra are more mobile than the sodium ions associated with the formation of NBOs since the attraction of the negatively charged AlO4 units and Na+ ions is more ionic in character as compared to the stabilization of a NBO with Na+. Thus, Na+ is not bound as strongly when it charge-balances AlO43-, and this creates a dynamic network structure with more symmetric AlO4 tetrahedra. Reliability of the 29Si MAS NMR Line Shape Simulations. The structural models and the simultaneous fitting procedure, employed for the 29Si MAS NMR spectra of the glasses, are restricted to several assumptions and constraints. These are chosen to approach a balance between flexible fitting and physically meaningful parameters, while highlighting any shortcomings of the model. If fitted individually, the experimental and simulated spectra may match perfectly, but the fitting parameters may not be comparable to those found in the literature. In reality, however, the fitting parameters are not fixed for each sub-peak’s contribution to the full 29Si NMR spectrum. For example, SiO4 sites connected with charge-balanced Al, by either Na+, Ca2+, or SiO4 sites associated with NBOs, display 29Si NMR resonances with varying line widths and chemical shifts.10 This has a significant impact on the appearance of the 29Si MAS NMR spectra and implies the necessity for using different fit parameters for each glass series in order to isolate the effects of Na, K, and/or Ca modifiers within each series. The use of fixed parameters for each sub-peak in the simulations, to account for the effects of the various modifiers on the 29Si line shape, makes it possible to evaluate the impact of the change in modifier environments. With increasing alkali content, any changes in modifier environment in the glass network

become apparent when the models fail to accommodate these changes due to restrictions. The analyses in Fig. 3a show that the changes in average peak position and line width for the 29Si resonance line shapes are most pronounced at sodium contents above 8 mol%. This critical sodium content coincides with the compositions where sodium starts to contribute to the formation of NBOs in the Si network, and below which the NBO formation is governed by calcium only (c.f. Table 1). This results in a decrease in line widths and a high-frequency shift of the sub-peaks from the Si sites associated with NBO. The line width of the individual Si(Qn(kAl)) sub-peaks is dependent on the specific chemical environment of Si as well as on the variations in bond angles and atomic distances of the structural SiO4 unit. These variations have a dominant impact on the line width since the topological disorder is enhanced by the rapid quenching of the melts. The use of the same melting and cooling conditions for the different glass syntheses should result in similar bond angles and atomic distances for similar compositions. Previous studies have reported only small, non-systematic line width variations of about 0.1 to 2 ppm for different calcium aluminosilicate glasses and about 0.1 to 4 ppm for different sodium aluminosilicate glasses among the various Si(Q4(kAl)) units.10,16,25 These variations are relatively small, which justifies our use of a constant line width for the different Si(Q4(kAl)) units within each series. In practice, larger variations may occur within each series with increasing alkali contents, however, a restricted linewidth emphasizes the impact of the increasing alkali content and its role in the glass network. The implementation of non-systematic variations in line width also generates additional flexibility to the simulations that may not result in physically meaningful fitting parameters. Typical 29Si NMR line widths (FWHM), reported for the Si(Qn(kAl)) units, are within the range of ~10–15 ppm10,14-20,24-26 and thereby of the same magnitude as those found in this work (Table 4). In the present study, the simulations employ a constant line width for all Si(Qn(kAl)) sub-peaks within each series, which obviously is an approximation. However, if the line widths are dominated by topological disorder rather than variations in the chemical environments, the approximation may be justified. The assumption of a fixed line width for the Si(Qn(kAl)) sub-peaks implies also that the difference between the simulated and experimental 29Si NMR line shapes is more pronounced when a clear change in the sub-peak linewidth occurs due to the introduction of a new network modifier. The fitting parameters in Table 4 reveal that the simulation of the C0.38A0.19S series, with its high Si/Al ratio, results in a broad linewidth of sub-peaks according to the fitting with model 1 (15.22 ppm) as a result of the high-frequency shifts with increasing sodium content in this series, i.e. by increasing only the NBO contents. These shifts are larger than those caused by Al in the second coordination sphere of Si, and hence the linewidths must be broad to cover the full spectrum. The other two series generally have lower Si/Al ratios (except for the NCAS0.15 glass) and also larger modifier contents,


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resulting in reduced linewidths (Table 4), according to the fitting with model 1. The simulation using model 2 shows a narrower linewidth (Table 4) for the C0.38A0.19S sub-peaks due to the increased number of simulated subpeaks (c.f. Fig. 1 and Table 3). However, the other two series display linewidths similar to those obtained with model 1. The simulations for the NCASy and KiNjC0.78A0.43S series also utilize similar chemical shifts for the Si(Q4(0Al)) sub-peak, whereas this fitting parameter varies in the simulations of the C0.38A0.19S series for the two models. The chemical shifts for the various sub-peaks depend on the introduction of NBOs in the Si network (parameter A) and on Al in the second coordination sphere of Si (parameter B). Using a constant value for the A and B parameters gives identical shifts for all consecutive cases of either NBO formation or replacement of Si by Al in the second coordination sphere to Si. It is well-known that a decrease in condensation (n in Qn) due to NBO formation results in a high-frequency shift, and linear regression of the values from previous studies reveal an average shift of +9.2 ± 3.8 ppm per NBO formed.24-27 Similarly, the introduction of Al into the second coordination sphere of Si de-shields the Si atom, resulting in a high-frequency shift of +6.3 ± 0.2 ppm for the Si(Q4(kAl)) units, according to linear regression of the average values for each introduced Al (k value).10,30 However, each increase of k induces a reduced high-frequency shift, relative to the shift for low k values (k = 1 and k =2). Moreover, a shift to higher frequency has been observed when the Si/Al ratio is increased,26 justifying the use of separate simulations for the three distinct series. With this method, the effect of the modifiers on the high-frequency shift is isolated from the effects of different CaO–Al2O3–SiO2 ratios between the series. Thus, the use of constant values for the A and B parameters provides a physical basis for the fits and for the distinction between effects of different network modifiers (Fig. 3), although the simulations using the two models result in some variations between the simulated and experimental spectra. The A parameter used in this study for both models is in good agreement with the values reported in literature for the C0.39A0.19S series, whereas for the two other series, A is lower and B is higher.10,24-27,30 This may be explained by the increased Al/modifier ratio in the latter two cases, as compared to the C0.39A0.19S series, since a larger Al content gives more weight to the B parameter. Generally, B adopts a lower value compared to those reported in the literature due to the larger contents of modifier used in this study, resulting in strongly peralkaline compositions. Thus, the A parameter dominantly affects the simulations. The constant chemical shift of -100 ppm employed for the simulated Si(Q4(0Al)) sub-peak for the NCASy and K8zNzC0.78A0.43S series in both models (Table 4) causes some error in the fitting procedure. The ionic character of the SiO4 network increases with increasing modifier contents, causing a decrease in Si–O–T bond angles. As with variations in line-widths and chemical shifts for the various sub-peaks with increasing modifier contents, the frequencies for the Si(Q4(0Al)) sub-peak are also expected

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to vary with glass composition, and especially to differ between the individual series. Since the restriction on the other fitting parameters serves to highlight the isolated impact of the change in modifier environments, the Si(Q4(0Al)) chemical shift may reach a rather unlikely value if not constrained. From Table 4 it is apparent that only the C0.38A0.19S series resulted in realistic δ(Si(Q4(0Al))) values, while the remaining two series gave shifts much above -100 ppm. If each simulated series is fitted using varying Si(Q4(0Al)) sub-peak positions, the increased fitting flexibility will allow for better fits but provide a reduced physical basis, hence eliminating the isolated impact of the alkali content. Therefore, this constraint is a necessary assumption for the intended purpose of the fitting procedure. The induced errors also artificially decrease the uncertainties of the fitting parameters, as some of the reported uncertainties are very low despite no restrictions were forced on the w, A, and B parameters in the fitting procedure. Finally, the fitting parameters in Table 4 are also in good agreement with those reported in a similar study by Moesgaard et al.11 They reported a chemical shift of -100.8 ppm for Si(Q4(0Al)), a line width of 10.7 ppm, a NBO shift of 8.6 ppm (A parameter), and a shift of 3.9 ppm for each Al coordinated to Si in the second coordination sphere (B parameter). However, their study focused on alkali-free CaO–Al2O3– SiO2 compositions, for which the average line widths and chemical shifts of the Si(Qn(kAl)) structural units were not affected by additional network modifiers, resulting in rather good agreements between all the experimental and simulated 29Si NMR spectra11. Comparison of the two different approaches used to calculate the Si(Qn(kAl)) distributions in the glass compositions reveals that both models give similar results when fitted to the 29Si MAS NMR spectra (Fig. 2) and both result in realistic fitting parameters. The main differences in the fitting parameters can be attributed to the higher number of sub-peaks used in the fitting of model 2 compared to model 1. Only the simulations of the NCAS0.29 glass (Fig. 2b) show some disagreement for the two models, although this may reflect the varying Al/Si ratio for the NCASy series. In both models, NBOs are assumed to be integrated only in the SiO4 network. However, some of the NCASy compositions exhibit high Al/Si ratios, and in order to accommodate both this assumption and a complete Al–O–Al avoidance in these glasses, model 2 gives some Si(Qn(kAl)) distributions similar to model 1 for this series, and some with a larger Al distribution among the Si(Qn) species (c.f. Table 3). This results in differences in the fitting parameters for glasses with high and low Al/Si ratios, which was not accounted for in the simulation process. Model 1 assumes a more binary distribution of Si(Qn(kAl)) species (c.f. Table 2), and this may partly accommodate this limitation in the simulations. Model 1 has been shown to be better than a fully random distribution model,11 and studies have shown that Al tetrahedra may occupy more polymerized positions in the network, which also supports this model.34 however, a further improvement can most likely


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be achieved if the real degree of Al avoidance is incorporated in the models.

(No. 11-116724) and the collaborators of this project for useful discussions.

CONCLUSIONS

REFERENCES 1.

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Snellings, R.; Mertens, G.; Elsen, J. Supplementary Cementitious Materials. Rev. Mineral Geochem. 2012, 74, 211-278.

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Moesgaard, M.; Herfort, D.; Skibsted, J.; Yue, Y. Z. Calcium Aluminosilicate Glasses as Supplementary Cementitious Materials. Glass Technol.: Eur. J. Glass Sci. Technol., Part A 2010, 51, 183-190.

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Moesgaard, M.; Herfort, D.; Steenberg, M.; Kirkegaard, L. F.; Yue, Y. Z. Physical Performances of Blended Cements Containing Calcium Aluminosilicate Glass Powder and Limestone. Cem. Concr. Res. 2011, 41, 359364.

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Thomsen, R. M.; Garzón, S. F.; Herfort, D.; Skibsted, J.; Yue, Y. Z. Physical Performances of Alkali-Activated Portland Cement-Glass-Limestone Blends. J. Am. Ceram. Soc. 2017, 100, 4159-4172.

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Snellings, R. Solution-Controlled Dissolution of Supplementary Cementitious Material Glasses at pH 13: The Effect of Solution Composition on Glass Dissolution Rate. J. Am. Ceram. Soc. 2013, 96, 24672475.

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Lee, S. K.; Stebbins, J. F. The Degree of Aluminum Avoidance in Aluminosilicate Glasses. Am. Mineral. 1999, 84, 937-945.

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All authors have given approval to the final version of the manuscript.

Moesgaard, M.; Keding, R.; Skibsted, J.; Yue, Y. Z. Evidence of Intermediate-Range Order Heterogeneity in Calcium Aluminosilicate Glasses. Chem. Mater. 2010, 22, 4471-4483.

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ACKNOWLEDGMENT

Greaves, G. N. EXAFS and the Structure of Glass. J. Non-Cryst. Solids. 1985, 71, 203-217.

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Jund, P.; Kob, W.; Jullien, R. Channel Diffusion of Sodium in a Silicate Glass. Phys. Rev. B 2001, 64, 134303.

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Baasner, A.; Schmidt, B. C.; Dupree, R.; Webb, S. L. Fluorine Speciation as a Function of Composition in Peralkaline and Peraluminous Na2O–CaO–Al2O3–SiO2 Glasses: A Multinuclear NMR Study. Geochim. Cosmochim. Acta 2014, 132, 151-169.

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Le Losq, C.; Neuville, D. R.; Chen, W.; Florian, P.; Massiot, D.; Zhou, Z.; Greaves, G. N. Percolation Channels: A Universal Idea to Describe the Atomic Structure and Dynamics of Glasses and Melts. Sci. Rep. 2017, 7, 16490.

16.

Murdoch, J. B.; Stebbins, J. F. High-Resolution 29Si NMR Study of Silicate and Aluminosilicate Glasses:

Three series of peralkaline calcium aluminosilicate glasses, modified with increasing amounts of alkali oxides and mixed alkali oxides, have been simulated according to two different approaches for distributing tetrahedral aluminum in the silicon glass network. A quasiheterogeneous structural model that employs a qualitative hierarchy (model 1) and a model that is based on differences in lattice energies (model 2) have been used to calculate the distribution of Si(Qn(kAl)) species and in numerical fitting to experimental 29Si MAS NMR spectra for the three series of glasses. A systematic divergence, observed for both models, between the simulated and experimental spectra indicates a strong preference for alkali ions as the primary charge-balancing modifiers in the polymerized Si–O–Al domains of the glass network, whereas the less mobile calcium ions primarily act as NBO formers causing network depolymerization. This finding is supported by the observed decrease in average 27Al isotropic chemical shift of the resonance from tetrahedral Al in the 27Al MAS NMR spectra with a decrease in the Na2O/(Na2O+K2O) ratio, since the 27Al isotropic chemical shifts for tetrahedral Al in glasses without mixed alkalis exhibit a linear relation with the SiO2 content, from which the shifts for the mixed alkali series deviates (below the linear line). Of the two structural models employed in the present work, model 2 is most likely more accurate, considering the assumed binomial Al distribution used in model 1. However, the strong similarity in the simulated 29 Si NMR spectra from the two models, suggests that model 1 may be of higher practical value due to the larger number of Si(Qn(kAl)) sub-species used in model 2.

AUTHOR INFORMATION Corresponding Author * E-mail: [email protected]

Author Contributions

The use of the solid-state NMR facilities at the Department of Chemistry, Aarhus University, sponsored by the Danish Research Councils for Independent Research and Carlsbergfondet, is acknowledged. Useful discussions with Pierre Florian, CEMHTI-CNRS UPR3079, France, as well as with PhD students Laura Paraschiv and Mouritz Nolsøe Svenson, and post doc Saurabh Kapoor at Aalborg University, Denmark are greatly appreciated. Mette Solvang, ROCKWOOL International A/S, Andreas Bagger Thomsen, Bjarke Buchbjerg, and Camilla Bøge Slej Øhlenschlæger are acknowledged for providing the NCASy glass series. We thank the Innovation Fund Denmark for financial support to the LowE-CEM project


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The Effect of Network-Modifying Mineral. 1985, 70, 332-343.

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Allwardt, J. R.; Lee, S. K.; Stebbins, J. F. Bonding Preferences of Non-Bridging O Atoms: Evidence from 17 O MAS and 3QMAS NMR on Calcium Aluminate and Low-Silica Ca-Aluminosilicate Glasses. Am. Miner. 2003, 88, 949-954.

19. Park, S. Y.; Lee, S. K. Structure and Disorder in Basaltic Glasses and Melts: Insights from High-Resolution Solid-State NMR Study of Glasses in Diopside-CaTschermakite Join and Diopside-Anorthite Eutectic Composition. Geochim. Cosmochim. Acta 2012, 80, 125142. 20. Pedone, A.; Gambuzzi, E.; Menziani, M. C. Unambiguous Description of the Oxygen Environment in Multicomponent Aluminosilicate Glasses from 17O Solid State NMR Computational Spectroscopy. J. Phys. Chem. C 2012, 116, 14599-14609. 21.

Neuville, D. R.; Cormier, L; Massiot, D. Al Coordination and Speciation in Calcium Aluminosilicate Glasses: Effects of Composition Determined by 27Al MQ-MAS NMR and Raman Spectroscopy. Chem. Geol. 2006, 229, 173-185.

22. Engelhardt, G. Structural Studies of Calcium Aluminosilicate Glasses by High Resolution Solid State 29 Si and 27Al Magic Angle Spinning Nuclear Magnetic Resonance. Phys. Chem. Glasses 1985, 26, 157-165. 23. Engelhardt, G.; Michel, D. High Resolution Solid-State NMR of Silicates and Zeolites. Wiley: New York. 1987. 24. Mahler, J.; Sebald, A. Deconvolution of 29Si MagicAngle Spinning Nuclear Magnetic Resonance Spectra of Silicate Glasses Revisited – Some Critical Comments. Solid State Nucl. Magn. Reson. 1995, 5, 63-78. 25. Schneider, J.; Mastelaro, V. R.; Panepucci, H.; Zanotto, E. D. 29Si MAS-NMR Studies of Qn Structural Units in Metasilicate Glasses and Their Nucleating Ability. J. Non-Cryst. Solids. 2000, 273, 8-18. 26. Duxson, P.; Provis, J. L.; Lukey, G. C.; Separovic, F.; van Deventer, J. S. J. 29Si NMR Study of Structural Ordering in Aluminosilicate Geopolymer Gels. Langmuir 2005, 21, 3028-3036. 27. Pedone, A.; Charpentier, T.; Menziani, M. C. Multinuclear NMR of CaSiO3 Glass: Simulation from First Principles. Phys. Chem. Chem. Phys. 2010, 12, 6054-6066.

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28. Karlsson, C.; Zanghellini, E.; Swenson, J.; Roling, B.; Bowron, D. T.; Börjesson, L. Structure of Mixed Alkali/Alkaline-Earth Silicate Glasses from Neutron Diffraction and Vibrational Spectroscopy. Phys. Rev. B 2005, 72, 064206. 29. Uchino, T.; Sakka, T.; Ogata, Y.; Iwasaki, M. Local Structure of Sodium Aluminosilicate Glass: An Ab Initio Molecular Orbital Study. J. Phys. Chem. 1993, 97, 9642-9649. 30. Gambuzzi, E.; Pedone, A.; Menziani, M. C.; Angeli, F.; Caurant, D.; Charpentier, T. Probing Silicon and Aluminium Chemical Environments in Silicate and Aluminosilicate Glasses by Solid State NMR Spectroscopy and Accurate First-Principles Calculations. Geochim. Cosmochim. Acta 2014, 125, 170185. 31.

Baltisberger, J. H.; Xu, Z.; Stebbins, J. F.; Wang, S. H.; Pines, A. Triple-Quantum Two-Dimensional 27Al Magic-Angle Spinning Nuclear Magnetic Resonance Spectroscopic Study of Aluminosilicate and Aluminate Crystals and Glasses. J. Am. Chem. Soc. 1996, 118, 72097214.

32. Massiot, D.; Fayon, F.; Capron, M.; King, I.; Le Calvé, S.; Alonso, B.; Durand, J.-O.; Bujoli, B.; Gan, Z.; Hoatson, G. Modelling One- and Two-Dimensional Solid-State NMR Spectra. Magn. Reson. Chem. 2002, 40, 70-76. 33. Neuville, D. R.; Cormier, L.; Massiot, D. Al Environment in Tectosilicate and Peraluminous Glasses: A 27Al MQ-MAS NMR, Raman, and XANES Investigation. Geochim. Cosmochim. Acta 2004, 68, 5071-5079. 34. Neuville, D. R.; Cormier, L.; Montouillout, V.; Massiot, D. Local Al Site Distribution in Aluminosilicate Glasses by 27Al MQMAS NMR. J. Non-Cryst. Solids 2007, 353, 180-184. 35. Stebbins, J. F.; Xu, Z. NMR Evidence for Excess NonBridging Oxygen in an Aluminosilicate Glass. Nature 1997, 390, 60-62. 36. Toplis, M. J.; Kohn, S. C.; Smith, M. E.; Poplett, I. J. F. Fivefold-Coordinated Aluminum in Tectosilicate Glasses Observed by Triple Quantum MAS NMR. Am. Miner. 2000, 85, 1556-1560. 37. Lippmaa, E.; Samoson, A.; Mägi, M. High-Resolution 27 Al NMR of Aluminosilicates. J. Am. Chem. Soc. 1986, 108, 1730-1735.


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TOC Graphic


15


Experimental (black) and simulated (red) 29Si MAS NMR spectra for the N8C0.39A0.19S glass. The simulated spectra reflect the distribution of Si(Qn(kAl)) species calculated by (a) model 1 and (b) model 2. The simulated spectra are composed of the Gaussian shaped sub-peaks (black, dashed lines). The bold dashed lines in (b) correspond to the sub-peaks which are also present in (a). 119x173mm (600 x 600 DPI)


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Experimental 29Si MAS NMR spectra (black) and simulations based on the hierarchical, quasi-heterogeneous model (red, solid) or the model based on differences in lattice energies (red, dotted) for (a) the C0.39A0.19S series modified with up to 20 mol% Na2O, (b) the NCASy series with increasing Si/Al ratio, and (c) the mixed alkali series. 222x599mm (600 x 600 DPI)



The full width at half maximum (FWHM, black) line width and the isotropic chemical shifts (δiso, red) from the 29Si MAS NMR spectra for (a) the sodium-modified C0.39A0.19S glasses against the sodium content, (b) the NCASy series against its Si/Al ratio, and (c) the mixed alkali series (KiNjC0.78A0.43S) against the Na2O/(Na2O+Ka2O) ratio. The lines are guides for the eyes. 156x296mm (600 x 600 DPI)


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Selected 27Al MAS NMR spectra representing glass compositions over the studied range of SiO2 contents. The dashed line indicates the clear shift of the peak position towards higher chemical shifts with decreasing SiO2 content. 70x59mm (600 x 600 DPI)



Variation of the 27Al isotropic chemical shifts for the tetrahedral Al sites in the glasses (Table 5) as a function of the SiO2 content. The open circles represent the end members of the mixed alkali series. The inset shows the variation in 27Al isotropic chemical shifts for the entire mixed alkali series, and the red lines indicate this variation in both graphs. 67x55mm (600 x 600 DPI)


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47x43mm (600 x 600 DPI)


The Charge-Balancing Role of Calcium and Alkali Ions in Per-Alkaline

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