Four types of pre-management glass waste were chosen: clear, green, brown and mixed. These materials were characterised with the following techniques:

The glass samples were crushed, ground and sieved to obtain three particle size fractions: i) <45 µm; ii) 45–90 µm; iii) >125 µm.

Table 1 gives the chemical composition (by XRF) of the glass waste for each particle size chosen for the study (from under 45 to over 125 µm). The XRF values showed that the proportions of the majority oxides (SiO₂, Na₂O and CaO) were similar across the entire range of particle sizes and types of glass. Some minor differences were detected in the trace elements, and specifically Cr, which plays a role in glass hue (with higher levels in green, brown and mixed glass).

The XRD mineralogical characterisation (see Figure 1) showed the glass to be an amorphous material, with low range structural order. Crystalline phases were not detected via XRD, consistent with the highly amorphous nature of the materials used in this study at 2θ values of around 20–40°, an indication of the vitrification of silicates, sodium silicate (Na₂SiO₃·nH₂O) and calcium silicate type phases, along with potassium and barium. The IR spectra for these glass waste (Figure 2) contained a wide and intense band at 900–1100 cm⁻¹ associated with the Si-O (ν₃ (Si-O)) asymmetric stretching vibrations in the SiO₄ tetrahedral groups present in the silicates, and another at 450–470 cm⁻¹ due to the ν₄ (O-Si-O) bending vibrations in the SiO₄ groups, likewise in the silicates. The assignment of the spectrum bands is listed in Table 2. The XRD and FTIR findings were practically identical in the four types of glass waste.

The variables studied to determine the solubility of the four types of glass in highly alkaline media were: type of process, nature of the activating solution, particle size of the glass waste, temperature and process/solution time.

Glass solubility was determined in the following three processes:

At ambient temperature (22 ± 2°C)

Chemical process, which consisted of magnetic stirring at ambient (22 ± 2°C) temperature and a constant speed, was studied in the four types of glass specified. The solid: liquid ratio was 1 g of glass material per 100 mL of solution (1:100). These conditions were constant for the four types of glass and three particle sizes (<45, 45–90 and >125 µm). Stirring times of 10 min and 2, 4 and 6 h were studied to determine the effect on the time on glass dissolution. The three dissolutions used were deionised water (pH = 7), the reference, a NaOH solution (pH = 13.8) and a 1:1 NaOH/Na₂CO₃ solution (pH = 13.6). The concentration of the two alkaline solutions was 4 M. A total of 144 trials were conducted to test all these variables in the four types of glass.

Dissolution trials for chemical process at 80°C were limited to the brown and mixed glass, based on the findings for chemical process at ambient temperature. All the other conditions remained unchanged: stirring times, glass particle size and the activating solutions (NaOH and NaOH/Na₂CO₃ at the same concentration as in chemical process at ambient temperature). Solution temperature was held steady at 80 ± 2°C. A total of 48 trials were conducted under these conditions.

Lastly, in the mechano-chemical process trials, the glass was ground in a steel ball grinder. In such procedures, the relationship between the size and number of balls and the solid: liquid ratio had an important effect on the solubility process. Several trials were run to establish the optimal conditions. The best grinding performance was found for 5 g of solid in 500 ml of solution and 1 kg of balls (with a size distribution of approximately 2.5% of balls 20 mm in diameter; and 12.3% measuring 15 mm, 29.3%, 9 mm and 56%, 5 mm in diameter). As in the case of chemical stirring at 80°C, only brown and green glass were studied, using the same particle size fractions and stirring times as established for the ambient temperature trials, and the same concentration of NaOH and NaOH/Na₂CO₃ solutions as for chemical process at ambient temperature. A total of 48 trials were conducted.

All the solutions obtained were gravity filtered and brought to a known volume for subsequent ICP-AES laboratory analysis of elements Si, Al, Ca and Mg. The analyses were conducted in a VARIAN 725-ES inductively coupled plasma atomic emission spectrometer.

The objective of studying all these variables was to determine the type of process, type of glass (clear, green, brown or mixed), particle size of the glass waste and type of activating solution that yielded the highest glass solubility. This information is essential to establishing the optimal conditions for obtaining sodium silicates, possible waterglass as family alkaline activators, from glass waste.

The experimental findings for each dissolution process were statistically corroborated. Variables and their respective levels were established to obtain a mathematical model able to determine the solubility of all the species present in the glass (Si, Al, Ca and Mg) in a single equation accommodating all the variables. STATGRAPHICS Plus 5.1 software was used in this analysis.

The data were obtained, i.e., the response variables were determined, by running a full factorial model on the experimental findings. Model parameter significance was analysed with the F-test (Fischer's test). The general criterion applied was to accord significance to factors as well as binary and ternary interactions whose p-values at 95% confidence were lower than <0.05.

Once the significant factors were determined, the equation parameters were estimated and the model initially proposed was validated. Further to the model, the variability found in a response variable is attributed to the sum of a series of terms assigned to each factor studied and its binary and ternary interactions. In addition to the variability attributed to the factors studied, the model envisages a term representing the variability due to experimental error. In the present study, where interactions >3 were assumed to be nil, the equation [1] developed to define the model, a priori, was as follows: y ijkl = μ + A i + B j + C k + D l + ( AB ) ij + ( AC ) ik + ( AD ) ik + ( BC ) jk + ( BD ) il + ( CD ) kl + ( ABC ) ijk + ( ABD ) i + ( ACD ) ikl + ( BCD ) jkl + ε ijkl

where y _ijkl is the estimated value of the response variable at levels i, j, k and l for the factors studied; µ is a scale factor indicating the mean of the estimated value; A _i , B _j , C _k , and D _l represent the effects of the change in factor values on the response variable; (AB) _ij , (AC) _ik , (AD) _jl , (BC) _jk , (BD) _il and (CD) _kl represent the effect of the binary interactions between factors on the response variable; eijkl, which represents the random measuring error, is independent of the factors and levels studied, subject to the assumption that ɛ_ijkl ≈ N (0, σ²); and σ² is the variance in the experimental error estimated from the available degrees of freedom, given that the interactions of an order higher than three are regarded as nil.

Table 3 lists the four factors studied in the factorial design for the experiments conducted, along with the associated levels for chemical activation at ambient temperature (for a total of 144 trials). Fewer levels of some of these factors were defined for chemical at 80°C and mechano-chemical processes (see Table 4).

The model was validated by plotting the experimental against the expected values. The model is valid when the points on the graph obtained are randomly distributed, i.e., where no pattern emerges.

The formation of sodium silicate solutions from the dissolution of glass waste is followed by the content of SiO₂ dissolved from these wastes by the different methods applied. The Na ions content cannot be used because the alkaline solutions used were NaOH or NaOH/Na₂CO₃. The main analytical and statistical results obtained according to the methods used are presented below.

The statistical study performed to take the solubilisation methods used (Table 9) into consideration yielded significant values for the all main factors as well as the binary and ternary interactions (Table 10). As noted earlier, the effect of the process used played an important role in glass waste solubilisation and sodium silicates formation, for both network forming and modifying oxides.

According to the p-values, the solubilisation method used (factor E) was significant in all the oxides studied, both separately and in the binary and ternary interactions. While all the binary interactions were significant for SiO₂ solubility, the interactions involving factor E were the most prominent. All the other main factors studied (A, B, C and D) were significant in binary and ternary interactions with factor E: in other words, regardless of the solubilisation process used, these factors would be altered. This reinforces the importance of temperature in the dissolution of SiO₂ oxide in glass. Aluminium oxide behaved in much the same way as silicon oxide. The solubilisation process was likewise significant for the modifying oxides, although the type of solution used and stirring time prevailed over the other factors.

The graphs in Figure 9 show that the highest solubility values for all the oxides in glass were attained with chemical process at 80°C. Only minor differences were observed between the other two methods, except for aluminium and magnesium oxide solubility. Mechano-chemical process proved to be more effective for the former and chemical process at ambient temperature for the latter.

From the previous analytical findings in this work has been proved and quantified the SiO₂ contents dissolved from glass wastes. The main objective of this work was to generate solutions of sodium silicates (as potential waterglass solutions) from the different solubilisation process. Figure 10, in turn, reproduces the ²⁹Si NMR spectrum for the original glass (mixed glass) and the spectrum for the liquid obtained after treating the glass waste with NaOH/Na₂CO₃ at 80 ± 2°C for 6 hours. The spectrum for the waste glass contained a single signal at around -93 ppm, indicative of the presence of the Q⁴ Si units that characterize silica glass. The spectrum for the post treatment liquid, even at short times, exhibited a single signal at around -71ppm, associated with the presence of Q⁰ units, i.e., dissolved Si monomers. This is important because according to the literature [30–32], the effectiveness of Si in waterglass systems rises with declining condensation and polymerization of the molecule in the medium.

Next step is to demonstrate that these sodium silicate solutions formed from glass wastes can be used as alkaline activator of waterglass family in the preparation of Alkali-Activated Materials (AAMs).