Effect of different ions on dissolution rates of silica and feldspars at high pH

Bagheri, Mahsa; Lothenbach, Barbara; Shakoorioskooie, Mahdieh; Scrivener, Karen

doi:10.1016/j.cemconres.2021.106644

Cited by 46 publications

(23 citation statements)

References 54 publications

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“…The Al content, however, is seen to have a negative impact on quartz dissolution according to the RF and ANN models, especially for the latter, where the mean SHAP value is clearly more negative. This hindrance effect of Al species on silica dissolution in alkaline solutions has also been reported in the literature for both quartz (Bickmore et al, 2006;Bagheri et al, 2022) and silicate-based glasses (Snellings, 2013;Bagheri et al, 2022). This hindrance effect has been considered a major reason for another widely observed phenomenon in the cement and concrete community: Al additives or supplementary cementitious materials (e.g., slag, fly ash and metakaolin that contains dissolvable Al species) suppress ASR in concrete and the associated deleterious expansion (Chappex and Scrivener, 2012;Leemann et al, 2015;Zhou et al, 2019;Tapas et al, 2021).…”

Section: Model Interpretationsupporting

confidence: 63%

“…Although there are very few silica dissolution rate data from the cements and concrete community (Snellings, 2013;Bagheri et al, 2022), data mining reveals abundant quartz dissolution data across different disciplines. Data-driven ML models are seen to give accurate prediction of quartz dissolution rates as a function of different dissolution conditions.…”

Section: Broader Impact and Limitationsmentioning

confidence: 99%

“…However, as stated in this recent review article (Rajabipour et al, 2015), "literature on this subject is limited and significant knowledge gaps exist with respect to quantifying the dissolution rate as a function of aggregate composition, mineralogy, and surface properties, pore solution composition, temperature, and pressure". Most existing studies on the dissolution kinetics of silica polymorph (e.g., quartz and amorphous silica) come from the fields of geochemistry and earth science (Dove, 1994), with only a few exceptions from the cement and concrete field (Snellings, 2013;Pignatelli et al, 2016;Bagheri et al, 2022). For example, as mentioned earlier, most of these studies (Dove, 1994) focus on pH < 12, and there are significantly fewer dissolution rate data for quartz (and its polymorph) at pH > ~13, which is more relevant to concrete, especially for AAM-based concrete.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Prediction of Quartz Dissolution Rates at Near-Neutral and Alkaline Environments

et al. 2022

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Dissolution of silicate-based materials is important to many natural processes and engineering applications, including cement and concrete production. Here, we present a data-driven study to predict the dissolution rates of crystalline silica (i.e., quartz) in near-neutral and alkaline environments. We present a quartz dissolution database containing both dissolution rates and five major dissolution conditions (i.e., temperature, pressure, pH at the experimental temperature T (pHT), and the sodium and alumina content in the solution) via data mining from the literature. We supplement the database with experimental data of quartz dissolution rate in sodium hydroxide solutions (0–5 M) at different target temperatures (25–90°C), which are significantly less covered by the existing literature. We build two data-driven models (i.e., random forest (RF) and artificial neural network (ANN)) to predict the dissolution rate of quartz (i.e., output target) as a function of dissolution conditions (i.e., input features). The results show that both RF and ANN models exhibit high predictive capability, with R2 values of 0.97–0.98, MAPEs of 2.95–4.24% and RMSEs of ∼0.31–0.44 log (mole/m2/s) for the test set. These prediction errors are much smaller than linear regression models (RMSE of ∼1.25 log) also presented here and comparable with those achieved in previous studies using reaction models based on a smaller and less complex dataset (RMSE of ∼0.35–0.44 log). We further evaluate the interpretability and performance of the data-driven models, and the results show that the model predictions are generally consistent with literature observations, including the different impacts of input features on dissolution rate. In particular, the ANN model appears to exhibit a certain level of ability to extrapolate, i.e., making predictions in feature space not covered in the database.

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Section: Model Interpretationsupporting

confidence: 63%

Section: Broader Impact and Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Prediction of Quartz Dissolution Rates at Near-Neutral and Alkaline Environments

et al. 2022

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“…Relatively low concentrations of Al (1-5 mM) can reduce the dissolution rate of silica by as much as 90%. However, the sorption of Al(OH) 4 on the surface of silica and the slowing down of silica dissolution silica is more distinct at intermediate pH values (<12), while at pH 13 and above the sorption of Al(OH) 4 on silica becomes weak resulting in only feeble suppression of the dissolution rate (Yokoyama et al, 1988;Chappex and Scrivener, 2012b;Nicoleau et al, 2014) as illustrated in Figure 3 (Bagheri et al, 2022). A comparison with the Al concentration in the pore solution of Portland cement and blended cements, show that blending of Portland cement with fly ash or metakaolin could in fact increase the Al concentration to 1 mM and above, i.e., to Al concentrations efficient in slowing down silica dissolution (see Figure 3).…”

Section: Slow Down the Dissolution Rate Of The Reactive Silicamentioning

confidence: 98%

“…The role of Al in mitigating ASR has been at least partially related to a slowing down of the dissolution of reactive silica (Chappex and Scrivener, 2012b;Bagheri et al, 2022). The presence of Al may also alter the structure of crystalline ASR products to zeolite or its precursor at 80 °C, while their formation kinetics at ambient temperatures seem to be too slow to have a relevant effect (Hünger, 2007;Shi et al, 2018;Shi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Role of Aluminum and Lithium in Mitigating Alkali-Silica Reaction—A Review

Shi

Lothenbach

2022

Front. Mater.

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Effective mitigation of alkali-silica reaction (ASR) is critical for producing durable concrete. The use of alumina-rich supplementary cementitious materials (SCMs) and chemical admixtures such as lithium salts to prevent expansion caused by ASR was first reported 70 years ago, shortly after the discovery of ASR in 1940s. Despite numerous investigations, the understanding of the mechanisms of Al and Li for mitigating ASR remain partially inexplicit in the case of Al, and hardly understood in the case of Li. This paper reviews the available information on the effect of Al and Li on ASR expansion, the influencing factors, possible mechanisms and limitations. The role of Al in mitigating ASR is likely related to the reduction of dissolution rate of reactive silica. Moreover, the presence of Al may alter the structure of crystalline ASR products to zeolite or its precursor, but such effect seems to be not that significant at ambient conditions due to the slow kinetics of zeolite formation. Several mechanisms for the lithium salts in mitigating ASR have been proposed, but most of them are not conclusive primarily due to the lack of knowledge about the formed reaction products. Combination of Al-rich SCMs and lithium salts may be used as an economic solution for ASR mitigation, although systematic studies are necessary prior to the applications.

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