Geopolymers have the potential to function as an environmentally friendly substitute for ordinary Portland cement, with up to 80% less CO2 emission during production. The effect is best utilized for geopolymers prepared with amorphous silica instead of waterglass (Na2xSiyO2y+x) to adjust the Si:Al ratio. The reactivity of the precursors with the alkaline activator affects the final mineralogical properties of the binder. The purpose of the present study was to investigate the amount of different phases formed during geopolymerization and to understand the quantitative evolution of carbonation during geopolymer synthesis by determining the solubility of metakaolinite and amorphous SiO2 in NaOH at various concentrations. The solubility was studied by ICP-OES measurements. X-ray diffraction was used for qualitative and quantitative phase analysis of the geopolymers. The solubility of the precursors increased with calcination temperature of metakaolinite, reaction time for amorphous SiO2, and at higher NaOH concentrations. Partial dissolution resulted in free Na+, which is a source for the formation of carbonates in the geopolymers. Thermonatrite occurred prior to trona formation in all samples.

Development of robust concrete mixes with a lower environmental impact is challenging due to natural variability in constituent materials and a multitude of possible combinations of mix proportions. Making reliable property predictions with machine learning can facilitate performance-based specification of concrete, reducing material inefficiencies and improving the sustainability of concrete construction. In this work, we develop a machine learning algorithm that can utilize intermediate target variables and their associated noise to predict the final target variable. We apply the methodology to specify a concrete mix that has high resistance to carbonation, and another concrete mix that has low environmental impact. Both mixes also fulfill targets on the strength, density, and cost. The specified mixes are experimentally validated against their predictions. Our generic methodology enables the exploitation of noise in machine learning, which has a broad range of applications in structural engineering and beyond.

The consumption of sugar-sweetened beverages is associated with increased incidence of overweight and obesity, and a factor underlying this putative link could be the relatively low levels of satiety that may be induced by these beverages. Although many sugar-sweetened beverages are carbonated, little attention has been given to the potential effects of level of carbonation on satiety and subsequent intakes. We hypothesized that increasing the level of carbonation in a sugar-sweetened beverage would increase satiety and decrease intakes in the short term. Using a randomized, within-subject cross-over design, thirty non-obese subjects (fifteen women, fifteen men) participated on three occasions, 1 week apart. Following a standard breakfast, subjects consumed a beverage preload 10 min before consuming a lunch ad libitum. Preloads were the same sugar-sweetened beverage (400 ml, 639 kJ) with three levels of carbonation, which were low (1·7 volumes), medium (2·5 volumes) and high (3·7 volumes). Satiety was assessed using visual analogue scales and intakes were measured at the lunch and for the rest of the day. Compared with the beverage with low carbonation, consumption of the beverages with medium and high carbonation led to significantly (P < 0·05) higher satiety until lunch, when intakes of food and energy were significantly (P < 0·05) lower. There were no significant effects on satiety following lunch or on intakes for the rest of the day. This short-term study suggests that the level of carbonation may need to be taken into account when assessing potential effects of beverages on satiety and intake.