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Conventional drying of colloidal materials and gels (including cement) can lead to detrimental effects due to the buildup of internal stresses as water evaporates from the nano/microscopic pores. However, for these gel materials the underlying nanoscopic alterations that are, in part, responsible for macroscopically-measured strain values (especially at low relative humidity) remain a topic of open debate in the literature. In this study, sodium-based calcium-alumino-silicate-hydrate (C-(N)-A-S-H) gel, the major binding phase of silicate-activated blast furnace slag (one type of low-CO 2 cement), is investigated from a drying perspective, since it is known to suffer extensively from drying-induced microcracking. By employing in situ synchrotron X-ray total scattering measurements and pair distribution function (PDF) analysis we show that the significant contributing factor to the strain development in this material at extremely low relative humidity (0%) is the local atomic structural rearrangement of the C-(N)-A-S-H gel, including collapse of interlayer spacing and slight disintegration of the gel. Moreover, analysis of the medium range (1.0–2.2 nm) ordering in the PDF data reveals that the PDF-derived strain values are in much closer agreement (same order of magnitude) with the macroscopically measured strain data, compared to previous results based on reciprocal space X-ray diffraction data. From a mitigation standpoint, we show that small amounts of ZrO 2 nanoparticles are able to actively reinforce the structure of silicate-activated slag during drying, preventing atomic level strains from developing. Mechanistically, these nanoparticles induce growth of a silica-rich gel during drying, which, via density functional theory calculations, we show is attributed to the high surface reactivity of tetragonal ZrO 2 . 

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.