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The high density of aluminum nanocrystals (>10 21  m −3 ) that develop during the primary crystallization in Al-based metallic glasses indicates a high nucleation rate (∼10 18  m −3  s −1 ). Several studies have been advanced to account for the primary crystallization behavior, but none have been developed to completely describe the reaction kinetics. Recently, structural analysis by fluctuation electron microscopy has demonstrated the presence of the Al-like medium range order (MRO) regions as a spatial heterogeneity in as-spun Al 88 Y 7 Fe 5 metallic glass that is representative for the class of Al-based amorphous alloys that develop Al nanocrystals during primary crystallization. From the structural characterization, an MRO seeded nucleation configuration is established, whereby the Al nanocrystals are catalyzed by the MRO core to decrease the nucleation barrier. The MRO seeded nucleation model and the kinetic data from the delay time ( τ) measurement provide a full accounting of the evolution of the Al nanocrystal density (N v ) during the primary crystallization under isothermal annealing treatments. Moreover, the calculated values of the steady state nucleation rates ( J ss ) predicted by the nucleation model agree with the experimental results. Moreover, the model satisfies constraints on the structural, thermodynamic, and kinetic parameters, such as the critical nucleus size, the interface energy, and the volume-free energy driving force that are essential for a fully self-consistent nucleation kinetics analysis. The nucleation kinetics model can be applied more broadly to materials that are characterized by the presence of spatial heterogeneities. 

Metal halide perovskite nanocrystals (NCs) have emerged as new-generation light-emitting materials with narrow emissions and high photoluminescence quantum efficiencies (PLQEs). Various types of perovskite NCs, e.g., platelets, wires, and cubes, have been discovered to exhibit tunable emissions across the whole visible spectrum. Despite remarkable advances in the field of perovskite NCs, many nanostructures in inorganic NCs have not yet been realized in metal halide perovskites, and producing highly efficient blue-emitting perovskite NCs remains challenging and of great interest. Here, we report the discovery of highly efficient blue-emitting cesium lead bromide (CsPbBr 3 ) perovskite hollow NCs. By facile solution processing of CsPbBr 3 precursor solution containing ethylenediammonium bromide and sodium bromide, in situ formation of hollow CsPbBr 3 NCs with controlled particle and pore sizes is realized. Synthetic control of hollow nanostructures with quantum confinement effect results in color tuning of CsPbBr 3 NCs from green to blue, with high PLQEs of up to 81%. 

While the popularity of online social network (OSN) apps continues to grow, little attention has been drawn to the increasing cases of Social Network Addictions (SNAs). In this paper, we argue that by mining OSN data in support of online intervention treatment, data scientists may assist mental healthcare professionals to alleviate the symptoms of users with SNA in early stages. Our idea, based on behavioral therapy, is to incrementally substitute highly addictive newsfeeds with safer, less addictive, and more supportive newsfeeds. To realize this idea, we propose a novel framework, called Newsfeed Substituting and Supporting System (N3S), for newsfeed filtering and dissemination in support of SNA interventions. New research challenges arise in 1) measuring the addictive degree of a newsfeed to an SNA patient, and 2) properly substituting addictive newsfeeds with safe ones based on psychological theories. To address these issues, we first propose the Additive Degree Model (ADM) to measure the addictive degrees of newsfeeds to different users. We then formulate a new optimization problem aiming to maximize the efficacy of behavioral therapy without sacrificing user preferences. Accordingly, we design a randomized algorithm with a theoretical bound. A user study with 716 Facebook users and 11 mental healthcare professionals around the world manifests that the addictive scores can be reduced by more than 30%. Moreover, experiments show that the correlation between the SNA scores and the addictive degrees quantified by the proposed model is much greater than that of state-of-the-art preference based models. 

Abstract Segmented polyureas (PUa) are industrially important class of polymers widely used in coatings, sealant, and adhesive applications. Here, we report synthesis, characterization, and modeling of Isophorone Diisocyanate‐Diethyl‐Toluene‐Diamine‐Polyether amine (IPDI‐DETDA‐PO PUa) with varied hard segment contents of 20, 30, and 40 weight percent. For each of the three materials, we study its structure and phase behavior using FTIR, DSC, and TEM, and clearly show the presence of microphase separation between the hard and soft nanodomains. We then measure the linear viscoelastic response of the PUa‐s using DMA (frequency sweeps at multiple temperatures). The DMA data are shown to obey the time‐temperature superposition. Finally, we develop a new micromechanical model describing the DMA results; the model describes a phase‐separated PUa as two “Fractional‐order Maxwell gels” branches, connected in parallel, with the first FMG branch representing the “percolated hard phase and the second one modeling the “filled soft phase. In agreement with the earlier thermodynamic theories, the volume‐fraction of the percolated hard phase is related to the hard segment weight‐fraction (HSWF), defined as the combined mass of IPDI and DETDA normalized to the total mass of the polymer. The data and model are found to be in a good qualitative and quantitative agreement.