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Register a new userCrystallization Inhibitors: Explaining Experimental Data through Mathematical Models
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In this paper we propose a new mathematical model describing the effect of phosphocitrate (PC) on sodium sulphate crystallization inside bricks. This model describes salt and water transport, and crystal formation in a one dimensional symmetry. This is the first study that takes into account mathematically the effects of inhibitors inside a porous stone. To this aim, we introduce two model parameters: the crystallization rate, which depends on the nucleation rate, and the specific volume of precipitated salt. These two parameters are determined by numerical calibration of our system model for both the treated and non treated case.
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By means of Raman spectroscopy of liquid microjets we have investigated the crystallization process of supercooled quantum liquid mixtures composed of parahydrogen (pH$_2$) diluted with small amounts of up to 5% of either neon or orthodeuterium (oD$_2$), and of oD$_2$ diluted with either Ne or pH$_2$. We show that the introduction of Ne impurities affects the crystallization kinetics in both the pH$_2$-Ne and oD$_2$-Ne mixtures in terms of a significant reduction of the crystal growth rate, similarly to what found in our previous work on supercooled pH$_2$-oD$_2$ liquid mixtures [M. Kuhnel et {it al.}, Phys. Rev. B textbf{89}, 180506(R) (2014)]. Our experimental results, in combination with path-integral simulations of the supercooled liquid mixtures, suggest in particular a correlation between the measured growth rates and the ratio of the effective particle sizes originating from quantum delocalization effects. We further show that the crystalline structure of the mixture is also affected to a large extent by the presence of the Ne impurities, which likely initiate the freezing process through the formation of Ne crystallites.
We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal-organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.
The hematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. Feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in hematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. Developing a proper experimental design for these studies is an extremely challenging task. To address this issue, we have developed a novel Bayesian framework for optimal design of perturbation experiments. We model the numbers of hematopoietic stem and progenitor cells in mice that are exposed to a low dose of radiation. We use a differential equations model that accounts for feedback and feedforward regulation. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. This model is embedded in a hierarchical framework with latent variables that capture unobserved cellular population levels. We select the optimum design based on the amount of information gain, measured by the Kullback-Leibler divergence between the probability distributions before and after observing the data. We evaluate our approach using synthetic and experimental data. We show that a proper design can lead to better estimates of model parameters even with relatively few subjects. Additionally, we demonstrate that the model parameters show a wide range of sensitivities to design options. Our method should allow scientists to find the optimal design by focusing on their specific parameters of interest and provide insight to hematopoiesis. Our approach can be extended to more complex models where latent components are used.
Sparse matter is characterized by regions with low electron density and its understanding calls for methods to accurately calculate both the van der Waals (vdW) interactions and other bonding. Here we present a first-principles density functional theory (DFT) study of a layered oxide (V2O5) bulk structure which shows charge voids in between the layers and we highlight the role of the vdW forces in building up material cohesion. The result of previous first-principles studies involving semilocal approximations to the exchange-correlation functional in DFT gave results in good agreement with experiments for the two in-plane lattice parameters of the unit cell but overestimated the parameter for the stacking direction. To recover the third parameter we include the nonlocal (dispersive) vdW interactions through the vdW-DF method [Dion et al., Phys. Rev. Lett. 92, 246401 (2004)] testing also various choices of exchange flavors. We find that the transferable first-principle vdW-DF calculations stabilizes the bulk structure. The vdW-DF method gives results in fairly good agreement with experiments for all three lattice parameters.
The conversion of optical and electrical energy in novel materials is key to modern optoelectronic and light-harvesting applications. Here, we investigate the equilibration dynamics of photoexcited 2,7-bis(biphenyl-4-yl)-2,7-ditertbutyl-9,9-spirobiuorene (SP6) molecules adsorbed on ZnO(10-10) using femtosecond time-resolved two-photon photoelectron (2PPE) and optical spectroscopy. We find that, after initial ultrafast relaxation on fs and ps timescales, an optically dark state is populated, likely the SP6 triplet (T) state, that undergoes Dexter-type energy transfer ($r_{mathrm{Dex}} = 1.3~mathrm{nm}$) and exhibits a long decay time of 0.1 s. Because of this long lifetime a photostationary state with average T-T distances below 2 nm is established at excitation densities in the $10^{20}~mathrm{cm}^{-2}~mathrm{s}^{-1}$ range. This large density enables decay by T-T annihilation (TTA) mediating autoionization despite an extremely low TTA rate of $k_{mathrm{TTA}} = 4.5~10^{-26}~mathrm{m}^3~mathrm{s}^{-1}$. The large external quantum efficiency of the autoionization process (up to 15 %) and photocurrent densities in the mathrm{mA}~mathrm{cm}^{-2}$ range offer great potential for light-harvesting applications.
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