Molecular dynamics of n-hexane: A quasi-elastic neutron scattering study on the bulk and spatially nanochannel-confined liquid

We present incoherent quasi-elastic neutron scattering measurements in a wavevector transfer range from 0.4 AA^{-1} to 1.6AA^{-1} on liquid n-hexane confined in cylindrical, parallel-aligned nanochannels of 6 nm mean diameter and 260 micrometer length in monolithic, mesoporous silicon. They are complemented with, and compared to, measurements on the bulk system in a temperature range from 50K to 250K. The time-of-flight spectra of the bulk liquid can be modeled by microscopic translational as well as fast localized rotational, thermally-excited, stochastic motions of the molecules. In the nano-confined state of the liquid, which was prepared by vapor condensation, we find two molecular populations with distinct dynamics, a fraction which is immobile on the time scale of 1ps to 100ps probed in our experiments and a second component with a self-diffusion dynamics slightly slower than observed for the bulk liquid. No hints of an anisotropy of the translational diffusion with regard to the orientation of the channels' long axes have been found. The immobile fraction amounts to about 5% at 250K, gradually increases upon cooling and exhibits an abrupt increase at 160K (20K below bulk crystallization), which indicates pore freezingComment: 10 pages, 7 figure

Inelastic Neutron Scattering Analysis with Time-Dependent Gaussian-Field Models

Converting neutron scattering data to real-space time-dependent structures can only be achieved through suitable models, which is particularly challenging for geometrically disordered structures. We address this problem by introducing time-dependent clipped Gaussian field models. General expressions are derived for all space- and time-correlation functions relevant to coherent inelastic neutron scattering, for multiphase systems and arbitrary scattering contrasts. Various dynamic models are introduced that enable one to add time-dependence to any given spatial statistics, as captured e.g. by small-angle scattering. In a first approach, the Gaussian field is decomposed into localised waves that are allowed to fluctuate in time or to move, either ballistically or diffusively. In a second approach, a dispersion relation is used to make the spectral components of the field time-dependent. The various models lead to qualitatively different dynamics, which can be discriminated by neutron scattering. The methods of the paper are illustrated with oil/water microemulsion studied by small-angle scattering and neutron spin-echo. All available data - in both film and bulk contrasts, over the entire range of $q$ and $\tau$- are analyzed jointly with a single model. The analysis points to static large-scale structure of the oil and water domains, while the interfaces are subject to thermal fluctuations. The fluctuations have an amplitude around 6 nm and contribute to 30 % of the total interface area.Comment: The following article has been accepted by Journal of Chemical Physics. After it is published, it will be found at https://aip.scitation.org/journal/jcp

Low frequency vibrations and diffusion in disordered polymers bearing an intrinsic microporosity as revealed by neutron scattering

The microscopic diffusion and the low frequency density of states (VDOS) of PIM-EA-TB(CH3) are investigated by inelastic and quasi-elastic neutron scattering where also the demethylated counterpart of PIM-EA-TB(H2) is considered. These intrinsic microporous polymers are characterized by large BET surface area values of several hundred m2/g and pore sizes between 0.5 and 2 nm. Detailed comparison is made to the archetype of polymers of intrinsic microporosity, PIM-1, and polynorbornenes also bearing a microporosity. Due to the wavelength of neutrons, the diffusion and vibrations can be addressed on microscopic length and time scales. From the inelastic neutron scattering experiments the low frequency density of states (VDOS) is estimated which shows excess contributions to the Debye-type VDOS known as Boson peak. It was found that the maximum frequency of the Boson peak decreases with increasing microporosity characterized by the BET surface area. However, besides the BET surface area, additional factors such as the backbone stiffness govern the maximum frequency of the Boson peak. Further the mean squared displacement related to microscopic motions was estimated from elastic fixed window scans. At temperatures above 175 K, the mean squared displacement PIM-EA-TB(CH3) is higher than that for the demethylated counterpart PIM-EA-TB(H2). The additional contribution found for PIM-EA-TB(CH3) is ascribed to the rotation of the methyl group in this polymer because the only difference between the two structures is that PIM-EA-TB(CH3) has methyl groups where PIM-EA-TB(H2) has none. A detailed comparison of the molecular dynamics is also made to that of PIM-1 and the microporous polynorbornene PTCNSi1. The manuscript focuses on the importance of vibrations and the localized molecular mobility characterized by the microscopic diffusion on the gas transport in polymeric separation membranes. In the frame of the random gate model localized fluctuations can open or close bottlenecks between pores to enable the diffusion of gas molecules

Fractal diffusion in high temperature polymer electrolyte fuel cell membranes

© 2018 Author(s). The performance of fuel cells depends largely on the proton diffusion in the proton conducting membrane, the core of a fuel cell. High temperature polymer electrolyte fuel cells are based on a polymer membrane swollen with phosphoric acid as the electrolyte, where proton conduction takes place. We studied the proton diffusion in such membranes with neutron scattering techniques which are especially sensitive to the proton contribution. Time of flight spectroscopy and backscattering spectroscopy have been combined to cover a broad dynamic range. In order to selectively observe the diffusion of protons potentially contributing to the ion conductivity, two samples were prepared, where in one of the samples the phosphoric acid was used with hydrogen replaced by deuterium. The scattering data from the two samples were subtracted in a suitable way after measurement. Thereby subdiffusive behavior of the proton diffusion has been observed and interpreted in terms of a model of fractal diffusion. For this purpose, a scattering function for fractal diffusion has been developed. The fractal diffusion dimension dw and the Hausdorff dimension df have been determined on the length scales covered in the neutron scattering experiments

Epigenetic Control of the foxp3 Locus in Regulatory T Cells

Compelling evidence suggests that the transcription factor Foxp3 acts as a master switch governing the development and function of CD4(+) regulatory T cells (Tregs). However, whether transcriptional control of Foxp3 expression itself contributes to the development of a stable Treg lineage has thus far not been investigated. We here identified an evolutionarily conserved region within the foxp3 locus upstream of exon-1 possessing transcriptional activity. Bisulphite sequencing and chromatin immunoprecipitation revealed complete demethylation of CpG motifs as well as histone modifications within the conserved region in ex vivo isolated Foxp3(+)CD25(+)CD4(+) Tregs, but not in naïve CD25(−)CD4(+) T cells. Partial DNA demethylation is already found within developing Foxp3(+) thymocytes; however, Tregs induced by TGF-β in vitro display only incomplete demethylation despite high Foxp3 expression. In contrast to natural Tregs, these TGF-β–induced Foxp3(+) Tregs lose both Foxp3 expression and suppressive activity upon restimulation in the absence of TGF-β. Our data suggest that expression of Foxp3 must be stabilized by epigenetic modification to allow the development of a permanent suppressor cell lineage, a finding of significant importance for therapeutic applications involving induction or transfer of Tregs and for the understanding of long-term cell lineage decisions

The effect of cross-linking on the molecular dynamics of the segmental and β Johari–Goldstein processes in polyvinylpyrrolidone-based copolymers

The effect of the cross-link density on the molecular dynamics of copolymers composed of vinylpyrrolidone (VP) and butyl acrylate (BA) was studied using differential scanning calorimetry (DSC) and dielectric relaxation spectroscopy (DRS). A single glass transition was detected by DSC measurements. The dielectric spectra exhibit conductive processes and three dipolar relaxations labeled as a, b and g in the decreasing order of temperatures. The cross-linker content affects both a and b processes, but the fastest g process is relatively unaffected. An increase of cross-linking produces a typical effect on the a process dynamics: (i) the glass transition temperature is increased, (ii) the dispersion is broadened, (iii) its strength is decreased and (iv) the relaxation times are increased. However, the b process, which possesses typical features of a pure Johari Goldstein relaxation, unexpectedly loses the intermolecular character for the highest cross-linker content.B.R.F., M.J.S., P.O.S. and M.C. gratefully acknowledge CICYT for grant MAT2012-33483. F.G. and J.M.G. acknowledge the Spanish Ministerio de Economia y Competitividad-FEDER (MAT2014-54137-R) and the Junta de Castilla y Leon (BU232U13).Redondo Foj, MB.; Sanchis Sánchez, MJ.; Ortiz Serna, MP.; Carsí Rosique, M.; García, JM.; García, FC. (2015). The effect of cross-linking on the molecular dynamics of the segmental and β Johari–Goldstein processes in polyvinylpyrrolidone-based copolymers. Soft Matter. 11:7171-7180. https://doi.org/10.1039/c5sm00714cS7171718011V. Bühler , Polyvinylpyrrolidone Excipients for Pharmaceuticals: Povidone, Crospovidone and Copovidone , Springer , Berlin , 2005Haaf, F., Sanner, A., & Straub, F. (1985). Polymers of N-Vinylpyrrolidone: Synthesis, Characterization and Uses. 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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages