Clay Composites by In Situ Polymerization of Ionic Liquid-Based Dispersions

Flexible composite materials were prepared by in situ copolymerization of ionic liquid like monomers-namely 1-vinyl-3- ethyl imidazolium bis(trifluoromethane)sulfonimide (M1) and 1-(2-acryloyloxyundecyl)-3-methylimidazolium bis(trifluoromethane)sulfonimide (M2) that were cross-linked with 1,1 '-octane-1,8-diylbis(3-vinyl imidazolium) di[bis(trifluoromethane)sulfonimide] (CL). Mixtures of polymerizable ionic liquids were used to disperse organo-modified montmorillonite clay as a filler. Polymerization of the mixtures resulted in copolymer composites. The glass transition temperature of the composites could be tuned in the range of -2-127 degrees C by varying the ratio of the ionic liquid monomers M1 and M2, which is presented in the article for the first time along with its homopolymer. The mechanical properties were significantly enhanced by using a copolymer matrix instead of either of the respective homopolymers. The toughest M1-M2 copolymer composite exhibited a toughness of 5.3 +/- 1.4 MPa, while the toughnesses of corresponding poly(M1) and poly(M2) films were 0.6 +/- 0.2 and 0.5 +/- 0.003 MPa, respectively. The composite could be filled uniformly with large amounts of montmorillonite clay. The copolymer matrix was able to take up large amounts of clay while still exhibiting mechanical properties that surpassed the unfilled matrix.Peer reviewe

New insights into the environmental factors controlling the ground thermal regime across the Northern Hemisphere : a comparison between permafrost and non-permafrost areas

The thermal state of permafrost affects Earth surface systems and human activity in the Arctic and has implications for global climate. Improved understanding of the local-scale variability in the global ground thermal regime is required to account for its sensitivity to changing climatic and geoecological conditions. Here, we statistically related observations of mean annual ground temperature (MAGT) and active-layer thickness (ALT) to high-resolution (similar to 1 km(2)) geospatial data of climatic and local environmental conditions across the Northern Hemisphere. The aim was to characterize the relative importance of key environmental factors and the magnitude and shape of their effects on MAGT and ALT. The multivariate models fitted well to both response variables with average R-2 values being similar to 0.94 and 0.78. Corresponding predictive performances in terms of root-mean-square error were similar to 1.31 degrees C and 87 cm. Freezing (FDD) and thawing (TDD) degree days were key factors for MAGT inside and outside the permafrost domain with average effect sizes of 6.7 and 13.6 degrees C, respectively. Soil properties had marginal effects on MAGT (effect size = 0.4-0.7 degrees C). For ALT, rainfall (effect size = 181 cm) and solar radiation (161 cm) were most influential. Analysis of variable importance further underlined the dominance of climate for MAGT and highlighted the role of solar radiation for ALT. Most response shapes for MAGTPeer reviewe

A survey of low-velocity collisional features in Saturn's F ring

Small (~50km scale), irregular features seen in Cassini images to be emanating from Saturn's F ring have been termed mini-jets by Attree et al. (2012). One particular mini-jet was tracked over half an orbital period, revealing its evolution with time and suggesting a collision with a local moonlet as its origin. In addition to these data we present here a much more detailed analysis of the full catalogue of over 800 F ring mini-jets, examining their distribution, morphology and lifetimes in order to place constraints on the underlying moonlet population. We find mini-jets randomly located in longitude around the ring, with little correlation to the moon Prometheus, and randomly distributed in time, over the full Cassini tour to date. They have a tendency to cluster together, forming complicated `multiple' structures, and have typical lifetimes of ~1d. Repeated observations of some features show significant evolution, including the creation of new mini-jets, implying repeated collisions by the same object. This suggests a population of <~1km radius objects with some internal strength and orbits spread over 100km in semi-major axis relative to the F ring but with the majority within 20km. These objects likely formed in the ring under, and were subsequently scattered onto differing orbits by, the perturbing action of Prometheus. This reinforces the idea of the F ring as a region with a complex balance between collisions, disruption and accretion.Comment: 21 pages, 12 figures. Accepted for publication in Icarus. Supplementary information available at http://www.maths.qmul.ac.uk/~attree/mini-jets

A Subspace Method for Dynamical Estimation of Evoked Potentials

It is a challenge in evoked potential (EP) analysis to incorporate prior physiological knowledge for estimation. In this paper, we address the problem of single-channel trial-to-trial EP characteristics estimation. Prior information about phase-locked properties of the EPs is assesed by means of estimated signal subspace and eigenvalue decomposition. Then for those situations that dynamic fluctuations from stimulus-to-stimulus could be expected, prior information can be exploited by means of state-space modeling and recursive Bayesian mean square estimation methods (Kalman filtering and smoothing). We demonstrate that a few dominant eigenvectors of the data correlation matrix are able to model trend-like changes of some component of the EPs, and that Kalman smoother algorithm is to be preferred in terms of better tracking capabilities and mean square error reduction. We also demonstrate the effect of strong artifacts, particularly eye blinks, on the quality of the signal subspace and EP estimates by means of independent component analysis applied as a prepossessing step on the multichannel measurements

Hamiltonian inference from dynamical excitations in confined quantum magnets

Quantum-disordered models provide a versatile platform to explore the emergence of quantum excitations in many-body systems. The engineering of spin models at the atomic scale with scanning tunneling microscopy and the local imaging of excitations with electrically driven spin resonance has risen as a powerful strategy to image spin excitations in finite quantum spin systems. Here, focusing on $S=1/2$ lattices as realized by Ti in MgO, we show that dynamical spin excitations provide a robust strategy to infer the nature of the underlying Hamiltonian. We show that finite-size interference of the dynamical many-body spin excitations of a generalized long-range Heisenberg model allows the underlying spin couplings to be inferred. We show that the spatial distribution of local spin excitations in Ti islands and ladders directly correlates with the underlying ground state in the thermodynamic limit. Using a supervised learning algorithm, we demonstrate that the different parameters of the Hamiltonian can be extracted by providing the spatially and frequency-dependent local excitations that can be directly measured by electrically driven spin resonance with scanning tunneling microscopy. Our results put forward local dynamical excitations in confined quantum spin models as versatile witnesses of the underlying ground state, providing an experimentally robust strategy for Hamiltonian inference in complex real spin models.Comment: 11 pages, 10 figure

Benzothiadiazole induces the accumulation of phenolics and improves resistance to powdery mildew in strawberries

Benzothiadiazole (BTH) enhanced the accumulation of soluble and cell-wall-bound phenolics in strawberry leaves and also improved the resistance to powdery mildew infection under greenhouse conditions. The most pronounced change was seen in the levels of ellagitannins, which increased up to 2- to 6-fold 4 days after the BTH application, but persisted only in the inoculated plants. The induction of phenolic metabolism by BTH was also reflected in the fruits, several compounds being increased in inoculated, BTH-treated plants. Basal salicylic acid (SA) content was high in strawberry leaves, but increased in a similar fashion to other phenolics after the treatments. Several phenolic compounds were identified in strawberries for the first time. For example, ellagic acid deoxyhexose, three agrimoniin-like ellagitannins, sanguiin H-10- and lambertianin C-like ellagitannins in the leaves, ellagic acid, p-coumaric acid, gallic acid, and kaempferol hexose in the cell-wall-bound fraction of the leaves, and kaempferol malonylglucoside in the fruits. The findings show that BTH can enhance the accumulation of phenolics in strawberry plants which may then be involved in the BTH-induced resistance to powdery mildew

Detecting Distal Radius Fractures Using a Segmentation-Based Deep Learning Model

Deep learning algorithms can be used to classify medical images. In distal radius fracture treatment, fracture detection and radiographic assessment of fracture displacement are critical steps. The aim of this study was to use pixel-level annotations of fractures to develop a deep learning model for precise distal radius fracture detection. We randomly divided 3785 consecutive emergency wrist radiograph examinations from six hospitals to a training set (3399 examinations) and test set (386 examinations). The training set was used to develop the deep learning model and the test set to assess its validity. The consensus of three hand surgeons was used as the gold standard for the test set. The area under the ROC curve was 0.97 (CI 0.95-0.98) and 0.95 (CI 0.92-0.98) for examinations without a cast. Fractures were identified with higher accuracy in the postero-anterior radiographs than in the lateral radiographs. Our deep learning model performed well in our multi-hospital and multi-radiograph system manufacturer settings. Thus, segmentation-based deep learning models may provide additional benefit. Further research is needed with algorithm comparison and external validation.Peer reviewe

Parameter estimators of random intersection graphs with thinned communities

This paper studies a statistical network model generated by a large number of randomly sized overlapping communities, where any pair of nodes sharing a community is linked with probability $q$ via the community. In the special case with $q=1$ the model reduces to a random intersection graph which is known to generate high levels of transitivity also in the sparse context. The parameter $q$ adds a degree of freedom and leads to a parsimonious and analytically tractable network model with tunable density, transitivity, and degree fluctuations. We prove that the parameters of this model can be consistently estimated in the large and sparse limiting regime using moment estimators based on partially observed densities of links, 2-stars, and triangles.Comment: 15 page