Dynamics of the two-dimensional S=1/2 dimer system (C5H6N2F)2CuCl4

Inelastic neutron scattering was used to study a quantum S=1/2 antiferromagnetic Heisenberg system-Bis(2-amino-5-fluoropyridinium) Tetrachlorocuprate(II). The magnetic excitation spectrum was shown to be dominated by long-lived excitations with an energy gap as 1.07(3) meV. The measured dispersion relation is consistent with a simple two-dimensional square lattice of weakly-coupled spin dimers. Comparing the data to a random phase approximation treatment of this model gives the intra-dimer and inter-dimer exchange constants J=1.45(2) meV and J'=0.31(3) meV, respectively.Comment: 4 pages, 4 figure

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multi-Molecular and Solvent-Inclusive Collective Variables

The typically rugged nature of molecular free energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for data-driven discovery of CVs parameterizing the important large scale motions of the system. A challenge to CV discovery is learning CVs invariant to symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance have proved a persistent challenge in frustrating the the data-driven discovery of multi-molecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate Permutation Invariant Vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation, and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand sampling of configurational phase space and obtain converged CVs and free energy landscapes. We demonstrate the Permutationally Invariant Network for Enhanced Sampling (PINES) approach in applications to the self-assembly of a 13-atom Argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C45H92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries

Wilson ratio of a Tomonaga-Luttinger liquid in a spin-1/2 Heisenberg ladder

Using micromechanical force magnetometry, we have measured the magnetization of the strong-leg spin-1/2 ladder compound (C$_7$H$_{10}$N)$_2$CuBr$_2$ at temperatures down to 45 mK. Low-temperature magnetic susceptibility as a function of field exhibits a maximum near the critical field H_c at which the magnon gap vanishes, as expected for a gapped one-dimensional antiferromagnet. Above H_c a clear minimum appears in the magnetization as a function of temperature as predicted by theory. In this field region, the susceptibility in conjunction with our specific heat data yields the Wilson ratio R_W. The result supports the relation R_W=4K, where K is the Tomonaga-Luttinger-liquid parameter

Production technology of Nabataean painted pottery compared with that of Roman terra sigillata

The Nabataeans, who founded the city of Petra (southern Jordan) in the late first millennium BCE, are noted for the production of a distinctive very fine pottery with painted decoration and a wall thickness sometimes as little as 1.5 mm; this pottery appears largely locally made and not widely circulated. Using a combination of OM, SEM with attached EDS, surface XRF, and XRD, it is shown that the Nabataean fine pottery bodies were produced using semi-calcareous clays which were fired to temperatures of about 950 °C. In contrast, published data indicate that contemporary and in many ways apparently functionally equivalent Roman terra sigillata, which was traded throughout the Roman Empire, was produced using fully-calcareous clays which were fired to temperatures in the range 1000–1100 °C. Furthermore, the high gloss slip applied to Roman terra sigillata is fully vitrified whereas the red-painted decoration applied to the Nabataean pottery is unvitrified. The more robust Roman terra sigillata is therefore better suited as tableware for serving and consuming food than would be the case for Nabataean fine pottery, and would be a more successful export material

Neutron Imaging of Archaeological Bronzes at the Oak Ridge National Laboratory

AbstractThis article presents the initial results of 2-D and 3-D neutron imaging of bronze artifacts using the CG-1D prototype beamline at the High Flux Isotope Reactor (HFIR) located at the Oak Ridge National Laboratory (ORNL). Neutron imaging is a non-destructive technique capable of producing unprecedented three-dimensional information on archaeomaterials, including qualitative, quantitative, and visual data on impurities, composition change, voids, and structure at macro-scale levels. The initial results presented in this publication highlight how information from neutron imaging can provide otherwise inaccessible details about the methods and materials that ancient craftspeople used in creating bronze objects

Real-time Epidemic Forecasting: Challenges and Opportunities.

Infectious disease outbreaks play an important role in global morbidity and mortality. Real-time epidemic forecasting provides an opportunity to predict geographic disease spread as well as case counts to better inform public health interventions when outbreaks occur. Challenges and recent advances in predictive modeling are discussed here. We identified data needs in the areas of epidemic surveillance, mobility, host and environmental susceptibility, pathogen transmissibility, population density, and healthcare capacity. Constraints in standardized case definitions and timely data sharing can limit the precision of predictive models. Resource-limited settings present particular challenges for accurate epidemic forecasting due to the lack of granular data available. Incorporating novel data streams into modeling efforts is an important consideration for the future as technology penetration continues to improve on a global level. Recent advances in machine-learning, increased collaboration between modelers, the use of stochastic semi-mechanistic models, real-time digital disease surveillance data, and open data sharing provide opportunities for refining forecasts for future epidemics. Epidemic forecasting using predictive modeling is an important tool for outbreak preparedness and response efforts. Despite the presence of some data gaps at present, opportunities and advancements in innovative data streams provide additional support for modeling future epidemics