Enhanced Electron Correlation and Significantly Suppressed Thermal Conductivity in Dirac Nodal-Line Metal Nanowires by Chemical Doping

Enhancing electron correlation in a weakly interacting topological system has great potential to promote correlated topological states of matter with extraordinary quantum properties. Here, the enhancement of electron correlation in a prototypical topological metal, namely iridium dioxide (IrO2), via doping with 3d transition metal vanadium is demonstrated. Single-crystalline vanadium-doped IrO2 nanowires are synthesized through chemical vapor deposition where the nanowire yield and morphology are improved by creating rough surfaces on substrates. Vanadium doping leads to a dramatic decrease in Raman intensity without notable peak broadening, signifying the enhancement of electron correlation. The enhanced electron correlation is further evidenced by transport studies where the electrical resistivity is greatly increased and follows an unusual √ T dependence on the temperature (T). The lattice thermal conductivity is suppressed by an order of magnitude via doping even at room temperature where phonon-impurity scattering becomes less important. Density functional theory calculations suggest that the remarkable reduction of thermal conductivity arises from the complex phonon dispersion and reduced energy gap between phonon branches, which greatly enhances phase space for phonon–phonon Umklapp scattering. This work demonstrates a unique system combining 3d and 5d transition metals in isostructural materials to enrich the system with various types of interactions

Development of an empirical model to predict malaria outbreaks based on monthly case reports and climate variables in Hefei, China, 1990–2011

Malaria remains a significant public health concern in developing countries. Drivers of malaria transmission vary across different geographical regions. Climatic variables are major risk factor in seasonal and secular patterns of P. vivax malaria transmission along Anhui province. The study aims to forecast malaria outbreaks using empirical model developed in Hefei, China. Data on the monthly numbers of notified malaria cases and climatic factors were obtained for the period of January 1st 1990 to December 31st 2011 from the Hefei CDC and Anhui Institute of Meteorological Sciences, respectively. Two logistic regression models with time series seasonal decomposition were used to explore the impact of climatic and seasonal factors on malaria outbreaks. Sensitivity and specificity statistics were used for evaluating the predictive power. The results showed that relative humidity (OR = 1.171, 95% CI = 1.090–1.257), sunshine (OR = 1.076, 95% CI = 1.043–1.110) and barometric pressure (OR = 1.051, 95% CI = 1.003–1.100) were significantly associated with malaria outbreaks after adjustment for seasonality in Hefei area. The validation analyses indicated the overall agreement of 70.42% (sensitivity: 70.52%; specificity: 70.30%). The research suggested that the empirical model developed based on disease surveillance and climatic conditions may have applications in malaria control and prevention activities.</p