Classification of biomass reactions and predictions of reaction energies through machine learning.

Elementary steps and intermediate species of linearly structured biomass compounds are studied. Specifically, possible intermediates and elementary reactions of 15 key biomass compounds and 33 small molecules are obtained from a recursive bond-breaking algorithm. These are used as inputs to the unsupervised Mol2Vec algorithm to generate vector representations of all intermediates and elementary reactions. The vector descriptors are used to identify sub-classes of elementary steps, and linear discriminant analysis is used to accurately identify the reaction type and reduce the dimension of the vectors. The resulting descriptors are applied to predict gas-phase reaction energies using linear regression with accuracies that exceed the commonly employed group additivity approach. They are also applied to quantitatively assess model compound similarity, and the results are consistent with chemical intuition. This workflow for creating vector representations of complex molecular systems requires no input from electronic structure calculations, and it is expected to be applicable to other similar systems where vector representations are needed.

Highly Hydroxide-Conductive Nanostructured Solid Electrolyte via Predesigned Ionic Nanoaggregates.

The creation of interconnected ionic nanoaggregates within solid electrolytes is a crucial yet challenging task for fabricating high-performance alkaline fuel cells. Herein, we present a facile and generic approach to embedding ionic nanoaggregates via predesigned hybrid core-shell nanoarchitecture within nonionic polymer membranes as follows: (i) synthesizing core-shell nanoparticles composed of SiO2/densely quaternary ammonium-functionalized polystyrene. Because of the spatial confinement effect of the SiO2 "core", the abundant hydroxide-conducting groups are locally aggregated in the functionalized polystyrene "shell", forming ionic nanoaggregates bearing intrinsic continuous ion channels; (ii) embedding these ionic nanoaggregates (20-70 wt %) into the polysulfone matrix to construct interconnected hydroxide-conducting channels. The chemical composition, physical morphology, amount, and distribution of the ionic nanoaggregates are facilely regulated, leading to highly connected ion channels with high effective ion mobility comparable to that of the state-of-the-art Nafion. The resulting membranes display strikingly high hydroxide conductivity (188.1 mS cm-1 at 80 °C), which is one of the highest results to date. The membranes also exhibit good mechanical properties. The independent manipulation of the conduction function and nonconduction function by the ionic nanoaggregates and nonionic polymer matrix, respectively, opens a new avenue, free of microphase separation, for designing high-performance solid electrolytes for diverse application realms.

A highly proton-conducting, methanol-blocking Nafion composite membrane enabled by surface-coating crosslinked sulfonated graphene oxide.

Coating an ultrathin crosslinked graphene oxide film onto a Nafion support enables the tradeoff effect to be successfully overcome by the resulting composite membrane: 93% decrease of methanol permeability while retaining the high proton conductivity of Nafion, owing to the synergistic modulation of methanol-transport and proton-transport channels within the graphene oxide film.

The novel H1N1 Influenza A global airline transmission and early warning without travel containments.

A novel influenza A (H1N1) has been spreading worldwide. Early studies implied that international air travels might be key cause of a severe potential pandemic without appropriate containments. In this study, early outbreaks in Mexico and some cities of United States were used to estimate the preliminary epidemic parameters by applying adjusted SEIR epidemiological model, indicating transmissibility infectivity of the virus. According to the findings, a new spatial allocation model totally based on the real-time airline data was established to assess the potential spreading of H1N1 from Mexico to the world. Our estimates find the basic reproductive number R0 of H1N1 is around 3.4, and the effective reproductive number fall sharply by effective containment strategies. The finding also implies Spain, Canada, France, Panama, Peru are the most possible country to be involved in severe endemic H1N1 spreading.

Risk analysis for the highly pathogenic avian influenza in Mainland China using meta-modeling.

A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 22 d. The potential risk of occurrence of HPAI in Mainland China for the period from January 23 to February 17, 2004 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.