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.

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 SiO/densely quaternary ammonium-functionalized polystyrene. Because of the spatial confinement effect of the SiO "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.

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.

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 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.