Enhancing Electrochemical CO2 Reduction on Perovskite Oxide for Solid Oxide Electrolysis Cells through In Situ A-Site Deficiencies and Surface Carbonate Deposition Induced by Lithium Cation Doping and Exsolution.

Solid oxide electrolysis cells (SOECs) hold enormous potential for efficient conversion of CO2 to CO at low cost and high reaction kinetics. The identification of active cathodes is highly desirable to promote the SOEC's performance. This study explores a lithium-doped perovskite La0.6- x Lix Sr0.4 Co0.7 Mn0.3 O3-δ (x = 0, 0.025 0.05, and 0.10) material with in situ generated A-site deficiency and surface carbonate as SOEC cathodes  for CO2 reduction. The experimental results indicate that the SOEC with the La0.55 Li0.05 Sr0.4 Co0.7 Mn0.3 O3-δ cathode exhibits a current density of 0.991 A cm-2 at 1.5 V/800 °C, which is an improvement of ≈30% over the pristine sample. Furthermore, SOECs based on the proposed cathode demonstrate excellent stability over 300 h for pure CO2 electrolysis. The addition of lithium with high basicity, low valance, and small radius, coupled with A-site deficiency, promotes the formation of oxygen vacancy and modifies the electronic structure of active sites, thus enhancing CO2 adsorption, dissociation process, and CO desorption steps as corroborated by the experimental analysis and the density functional theory calculation. It is further confirmed that Li-ion migration to the cathode surface forms carbonate and consequently provides the perovskite cathode with an impressive anti-carbon deposition capability, as well as electrolysis activity.

Mutual Conversion of CO-CO2 on a Perovskite Fuel Electrode with Endogenous Alloy Nanoparticles for Reversible Solid Oxide Cells.

Reversible solid oxide cells (RSOCs) can efficiently render the mutual conversion between electricity and chemicals, for example, electrolyzing CO2 to CO under a solid oxide electrolysis cell (SOEC) mode and oxidizing CO to CO2 under a solid oxide fuel cell (SOFC) mode. Nevertheless, the development of RSOCs is still hindered, owing to the lack of catalytically active and carbon-tolerant fuel electrodes. For improving mutual CO-CO2 conversion kinetics in RSOCs, here, we demonstrate a high-performing and durable fuel electrode consisting of redox-stable Sr2(Fe, Mo)2O6-δ perovskite oxide and epitaxially endogenous NiFe alloy nanoparticles. The electrochemical impedance spectrum (EIS) and distribution of relaxation time (DRT) analyses reveal that surface/interface oxygen exchange kinetics and the CO/CO2 activation process are both greatly accelerated. The assembled single cell produces a maximum power density (MPD) of 443 mW cm-2 at 800 °C under the SOFC mode, with the corresponding CO oxidation rate of 5.524 mL min-1 cm-2. On the other hand, a current density of -0.877 A cm-2 is achieved at 1.46 V under the SOEC mode, equivalent to a CO2 reduction rate of 6.108 mL min cm-2. Furthermore, reliable reversible conversion of CO-CO2 is proven with no performance degradation in 20 cycles under SOEC (1.3 V) and SOFC (0.6 V) modes. Therefore, our work provides an alternative way for designing highly active and durable fuel electrodes for RSOC applications.

Highly improved aqueous lubrication of polymer surface by noncovalently bonding hyaluronic acid-based hydration layer for endotracheal intubation.

Hydration lubrication is the key responsible for the exceptionally low boundary friction between biosurfaces. However, it is a challenge to settle a hydration layer on a polymer surface via a noncovalent manner. Herein, we develop a highly lubricated coating absorbed onto the polymer surface via intermolecular association of hyaluronic acid (HA)-based micelles. A poly(ethylene oxide)-b-poly(propylene oxide)-b-poly(ethylene oxide) triblock copolymer (Pluronic, F127) is recruited to complex with HA and further self-assembled to form a thick micelle layer. High water-retaining capacity of the HA/F127 coating enables the decorated surface with excellent hydrophilicity and boundary lubrication, where the coefficient of friction in aqueous media is reduced by 60% compared with the bare polymer surface. The HA/F127 coating suppresses nonspecific protein adsorption and exhibits good biocompatibility. More remarkably, an in vivo cynomolgus monkey model, demonstrates the utility of the HA/F127 coating in alleviating or preventing complications of endotracheal intubation, such as foreign irritation, airway mucosal damage, and inflammatory response. This cost-effective and scalable approach is suitable to manufacture interventional devices especially disposable medical devices with highly lubricated surface.

Integrating Multiple Data Sources and Learning Models to Predict Infectious Diseases in China.

The outbreaks of infectious diseases do not only endanger people's lives and property, but can also result in negative social impact and economic loss. Therefore, establishing early warning technologies for infectious diseases is of great value. This paper was built on the historical morbidity and mortality incidence data of infectious diseases, including typhoid fever, Hemorrhagic Fever with Renal Syndrome (HFRS), mumps, scarlatina, malaria, dysentery, pertussis, conjunctivitis, pulmonary tuberculosis, diarrhea from 2012 to 2016 in China. We also integrated search engine query data and seasonal information into the prediction models. Multiple models for prediction, including linear model, time series analysis model, boosting tree model and deep learning model (recurrent neural network, RNN) were constructed in order to predict the morbidity incidence of 10 infectious diseases. The RNN model has better predictive capability for these diseases. The improvement of techniques for infectious disease prediction can facilitate constructive and positive change towards disease prevention.

Role of plant MicroRNA in cross-species regulatory networks of humans.

BACKGROUND: It has been found that microRNAs (miRNAs) can function as a regulatory factor across species. For example, food-derived plant miRNAs may pass through the gastrointestinal (GI) tract, enter into the plasma and serum of mammals, and interact with endogenous RNAs to regulate their expression. Although this new type of regulatory mechanism is not well understood, it provides a fresh look at the relationship between food consumption and physiology. To investigate this new type of mechanism, we conducted a systematic computational study to analyze the potential functions of these dietary miRNAs in the human body. RESULTS: In this paper, we predicted human and plant target genes using RNAhybrid and set some criteria to further filter them. Then we built the cross-species regulatory network according to the filtered targets, extracted central nodes by PageRank algorithm and built core modules. We summarized the functions of these modules to three major categories: ion transport, metabolic process and stress response, and especially some target genes are highly related to ion transport, polysaccharides and the lipid metabolic process. Through functional analysis, we found that human and plants have similar functions such as ion transport and stress response, so our study also indicates the existence of a close link between exogenous plant miRNA targets and digestive/urinary organs. CONCLUSIONS: According to our analysis results, we suggest that the ingestion of these plant miRNAs may have a functional impact on consuming organisms in a cross-kingdom way, and the dietary habit may affect the physiological condition at a genetic level. Our findings may be useful for discovering cross-species regulatory mechanism in further study.

A computational method for predicting regulation of human microRNAs on the influenza virus genome.

BACKGROUND: While it has been suggested that host microRNAs (miRNAs) may downregulate viral gene expression as an antiviral defense mechanism, such a mechanism has not been explored in the influenza virus for human flu studies. As it is difficult to conduct related experiments on humans, computational studies can provide some insight. Although many computational tools have been designed for miRNA target prediction, there is a need for cross-species prediction, especially for predicting viral targets of human miRNAs. However, finding putative human miRNAs targeting influenza virus genome is still challenging. RESULTS: We developed machine-learning features and conducted comprehensive data training for predicting interactions between H1N1 genome segments and host miRNA. We defined our seed region as the first ten nucleotides from the 5' end of the miRNA to the 3' end of the miRNA and integrated various features including the number of consecutive matching bases in the seed region of 10 bases, a triplet feature in seed regions, thermodynamic energy, penalty of bulges and wobbles at binding sites, and the secondary structure of viral RNA for the prediction. CONCLUSIONS: Compared to general predictive models, our model fully takes into account the conservation patterns and features of viral RNA secondary structures, and greatly improves the prediction accuracy. Our model identified some key miRNAs including hsa-miR-489, hsa-miR-325, hsa-miR-876-3p and hsa-miR-2117, which target HA, PB2, MP and NS of H1N1, respectively. Our study provided an interesting hypothesis concerning the miRNA-based antiviral defense mechanism against influenza virus in human, i.e., the binding between human miRNA and viral RNAs may not result in gene silencing but rather may block the viral RNA replication.