Size-Dependent Response of Hydrothermally Grown SnO2 for a High-Performance NO2 Sensor and the Impact of Oxygen.

A NO2 sensor with a detection limit down to the ppb level based on pristine SnO2 has been developed through a facile poly(acrylic acid)-mediated hydrothermal method. SnO2 particles of solid microsphere, hollow microsphere, and nanosphere morphologies were synthesized, with respective constitutional crystallite of size ∼2 µm in length and 10-20 nm and ∼7 nm in diameter. All sensors show great selectivity to NO2. The hollow microsphere sensor exhibits the best performance, with medium specific surface area (SSA), followed by the nanosphere sensor with the largest SSA. This is attributed to the superposition of two opposite effects on sensor response with increased SSA: more adsorption sites and fewer electrons to be taken out with overly small crystallite that may reach complete depletion. O2 is found to speed up the response and recovery times but reduce the response because O adsorbates facilitate the adsorption/desorption of NO2 thermodynamically, and the two oxidizing gases compete in harvesting electrons from SnO2. The adverse effect of humidity can be minimized by operating the sensor at 110 °C. The response of the hollow microsphere sensor to 50 ppb of NO2 is 8.8 (Rg/Ra) at room temperature, and it increases to 15.1 at 110 °C. These findings are useful for developing other oxidizing gas semiconductor sensors.

Nano-porous Al/Au skeleton to support MnO2 with enhanced performance and electrodeposition adhesion for flexible supercapacitors.

A nano-porous Al/Au skeleton is constructed to effectively improve the utilization rate of the active MnO2 and the overall adhesion between the current collector and MnO2 in an electrodeposition system. The Al/Au current collector is prepared by first forming a nano-porous structure on the surface of Al foil through etching modification, and subsequently coating an ultra-thin Au layer onto the Al foil. The active MnO2 is electrodeposited on the Al/Au current collector to fabricate a novel Al/Au/MnO2 electrode. The nano-porous skeleton supports MnO2 to grow autonomously inside-out. The ultra-thin Au layer acts as a transition layer to improve the overall conductivity of the current collector (0.35 Ω m-1) and to improve the adhesion with MnO2 as well. Owing to the highly porous structure, the electrochemical properties of the electrode are greatly improved, as evidenced by a remarkable specific capacitance of 222.13 mF cm-2 at 0.2 mA cm-2 and excellent rate capability of 63% capacitance retention at 6.0 mA cm-2. Furthermore, the assembled solid-state symmetric supercapacitor exhibits a high energy density of 0.68 mW h cm-3, excellent cyclic stability (86.3% capacitance retention after 2000 cycles), and prominent flexibility.

HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks.

BACKGROUND: As a standardized vocabulary of phenotypic abnormalities associated with human diseases, the Human Phenotype Ontology (HPO) has been widely used by researchers to annotate phenotypes of genes/proteins. For saving the cost and time spent on experiments, many computational approaches have been proposed. They are able to alleviate the problem to some extent, but their performances are still far from satisfactory. METHOD: For inferring large-scale protein-phenotype associations, we propose HPOAnnotator that incorporates multiple Protein-Protein Interaction (PPI) information and the hierarchical structure of HPO. Specifically, we use a dual graph to regularize Non-negative Matrix Factorization (NMF) in a way that the information from different sources can be seamlessly integrated. In essence, HPOAnnotator solves the sparsity problem of a protein-phenotype association matrix by using a low-rank approximation. RESULTS: By combining the hierarchical structure of HPO and co-annotations of proteins, our model can well capture the HPO semantic similarities. Moreover, graph Laplacian regularizations are imposed in the latent space so as to utilize multiple PPI networks. The performance of HPOAnnotator has been validated under cross-validation and independent test. Experimental results have shown that HPOAnnotator outperforms the competing methods significantly. CONCLUSIONS: Through extensive comparisons with the state-of-the-art methods, we conclude that the proposed HPOAnnotator is able to achieve the superior performance as a result of using a low-rank approximation with a graph regularization. It is promising in that our approach can be considered as a starting point to study more efficient matrix factorization-based algorithms.

Electronically Controlled Chemical Stability of Compound Semiconductor Surfaces.

Effects of a humid environment on the degradation of semiconductors were studied to understand the role of the surface charge on material stability. Two distinctly different semiconductors with the Fermi level stabilization energy EFS located inside the conduction band (CdO) and valence band (SnTe) were selected, and effects of an exposure to 85 °C and 85% relative humidity conditions on their electrical properties were investigated. Undoped CdO films with bulk Fermi level EF below EFS and positively charged surface are very unstable. The stability greatly improves with doping when EF shifts above EFS, and the surface becomes negatively charged. This charge-controlled reactivity is further confirmed by the superior stability of undoped p-type SnTe with EF above EFS. These distinct reactivities are explained by the surface attracting either the reactive OH- or passivating H+ ions. The present results have important implications for understanding the interaction of semiconductor surfaces with water or, in general, ionic solutions.

A solid-state chemical reduction approach to synthesize graphitic carbon nitride with tunable nitrogen defects for efficient visible-light photocatalytic hydrogen evolution.

Graphitic carbon nitride with nitrogen defects (g-C3N4-x) is prepared by a facile and effective solid-state chemical reduction technique at mild temperature conditions. The cyano groups and nitrogen vacancies, as evidenced by electron paramagnetic resonance (EPR), X-ray photoelectron spectrometer (XPS), Fourier transform infrared spectra (FTIR) and Solid-state 13C MAS NMR spectra, are controllable via adjusting chemical reduction temperature. Comparing to the pristine g-C3N4, the as-prepared g-C3N4-x shows much enhanced photocatalytic H2 evolution activity under visible-light irradiation. The maximum H2 evolution rate of 3068 µmol·g-1·h-1 is achieved with g-C3N4-x after chemical reduction treatment at 400 °C for 1 h, which is 4.85 times that of the pristine g-C3N4. Moreover, excellent reusability and storage stability have been shown by this photocatalyst as well. It is discovered that nitrogen defects can result in both the up-shift of the valance band and the down-shift of the conduction band, which benefit the absorption of longer wavelength photons and trapping of the photoinduced electrons, therefore reducing the recombination losses of the generated carriers. It is because of this improved visible-light absorption and charge carrier separation, g-C3N4-x displays better visible-light photocatalytic activity compared to the pristine g-C3N4. It is then concluded that the synthetic strategy presented here represents a straightforward and efficient way to synergistically optimize the chemical composition, optical response, and photocatalytic characteristics of g-C3N4-based photocatalysts.

DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.

MOTIVATION: Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. METHODS: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. RESULTS: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. AVAILABILITY: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank CONTACT: zhusf@fudan.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.