Photocatalytic reduction of CO to solar fuels is an ideal approach to simultaneously solve the global warming and energy crisis issues. Constructing a direct Z-scheme heterojunction is an effective way to overcome the drawbacks of single-component or conventional heterogeneous photocatalysts for photocatalytic CO reduction. Here, a novel type of direct Z-scheme g-C N /SnS heterojunction was constructed by depositing SnS quantum dots onto the g-C N surface in situ via a simple one-step hydrothermal method. -Cysteine not only acted as the sulfur source, but also grafted ammine groups onto g-C N in the hydrothermal process, which greatly enhanced the CO uptake of the composite. XPS analysis and density functional theory (DFT) calculation show that electron transfer occurred from g-C N to SnS , resulting in the formation of interfacial internal electric fields (IEF) between the two semiconductors at equilibrium. As a result, Z-scheme charge transfer took place under photoexcitation, with the electrons in SnS combining with the holes in g-C N , which improved the extraction and utilization of photoinduced electron in g-C N . The g-C N /SnS hybrid shows superior photocatalytic CO reduction as compared with individual g-C N and SnS , which should be attributed to the IEF-induced direct Z-scheme as well as improved CO adsorption capacity. In situ FTIR spectra illustrate that HCOOH appeared as an intermediate during the CO conversion, which can only be generated by g-C N according to the energy level of the photoinduced electrons, further confirming the Z-scheme configuration for the g-C N /SnS system.
Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fIVIRI data (both with the application of external stimuli and with the subject "at rest"). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxelwise time series. Given the set of ICA components, if the components representing "signal" (brain activity) can be distinguished form the "noise" components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the handclassification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL. (C) 2014 Elsevier Inc. All rights reserved.
We present an improved method for computing incompressible viscous flow around suspended rigid particles using a fixed and uniform computational grid. The main idea is to incorporate Peskin’s regularized delta function approach [Acta Numerica 11 (2002) 1] into a direct formulation of the fluid–solid interaction force in order to allow for a smooth transfer between Eulerian and Lagrangian representations while at the same time avoiding strong restrictions of the time step. This technique was implemented in a finite-difference and fractional-step context. A variety of two- and three-dimensional simulations are presented, ranging from the flow around a single cylinder to the sedimentation of 1000 spherical particles. The accuracy and efficiency of the current method are clearly demonstrated.
Lump solutions are analytical rational function solutions localized in all directions in space. We analyze a class of lump solutions, generated from quadratic functions, to nonlinear partial differential equations. The basis of success is the Hirota bilinear formulation and the primary object is the class of positive multivariate quadratic functions. A complete determination of quadratic functions positive in space and time is given, and positive quadratic functions are characterized as sums of squares of linear functions. Necessary and sufficient conditions for positive quadratic functions to solve Hirota bilinear equations are presented, and such polynomial solutions yield lump solutions to nonlinear partial differential equations under the dependent variable transformations and , where is one spatial variable. Applications are made for a few generalized KP and BKP equations.
A survey revealed that researchers still seem to encounter difficulties to cope with outliers. Detecting outliers by determining an interval spanning over the mean plus/minus three standard deviations remains a common practice. However, since both the mean and the standard deviation are particularly sensitive to outliers, this method is problematic. We highlight the disadvantages of this method and present the median absolute deviation, an alternative and more robust measure of dispersion that is easy to implement. We also explain the procedures for calculating this indicator in SPSS and R software.
The dispersion behaviour of graphene oxide (GO) and chemically reduced GO (rGO) has been investigated in a wide range of organic solvents. The effect of the reduction process on the GO solubility in eighteen different solvents was examined and analysed, taking into consideration the solvent polarity, the surface tension and the Hansen and Hildebrand solubility parameters. rGO concentrations up to similar to 9 mu g/mL in chlorinated solvents were achieved, demonstrating an efficient solubilization strategy, extending the scope for scalable liquid-phase processing of conductive rGO inks for the development of printed flexible electronics. (C) 2014 Elsevier Inc. All rights reserved.
Magnetic magnetite (Fe O ) nanoparticles synthesized by chemical co-precipitation were characterized using XRD, TEM, SEM-EDX, FT-IR, ED-XRF, PPMS, point of zero charge (pH ) and surface area measurements. As-prepared Fe O nanoparticles were successful for aqueous Cr and Pb removal. Batch adsorption experiments systematically investigated the influence of pH, temperature, contact time and adsorbate/adsorbent concentration on Cr and Pb adsorption. Maximum Cr and Pb removal occurred at pH 2.0 and 5.0, respectively. Sorption data fit pseudo-second order kinetics, indicating a chemical adsorption. The Freundlich, Langmuir, Redlich–Peterson, Toth, Radke and Sips adsorption isotherm models were applied to describe equilibrium data. The Sips and Langmuir models best described Cr and Pb adsorption on magnetite nanoparticles, respectively. The maximum Langmuir adsorption capacities were 34.87 (Cr ) and 53.11 (Pb ) mg/g at 45 °C, respectively. Fe O nanoparticles are promising potential adsorbents and exhibited remarkable reusability for metal ions removal in water and wastewater treatment.
Occurring at adenine (A) with the consensus motif GAC, N 6 -methyladenosine (m 6 A) is one of the most abundant modifications in RNA, which plays very important roles in many biological processes. The nonuniform distribution of m 6 A sites across the genome implies that, for better understanding the regulatory mechanism of m 6 A, it is indispensable to characterize its sites in a genome-wide scope. Although a series of experimental technologies have been developed in this regard, they are both time-consuming and expensive. With the avalanche of RNA sequences generated in the postgenomic age, it is highly desired to develop computational methods to timely identify their m 6 A sites. In view of this, a predictor called "iRNA-Methyl" is proposed by formulating RNA sequences with the "pseudo dinucleotide composition" into which three RNA physiochemical properties were incorporated. Rigorous cross-validation tests have indicated that iRNA-Methyl holds very high potential to become a useful tool for genome analysis. For the convenience of most experimental scientists, a web-server for iRNA-Methyl has been established at http://lin.uestc.edu.cn/server/iRNA-Methyl by which users can easily get their desired results without needing to go through the mathematical details.
N-6-methyladenine (6mA) is one kind of post-replication modification (PTM or PTRM) occurring in a wide range of DNA sequences. Accurate identification of its sites will be very helpful for revealing the biological functions of 6mA, but it is time-consuming and expensive to determine them by experiments alone. Unfortunately, so far, no bioinformatics tool is available to do so. To fill in such an empty area, we have proposed a novel predictor called iDNA6mA-PseKNC that is established by incorporating nucleotide physicochemical properties into Pseudo K-tuple Nucleotide Composition (PseKNC). It has been observed via rigorous cross-validations that the predictor's sensitivity (Sn), specificity (Sp), accuracy (Acc), and stability (MCC) are 93%, 100%, 96%, and 0.93, respectively. For the convenience of most experimental scientists, a user-friendly web server for iDNA6mA-PseKNC has been established at http://lin-group.cn/server/iDNA6mA-PseKNC, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
In order to boost the low-temperature activity, a series of Mn–Ce/TiO -X (X = Hk, N1, N2 and N3) were prepared by adopting incipient wetness technique and investigated for the low-temperature selective catalytic reduction (SCR) of NO with NH at industrial relevant conditions. Prior to that, hydrous TiO -nanosized samples were synthesized by a deposition technique at constant pH kept in the range of 5–8 and constant temperature 30–80 °C. Titanium oxide hydrates (N1, N2, and N3) possess high specific surface area as 620 m /g, 457 m /g, 398 m /g, whereas TiO (Hk) preserves 309 m /g surface area. In our studies, it was found that the NO conversion over Mn–Ce/TiO -Hk with the atomic ratio of Mn/Ce = 5.1 was apparently higher compared with that over Mn–Ce(5.1)/TiO -X (X = N1, N2, and N3). Our activity results showed that 93.0% NO conversion was obtained over Mn–Ce(5.1)/TiO -Hk at 100 °C at a space velocity of 80,000 h . Our XRD results suggest that the loading of manganese and ceria onto hydrated titania led to the evolution of diffraction peaks which can be attributed to the formation of crystalline manganese dioxide (MnO ). Among all the catalysts, Mn–Ce/TiO N2 showed high intensity diffraction peaks at 2 = 37.2°, which corresponds to the highly crystalline (1 0 1) plane of manganese dioxide. Once the catalysts with the best performance were identified, experiments were performed with the aim of optimizing these formulations with respect to the dopant and Mn/Ce atomic ratio. Both the ceria co-doping and Mn/Ce atomic ratios played a key role to achieve high NO conversions at 100 °C. The disappearance or low-temperature shift of ceria reduction peak in H -TPR indicates the increase of active components’ reduction potential, oxygen vacancies, and the existence of surface-capping oxygen species in Mn–Ce/TiO (Hk). The H -TPR results are in good accordance with our XPS analysis where the relative atomic ratios of Mn /Mn , Ce /Ce , and the existence of surface oxygen species greatly enhanced in Mn–Ce/TiO (Hk) compared to other catalysts in this work. The relative atomic ratio of Mn /Mn (2.19) in Mn–Ce(5.1)/TiO -Hk calculated from deconvoluted XPS spectra is much higher than that of other catalysts (1.90, 0.89, and 2.03 for N1, N2, and N3, respectively). Moreover, the superior ratio of Ce /Ce can generate a charge imbalance, oxygen vacancies, and unsaturated chemical bonds over the catalyst surface to promote the oxidation of NO to NO . It is highly remarkable to note that the deNO efficiency of all the prepared catalysts is indeed correlated with the surface concentrations of Ce /Ce and Mn /Mn . NH -TPD results imply that the co-doping of manganese and ceria onto Hk TiO can remarkably improve the acid sites distribution and the concentration of acid sites of the Mn–Ce/TiO catalyst. Our investigation results illustrate that the enhancement in reduction potential of active components, broadening of acid sites distribution, and the promotion of Mn /Mn , Ce /Ce ratios including surface labile oxygen and small pore openings seem to be the reason for high deNO efficiency of Mn–Ce/TiO (Hk) at low temperatures.