Synthesis of Uniform Bi₂WO₆‑Reduced Graphene Oxide Nanocomposites with Significantly Enhanced Photocatalytic Reduction Activity

In this work, the uniform B₂WO₆-reduced graphene oxide (BWO–RGO) nanocomposites are prepared via electrostatic self-assembly of positively charged BWO with negatively charged GO sheets and then the composited GO is reduced via the hydrothermal treatment. The close interfacial contact and strong electronic interaction between BWO and RGO are achieved by this facile and efficient self-assembly route. Photocatalytic degradation of pollutant bisphenol A, selective oxidation of benzyl alcohol, removal of heavy metal ion Cr­(VI), and selective reduction of 4-nitrophenol are selected as the probe reactions to investigate the photocatalytic activities of as-obtained BWO–RGO nanocomposites. The experimental results demonstrate the photocatalytic redox activities of BWO–RGO composites are predominantly dependent on the energy levels of photoinduced electrons or holes. In particular, the upshift of the valence band and conduction band edge of catalysts induced by the electronic interaction between BWO and RGO has an inconsistent influence on the photocatalytic reduction and oxidation reactions, respectively. As a result, the photocatalytic activity of reduction reactions is significantly enhanced, owing to the synergetic effect of the upshift of conduction band edge and the improved separation of photogenerated electrons/holes, while the oxidation ability of BWO–RGO nanocomposite is improved to a slight extent compared with bare BWO. The energy levels of photogenerated carriers should be the origins accounting for the different enhancement of photocatalytic activities for the different reactions. According to the discussion, one important conclusion can be drawn, that is, the results should be analyzed on the basis of specific reactions when discussing the effect of graphene or RGO on the photocatalytic properties of semiconductor particles

Can Silica Particles Reduce Air Pollution by Facilitating the Reactions of Aliphatic Aldehyde and NO₂?

This study investigated the heterogeneous atmospheric reactions of acetaldehyde, propanal, and butanal with NO₂ onto silica (SiO₂) clusters using a theoretical approach. By analyzing spectral features and adsorption parameters, the formation of hydrogen bonds and negative adsorption energies provide evidence that an efficient spontaneous uptake of aliphatic aldehydes onto SiO₂ could occur. The atmospheric reaction mechanisms show that when aldehydes and NO₂ react on the surface model, the H atom abstraction reaction from the aldehydic molecule by NO₂ is an exclusive channel, forming nitrous acid and acyl radicals. This study included kinetics exploring the reaction of aldehydes with NO₂ using a canonical variational transition state theory. The reaction rate constants are increased in the presence of SiO₂ between the temperatures 217 and 298 K. This may explain how aldehydes can temporarily stay on mineral particles without chemical reactions. The results suggest that silica can depress the rate at which the studied aldehydes react with NO₂ and possibly reduce air pollution generated by these atmospheric reactions

Chi-squared Q-Q plots for the additive-additive model with main effect at both locus (Schema 3).

<p>Top panels: <b>A</b>. <i>GenoMI</i>; <b>B</b>. <i>GenoCMI</i>; <b>C</b>. <i>GameteCMI</i>. Middle panels: <b>D</b>. original Wu et al statistic; <b>E</b>. adjusted Wu statistic; <b>F</b>. joint effect statistic. Bottom panel: <b>G</b>. logistic regression model with 1 df test; <b>H</b>. logistic regression model with 4 df test.</p

Description of simulation schemas.

a<p>In each schema, three two-locus interaction models (additive × additive, dominant × dominant and recessive × recessive) were evaluated.</p>b<p><i>OR_G</i>, <i>OR_H</i>, and <i>OR_GH</i> denote the main effect for locus <i>G</i>, main effect for locus <i>H</i>, and their interaction effect, respectively. “√” indicates that the effect is present. “–” indicates that the effect is absent.</p>c<p>Disease prevalence (baseline penetrance).</p>d<p>For Schemas 8 and 9, the interaction effect <i>OR_GH</i> was increased from 1.0 to a value at which the power of the optimal metric achieved 100% at significance level 0.01.</p

Application of entropy-based statistics for testing gene-gene interaction between SNP309 in <i>MDM2</i> gene and codon72 polymorphism in <i>p53</i> gene.

a<p>frequencies were shown as No. of individuals genotyped as <i>TT</i>/<i>TG</i>/<i>GG</i> of <i>MDM2 309T</i>><i>G</i> in case.</p>b<p>frequencies were shown as No. of individuals genotyped as <i>TT</i>/<i>TG</i>/<i>GG</i> of <i>MDM2 309T</i>><i>G</i> in control.</p>1<p>obtained by logistic regression model assuming additive × additive model.</p>2<p>obtained by logistic regression model assuming dominant × dominant model.</p>3<p>obtained by logistic regression model assuming recessive × recessive model.</p>4<p>obtained by logistic regression model by coding genotypes as factors.</p><p>GCC: gaster cardia cancer; LC: lung cancer; HCC: hepatacelluar cancer; BC: breast cancer.</p

Comparison of <i>P</i>-values in testing gene-gene interaction between hemoglobin (<i>Hb</i>) gene and <i>α</i>⁺-thalassemia gene.

a<p>frequencies were shown as No. of case/No. of control.</p>b<p>P-values reported by Williams et al.</p>c<p>the lowest <i>P</i>-value among logistic regression models by assuming additive × additive, dominant × dominant and recessive × recessive interaction models, respectively.</p>d<p>obtained by logistic regression model by coding genotypes as factors.</p

To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches

<div><p>Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects.</p></div

Chi-squared Q-Q plots for the recessive-recessive model with main effect at both loci, when case/control ratios varied (Schema 7).

<p>Assuming main effects at both locus (<i>OR_G</i> = <i>OR_H</i> = 2.0) and disease prevalence 0.02. Top panels: <b>A</b>. <i>GenoMI</i>; <b>B</b>. <i>GenoCMI</i>; <b>C</b>. <i>GameteCMI</i>. Middle panels: <b>D</b>. original Wu et al statistic; <b>E</b>. adjusted Wu statistic; <b>F</b>. joint effect statistic. Bottom panel: <b>G</b>. logistic regression model with 1 df test; <b>H</b>. logistic regression model with 4 df test.</p

False positive rates (type 1 error rates) for testing interaction in common disease with main effect at one locus (Schema 2).

a<p>logistic regression model with 1 df test for the correct genetic model.</p>b<p>logistic regression model with 4 df test by coding genotypes as factors.</p><p>The disease prevalence is assumed 0.02. The significance level is set as 0.01.</p

Power curves for testing interaction under the dominant-dominant interaction model.

<p><b>A</b>. Assuming no main effect at both loci (<i>OR_G</i> = <i>OR_H</i> = 1.0); <b>B</b>. Assuming main effect at one locus (<i>OR_G</i> = 2.0). G-MI: <i>GenoMI</i>; G-CMI: <i>GenoCMI</i>; H-CMI: <i>GameteCMI</i>; Wu-adj: Adjusted Wu statistics; JE: Joint Effects statistics; Logit_1 df: logistic regression model with 1 df test; Logit_4 df: logistic regression model with 4 df test. Disease prevalence was chosen at 0.02.</p