Visible light enhanced oxidant free dehydrogenation of aromatic alcohols using Au-Pd alloy nanoparticle catalysts

We find that visible light irradiation of gold–palladium alloy nanoparticles supported on photocatalytically inert ZrO2 significantly enhances their catalytic activity for oxidant-free dehydrogenation of aromatic alcohols to the corresponding aldehydes at ambient temperatures. Dehydrogenation is also the dominant process in the selective oxidation of the alcohols to the corresponding aldehydes with molecular oxygen. The alloy nanoparticles strongly absorb light and exhibit superior catalytic and photocatalytic activity when compared to either pure palladium or gold nanoparticles. Analysis with a free electron gas model for the bulk alloy structure reveals that the alloying increases the surface charge heterogeneity on the alloy particle surface, which enhances the interaction between the alcohol molecules and the metal NPs. The increased surface charge heterogeneity of the alloy particles is confirmed with density function theory applied to small alloy clusters. Optimal catalytic activity was observed with a Au : Pd molar ratio of 1 : 186, which is in good agreement with the theoretical analysis. The rate-determining step of the dehydrogenation is hydrogen abstraction. The conduction electrons of the nanoparticles are photo-excited by the incident light giving them the necessary energy to be injected into the adsorbed alcohol molecules, promoting the hydrogen abstraction. The strong chemical adsorption of alcohol molecules facilitates this electron transfer. The results show that the alloy nanoparticles efficiently couple thermal and photonic energy sources to drive the dehydrogenation. These findings provide useful insight into the design of catalysts that utilize light for various organic syntheses at ambient temperatures.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

Strong evidence for a novel schizophrenia risk locus on chromosome 1p31.1 in homogeneous pedigrees from Tamil Nadu, India

OBJECTIVE: The study of ethnically homogeneous populations may help to identify schizophrenia risk loci. The authors conducted a genomewide linkage scan for schizophrenia in an Indian population. METHOD: Participants were 441 individuals (262 affected probands and siblings) who were recruited primarily from one ethnically homogeneous group, the Tamil Brahmin caste, although individuals from other geographically proximal castes also participated. Genotyping of 124 affected sibling pair pedigrees was performed with 402 short tandem repeat polymorphisms. Linkage analyses were conducted using nonparametric exponential LOD (logarithm of the odds ratio for linkage) scores and parametric heterogeneity LOD scores. Parametric heterogeneity scores were calculated using simple dominant and recessive models, correcting for multiple statistics. The data were examined for evidence of consanguinity. Genomewide significance levels were determined using 10,000 gene dropping simulations. RESULTS: These findings revealed genomewide significant linkage to chromosome 1p31.1, through the use of both exponential and heterogeneity LOD scores, incorporating correction for multiple statistics and mild consanguinity. The estimated sibling recurrence risk associated with this putative locus was 1.95. Analysis for heterogeneity LOD scores also detected suggestive linkage to chromosomes 13q22.1 and 16q12.2. Using 117 tag single nucleotide polymorphisms (SNPs), family-based association analyses of phosphodiesterase 4B (PDE4B), the closest schizophrenia candidate gene, detected no convincing evidence of association, suggesting that the chromosome 1 peak represents a novel risk locus. CONCLUSIONS: This is the first study-to the authors' knowledge-to report significant linkage of schizophrenia to chromosome 1p31.1. Further investigation of this chromosome region in diverse populations is warranted to identify underlying sequence variants.