Although sequential adsorption of dyes in a single TiO(2) electrode is ideal to extend the range of light absorption in dye-sensitized solar cells, high-temperature processing has so far limited its application. We report a method for selective positioning of organic dye molecules with different absorption ranges in a mesoporous TiO(2) film by mimicking the concept of the stationary phase and the mobile phase in column chromatography, where polystyrene-filled mesoporous TiO(2) film is explored for use as a stationary phase and a Brønsted-base-containing polymer solution is developed for use as a mobile phase for selective desorption of the adsorbed dye. By controlling the desorption and adsorption depth, yellow, red and green dyes were vertically aligned within a TiO(2) film, which is confirmed by an electron probe micro-analyser. The external quantum efficiency (EQE) spectrum from a solar cell with three selectively positioned dyes reveals the EQE characteristics of each single-dye cell.
Density functional theory is used to study the adsorption of guaiacol and its initial hydrodeoxygenation (HDO) reactions on Pt(111). Previous Brønsted-Evans-Polanyi (BEP) correlations for small open-chain molecules are inadequate in estimating the reaction barriers of phenolic compounds except for the side group (methoxy) carbon-dehydrogenation. New BEP relations are established using a select group of phenolic compounds. These relations are applied to construct a potential-energy surface of guaiacol-HDO to catechol. Analysis shows that catechol is mainly produced via dehydrogenation of the methoxy functional group followed by the CHx (x<3) removal of the functional group and hydrogenation of the ring carbon, in contrast to a hypothesis of a direct demethylation path. Dehydroxylation and demethoxylation are slow, implying that phenol is likely produced from catechol but not through its direct dehydroxylation followed by aromatic carbon-ring hydrogenation.
BackgroundInsects and animals can recognize surrounding environments by detecting thousands of chemical odorants. Olfaction is a complicated process that begins in the olfactory epithelium with the specific binding of volatile odorant molecules to dedicated olfactory receptors (ORs). OR proteins are encoded by the largest gene superfamily in the mammalian genome.ResultsWe report here the whole genome analysis of the olfactory receptor genes of S. scrofa using conserved OR gene specific motifs and known OR protein sequences from diverse species. We identified 1,301 OR related sequences from the S. scrofa genome assembly, Sscrofa10.2, including 1,113 functional OR genes and 188 pseudogenes. OR genes were located in 46 different regions on 16 pig chromosomes. We classified the ORs into 17 families, three Class I and 14 Class II families, and further grouped them into 349 subfamilies. We also identified inter- and intra-chromosomal duplications of OR genes residing on 11 chromosomes. A significant number of pig OR genes (n = 212) showed less than 60% amino acid sequence similarity to known OR genes of other species.ConclusionAs the genome assembly Sscrofa10.2 covers 99.9% of the pig genome, our analysis represents an almost complete OR gene repertoire from an individual pig genome. We show that S. scrofa has one of the largest OR repertoires, suggesting an expansion of OR genes in the swine genome. A significant number of unique OR genes in the pig genome may suggest the presence of swine specific olfactory stimulation.
Supported nickel catalysts are widely used in hydrocarbon steam reforming for producing hydrogen. Carbon deposition is a major cause of Ni catalyst deactivation. In this work, supported Ni and Ni/Rh catalysts were synthesized and tested for liquid hydrocarbon steam reforming. Carbon analysis on the spent catalysts illustrates that the carbon deposition is significantly reduced with inclusion of Rh. Extended X-ray Absorption Fine Structure (EXAFS) analysis indicates close interactions between Ni and Rh atoms. Density functional theory (DFT) results show that a Ni/Rh alloy is thermodynamically more stable than Ni and Rh alone at the synthesis and reaction temperatures. Adsorption, diffusion, and oxidation of carbon atoms over Ni and Ni/Rh alloy surfaces were examined by DFT calculations. Alloying with Rh reduces the stability of deposited carbon atoms and clusters over both terraces and steps of the catalyst. Moreover, Rh addition enhances the competition of oxidation reactions against carbon deposition by altering the relative diffusion rates and bond formation rates of the two processes.
There is intense interest in uncovering design rules that govern the formation of various structural phases as a function of chemical composition in multi-principal element alloys (MPEAs). In this paper, we develop a machine learning (ML) approach built on the foundations of ensemble learning, post hoc model interpretability of black-box models, and clustering analysis to establish a quantitative relationship between the chemical composition and experimentally observed phases of MPEAs. The originality of our work stems from performing instance-level (or local) variable attribution analysis of ML predictions based on the breakdown method, and then identifying similar instances based on k-means clustering analysis of the breakdown results. We also complement the breakdown analysis with Ceteris Paribus profiles that showcase how the model response changes as a function of a single variable, when the values of all other variables are fixed. Results from local model interpretability analysis uncover key insights into variables that govern the formation of each phase. Our developed approach is generic, model-agnostic, and valuable to explain the insights learned by the black-box models. An interactive web application is developed to facilitate model sharing and accelerate the design of MPEAs with targeted properties.
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