Charge-controlled switchable CO2 capture on boron nitride nanomaterials

Increasing concerns about the atmospheric CO2 concentration and its impact on the environment are motivating researchers to discover new materials and technologies for efficient CO2 capture and conversion. Here, we report a study of the adsorption of CO2, CH4, and H2 on boron nitride (BN) nanosheets and nanotubes (NTs) with different charge states. The results show that the process of CO2 capture/release can be simply controlled by switching on/off the charges carried by BN nanomaterials. CO2 molecules form weak interactions with uncharged BN nanomaterials and are weakly adsorbed. When extra electrons are introduced to these nanomaterials (i.e., when they are negatively charged), CO2 molecules become tightly bound and strongly adsorbed. Once the electrons are removed, CO2 molecules spontaneously desorb from BN absorbents. In addition, these negatively charged BN nanosorbents show high selectivity for separating CO2 from its mixtures with CH4 and/or H2. Our study demonstrates that BN nanomaterials are excellent absorbents for controllable, highly selective, and reversible capture and release of CO2. In addition, the charge density applied in this study is of the order of 1013 cm–2 of BN nanomaterials and can be easily realized experimentally.

CO2 capture and gas separation on boron carbon nanotubes

Concern about the increasing atmospheric CO2 concentration and its impact on the environment has led to increasing attention directed toward finding advanced materials and technologies suited for efficient CO2 capture, storage and purification of clean-burning natural gas. In this letter, we have performed comprehensive theoretical investigation of CO2, N2, CH4 and H2 adsorption on B2CNTs. Our study shows that CO2 molecules can form strong interactions with B2CNTs with different charge states. However, N2, CH4 and H2 can only form very weak interactions with B2CNTs. Therefore, the study demonstrates B2CNTs could sever as promising materials for CO2 capture and gas separation.

Neutral and charged boron-doped fullerenes for CO2 adsorption

Recently, the capture and storage of CO2 have attracted research interest as a strategy to reduce the global emissions of greenhouse gases. It is crucial to find suitable materials to achieve an efficient CO2 capture. Here we report our study of CO2 adsorption on boron-doped C60 fullerene in the neutral state and in the 1e−-charged state. We use first principle density functional calculations to simulate the CO2 adsorption. The results show that CO2 can form weak interactions with the BC59 cage in its neutral state and the interactions can be enhanced significantly by introducing an extra electron to the system.

Halogen bonds in 2D supramolecular self-assembly of organic semiconductors

Weak interactions between bromine, sulphur, and hydrogen are shown to stabilize 2D supramolecular monolayers at the liquid–solid interface. Three different thiophene-based semiconducting organic molecules assemble into close-packed ultrathin ordered layers. A combination of scanning tunneling microscopy (STM) and density functional theory (DFT) elucidates the interactions within the monolayer. Electrostatic interactions are identified as the driving force for intermolecular Br⋯Br and Br⋯H bonding. We find that the S⋯S interactions of the 2D supramolecular layers correlate with the hole mobilities of thin film transistors of the same materials.

Interactions between energy intake and expenditure in the development and treatment of obesity.

Summary There are four interactions to consider between energy intake (EI) and energy expenditure (EE) in the development and treatment of obesity. (1) Does sedentariness alter levels of EI or subsequent EE? and (2) Do high levels of EI alter physical activity or exercise? (3) Do exercise-induced increases in EE drive EI upwards and undermine dietary approaches to weight management and (4) Do low levels of EI elevate or decrease EE? There is little evidence that sedentariness alters levels of EI. This lack of cross-talk between altered EE and EI appears to promote a positive EB. Lifestyle studies also suggest that a sedentary routine actually offers the opportunity for over-consumption. Substantive changes in non exercise activity thermogenesis are feasible, but not clearly demonstrated. Cross talk between elevated EE and EI is initially too weak and takes too long to activate, to seriously threaten dietary approaches to weight management. It appears that substantial fat loss is possible before intake begins to track a sustained elevation of EE. There is more evidence that low levels of EI does lower physical activity levels, in relatively lean men under conditions of acute or prolonged semi-starvation and in dieting obese subjects. During altered EB there are a number of small but significant changes in the components of EE, including (i) sleeping and basal metabolic rate, (ii) energy cost of weight change alters as weight is gained or lost, (iii) exercise efficiency, (iv) energy cost of weight bearing activities, (v) during substantive overfeeding diet composition (fat versus carbohydrate) will influence the energy cost of nutrient storage by ~ 15%. The responses (i-v) above are all “obligatory” responses. Altered EB can also stimulate facultative behavioural responses, as a consequence of cross-talk between EI and EE. Altered EB will lead to changes in the mode duration and intensity of physical activities. Feeding behaviour can also change. The degree of inter-individual variability in these responses will define the scope within which various mechanisms of EB compensation can operate. The relative importance of “obligatory” versus facultative, behavioural responses -as components of EB control- need to be defined.

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Front interactions in a three-component system

The three-component reaction-diffusion system introduced in [C. P. Schenk et al., Phys. Rev. Lett., 78 (1997), pp. 3781–3784] has become a paradigm model in pattern formation. It exhibits a rich variety of dynamics of fronts, pulses, and spots. The front and pulse interactions range in type from weak, in which the localized structures interact only through their exponentially small tails, to strong interactions, in which they annihilate or collide and in which all components are far from equilibrium in the domains between the localized structures. Intermediate to these two extremes sits the semistrong interaction regime, in which the activator component of the front is near equilibrium in the intervals between adjacent fronts but both inhibitor components are far from equilibrium there, and hence their concentration profiles drive the front evolution. In this paper, we focus on dynamically evolving N-front solutions in the semistrong regime. The primary result is use of a renormalization group method to rigorously derive the system of N coupled ODEs that governs the positions of the fronts. The operators associated with the linearization about the N-front solutions have N small eigenvalues, and the N-front solutions may be decomposed into a component in the space spanned by the associated eigenfunctions and a component projected onto the complement of this space. This decomposition is carried out iteratively at a sequence of times. The former projections yield the ODEs for the front positions, while the latter projections are associated with remainders that we show stay small in a suitable norm during each iteration of the renormalization group method. Our results also help extend the application of the renormalization group method from the weak interaction regime for which it was initially developed to the semistrong interaction regime. The second set of results that we present is a detailed analysis of this system of ODEs, providing a classification of the possible front interactions in the cases of $N=1,2,3,4$, as well as how front solutions interact with the stationary pulse solutions studied earlier in [A. Doelman, P. van Heijster, and T. J. Kaper, J. Dynam. Differential Equations, 21 (2009), pp. 73–115; P. van Heijster, A. Doelman, and T. J. Kaper, Phys. D, 237 (2008), pp. 3335–3368]. Moreover, we present some results on the general case of N-front interactions.

Cytochrome p450 1b1, a new keystone in gene-environment interactions related to human head and neck cancer?

Alcohol consumption and tobacco smoking are major causes of head and neck cancers, and regional differences point to the importance of research into gene-environment interactions. Much interest has been focused on polymorphisms of CYP1A1 and of GSTM1 and GSTT1, but a number of studies have not demonstrated significant effects. This has mostly been ascribed to small sample sizes. In general, the impact of polymorphisms of metabolic enzymes appears inconsistent, with some reports of weak-to-moderate associations, and with others of no elevation of risks. The classical cytochrome P450 isoenzyme considered for metabolic activation of polycyclic aromatic hydrocarbons (PAH) is CYP1A1. A new member of the CYP1 family, CYP1B1, was cloned in 1994, currently representing the only member of the CYP1B subfamily. A number of single nucleotide polymorphisms of the CYP1B1 gene have been reported. The amino acid substitutions Val432Leu (CYP1B1*3) and Asn453Ser (CYP1B1*4), located in the heme binding domain of CYP1B1, appear as likely candidates to be linked with biological effects. CYP1B1 activates a wide range of PAH, aromatic and heterocyclic amines. Very recently, the CYP1B1 codon 432 polymorphism (CYP1B1*3) has been identified as a susceptibility factor in smoking-related head-and-neck squamous cell cancer. The impact of this polymorphic variant of CYP1B1 on cancer risk was also reflected by an association with the frequency of somatic mutations of the p53 gene. Combined genotype analysis of CYP1B1 and the glutathione transferases GSTM1 or GSTT1 has pointed to interactive effects. This provides new molecular evidence that tobacco smoke-specific compounds relevant to head and neck carcinogenesis are metabolically activated through CYP1B1 and is consistent with a major pathogenetic relevance of PAH as ingredients of tobacco smoke.

Raman spectroscopic characteristics of phthalocyanine and naphthalocyanine in sandwich-type phthalocyaninato and porphyrinato rare earth complexes Part 5. – Raman spectroscopic characteristics of naphthalocyanine in mixed [tetrakis(4-tert-butylphenyl)porphyrinato]-(naphthalocyaninato) rare earth double-deckers

Raman spectra were recorded in the range 400–1800 cm−1 for a series of 15 mixed \[tetrakis(4-tert-butylphenyl)porphyrinato](2,3-naphthalocyaninato) rare earth double-deckers M(TBPP)(Nc) (M = Y; La–Lu except Pm) using laser excitation at 632.8 and 785 nm. Comparisons with bis(naphthalocyaninato) rare earth counterparts reveal that the vibrations of the metallonaphthalocyanine M(Nc) fragment dominate the Raman features of M(TBPP)(Nc). When excited with radiation of 632.8 nm, the most intense vibration appears at about 1595 cm−1, due to the naphthalene stretching. These complexes exhibit the marker Raman band for Nc•− as a medium-intense band in the range 1496–1507 cm−1, attributed to the coupling of pyrrole and aza stretching, while the marker Raman band of Nc2− in intermediate-valence Ce(TBPP)(Nc) appears as a strong band at 1493 cm−1 and is due to the isoindole stretchings. By contrast, when excited with radiation of 785 nm that is in close resonance with the main Q absorption band of the naphthalocyanine ligand, the ring radial vibrations at ca 680 and 735 cm−1 for MIII(TBPP)(Nc) are selectively intensified and are the most intense bands. For the cerium double-decker, the most intense vibration also acting as the marker Raman band of Nc2− appears at 1497 cm−1 with contributions from both pyrrole CC and aza CN stretches. The same vibrational modes show weak to medium intensity scattering at 1506–1509 cm−1 for MIII(TBPP)(Nc) and this is the marker Raman band of Nc•− when thus excited. The scatterings due to the Nc breathings, ring radial vibration, aza group stretchings, naphthalene stretchings, benzoisoindole stretchings and the coupling of pyrrole CC and aza CN stretchings in MIII(TBPP)(Nc) are all slightly blue shifted along with the decrease in rare earth ionic radius, confirming the effects of increased ring–ring interactions on the Raman characteristics of naphthalocyanine in the mixed ring double-deckers.

8-Ammonionaphthalene-2-sulfonate monohydrate : the zwitterionic hydrate of 1,7-Cleve's acid

The structure of 8-amino-2-naphthalenesulfonic acid monohydrate (1,7-Cleve's acid hydrate), C10H9NO3S.H2O, shows the presence of a sulfonate-aminium group zwitterion, both groups and the water molecule of solvation giving cyclic R3/3(8) intermolecular hydrogen-bonding interactions forming chains which extend down a axis of the unit cell. Additional peripheral associations, including weak aromatic ring pi-pi interactions [centroid-centroid distance 3.6299(15)A], result in a two-dimensional sheet structure.