Protic ionic liquids based on the dimeric and oligomeric anions: [(AcO)xHx−1]−

We describe a fluidity and conductivity study as a function of composition in N-methylpyrrolidine–acetic acid mixtures. The simple 1:1 acid–base mixture appears to form an ionic liquid, but its degree of ionicity is quite low and such liquids are better thought of as poorly dissociated mixtures of acid and base. The composition consisting of 3 moles acetic acid and 1 mole N-methylpyrrolidine is shown to form the highest ionicity mixture in this binary due to the presence of oligomeric anionic species [(AcO)_xH_x−1]− stabilised by hydrogen bonds. These oligomeric species, being weaker bases than the acetate anion, shift the proton transfer equilibrium towards formation of ionic species, thus generating a higher degree of ionicity than is present at the 1:1 composition. A Walden plot analysis, thermogravimetric behaviour and proton NMR data, as well as ab initio calculations of the oligomeric species, all support this conclusion.

Ftir study of ion-pairing effects in plasticized polymer electrolytes

The effect of plasticizer on the ubiquitous ion-pairing observed in polymer electrolytes has been investigated using FTIR as a probe of the local environment of the triflate ion in sodium and lithium triflate based electrolytes. Plasticizers having a range of properties, such as, propylene carbonate, and dimethyl formamide (DMF), have been investigated in the pure state for comparison with the polymer (a random copolymer of ethylene oxide at propylene oxide (mol ratio 3: 1)). The different plasticizers exhibited strikingly different effects on the triflate ion bands normally observed in polyether salt systems. In particular, the cation associated triflate ion bands at 1288 and 1248 cm⁻¹ and the band at 1272 cm⁻¹ which has variously been assigned to the free ion and also to the strongly aggregated anion, are different. PC produces a rapid disappearance of the “free” ion band in favour of the monodentate ion pair. On the other hand, DMF strongly enhances the band near 1270 cm⁻¹ at salt concentrations higher than 0.7 mol kg⁻¹. These observations are discussed in terms of recent ab initio calculations of the triflate vibrational bands.

Aluminium speciation in 1-Butyl-1-Methylpyrrolidinium Bis(trifluoromethylsulfonyl)amide/AlCl3 mixtures

Aluminium speciation: Aluminium speciation in NTf₂ ionic liquids has a strong influence on its electrodeposition from the liquid mixture. This work probed the nature of these species and proposes that the electroactive species involved are either [AlCl₃(NTf₂)]⁻ or [AlCl₂(NTf₂)₂]⁻ (e.g., see figure).


Electrodeposition of aluminium is possible from solutions of AlCl₃ dissolved in the 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)amide (C₄mpyrNTf₂) ionic liquid. However, electrodeposition is dependant on the AlCl₃ concentration as it only occurs at concentrations >1.6 mol L⁻¹. At these relatively high AlCl₃ concentrations the C₄mpyrNTf₂/AlCl₃ mixtures exhibit biphasic behaviour. Notably, at 1.6 mol L⁻¹ AlCl₃, aluminium can only be electrodeposited from the upper phase. Conversely, we found that at 3.3 mol L⁻¹ aluminium electrodeposition can only occur from the lower phase. The complex chemistry of the C₄mpyrNTf₂/AlCl₃ system is described and implications of aluminium speciation in several C₄mpyrNTf₂/AlCl₃ mixtures, as deduced from Raman and ²⁷Al NMR spectroscopic data, are discussed. The ²⁷Al NMR spectra of the C₄mpyrNTf₂/AlCl₃ mixtures revealed the presence of both tetrahedrally and octahedrally coordinated aluminium species. Raman spectroscopy revealed that the level of uncoordinated NTf₂⁻ anions decreased with increasing AlCl₃ concentration. Quantum chemical calculations using density functional and ab initio theory were employed to identify plausible aluminium-containing species and to calculate their vibrational frequencies, which in turn assisted the assignment of the observed Raman bands. The data indicate that the electroactive species involved are likely to be either [AlCl₃(NTf₂)]⁻ or [AlCl₂(NTf₂)₂]⁻.

Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

Electrochemistry at negative potentials in Bis(trifluoromethanesulfonyl)amide ionic liquids

The bis(trifluoromethanesulfonyl)amide (TFSA) anion is widely studied as an ionic liquid (IL) forming anion which imparts many useful properties, notably electrochemical stability. Here we present electrochemical and spectroscopic evidence indicating that reductive decomposition of the bis(trifluoromethanesulfonyl)amide (TFSA) anion begins at ~ −2.0 V vs. Fc+/Fc, well before the reported cathodic limit for many of these ILs. These processes are shown to be dependent upon the electrode substrate and are influenced by the water content of the IL. Supporting ab initio calculations are presented which suggest a possible mechanism for the anion decomposition. The products appear to passivate the electrode surface and the implications of this behaviour are discussed.

Compression and probing C−H···I hydrogen bonds of iodoform under high pressure by x-ray diffraction and raman scattering

High-pressure methods were applied to investigate the structural stability and hydrogen bonding of polar molecules of iodoform by synchrotron radiation X-ray diffraction and Raman spectra measurements, respectively. Up to a pressure of 40 GPa, no phase transitions were observed. The discontinuous frequency shift of the C−H stretching band is believed to be related to the enhancement of the C−H···I weak hydrogen bonds under high pressures. Ab initio calculations were performed, and the results predict the frequency shift of the C−H stretching vibration as C−H···I interacts via hydrogen bonding. The bulk modulus is 17.3 ± 0.8 GPa, with a pressure derivative of 5.2.

Simplified or erroneous? It’s a fine line

Novice learners need to have simplified explanations because they are unable to understand fuller, more-involved explanations. However, there is a dangerously thin line between simplified explanations and over-simplified erroneous explanations, which lead to later misunderstandings and misconceptions. It is harder to unlearn misunderstandings and misconceptions, than to learn something new ab initio.

It is virtually impossible for any teacher to know everything that students will need for future study and careers, as each subject will lead to a myriad of pathways. For example, in my undergraduate 1st year class, students will go into numerous majors across more than 16 degree programs ranging from arts to zoology and from engineering to food-and-nutrition. 

Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease

Current single-locus-based analyses and candidate disease gene prediction methodologies used in genome-wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple-locus-based approach, drawing on protein pathway- or domain-based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC-implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.

Impact of nanodiffusion on the stacking fault energy in high-strength steels

A key requirement of modern steels – the combination of high strength and high deformability – can best be achieved by enabling a local adaptation of the microstructure during deformation. A local hardening is most efficiently obtained by a modification of the stacking sequence of atomic layers, resulting in the formation of twins or martensite. Combining ab initio calculations with in situ transmission electron microscopy, we show that the ability of a material to incorporate such stacking faults depends on its overall chemical composition and, importantly, the local composition near the defect, which is controlled by nanodiffusion. Specifically, the role of carbon for the stacking fault energy in high-Mn steels is investigated. Consequences for the long-term mechanical properties and the characterisation of these materials are discussed.

Binding affinities of amino acid analogues at the charged aqueous titania interface : implications for titania-binding peptides

Despite the extensive utilization of biomolecule-titania interfaces, biomolecular recognition and interactions at the aqueous titania interface remain far from being fully understood. Here, atomistic molecular dynamics simulations, in partnership with metadynamics, are used to calculate the free energy of adsorption of different amino acid side chain analogues at the negatively-charged aqueous rutile TiO2 (110) interface, under conditions corresponding with neutral pH. Our calculations predict that charged amino acid analogues have a relatively high affinity to the titania surface, with the arginine analogue predicted to be the strongest binder. Interactions between uncharged amino acid analogues and titania are found to be repulsive or weak at best. All of the residues that bound to the negatively-charged interface show a relatively stronger adsorption compared with the charge-neutral interface, including the negatively-charged analogue. Of the analogues that are found to bind to the titania surface, the rank ordering of the binding affinities is predicted to be "arginine" > "lysine" ≈ aspartic acid > "serine". This is the same ordering as was found previously for the charge-neutral aqueous titania interface. Our results show very good agreement with available experimental data and can provide a baseline for the interpretation of peptide-TiO2 adsorption data.

Oxygen-doped boron nitride nanosheets with excellent performance in hydrogen storage

Hydrogen is considered one of the best energy sources. However, the lack of effective, stable, and safe storage materials has severely prevented its practical application. Strong effort has been made to try new nanostructured materials as new storage materials. In this study, oxygen-doped boron nitride (BN) nanosheets with 2-6 atomic layers, synthesized by a facile sol-gel method, show a storage capacity of 5.7wt% under 5MPa at room temperature, which is the highest hydrogen storage ever reported for any BN materials. Importantly, 89% of the stored hydrogen can be released when the hydrogen pressure is reduced to ambient conditions. Furthermore, the BN nanosheets exhibit an excellent storage cycling stability due to the stable two-dimensional nanostructure. The first principles calculations reveal that the high hydrogen storage mainly origins from the oxygen-doping of the BN nanosheets with increased adsorption energies of H2 on BN by 20-80% over pure BN sheets at the different coverage. © 2014 Elsevier Ltd.

Spectroscopic and computational characterizations of alkaline-earth- and heavy-metal-exchanged natrolites

Synchrotron infrared (IR) and micro-Raman spectra of natrolites containing alkaline-earth ions (Ca2+, Sr2+, and Ba2+) and heavy metals (Cd2+, Pb2+, and Ag+) as extra-framework cations (EFCs) were measured under ambient conditions. Complementing our previous spectroscopic investigations of natrolites with monovalent alkali metal (Li+, Na+, K+, Rb +, and Cs+) EFCs, we establish a correlation between the redshifts of the frequencies of the 4-ring and helical 8-ring units and the size of the EFCs in natrolite. Through ab initio calculations we have derived structural models of Ca2+- and Ag+-exchanged natrolites with hydrogen atoms, and found that the frequency shifts in the H - O - H bending mode and the differences in the O - H stretching vibration modes can be correlated with the orientations of the water molecules along the natrolite channel. Assuming that the members of a solid solution series behave as an ideal mixture, we will be able to use spectroscopy to probe compositions. Deviation from ideal behavior might indicate the occurrence of phase separation on various length scales. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.