Inhibitory Effect of Phosphonium-Based Ionic Liquids on Coal Oxidation

To develop a chemical inhibitor that can efficiently suppress coal oxidation, nine tetraalkylphosphonium-based ionic liquids (ILs) and one imidazolium-based IL [1-allyl-3-methylimidazolium chloride ([AMIm]Cl)] were examined as additives. These ILs were used to treat and investigate the inhibitory effect on the oxidation activity and the structure of lignite coal. Characterization using thermogravimetric analysis showed that phosphonium-based ILs are able to inhibit coal oxidation up to 400 degrees C with the tributylethylphosphonium diethylphosphate ([P-4,P-4,P-4,P-2][DEP]) found to be the most effective. In contrast to the tetraalkylphosphonium-based ILs, inhibition using [AMIm]Cl was only found to be effective at temperatures below 250 degrees C, indicating that the tetraallcylphosphonium-based ILs may be more suitable for the future application of suppressing coal spontaneous combustion over a wide range of temperatures. Fourier transform infrared spectroscopic data showed that the various functional groups change in the coal following IL treatment, which are a decrease in the minerals and hydrogen bonds in all treated coals, while decreased aliphatic hydrocarbon and increased carbonyl bonds only appeared in some samples. During the oxidation of coal, the decomposition of aliphatic hydrocarbon groups is inhibited and the formation of carbonyl groups is delayed, so that the evolved gas concentration decreased, as shown by the temperature-programmed oxidation-mass spectrometry results. The deployment of the [P-4,P-4,P-4,P-2][ DEP] and tributylmethylphosphonium methylsulfate Its as additives also show good inhibitory effect on coal oxidation over the temperature range studied, and a relatively stronger interaction between [P-4,P-4,P-4,P-2] [DEP] and coal is demonstrated by the additive model.

Assessing hydrothermal liquefaction for the production of bio-oil and enhanced metal recovery from microalgae cultivated on acid mine drainage

The hydrothermal liquefaction(HTL) of algal biomass is a promising route to viable second generation biofuels. In this investigation HTL was assessed for the valorisation of algae used in the remediation of acid mine drainage (AMD). Initially the HTL process was evaluated using Arthrospira platensis (Spirulina) with additional metal sulphates to simulate metal remediation. Optimised conditions were then used to process a natural algal community (predominantly Chlamydomonas sp.) cultivated under two scenarios: high uptake and low uptake of metals from AMD. High metal concentrations appear to catalyse the conversion to bio-oil, and do not significantly affect the heteroatom content or higher heating value of the bio-oil produced. The associated metals were found to partition almost exclusively into the solid residue, favourable for potential metal recovery. High metal loadings also caused partitioning of phosphates from the aqueous phase to the solid phase, potentially compromising attempts to recycle process water as a growth supplement. HTL was therefore found to be a suitable method of processing algae used in AMD remediation, producing a crude oil suitable for upgrading into hydrocarbon fuels, an aqueous and gas stream suitable for supplementing the algal growth and the partitioning of most contaminant metals to the solid residue where they would be readily amenable for recovery and/or disposal.

Assessing hydrothermal liquefaction for the production of bio-oil and enhanced metal recovery from microalgae cultivated on acid mine drainage

The hydrothermal liquefaction(HTL) of algal biomass is a promising route to viable second generation biofuels. In this investigation HTL was assessed for the valorisation of algae used in the remediation of acid mine drainage (AMD). Initially the HTL process was evaluated using Arthrospira platensis (Spirulina) with additional metal sulphates to simulate metal remediation. Optimised conditions were then used to process a natural algal community (predominantly Chlamydomonas sp.) cultivated under two scenarios: high uptake and low uptake of metals from AMD. High metal concentrations appear to catalyse the conversion to bio-oil, and do not significantly affect the heteroatom content or higher heating value of the bio-oil produced. The associated metals were found to partition almost exclusively into the solid residue, favourable for potential metal recovery. High metal loadings also caused partitioning of phosphates from the aqueous phase to the solid phase, potentially compromising attempts to recycle process water as a growth supplement. HTL was therefore found to be a suitable method of processing algae used in AMD remediation, producing a crude oil suitable for upgrading into hydrocarbon fuels, an aqueous and gas stream suitable for supplementing the algal growth and the partitioning of most contaminant metals to the solid residue where they would be readily amenable for recovery and/or disposal.

Synthesis of a heparan sulfate mimetic disaccharide with a conformationally locked residue from a common intermediate

A simple mimetic of a heparan sulfate disaccharide sequence that binds to the growth factors FGF-1 and FGF-2 was synthesized by coupling a 2-azido-2-deoxy-D-glucosyl trichloroacetimidate donor with a 1,6-anhydro-2-azido-2-deoxy--D-glucose acceptor. Both the donor and acceptor were obtained from a common intermediate readily obtained from D-glucal. Molecular docking calculations showed that the predicted locations of the disaccharide sulfo groups in the binding site of FGF-1 and FGF-2 are similar to the positions observed for co-crystallized heparin-derived oligosaccharides obtained from published crystal structures.

Predicting residue-wise contact orders in proteins by support vector regression

Background The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.