Rapid, non-invasive characterization of the dispersity of emulsions via microwaves

A rapid and non-invasive method to determine the dispersity of emulsions is developed based on the interrelationship between the droplet size distribution and the dielectric properties of emulsions. A range of water-in-oil emulsions with different water contents and droplet size distributions were analysed using a microwave cavity perturbation technique together with dynamic light scattering. The results demonstrate that the dielectric properties, as measured by non-invasive microwave cavity analysis, can be used to characterise the dispersity of emulsions, and is also capable of characterizing heavy oil emulsions. This technique has great potential for industrial applications to examine the sedimentation, creaming and hence the stability of emulsions

The decarbonization of coal tar via microwave-initiated catalytic deep dehydrogenation

Coal tar, a major by-product of the coal industry, presents considerable difficulties in its refining and conversion into fuels due to its complex chemical composition and physical properties, such as high viscosity, corrosiveness, thermal instability, etc. Here we report a new route for producing hydrogen-rich gases together with carbonaceous materials, including carbon nanotubes, through the microwave-initiated catalytic deep dehydrogenation of coal tar using inexpensive iron catalysts. The resulting carbonaceous materials generated over the catalyst were investigated using a variety of techniques including scanning electron microscopy (SEM), transmission electron microscopy (TEM), temperature programmed oxidation (TPO) and Raman spectroscopy. Importantly, we have found that an aqueous emulsion feed of the coal tar enables considerably easier handling and an enhanced hydrogen production whilst also significantly reducing the extent of catalyst deactivation. This behaviour is shown to be assisted by the phenomenon of micro-explosion that enhances mass and heat transfer during the catalytic reactions

A Carbon Catalyst Co-Doped with P and N for Efficient and Selective Oxidation of 5-Hydroxymethylfurfural into 2,5-Diformylfuran

A newly designed N and P co-doped carbon material has been developed to catalyze the conversion of 5-hydroxymethylfurfural (HMF) to 2,5-furandialdehyde (DFF) with unprecedented yield and selectivity and demonstrating a synergistic effect between the heteroatoms. The desired catalyst was first synthesized via a pyrolysis method using urea as the nitrogen and carbon source followed by calcination with phytic acid solution as the phosphorus source. The mass ratio of phytic acid to C3N4 and calcination temperature were varied to investigate their effects on catalyst synthesis and microstructure as well as subsequent catalytic activity in simple reaction systems under oxygen. The effect of reaction conditions on the final HMF conversion and DFF selectivity were also investigated systematically. The P−C−N-5-800 catalyst obtained with the optimized annealing temperature of 800 °C and mass ratio of phytic acid/C3N4 of 5 enabled a 99.5 % DFF yield at 120 °C for 9 h under 10 bar oxygen pressure, being the highest among any reported metal-free heterogeneous catalyst to date. The excellent performance of P−C−N-5-800 could be ascribed to the synergy between N and P heteroatoms as well as the high content of graphitic-N and the P−C species within the carbon structure. Reusability studies show that the P−C−N-5-800 catalyst was stable and reusable without deactivation. These results strongly suggest that P−C−N-5-800 is a promising catalyst for large-scale production of DFF in a green manner

Photo-catalytic conversion of oxygenated hydrocarbons to hydrogen over heteroatom-doped TiO2 catalysts

Here we report a sustainable process for photo-induced hydrogen production from aqueous solutions of oxygenated hydrocarbons. Doping N and B into TiO2 noticeably improves its activity for hydrogen production. The addition of oxygenated hydrocarbons into water serves both as a hydrogen source and as an electron donor, which substantially enhances hydrogen production as compared with that for the photo-catalytic splitting of pure water. Other biomass-derived compounds such as glucose and sucrose are also shown to have potential for hydrogen production by this photo-catalytic conversion

Intelligent Molecular Identification for High Performance Organosulfide Capture Using Active Machine Learning Algorithm

Machine learning and computer-aided approaches significantly accelerate molecular design and discovery in scientific and industrial fields increasingly relying on data science for efficiency. The typical method used is supervised learning which needs huge datasets. Semi-supervised machine learning approaches are effective to train unlabeled data with improved modeling performance, whereas they are limited by the accumulation of prediction errors. Here, to screen solvents for removal of methyl mercaptan, a type of organosulfur impurities in natural gas, we constructed a computational framework by integrating molecular similarity search and active learning methods, namely, molecular active selection machine learning (MASML). This new model framework identifies the optimal molecules set by molecular similarity search and iterative addition to the training dataset. Among all 126,068 compounds in the initial dataset, 3 molecules were identified to be promising for methyl mercaptan (MeSH) capture, including benzylamine (BZA), p-methoxybenzylamine (PZM), and N,N-diethyltrimethylenediamine (DEAPA). Further experiments confirmed the effectiveness of our modeling framework in efficient molecular design and identification for capturing methyl mercaptan, in which DEAPA presents a Henry\u27s law constant 89.4% lower than that of methyl diethanolamine (MDEA)