Spray Congealed Lipid Microparticles with High Protein Loading: Preparation and Solid State Characterization

The spray-congealing technique, a solvent-free drug encapsulation process, was successfully employed to obtain lipid-based particulate systems with high (10–20% w/w) protein loading. Bovine serum albumin (BSA) was utilised as model protein and three low melting lipids (glyceryl palmitostearate, trimirystin and tristearin) were employed as carriers. BSA-loaded lipid microparticles were characterised in terms of particle size, morphology and drug loading. The results showed that the microparticles exhibited a spherical shape, mean diameter in the range 150–300 µm and an encapsulation efficiency higher than 90%. Possible changes in the protein structure as a result of the manufacturing process was then investigated for the first time using UV spectrophotometry in fourth derivative mode and FT-Raman spectroscopy. The results suggested that the structural integrity of the protein was maintained within the particles. Thermal analysis indicated that the effect of protein on the thermal properties of the carriers could be detected. Spray-congealing could thus be considered a suitable technique to produce highly BSA-loaded microparticles preserving the structure of the protein.

Assessment of rolling resistance models in discrete element simulations

Particulate systems are of interest in many disciplines. They are often investigated using the discrete element method because of its capability to investigate particulate systems at the individual particle scale. To model the interaction between two particles and between a particle and a boundary, conventional discrete element models use springs and dampers in both the normal and tangential directions. The significance of particle rotation has been highlighted in both numerical studies and physical experiments. Several researchers have attempted to incorporate a rotational torque to account for the rolling resistance or rolling friction by developing different models. This paper presents a review of the commonly used models for rolling resistance and proposes a more general model. These models are classified into four categories according to their key characteristics. The robustness of these models in reproducing rolling resistance effects arising from different physical situations was assessed by using several benchmarking test cases. The proposed model can be seen to be more general and suitable for modelling problems involving both dynamic and pseudo-static regimes. An example simulation of the formation of a 2D sandpile is also shown. For simplicity, all formulations and examples are presented in 2D form, though the general conclusions are also applicable to 3D systems.

Recent Innovations in Antibody-Mediated, Targeted Particulate Nanotechnology and Implications for Advanced Visualisation and Drug Delivery

Research into the targeting of drug substances to a specific disease site has enjoyed sustained activity for many decades. The reason for such fervent activity is the considerable clinical advantages that can be gained when the delivery system plays a pivotal role in determining where the drug is deposited. When compared to conventional formulations where no such control exists, such as parenteral and oral systems, the sophisticated targeting device can reduce side effects and limit collateral damage to surrounding normal tissue. No more so is this important than in the area of oncology when dose-limiting side effects are often encountered as an ever present difficulty. In this review, the types of colloidal carrier commonly used in targeted drug delivery are discussed, such as gold and polymeric colloids. In particular, the process of attaching targeting capabilities is considered, with reference to antibody technologies used as the targeting motifs. Nanotechnology has brought together a means to carry both a drug and targeting ligand in self-contained constructs and their applications to both clinical therapy and diagnosis are discussed.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.