A new way of valorizing biomaterials: the use of sunflower protein for 1 a-tocopherol microencapsulation

Biopolymer based microparticles were efficiently prepared from sunflower protein (SP) wall material and a-tocopherol (T) active core using a spray-drying technique. Protein enzymatic hydrolysis and/or N-acylation were carried out to make some structural modifications to the vegetable protein. Native and hydrolyzed SP were characterized by Asymmetrical Flow Field-Flow Fractionation (AsFlFFF). Results of AsFlFFF confirmed that size of proteinic macromolecules was influenced by degree of hydrolysis. The effect of protein modifications and the influence of wall/core ratio on both emulsions and microparticle properties were evaluated. Concerning emulsion properties, enzymatic hydrolysis involved a decrease in viscosity, whereas acylation did not significantly affect emulsion droplet size and viscosity. Microparticles obtained with hydrolyzed SP wall material showed lower retention efficiency (RE) than native SP microparticles (62-80% and 93% respectively). Conversely, acylation of both hydrolyzed SP and native SP allowed a higher RE to be reached (up to 100%). Increasing T concentration increased emulsion viscosity, emulsion droplet size, microparticle size, and enhanced RE. These results demonstrated the feasibility of high loaded (up to 79.2% T) microparticles

Relative Efficiency of Stochastic and Genetic Algorithms for Characterizing Clusters with Unique Bonding Patterns

This thesis investigates the relative efficiencies of two isomeric search procedures to survey potential energy surfaces with the objective of rapidly assessing the relevancy of compounds possessing atypical structural patterns. Variants of both the standard stochastic procedure and the genetic algorithm (GA) were implemented, benchmarked, and compared using a series of homoatomic silicon clusters and hypercoordinated planar-carbon containing species. Both of these approaches emerge as valuable tools for the objective of locating small low-lying energy clusters on the potential energy surface. For systems with ten or fewer atoms, the stochastic search locates every global and low-lying minima for reference cluster compounds. For larger systems, the GA surpasses stochastic approaches as the best alternative. Geometry optimization of the generated structures performed at the quantum chemistry level represents, by far, the most time-consuming step. Replacement of preliminary density functional computations with a semi-empirical alternative results in a dramatic reduction (∼20x) in the time needed to achieve final results. The benchmarking of the two search procedures is followed by explorations authenticating that high-symmetry (Be, B+, C2+)@Si6-10 clusters correspond to low-lying energy isomers. This emerging class of endohedral silicon-based structures, whose stability depends upon the nature of the doping atom, represents an intriguing alternative to their carbon analogues. Extensive analysis of the electronic structure of these compounds corroborates a reverse charge transfer (i.e. away from the cage) as compared to the archetypal metal-containing endohedral fullerenes. The key factors leading to enhanced stabilization can be attributed to a maximization of multicenter-type bonding along with an appropriate balance between electronegativity and atom size. Overall, the methodologies proposed throughout this thesis provide general guidelines on the best ways to validate computational structural design

Identifying clusters as low-lying minima - Efficiency of stochastic and genetic algorithms using inexpensive electronic structure levels

Molecular candidates possessing unconventional chemical bonding paradigms (e.g., boron wheels, molecular stars, and multicenter bonding) have attracted a great deal of attention by the computational community. The viability of such systems is necessarily assessed through the identification of the lowest lying energy forms of a given chemical composition on the potential energy surface (PES). Although dozens of search algorithms have been developed, only a few are general and simple enough to become standard everyday procedures for this purpose. The simple random search and genetic algorithm (GA) are among these: but how do these approaches perform on typical isomeric searches? The performance of three specific variants for the ab initio exploration of the PES of prototype planar tetracoordinated and hypercoordinated carbon-containing systems C2Al4 and CB62- are compared. The advantages of preoptimizing with a low-cost semiempirical method (e.g., PM6) together with the most cost-efficient GA-based variant are discussed, and the trends verified by the isomer search of the larger Si5Li7+ clusters. (c) 2011 Wiley Periodicals, Inc. J Comput Chem, 201

Efficiency of random search procedures along the silicon cluster series Sin (n=5-10, 15 and 20)

The efficiency of the simplest isomeric search procedure consisting in random generation of sets of atomic coordinates followed by density functional theory geometry optimization is tested on the silicon cluster series (Si-5-10,Si-15,Si-20). Criteria such as yield, isomer distributions and recurrences are used to clearly establish the performance of the approach with respect to increasing cluster size. The elimination of unphysical candidate structures and the use of distinct box shapes and theoretical levels are also investigated. For the smaller Si-n (n = 5-10) clusters, the generation of random coordinates within a spherical box is found to offer a reasonable alternative to more complex algorithms by allowing straightforward identification of every known low-lying local minima. The simple stochastic search of larger clusters (i.e. Si-15 and Si-20) is however complicated by the exponentially increasing number of both low-and high-lying minima leading to rather arbitrary and non-comprehensive results. (C) 2011 Wiley Periodicals, Inc. J Comput Chem 32: 1869-1875, 201

How are small endohedral silicon clusters stabilized?

Clusters in the (Be, B, C)@Si-n((0,1,2+)) (n = 6-10) series, isoelectronic to Si-n(2-), present multiple symmetric structures, including rings, cages and open structures, which the doping atom stabilizes using contrasting bonding mechanisms. The most striking feature of these clusters is the absence of electron transfer (for Be) or even the inversion (for B and C) in comparison to classic endohedral metallofullerenes (e.g. from the outer frameworks towards the enclosed atom). The relatively small cavity of the highly symmetric Si-8 cubic cage benefits more strongly from the encapsulation of a boron atom than from the insertion of a too large beryllium atom. Overall, the maximization of multicenter-type bonding, as visualized by the Localized Orbital Locator (LOL), is the key to the stabilization of the small Sin cages. Boron offers the best balance between size, electronegativity and delocalized bonding pattern when compared to beryllium and carbon

Antennifying Orthopedic Bone-Plate Fixtures for the Wireless Monitoring of Local Deep Infections

Infection is the unavoidable threat to any orthopedic implant that can also force its removal as extreme remedy. The diagnosis of infections is currently achieved by time consuming imaging (X-Rays, MRI, CT) or just by the onset of the patient's pain, when the problem is in an advanced status. Instead, by equipping the prosthesis with a local sensor (for the temperature as a first) and with a wireless communication radio, an early-time identification of the infection could be achieved. This paper proposes a method to transform an orthopedic device provided with holes (like a bone fixation plate) into an harvesting antenna integrating an RFID sensor, with no battery onboard. A miniaturized antenna adapter, fully embedded into a free hole, with tuning capability, collects the electromagnetic power intercepted by the medical device and transfers it to the RFID circuit. Simulations and experimentations with several prototypes demonstrated that the augmented implanted device can establish a stable RFID link up to 0.5 m and that it is able to correctly sample the variation (37°C - 40°C) of the local temperature of the bone as in case of typical deep infections

Orthopedic fixture-integrated RFID temperature sensor for the monitoring of deep inflammations

Orthopedic implants could be subjected to infections. Conventional diagnostic tools involve X-Rays, MRI, CT imaging or, more commonly, the onset of the patient's pain. Monitoring the health state of a prosthesis from the outside the limb can be accomplished by through-the-body wireless communication link. However, techniques for integrating a wireless sensor into orthopedic implants require a structural modification of the prosthesis. To overcome this limitation, a non-invasive way to augment a prosthesis with wireless monitoring capability is here proposed for the early detection of local tissue infection. The idea can be applied to an orthopedic fixation plate provided with holes, with no changes to its structure. The fixation bar is transformed into an RFID tag by exciting voltage gap into an unused screw hole. The electrical and geometrical parameters of the exciter enable a convenient two-steps tuning mechanism (coarse and fine) to adjust the working frequency. Preliminary simulations predicted a read range of more than 50 cm outside the body that is suited to an early and non-collaborative diagnosis in the emerging Personalized Healthcare