Effect of hydroxyapatite nanoparticles on the degradability of random poly(butylene terephthalate-co-aliphatic dicarboxylate)s having a high content of terephthalic units

Copolyesters derived from 1,4-butanediol and constituted also of aliphatic and aromatic dicarboxylate units in a molar ratio of 3:7 were synthesized by a two-step polycondensation procedure. Succinic, adipic, and sebacic acids were specifically selected as the aliphatic component whereas terephthalic acid was chosen as the aromatic moiety. The second synthesis step was a thermal transesterification between the corresponding homopolymers, always attaining a random distribution as verified by NMR spectroscopy. Hybrid polymer composites containing 2.5 wt % of hydroxyapatite (HAp) were also prepared by in situ polymerization. Hydroxyl groups on the nanoparticle surface allowed the grafting of polymer chains in such a way that composites were mostly insoluble in the typical solvents of the parent copolyesters. HAp had some influence on crystallization from the melt, thermal stability, and mechanical properties. HAp also improved the biocompatibility of samples due to the presence of Ca2+ cations and the damping effect of phosphate groups. Interestingly, HAp resulted in a significant increase in the hydrophilicity of samples, which considerably affected both enzymatic and hydrolytic degradability. Slight differences were also found in the function of the dicarboxylic component, as the lowest degradation rates was found for the sample constituted of the most hydrophobic sebacic acid units. View Full-TextPeer ReviewedPostprint (published version

Biodegradability and biocompatibility of copoly(butylene sebacate-co-terephthalate)s

In the present study poly (butylene sebacate-co-terephthalate)s having different compositions were synthesized with a high yield and a random distribution by thermal transesterification of poly (butylene sebacate) and poly (butylene terephthalate) homopolymers. The copolymer with the highest comonomer ratio was the least crystalline sample, although the melting peaks corresponding to both, sebacate and terephthalate-rich phases were still observable in calorimetric heating runs. This copolymer was associated with interesting thermal and mechanical properties, as the maximum melting point was higher than 100 °C and the storage modulus was also high (i.e. 1.1 × 109 N/m2 and 1.7 108 N/m2 were determined just before and after the main glass transition temperature of -12 °C). As all studied samples were thermally stable up to temperatures clearly higher than the fusion temperature, they could be easily processed. Increasing the terephthalate content of the copolymers resulted in higher hydrophobicity, which had a minor influence on cell adhesion and proliferation of both fibroblast-like and epithelial-like cells. Hydrolytic and enzymatic degradability were assessed and the effect of composition and crystallinity on the degradation rate was investigated. Molecular weight measurements during exposure to a hydrolytic media indicated a first order kinetic mechanism during the initial stages of degradation before reaching a limiting molecular size, which was indicative of solubilization. The most amorphous sample appears as a highly promising biodegradable material since it showed a significant weight loss during exposure to all selected degradation media, but also exhibited good performance and properties that were comparable to those characteristic of polyethylenePeer ReviewedPostprint (author's final draft

Thermal degradation of random copolyesters based on 1,4-butanediol, terepthalic acid and different aliphatic dicarboxylic acids

Thermal stability and degradation kinetics have been studied for a series of aliphatic-aromatic copolyesters where the terephthalate content was varied between 30 mol-% and 70 mol-%. Succinate, adipate and sebacate were considered as the aliphatic dicarboxylate unit. All copolyesters were synthesized with a perfect random distribution by a thermal transesterification process from the corresponding homopolyesters. A complex degradation was deduced for all copolymers taking into account the increment of the activation energy with conversion. In fact, thermogravimetric curves showed a minor decomposition process in the low conversion region that was more significant for the succinate derivative and specifically for that having the lowest aromatic content. The sebacate derivative was characterized by the presence of an additional and minor decomposition process that took place at the highest conversion. All copolyesters were defined by a major decomposition process, which has similar values of activation energy regardless of the method used to calculate them (e.g. Kissinger, KAS or Friedman methodologies). This decomposition reaction followed a A4 Avrami-Erofeev mechanism when Coats-Redfern and Criado methodologies were applied. In summary, all the studied copolymers thermally decompose following a complex process but in all cases the main degradation step corresponds to a similar degradation mechanism.Postprint (author's final draft

Nucleating and retarding effects of nanohydroxyapatite on the crystallization of poly(butylene terephthalate-co-alkylene dicarboxylate)s with different lengths

New biodegradable and biocompatible composites are continuously developed for biomedical applications (e.g., from drug delivery devices to tissue engineering scaffolds). Properties of such systems may depend on their morphology and structure, which are attained after their processing, and therefore, the study of the crystallization kinetics has a particular relevance. The crystallization kinetics of hydroxyapatite-filled poly(butylene terephthalate-co-alkylene dicarboxylate)s has been studied under non-isothermal conditions, using a wide range of cooling rates and different kinetic models. Based on our results, nanohydroxyapatite (nHAp) particles were found to effectively act as additional nucleation sites for poly(butylene terephthalate-co-succinate) (PBST), giving rise to an increased crystallization rate with respect to pure PBST. However, the overall growth rate of HAp nanocomposites decreased compared to the corresponding homopolymers with longer aliphatic dicarboxylic acids (i.e., adipic and sebacic acid derivatives). In order to clarify this point, the activation energy for non-isothermal crystallization was evaluated using the Friedman method and significant differences were observed, suggesting a disturbing effect of nanoparticles on the motion of molecular chains that hindered their capability to reach the growing crystallization front. Isoconversional methods provided a good understanding of the kinetics of the crystallization process and significant information regarding the activation energy, relative crystallinity, and global and local Avrami exponents.Peer ReviewedPostprint (published version

Rhino-orbital Mucormycosis in an Immunocompetent Pediatric Patient, Resembling an Orbital Mass- A Case Report

Rhino-orbital mucormycosis in an immunocompetent pediatric patient can present as an orbital mass. We report a 9-year-old male that presented with periorbital swelling and limitation of left eye movement from one month ago. The patient was treated at another center with a diagnosis of mucormycosis but was referred due to worsening symptoms. Orbital and paranasal sinus CT scan revealed opacities in the left paranasal sinus and soft tissue density in the medial and inferior orbital wall. The patient underwent orbitotomy and mass debulking surgery on suspicion of a possible neoplastic mass. Pathologic evaluations revealed mucormycosis. After receiving intravenous liposomal amphotericin-B that was followed by oral posaconazole syrup for two months and sinus debridement, the symptoms regressed. In immunocompromised pediatric patients, mucormycosis should be considered in the differential diagnosis of an orbital mass

Physically-based dynamic model for the control of cavity pressure in thermoplastics injection molding

The injection molding process, due to its versatility, cost effectiveness, and ability to produce precise complex articles is widely used in plastics processing. Mold cavity pressure is a good indicator of the processes taking place in the cavity and plays an important role in determining the quality of the molded articles. The dynamic modeling and control of cavity pressure, based on a physically-based approach, is studied in this research project. The work deals with the filling and packing phases.A lumped physically-based model was developed in order to study the behavior of the system. The model is derived from conservation laws and incorporates a physical understanding of the process. The whole system was divided into subsystems including the hydraulic system, ram-screw, barrel, and polymer delivery system. It was found necessary to account for polymer melt elasticity as well as non-Newtonian behavior of the polymer melt flow. Consideration of the growing solid skin in the polymer delivery system was found to be necessary.The dynamics of the cavity pressure during the filling phase were investigated and found to be non-linear and time-varying in relation to the hydraulic servo-valve opening which is the manipulated variable. The dynamic behavior of the cavity pressure is approximated by piece-wise linearization of the non-linear governing equations to derive a transfer function using the physically-based model which is of fifth order. Adaptive PI, PID, and IMC controllers were designed and tested for the control of the cavity pressure. Various tuning techniques, along with changes in set-point, were used to determine conservative settings for the PI and PID controllers.A similar approach was used to study the dynamics of the cavity pressure during the packing phase. A sixth order transfer function, with piece-wise linearization, was derived to approximate the non-linear and time-varying behavior of the cavity pressure during packing. The adaptive PI, PID, and IMC controllers were successfully applied into the packing phase. The transition of the filling-to-packing was selected to be detected by the derivative of the cavity pressure and adaptive controllers were successfully used for this phase.Two commonly used injection molding grade thermoplastics, polyethylene and polystyrene, were used in experimental part of this work for model validation and controller testing

Preparation of Diethylenetriamine Modified Polyacrylonitrile Nanofibers for Cadmium Ion Adsorption

In this study, the electrospinning method was used to manufacture polyacrylonitrile (PAN) nanofibers. The procedure involved spinning a solution of 10%wt PAN in dimethyl formamide (DMF) in an electric field of 21 kV and with a tip to collector distance of 16 cm. The nanofibers thus obtained had an average diameter of 100 nm. Then, scanning electron microscopy (SEM) images were used to investigate the morphology of the nanofibers. In the next step, the nanofiner surface was modified with diethylenetriamine and FTIR was employed to ensure the presence of amines on the nanofiber surface. The functionalized nanofibers were then used for the first time to adsorb ions of cadmium (a heavy metal with industrial applications) and its adsorption capacity was evaluated. The chemical charactristics of the nanofibers and the effects of such parameters as pH, temprature, and contact time on adsorption efficiency were investigated. The results showed that maximum adsorption efficiency was achieved within the first 10 minutes of the process at a pH in the range of 5‒7 when about 80% of the cadmium ions were adsorbed.. Moreover, only slight changes were observed with longer contact times or with increasing temperature. Finally, the adsorption data fitted well with the Langmuir isother

An integrated Shannon Entropy and reference ideal method for the selection of enhanced oil recovery pilot areas based on an unsupervised machine learning algorithm

Pilot-scale enhanced oil recovery in hydrocarbon field development is often implemented to reduce investment risk due to geological uncertainties. Selection of the pilot area is important, since the result will be extended to the full field. The main challenge in choosing a pilot region is the absence of a systematic and quantitative method. In this paper, we present a novel quantitative and systematic method composed of reservoir-geology and operational-economic criteria where a cluster analysis is utilized as an unsupervised machine learning method. A field of study will be subdivided into pilot candidate areas, and the optimized pilot size is calculated using the economic objective function. Subsequently, the corresponding Covariance (COV) matrix is computed for the simulated 3-D reservoir quality maps in the areas. The areas are optimally clustered to select the dominant cluster. The operational-economic criteria could be applied for decision making as well as the proximity of each area to the center of dominant cluster as a geological-reservoir criterion. Ultimately, the Shannon entropy weighting and the reference ideal method are applied to compute the pilot opportunity index in each area. The proposed method was employed for a pilot study on an oil field in south west Iran