1,721,022 research outputs found
Image-Guided Mini-Invasive Treatments for Vascular and Oncologic Diseases: Embolization Therapy
No full textTranscatheter Embolization, also called Embolotherapy, is a mini-invasive, non-surgical therapeutic solution used in Interventional Radiology to close blood vessels deliberately. A wide range of embolic agents is available in clinical practice, including metal coils and liquid agents. More recent advances in biomaterials such as shape-memory foam and in-situ gelling solutions have led to the development of new pre-clinical embolic agents. This review offers a brief overview of current and emerging technologies in the field of endovascular embolization. The focus is on devices, materials and techniques
Superhydrophobic Coatings and Artificial Neural Networks: Design, Development and Optimization
No full textRecently, considerable attention has been devoted to the development of superhydrophobic surfaces due to their advantageous antifouling and antimicrobial capacity. While significant effort has been devoted to fabricating such surfaces, very few polymeric superhydrophobic surfaces can be considered durable against various externally imposed stresses. Pyrogenic hydrophobic silica nanoparticles were used to confer superhydrophobic properties to the coatings. 450 samples were prepared using a layer-by-layer approach, deposing an epoxy resin or PDMS layer as adhesive on a substrate (PC/ABS), followed by one or more layers of silica nanoparticles, or silica-resin mixed layers. The best coating obtained shows a contact angle of 157° and a tape peeling grade resistance. The applied method involves the spray deposition of a multilayer coating composed of: silica layer/epoxy resin layer/silica layer, followed by partial curing of the coating (15 min, 70 °C); another silica layer is then sprayed on the surface and is cured for 10 min. Given the high number of parameters involved, process optimization is quite tricky. Artificial Neural Networks are the best tool to deal with multivariate analysis problems and for this reason, data from all the prepared samples were collected into a matrix and was used to train a neural network capable of predicting the degree of hydrophobicity of a silica nanoparticles-based coating
CO production by AuAg/ZnO catalyst for CO2 valorization
No full textA controllable composition and morphology AuAg/ZnO catalyst, prepared by an easy scalable method, was for the first time explored for the electrocatalytic reduction of CO2. An experimental investigation reveals that the products are H2 and CO, which production rate increase in the presence of ZnO, up to 94.7 % of Faradic efficiency at 0.4 V. It was found that the composition of the bimetallic alloy contributes to the overall CO2 reduction performance. In particular, CO production increases decreasing the Au content in the catalyst alloy. To further investigate the catalytic activity, we have studied the electronic properties of the lattices Ag/Au, by density functional theory calculation. In particular, the Fermi level of transition metals is crucial in determining the binding strength between adsorbed species and surface. One can assume that the d-band centre represents the average energy of the d-electrons
Pseudo-semantic Approach to Study Model Membranes
No full textIt is well known that, during rapid temperature variations, lipid membranes are sensitive structures within cells. It is, therefore, not surprising that membranes are one of the most critical cellular targets for temperature adaptation. Many organisms adapt to the different temperature changing the degree of unsaturation of the lipids in the membrane. In this study, we describe a pseudo-semantic analysis approach applied to molecular dynamics. This approach is based on the encoding of data into strings and on the calculation of similarities. The described approach is universally applicable and, in this work, the fluidity of a POPC (palmitoyl-oleoyl-glycerophosphocholine) membrane under different conditions was monitored. In three simulations we varied the temperature above and below the phase transition temperature (Tm). In a fourth simulation we added an external molecule as fluidifier. The results of the clustering, obtained by similarity values, were consistent with the experimental data
Encoding Materials Dynamics for Machine Learning Applications
No full textRecent years witnessed an explosion of machine learning methods in all sectors. However, in the materials sector, and even more specifically in the biomaterials sector, although there have been numerous attempts at generalization, there has been a severe problem of coding the problem. The reason lies mainly in the temporal and spatial dimensions of the materials and their intrinsic complexity. In this contribution, we wish to suggest a possible universal coding of materials. This coding exploits a pseudo-semantic analysis and can be particularly advantageous in the study of polymeric biomaterials
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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