Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Fuzzy logic. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Hybrid silane coating reinforced with silanized graphene oxide nanosheets with improved corrosion protective performance

Abstract: Silane coating has been used as a steel surface treatment for the promotion of adhesion between a substrate and an organic coating. However, the corrosion protection properties of this coating are not sufficient. Incorporation of various additives and nano-fillers into the coating is a promising way to improve the coating barrier action. In this paper a new nano-filler based on graphene oxide is recommended for this type of coating. The graphene oxide is silanized in the first step through a sol–gel route by using aminopropyl triethoxysilane (ATPES). Then, it is employed in a hybrid silane coating based on the mixture of aminopropyl triethoxysilane and tetraethyl orthosilicate (TEOS). The graphene oxide functionalization was characterized by Fourier transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS). The silane coatings filled with silanized and untreated graphene oxide were also characterized by FT-IR, X-ray diffraction (XRD) and scanning electron microscopy (SEM). The results obtained from different characterizations showed successful silanization and dispersion of graphene oxide in the silane hybrid coating. The silane coating containing silanized graphene oxide showed superior corrosion protective performance compared to the unfilled silane coating in electrochemical impedance spectroscopy (EIS) and polarization measurements, indicating the considerable corrosion barrier effect of this nano-filler.

Reconstruction of reflectance spectra using weighted principal component analysis

Abstract: The weighted principal component analysis technique is employed for reconstruction of reflectance spectra of surface colors from the related tristimulus values A dynamic eigenvector subspace based on applying certain weights to reflectance data of Munsell color chips has been formed for each particular sample and the color difference value between the target, and Munsell dataset is chosen as a criterion for determination of weighting factors Implementation of this method enables one to increase the influence of samples which are closer to target on extracted principal eigenvectors and subsequently diminish the effect of those samples which benefit from higher amount of color difference The performance of the suggested method is evaluated in spectral reflectance reconstruction of three different collections of colored samples by the use of the first three Munsell bases The resulting spectra show considerable improvements in terms of root mean square error between the actual and reconstructed reflectance curves as well as CIELAB color difference under illuminant A in comparison to those obtained from the standard PCA method © 2008 Wiley Periodicals, Inc Col Res Appl, 33, 360–371, 2008

Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids

Abstract: Nanofluid viscosity is an important physical property in convective heat transfer phenomena. However, the current theoretical models for nanofluid viscosity prediction are only applicable across a ...

On-Chip Fabrication of Paclitaxel-Loaded Chitosan Nanoparticles for Cancer Therapeutics

Abstract: The use of solvent-free microfluidics to fine-tune the physical and chemical properties of chitosan nanoparticles for drug delivery is demonstrated. Nanoparticle self-assembly is driven by pH changes in a water environment, which increases biocompatibility by avoiding organic solvent contamination common with traditional techniques. Controlling the time of mixing (2.5–75 ms) during nanoparticle self-assembly enables us to adjust nanoparticle size and surface potential in order to maximize cellular uptake, which in turn dramatically increases drug effectiveness. The compact nanostructure of these nanoparticles preserves drug potency better than previous nanoparticles, and is more stable during long-term circulation at physiological pH. However, when the nanoparticles encounter a tumor cell and the associated drop in pH, the drug contents are released. Moreover, the loading efficiency of hydrophobic drugs into the nanoparticles increases significantly from previous work to over 95%. The microfluidic techniques used here have applications not just for drug-carrying nanoparticle fabrication, but also for the better control of virtually any self-assembly process.

Learning to Control Arm Stiffness Under Static Conditions

Abstract: We used a robotic device to test the idea that impedance control involves a process of learning or adaptation that is acquired over time and permits the voluntary control of the pattern of stiffness at the hand. The tests were conducted in statics. Subjects were trained over the course of 3 successive days to resist the effects of one of three different kinds of mechanical loads: single axis loads acting in the lateral direction, single axis loads acting in the forward/backward direction, and isotropic loads that perturbed the limb in eight directions about a circle. We found that subjects in contact with single axis loads voluntarily modified their hand stiffness orientation such that changes to the direction of maximum stiffness mirrored the direction of applied load. In the case of isotropic loads, a uniform increase in endpoint stiffness was observed. Using a physiologically realistic model of two-joint arm movement, the experimentally determined pattern of impedance change could be replicated by assuming that coactivation of elbow and double joint muscles was independent of coactivation of muscles at the shoulder. Moreover, using this pattern of coactivation control we were able to replicate an asymmetric pattern of rotation of the stiffness ellipse that was observed empirically. These findings are consistent with the idea that arm stiffness is controlled through the use of at least two independent co-contraction commands.