Zilli D.,PINMATE |
Bonelli P.R.,PINMATE |
Cukierman A.L.,PINMATE |
Cukierman A.L.,University of Buenos Aires
Sensors and Actuators, B: Chemical | Year: 2011
Multi-walled carbon nanotubes (MWCNTs) are successfully processed in the form of thin films (buckypapers), and their morphology and electrical behaviour are characterized. The MWCNTs are synthesized by the floating catalyst chemical vapour deposition process. The effects of a sequence of treatments applied for MWCNTs purification on the buckypapers electrical behaviour are also examined. Nanocomposite thin films constituted of pristine and purified MWCNTs and Pd nanoparticles are prepared in order to evaluate their viability as H 2 sensors at room temperature. For this purpose, the electrical resistance of the nanocomposite films in atmospheres with different H 2 concentrations, is determined. Scanning electron microscopy (SEM) images show that the buckypapers and the nanocomposite films are 2D structures constituted by randomly oriented MWCNTs. The buckypapers present a semiconductor-like electrical behaviour as determined by the standard four point method. Room temperature resistivity values of around 10 -3 Ω m are assessed. Nanocomposite films show different electrical behaviour depending on the purification treatment applied to the MWCNTs employed. Furthermore, the electrical resistance of the nanocomposite films is found to increase when the measurements are performed in H 2 atmosphere. Values of H 2 sensitivity at room temperature of the nanocomposite films up to 2.15% are determined for H 2 average concentration higher than 350 ppm with short recovery time. © 2011 Elsevier B.V. All rights reserved.
Jorge G.A.,Laboratorio Of Bajas Temperaturas |
Bekeris V.,Laboratorio Of Bajas Temperaturas |
Escobar M.M.,Laboratorio Of Polimeros Y Materiales Compuestos |
Goyanes S.,Laboratorio Of Polimeros Y Materiales Compuestos |
And 3 more authors.
Carbon | Year: 2010
We present micro-calorimetric specific heat measurements on different multiwall carbon nanotubes of large outer diameter, as a function of temperature in the range 10-120 K. A clear anomaly at 60 K with the shape of a peak is present in two of the samples, and both the height and the characteristic temperature of the peak are independent of magnetic field and do not exhibit thermal hysteresis, discarding magnetic degrees of freedom related to Fe seeds or contributions from adsorbed gases. These features suggest that the anomaly may be caused by a structural change. As the anomaly is also unaffected by induced intertube disorder, it may be related with a melting of orientational dislocations of individual tubes within multiwall nanotubes, an effect that was theoretically predicted to occur in carbon nanotubes and represents a distinctive feature of asymmetric molecular systems. © 2009 Elsevier Ltd. All rights reserved.
Maestri M.,PINMATE |
Farall A.,University of Buenos Aires |
Groisman P.,University of Buenos Aires |
Cassanello M.,PINMATE |
Computers and Chemical Engineering | Year: 2010
Many classical multivariate statistical process monitoring (MSPM) techniques assume normal distribution of the data and independence of the samples. Very often, these assumptions do not hold for real industrial chemical processes, where multiple plant operating modes lead to multiple nominal operation regions. MSPM techniques that do not take account of this fact show increased false alarm and missing alarm rates. In this work, a simple fault detection tool based on a robust clustering technique is implemented to detect abnormal situations in an industrial installation with multiple operation modes. The tool is applied to three case studies: (i) a two-dimensional toy example, (ii) a realistic simulation usually used as a benchmark example, known as the Tennessee-Eastman Process, and (iii) real data from a methanol plant. The clustering technique on which the tool relies assumes that the observations come from multiple populations with a common covariance matrix (i.e., the same underlying physical relations). The clustering technique is also capable of coping with a certain percentage of outliers, thus avoiding the need of extensive preprocessing of the data. Moreover, improvements in detection capacity are found when comparing the results to those obtained with standard methodologies. Hence, the feasibility of implementing fault detection tools based on this technique in the field of chemical industrial processes is discussed. © 2009 Elsevier Ltd. All rights reserved.