23 research outputs found

    Nondestructive monitoring of ageing of Alkali resistant Glass fiber reinforced cement (GRC)

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    Glass fiber reinforced cement (GRC) is a composite material made of portland cement mortar and alkali resistant (AR) fibers. AR fibers are added to portland cement to give the material additional flexural strength and toughness. However, ageing deteriorates the fibers and as a result the improvement in the mechanical properties resulted from the fiber addition disappears as the structure becomes old. The aim of this paper is monitoring GRC ageing by nondestructive evaluation (NDE) techniques. Two different NDE techniques (1) nonlinear impact resonant acoustic spectroscopy analysis and (2) propagating ultrasonic guided waves are used for this purpose. Both techniques revealed a reduction of the nonlinear behavior in the GRC material with ageing. Specimens are then loaded to failure to obtain their strength and stiffness. Compared to the un-aged specimens, the aged specimens are found to exhibit more linear behavior, have more stiffness but less toughness. Finally, undisturbed fragments on the fracture surface from mechanical tests are inspected under the electron microscope, to understand the fundamental mechanisms that cause the change in the GRC behavior with ageing.The authors want to acknowledge the financial support of the Ministerio de Ciencia e Innovacion MICINN, Spain, and FEDER funding (Ondacem Project: BIA 2010-19933) and BES-2011-044624. Also thanks to PAID-02-11 Program from Universitat Politecnica de Valencia.Eiras Fernández, JN.; Kundu, T.; Bonilla Salvador, MM.; Paya Bernabeu, JJ. (2013). Nondestructive monitoring of ageing of Alkali resistant Glass fiber reinforced cement (GRC). Journal of Nondestructive Evaluation - NDT and E International. 32:300-314. https://doi.org/10.1007/s10921-013-0183-yS30031432Bentur, A., Fibre, M.S.: Reinforced Cementitious Composites, 2nd edn. Taylor and Francis, New York (2007)Purnell, P., Short, N.R., Page, C.L.: A static fatigue model for the durability of glass fibre reinforced cement. J. 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    A thermosensitive electromechanical model for detecting biological particles

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    Miniature electromechanical systems form a class of bioMEMS that can provide appropriate sensitivity. In this research, a thermo-electro-mechanical model is presented to detect biological particles in the microscale. Identification in the model is based on analyzing pull-in instability parameters and frequency shifts. Here, governing equations are derived via the extended Hamilton’s principle. The coupled effects of system parameters such as surface layer energy, electric field correction, and material properties are incorporated in this thermosensitive model. Afterward, the accuracy of the present model and obtained results are validated with experimental, analytical, and numerical data for several cases. Performing a parametric study reveals that mechanical properties of biosensors can significantly affect the detection sensitivity of actuated ultra-small detectors and should be taken into account. Furthermore, it is shown that the number or dimension of deposited particles on the sensing zone can be estimated by investigating the changes in the threshold voltage, electrode deflection, and frequency shifts. The present analysis is likely to provide pertinent guidelines to design thermal switches and miniature detectors with the desired performance. The developed biosensor is more appropriate to detect and characterize viruses in samples with different temperatures

    Optical imaging and image analysis for high aspect ratio NEMS

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    A strategy for optical microscopy of high-aspect-ratio (HAR) nanoelectromechanical systems (NEMS) that combine large feature spacing and large height with sub-wavelength width is presented. Line images are simulated using a 2D model of incoherent imaging based on modal diffraction theory. Beyond a sufficient depth, it is shown that sub-wavelength features appear as dark lines, while wider features are visible as their edges. The results suggest NEMS and MEMS may be separated from background in images by detection of valleys in brightness. Results are confirmed by imaging of Si NEMS containing 100 nm wide features in a bright-field microscope. Algorithms for separation of NEMS, MEMS and background in microscope images based on valley detection, thresholding and masking are demonstrated
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