Real-time chloride diffusion coefficient in concrete using embedded resistivity sensors

Service life of concrete infrastructure is severely compromised because of chloride-induced corrosion and measuring the chloride content is crucial to determine the remaining service life. DuraCrete provides a chloride ingress model based on Fick’s 2nd law. Although the diffusion coefficient is modelled as a time-dependent variable, the DuraCrete solution averages it to a constant value. This simplification leads to inaccurate estimation of the chloride content. A new analytical solution that addresses the underlying mathematical discrepancy has been proposed. However, the time-dependent diffusion coefficient is still based on an empirical factor. In this study, a real-time durability monitoring system has been developed using remotely operated resistivity sensors. Such a system is able to monitor the time dependent diffusion coefficient without the need to incorporate empirical factors. Additionally, a numerical technique to find an approximation of the proposed improved analytical solution is presented using real-time resistivity measurements from laboratory and real structures. The results show that the discrete sensor data measurements over time provide a good approximation of the proposed analytical solution. The system developed in this study is used as a data-driven input parameter to supplement the existing chloride models.Concrete Structure

Biocompatible humidity sensor using paper cellulose fiber/GO matrix for human health and environment monitoring

Environmentally friendly humidity sensors with high sensing performance are considered crucial components for various wearable electronic devices. We developed a rapid-response and durable Paper Cellulose Fiber/Graphene Oxide Matrix (PCFGOM) humidity sensor using an all-carbon functional material. The fabricated sensor demonstrated a high sensitivity to humidity through an electrical impedance measurement, with an increase in response to humidity ranging from 10% to 90% at 1 kHz and 10 kHz, respectively, along with a response time of 1.2 s and a recovery time of 0.8 s. The stability of the sensor was also examined, with consistent performance over a period of 24 h. This novel sensor was employed in several applications, including non-contact proximity sensing, environmental humidity detection, and human respiration detection, to showcase its potential. Moreover, this work represents a significant milestone in developing inexpensive and eco-friendly humidity sensors, given the abundance of paper and graphene in nature and their biocompatibility.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Computer Engineerin

Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks

Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation is the ultimate goal of emerging memristor technology, in which the storage and computation can be done in the same memory crossbar. In this work, an analog memristor device is fabricated utilizing the unique characteristics of single-wall carbon nanotubes (SWCNTs) to act as the switching medium of the device. Via the planar structure, the memristor device exhibits analog switching ability with high state stability. The device’s conductance and capacitance can be tuned simultaneously, increasing the device's potential and broadening its applications' horizons. The multi-state storage capability and long-term memory are the key factors that make the device a promising candidate for bio-inspired computing applications. As a demonstrator, the fabricated memristor is deployed in spiking neural networks (SNN) to exploit its analog switching feature for energy-efficient classification operation. Results reveal that the computation-in-memory implementation performs Vector Matrix Multiplication with 95% inference accuracy and few femtojoules per spike energy efficiency. The memristor device presented in this work opens new insights towards utilizing the outstanding features of SWCNTs for efficient analog computation in deep learning systems.Computer Engineerin

Stopping Voltage-Dependent PCM and RRAM-Based Neuromorphic Characteristics of Germanium Telluride

Recently, phase change chalcogenides, such as monochalcogenides, are reported as switching materials for conduction-bridge-based memristors. However, the switching mechanism focused on the formation and rupture of an Ag filament during the SET and RESET, neglecting the contributions of the phase change phenomenon and the distribution and re-distribution of germanium vacancies defects. The different thicknesses of germanium telluride (GeTe)-based Ag/GeTe/Pt devices are investigated and the effectiveness of phase loops and defect loops future application in neuromorphic computing are explored. GeTe-based devices with thicknesses of 70, 100, and 200 nm, are fabricated and their electrical characteristics are investigated. Highly reproducible phase change and defect-based characteristics for a 100 nm-thick GeTe device are obtained. However, 70 and 200 nm-thick devices are unfavorable for the reliable memory characteristics. Upon further analysis of the Ag/GeTe/Pt device with 100 nm of GeTe, it is discovered that a state-of-the-art dependency of phase loops and defect loops exists on the starting and stopping voltage sweeps applied on the top Ag electrode. These findings allow for a deeper understanding of the switching mechanism of monochalcogenide-based conduction-bridge memristors.Computer Engineerin