Highly Sensitive Nanosensors for Electrochemical and Materials Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Materials Science and Engineering".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 3340

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Guest Editor
Department of Materials Science and Engineering, Gachon University, Seongnam-si 1342, Republic of Korea
Interests: FET biosensors; image sensors; fluorescent sensors; wireless smart sensors; RFID wireless sensing networks; functional polymers; conducting polymer nanomaterials; inorganic nanomaterials; composite nanomaterials; electronic signal transport in nanomaterials
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Special Issue Information

Dear Colleagues,

Recently, substantial efforts have been made to live a healthy, pleasant, and comfortable life along with an improvement in the quality of human life. Sensors, which measure changes in the environment or changes in disease factors, are one of the essential factors in improving the quality of life and the demand for them is expected to increase the future. Current sensor studies have been conducted using functional nanomaterials (i.e., nanosensors) with unique physical, chemical, mechanical, and optical properties derived from small scales. In the field of nanotechnology, nanosensors are instrumental for monitoring physical and chemical phenomena in regions difficult to reach, detecting biochemicals in cellular organelles, and measuring nanoscopic particles in the industry and environment. In addition, the integration of nanosensors into wireless communication networks is of particular interest because of the nonobtrusive nature of the installation, high nodal densities, and low costs (without the need for extensive wiring).

This Special Issue aims to attract leading researchers in the area of nanosensors to highlight the latest exciting developments to promote concrete applications. Accepted contributions will include (1) the synthesis and fundamental properties of functional nanomaterials for components in nanosensors, (2) the alternative design of the sensor device, and (3) their applications to chem-/biological detections.

Prof. Dr. Junseop Lee
Guest Editor

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Keywords

  • Nanosensor
  • Biosensor
  • Chemical sensor
  • Medical sensor
  • Wireless sensor
  • MEMS
  • Non-enzyme sensor
  • Conductive nanomaterials
  • Semiconducting materials
  • Composite nanomaterials
  • Aptamer
  • Nanotechnology

Published Papers (1 paper)

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Research

14 pages, 2721 KiB  
Article
Piezoelectric-Based Sensor Concept and Design with Machine Learning-Enabled Using COMSOL Multiphysics
by Bachar Mourched, Mario Hoxha, Ahmed Abdelgalil, Ndricim Ferko, Mariam Abdallah, Albert Potams, Ardit Lushi, Halil Ibrahim Turan and Sabahudin Vrtagic
Appl. Sci. 2022, 12(19), 9798; https://doi.org/10.3390/app12199798 - 29 Sep 2022
Cited by 4 | Viewed by 2953
Abstract
This paper presents the concept and design of a system that embeds piezoelectric sensors to measure the voltage of a mechanical load applied to it. COMSOL Multiphysics, a finite element simulation tool, was used to design the system and analyze the data to [...] Read more.
This paper presents the concept and design of a system that embeds piezoelectric sensors to measure the voltage of a mechanical load applied to it. COMSOL Multiphysics, a finite element simulation tool, was used to design the system and analyze the data to find a possible fingerprint of voltage changes. The sensors’ voltage readings were affected by the load applied to the surface of the structure with different magnitudes and speeds. The analyzed data show the effect of position and mass on the voltage readings and indicates the possibility of speed prediction. The obtained dataset results validated the concept of the proposed system, where the collected data can serve as a digital data pipeline model for future research on different artificial intelligence (AI) or machine learning (ML) modeling applications. From the obtained data, a reasonable view shows that voltage reading matrices can be utilized for the detection of vehicle speed, location, and mass if used as training data for machine learning modeling, which can benefit the Internet of Things (IoT) technology. Full article
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