The Effects of Adding Different Adhesive Layers with a Microstructure Fiber Sensor Based on Surface Plasmon Resonance: A Numerical Study

Shafkat, Araf; Rashed, Ahmed Nabih Zaki; El-Hageen, Hazem M.; Alatwi, Aadel M.

doi:10.1007/s11468-020-01352-y

Cited by 81 publications

(5 citation statements)

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“…The energy received by the optical fiber probe can be attenuated substantially to even render it undetectable in practice. The minimum losses of the sensors and the corresponding resonance wavelengths 36–102 are calculated and shown in Fig. 10.…”

Section: Numerical Resultsmentioning

confidence: 99%

Detection of kerosene adulteration in automobile fuel by a low-loss surface plasmon resonance (SPR) chemical sensor

Yang

Shi

et al. 2022

Anal. Methods

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Section: Numerical Resultsmentioning

confidence: 99%

Detection of kerosene adulteration in automobile fuel by a low-loss surface plasmon resonance (SPR) chemical sensor

Yang

Shi

et al. 2022

Anal. Methods

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“…Electron diffraction, on the other hand, is a powerful and versatile technique capable of identifying the structure of nanomaterials in a spatially resolved manner. Traditionally, this method is mostly applied to metals and ceramics, namely "hard materials", and has been the foundation of the theme of "microstructural engineering" that has led to recent advances in high-entropy alloys (HEAs), 258 ultrastrong ceramics under extreme conditions, 259 magnetic compounds, 260 plasmonic materials and fibers, 261 and various solid electrolytes. 262 The extension of the electron diffraction-based method to soft materials has been limited due to highly focused beam during diffraction and the lack of universally robust crystalline order (i.e., nanocrystalline or even amorphous properties).…”

Section: Local Structural Order and Defects Characterized By Electron...mentioning

confidence: 99%

Electron Microscopy Studies of Soft Nanomaterials

et al. 2023

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This review highlights recent efforts on applying electron microscopy (EM) to soft (including biological) nanomaterials. We will show how developments of both the hardware and software of EM have enabled new insights into the formation, assembly, and functioning (e.g., energy conversion and storage, phonon/photon modulation) of these materials by providing shape, size, phase, structural, and chemical information at the nanometer or higher spatial resolution. Specifically, we first discuss standard real-space two-dimensional imaging and analytical techniques which are offered conveniently by microscopes without special holders or advanced beam technology. The discussion is then extended to recent advancements, including visualizing three-dimensional morphology of soft nanomaterials using electron tomography and its variations, identifying local structure and strain by electron diffraction, and recording motions and transformation by in situ EM. On these advancements, we cover state-of-the-art technologies designed for overcoming the technical barriers for EM to characterize soft materials as well as representative application examples. The even more recent integration of machine learning and its impacts on EM are also discussed in detail. With our perspectives of future opportunities offered at the end, we expect this review to inspire and stimulate more efforts in developing and utilizing EM-based characterization methods for soft nanomaterials at the atomic to nanometer length scales in academic research and industrial applications.

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“…In sensor calibration experiments, there are large differences in the sources of uncertainty, and the uncertainty transfer coefficients and the covariance functions of different components are difficult to determine, leading to difficulties in the calculation process and poor applicability. A dynamic measurement uncertainty estimation method based on the frequency response characteristics of the sensor is proposed in the literature, and the dynamic measurement test of an acceleration sensor is taken as an example [12], and the amplitude-frequency characteristic and phase-frequency characteristic curves of the system are fitted by segments, and the uncertainty of the dynamic measurement is obtained according to the amplitude and frequency range of the input signal and the confidence factor [13]. The literature proposes a method for calculating the uncertainty of dynamic characteristic parameters of sensors based on model parameter identification, where the dynamic calibration of sensors is repeated and the generalized least squares method is applied to model the dynamic characteristics, and the sample values of dynamic characteristic parameters such as rise time, overshoot, and operating frequency band are derived from the identified time-domain model, and the standard uncertainty is calculated using Bessel's formula.…”

Section: Related Workmentioning

confidence: 99%

Sensor Mathematical Model Data Fusion Biobjective Optimization

Hou

Wei-yun

2022

Journal of Sensors

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Sensors are an important tool to quantify the changes and an important part of the information acquisition system; the performance and accuracy of sensors are more strictly desired. In this paper, a highly sensitive fiber optic sensor for measuring temperature and refractive index is prepared by using femtosecond laser micromachining technology and fiber fusion technology. The multimode fiber is first spliced together with single-mode fiber in a positive pair, and then, the multimode fiber is perforated using a femtosecond laser. The incorporation of data model sensors has led to a rapid increase in the development and application of sensors as well. Based on the design concept and technical approach of the wireless sensor network system, a general development plan of the indoor environmental monitoring system is proposed, including the system architecture and functional definition, wireless communication protocols, and design methods of node applications. The sensor has obvious advantages over traditional electrical sensors; the sensor is resistant to electromagnetic interference, electrical insulation, corrosion resistance, low loss, small size, high accuracy, and other advantages. The upper computer program of the indoor environment monitoring system was developed in a Visual Studio development environment using C# language to implement the monitoring, display, and alarm functions of the indoor environment monitoring system network. The sensor-data model interfusion with each other for mutual integration performs the demonstration of the application.

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The Effects of Adding Different Adhesive Layers with a Microstructure Fiber Sensor Based on Surface Plasmon Resonance: A Numerical Study

Cited by 81 publications

References 50 publications

Detection of kerosene adulteration in automobile fuel by a low-loss surface plasmon resonance (SPR) chemical sensor

Detection of kerosene adulteration in automobile fuel by a low-loss surface plasmon resonance (SPR) chemical sensor

Electron Microscopy Studies of Soft Nanomaterials

Sensor Mathematical Model Data Fusion Biobjective Optimization

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