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Optical Biosensors for Healthcare Monitoring
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Optical Biosensors for Healthcare Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (10 February 2023) | Viewed by 4355

Special Issue Editors

Dr. Shudong Yu

E-Mail Website
Guest Editor
National and Local Joint Engineering Research Center of Semiconductor Display and Optical Communication Devices, South China University of Technology, Guangzhou 510641, China
Interests: Light management; Biomimetics; Optoelectronic devices; Energy harvesting; Flexible sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Dahua Shou

E-Mail Website
Guest Editor
Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: wearable technology; thermoregulation devices; thermal comfort; personal thermal management; moisture management; responsive polymers; energy saving; energy generators; controllable transport; modeling; simulation; smart functional materials; biomimetics; textiles and clothing; sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Longshi Rao

E-Mail Website
Guest Editor
Department of Mechanical Engineering, College of Engineering, Shantou University, Shantou 515063, China
Interests: flexible sensor; carbon nanomaterials; perovskite nanocrystals; light-emitting diodes; microfluidics; ion detection
Dr. Jiasheng Li

E-Mail Website
Guest Editor
National & Local Joint Engineering Research Center of Semiconductor Display and Optical Communication Devices, South China University of Technology, Guangzhou 510641, China
Interests: nanomaterials; optoelectronic devices; micro/nano manufacturing; advanced packaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Covid-19 pandemic has stimulated an urgent demand for healthcare monitoring. Optical biosensors are an intriguing option for medical diagnosis due to their real-time and label-free detection characteristics. In recent years, optical biosensors have been widely exploited and developed in various aspects ranging from sensor architecture design to new implementations. 
This special issue is to collect recent advances in optical biosensors, especially for applications in healthcare monitoring. We aim to provide a platform for potential contributors to showcase their works such as novel materials and structures, smart wearable devices, and numerical approaches for optical biosensors. Sensors publishes original research articles and reviews. 

Our special issue aims to cover topics including but not limited to the development of new sensing materials, the structure design of optical sensors, and applications in healthcare monitoring. Note that the optical biosensors stated here include spectroscopic, fluorescence, colorimetric, surface plasmon resonance, and surface-enhanced Raman scattering devices, et al.

Dr. Shudong Yu
Dr. Dahua Shou
Dr. Longshi Rao
Dr. Jiasheng Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optical biosensors
  • healthcare monitoring
  • sensing materials
  • sensor structures
  • biophotonics

Published Papers (2 papers)

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Research

28 pages, 8590 KiB  
Article
Non-Invasive In Vivo Estimation of HbA1c Using Monte Carlo Photon Propagation Simulation: Application of Tissue-Segmented 3D MRI Stacks of the Fingertip and Wrist for Wearable Systems
by Shifat Hossain and Ki-Doo Kim
Sensors 2023, 23(1), 540; https://doi.org/10.3390/s23010540 - 03 Jan 2023
Cited by 4 | Viewed by 2043
Abstract
The early diagnosis of diabetes mellitus in normal people or maintaining stable blood sugar concentrations in diabetic patients requires frequent monitoring of the blood sugar levels. However, regular monitoring of the sugar levels is problematic owing to the pain and inconvenience associated with [...] Read more.
The early diagnosis of diabetes mellitus in normal people or maintaining stable blood sugar concentrations in diabetic patients requires frequent monitoring of the blood sugar levels. However, regular monitoring of the sugar levels is problematic owing to the pain and inconvenience associated with pricking the fingertip or using minimally invasive patches. In this study, we devise a noninvasive method to estimate the percentage of the in vivo glycated hemoglobin (HbA1c) values from Monte Carlo photon propagation simulations, based on models of the wrist using 3D magnetic resonance (MR) image data. The MR image slices are first segmented for several different tissue types, and the proposed Monte Carlo photon propagation system with complex composite tissue support is then used to derive several models for the fingertip and wrist sections with different wavelengths of light sources and photodetector arrangements. The Pearson r values for the estimated percent HbA1c values are 0.94 and 0.96 for the fingertip transmission- and reflection-type measurements, respectively. This is found to be the best among the related studies. Furthermore, a single-detector multiple-source arrangement resulted in a Pearson r value of 0.97 for the wrist. The Bland–Altman bias values were found to be −0.003 ± 0.36, 0.01 ± 0.25, and 0.01 ± 0.21, for the two fingertip and wrist models, respectively, which conform to the standards of the current state-of-the-art invasive point-of-care devices. The implementation of these algorithms will be a suitable alternative to the invasive state-of-the-art methods. Full article
(This article belongs to the Special Issue Optical Biosensors for Healthcare Monitoring)
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16 pages, 3568 KiB  
Article
A Design Method of Two-Dimensional Subwavelength Grating Filter Based on Deep Learning Series Feedback Neural Network
by Jun-Hua Guo, Ying-Li Zhang, Shuai-Shuai Zhang, Chang-Long Cai and Hai-Feng Liang
Sensors 2022, 22(20), 7758; https://doi.org/10.3390/s22207758 - 13 Oct 2022
Viewed by 1434
Abstract
Subwavelength grating structure has excellent filtering characteristics, and its traditional design method needs a lot of computational costs. This work proposed a design method of two-dimensional subwavelength grating filter based on a series feedback neural network, which can realize forward simulation and backward [...] Read more.
Subwavelength grating structure has excellent filtering characteristics, and its traditional design method needs a lot of computational costs. This work proposed a design method of two-dimensional subwavelength grating filter based on a series feedback neural network, which can realize forward simulation and backward design. It was programed in Python to study the filtering characteristics of two-dimensional subwavelength grating in the range of 0.4–0.7 µm. The shape, height, period, duty cycle, and waveguide layer height of two-dimensional subwavelength grating were taken into consideration. The dataset, containing 46,080 groups of data, was generated through numerical simulation of rigorous coupled-wave analysis (RCWA). The optimal network was five layers, 128 × 512 × 512 × 128 × 61 nodes, and 64 batch size. The loss function of the series feedback neural network is as low as 0.024. Meanwhile, it solves the problem of non-convergence of the network reverse design due to the non-uniqueness of data. The series feedback neural network can give the geometrical structure parameters of two-dimensional subwavelength grating within 1.12 s, and the correlation between the design results and the theoretical spectrum is greater than 0.65, which belongs to a strong correlation. This study provides a new method for the design of two-dimensional subwavelength grating, which is quicker and more accurate compared with the traditional method. Full article
(This article belongs to the Special Issue Optical Biosensors for Healthcare Monitoring)
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