Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.6
4.9
Journals
Biosensors
Special Issues
Terahertz Biosensing
share announcement

Terahertz Biosensing

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Optical and Photonic Biosensors".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 10701

Special Issue Editors

Prof. Dr. Jianquan Yao

E-Mail Website
Guest Editor
School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
Interests: optoelectronic technology
Prof. Dr. Weiling Fu

E-Mail Website
Guest Editor
Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
Interests: biosensor; clinical laboratory
Prof. Dr. Guozhong Zhao

E-Mail Website
Guest Editor
Department of Physics, Capital Normal University, Beijing 100048, China
Interests: terahertz technology and applications
Dr. Xiang Yang

E-Mail Website
Guest Editor
Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
Interests: terahertz biosensors; molecular diagnosis
Dr. Jia Shi

E-Mail Website
Guest Editor
Associate Professor, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China
Interests: microwave and terahertz photonics; optical fiber sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our great pleasure to announce a new Special Issue of Biosensors devoted to terahertz biosensing. It will cover both the theoretical and experimental advances of terahertz materials, spectroscopy, sensors, and imaging technology for biosensing. Particularly welcome are original works and reviews focusing on terahertz biosensors based on novel waveguides and devices. Taking into account that the validation of a supposition is usually realized via a cross-check, the use of combined approaches is also welcomed.

Prof. Dr. Jianquan Yao
Prof. Dr. Weiling Fu
Prof. Dr. Guozhong Zhao
Dr. Xiang Yang
Dr. Jia Shi
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. Biosensors is an international peer-reviewed open access monthly 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 2700 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

  • terahertz biosensors
  • terahertz metamaterials
  • terahertz spectroscopy
  • terahertz imaging
  • terahertz biomaterials
  • terahertz fiber
  • terahertz waveguides

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 4311 KiB  
Article
Experimental Detection and Simulation of Terahertz Spectra of Aqueous L-Arginine
by Lei Hou, Junnan Wang, Haiqing Wang, Lei Yang and Wei Shi
Biosensors 2022, 12(11), 1029; https://doi.org/10.3390/bios12111029 - 17 Nov 2022
Cited by 3 | Viewed by 1998
Abstract
Terahertz (THz) wave is a good candidate for biological sample detection, because vibration and rotation energy levels of biomolecule are in THz band. However, the strong absorption of THz wave by water in biological samples hinders its development. In this paper, a method [...] Read more.
Terahertz (THz) wave is a good candidate for biological sample detection, because vibration and rotation energy levels of biomolecule are in THz band. However, the strong absorption of THz wave by water in biological samples hinders its development. In this paper, a method for direct detection of THz absorption spectra of L-arginine suspension was proposed by using a strong field THz radiation source combined with a polyethylene cell with micrometer thickness in a THz time-domain spectroscopy system. And the THz absorption spectrum of L-arginine solution was simulated by the density functional theory and the simulation result is in good agreement with the experimental results. Finally, the types of chemical bond interaction that cause the absorption peak are identified based on the experimental and simulation results. This work paves a way to investigate the THz absorption spectra and intramolecular interactions of aqueous biological samples. Full article
(This article belongs to the Special Issue Terahertz Biosensing)
►▼ Show Figures

Figure 1

12 pages, 3239 KiB  
Article
Artificial Intelligence-Assisted Terahertz Imaging for Rapid and Label-Free Identification of Efficient Light Formula in Laser Therapy
by Jia Shi, Zekang Guo, Hongli Chen, Zhitao Xiao, Hua Bai, Xiuyan Li, Pingjuan Niu and Jianquan Yao
Biosensors 2022, 12(10), 826; https://doi.org/10.3390/bios12100826 - 5 Oct 2022
Cited by 6 | Viewed by 2331
Abstract
Photodynamic therapy (PDT) is considered a promising noninvasive therapeutic strategy in biomedicine, especially by utilizing low-level laser therapy (LLLT) in visible and near-infrared spectra to trigger biological responses. The major challenge of PDT in applications is the complicated and time-consuming biological methodological measurements [...] Read more.
Photodynamic therapy (PDT) is considered a promising noninvasive therapeutic strategy in biomedicine, especially by utilizing low-level laser therapy (LLLT) in visible and near-infrared spectra to trigger biological responses. The major challenge of PDT in applications is the complicated and time-consuming biological methodological measurements in identification of light formulas for different diseases. Here, we demonstrate a rapid and label-free identification method based on artificial intelligence (AI)-assisted terahertz imaging for efficient light formulas in LLLT of acute lung injury (ALI). The gray histogram of terahertz images is developed as the biophysical characteristics to identify the therapeutic effect. Label-free terahertz imaging is sequentially performed using rapid super-resolution imaging reconstruction and automatic identification algorithm based on a voting classifier. The results indicate that the therapeutic effect of LLLT with different light wavelengths and irradiation times for ALI can be identified using this method with a high accuracy of 91.22% in 33 s, which is more than 400 times faster than the biological methodology and more than 200 times faster than the scanning terahertz imaging technology. It may serve as a new tool for the development and application of PDT. Full article
(This article belongs to the Special Issue Terahertz Biosensing)
►▼ Show Figures

Figure 1

17 pages, 822 KiB  
Article
Multifrequency Investigation of Single- and Double-Stranded DNA with Scalable Metamaterial-Based THz Biosensors
by Christian Weisenstein, Merle Richter, Anna Katharina Wigger, Anja K. Bosserhoff and Peter Haring Bolívar
Biosensors 2022, 12(7), 483; https://doi.org/10.3390/bios12070483 - 1 Jul 2022
Cited by 12 | Viewed by 2363
Abstract
Due to the occurrence of THz-excited vibrational modes in biomacromolecules, the THz frequency range has been identified as particularly suitable for developing and applying new bioanalytical methods. We present a scalable THz metamaterial-based biosensor being utilized for the multifrequency investigation of single- and [...] Read more.
Due to the occurrence of THz-excited vibrational modes in biomacromolecules, the THz frequency range has been identified as particularly suitable for developing and applying new bioanalytical methods. We present a scalable THz metamaterial-based biosensor being utilized for the multifrequency investigation of single- and double-stranded DNA (ssDNA and dsDNA) samples. It is demonstrated that the metamaterial resonance frequency shift by the DNA’s presence depends on frequency. Our experiments with the scalable THz biosensors demonstrate a major change in the degree of the power function for dsDNA by 1.53 ± 0.06 and, in comparison, 0.34 ± 0.11 for ssDNA as a function of metamaterial resonance frequency. Thus, there is a significant advantage for dsDNA detection that can be used for increased sensitivity of biomolecular detection at higher frequencies. This work represents a first step for application-specific biosensors with potential advantages in sensitivity, specificity, and robustness. Full article
(This article belongs to the Special Issue Terahertz Biosensing)
►▼ Show Figures

Figure 1

12 pages, 1979 KiB  
Article
THz-ATR Spectroscopy Integrated with Species Recognition Based on Multi-Classifier Voting for Automated Clinical Microbial Identification
by Wenjing Yu, Jia Shi, Guorong Huang, Jie Zhou, Xinyu Zhan, Zekang Guo, Huiyan Tian, Fengxin Xie, Xiang Yang and Weiling Fu
Biosensors 2022, 12(6), 378; https://doi.org/10.3390/bios12060378 - 31 May 2022
Cited by 6 | Viewed by 2795
Abstract
The demand for rapid and accurate identification of microorganisms is growing due to considerable importance in all areas related to public health and safety. Here, we demonstrate a rapid and label-free strategy for the identification of microorganisms by integrating terahertz-attenuated total reflection (THz-ATR) [...] Read more.
The demand for rapid and accurate identification of microorganisms is growing due to considerable importance in all areas related to public health and safety. Here, we demonstrate a rapid and label-free strategy for the identification of microorganisms by integrating terahertz-attenuated total reflection (THz-ATR) spectroscopy with an automated recognition method based on multi-classifier voting. Our results show that 13 standard microbial strains can be classified into three different groups of microorganisms (Gram-positive bacteria, Gram-negative bacteria, and fungi) by THz-ATR spectroscopy. To detect clinical microbial strains with better differentiation that accounts for their greater sample heterogeneity, an automated recognition algorithm is proposed based on multi-classifier voting. It uses three types of machine learning classifiers to identify five different groups of clinical microbial strains. The results demonstrate that common microorganisms, once time-consuming to distinguish by traditional microbial identification methods, can be rapidly and accurately recognized using THz-ATR spectra in minutes. The proposed automatic recognition method is optimized by a spectroscopic feature selection algorithm designed to identify the optimal diagnostic indicator, and the combination of different machine learning classifiers with a voting scheme. The total diagnostic accuracy reaches 80.77% (as high as 99.6% for Enterococcus faecalis) for 1123 isolates from clinical samples of sputum, blood, urine, and feces. This strategy demonstrates that THz spectroscopy integrated with an automatic recognition method based on multi-classifier voting significantly improves the accuracy of spectral analysis, thereby presenting a new method for true label-free identification of clinical microorganisms with high efficiency. Full article
(This article belongs to the Special Issue Terahertz Biosensing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop