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Biosensors
Special Issues
Advances in Biosensing and Bioanalysis Based on Nanozymes
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Advances in Biosensing and Bioanalysis Based on Nanozymes

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1155

Special Issue Editors

Dr. Lei Jiao

E-Mail Website
Guest Editor
Institute of Molecular Metrology, College of Chemistry and Chemical Engineering, Qingdao University, Qingdao 266071, China
Interests: nanozymes; biosensing; single-atom biocatalysis
Dr. Ruijin Zeng

E-Mail Website
Guest Editor
School of Materials Science and Engineering, Peking University, Beijing 100871, China
Interests: nanozymes; single-atom bio-electroanalysis; photo-electrochemical biosensor

Special Issue Information

Dear Colleagues,

Nanozymes with superior enzyme-like activity and special physical and chemical characteristics have gradually emerged as excellent tools for biosensing and bioanalysis. Nanozymes can overcome the drawbacks of conventional enzymes and possess the advantages of being low cost and having simple preparation methods, high stability, and robust catalytic activity. As materials science and nanotechnology have been developed, different nanomaterials with unique physicochemical characteristics have been found to possess a similar catalytic performance compared with native enzymes, which have attracted much attention in the fields of sensing and biosensing, biotherapy, pollutant degradation, and so on. Compared with conventional sensing methods based on native enzymes, nanozyme-based biosensors exhibit greater merits including high sensitivity and selectivity, more specific target recognition, shorter detection time, and better signal readout for convenient and accurate analysis of target molecules. Therefore, this Special Issue, ”Advances in Biosensing and Bioanalysis Based on Nanozymes", focuses on the recent advances in the preparation of nanozymes with excellent catalytic performance, the understanding of nanozymes’ catalytic mechanism, and their applications in the detection of small molecules, protein biomarkers, metal ions, and other target molecules. This Special Issue will publish high-quality scholarly original research articles, review articles, news, and highlights.

Dr. Lei Jiao
Dr. Ruijin Zeng
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

  • nanozymes
  • biosensors
  • small molecules
  • biomarkers
  • metal ions
  • catalytic mechanism
  • activity regulation

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Published Papers (1 paper)

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Research

18 pages, 4283 KiB  
Article
A Machine Learning Assisted Non-Enzymatic Electrochemical Biosensor to Detect Urea Based on Multi-Walled Carbon Nanotube Functionalized with Copper Oxide Micro-Flowers
by Jitendra B. Zalke, Manish L. Bhaiyya, Pooja A. Jain, Devashree N. Sakharkar, Jayu Kalambe, Nitin P. Narkhede, Mangesh B. Thakre, Dinesh R. Rotake, Madhusudan B. Kulkarni and Shiv Govind Singh
Biosensors 2024, 14(10), 504; https://doi.org/10.3390/bios14100504 - 15 Oct 2024
Viewed by 788
Abstract
Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, [...] Read more.
Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, non-enzymatic biosensor was proposed to quantify urea concentrations. For the detection of urea, the biosensor was modified with a multi-walled carbon nanotube-zinc oxide (MWCNT-ZnO) nanocomposite functionalized with copper oxide (CuO) micro-flowers (MFs). Further, the CuO-MFs were synthesized using a standard sol-gel approach, and the obtained particles were subjected to various characterization techniques, including X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR) spectroscopy. The sensor’s performance for urea detection was evaluated by assessing the dependence of peak currents on analyte concentration using cyclic voltammetry (CV) at different scan rates of 50, 75, and 100 mV/s. The designed non-enzymatic biosensor showed an acceptable linear range of operation of 0.5–8 mM, and the limit of detection (LoD) observed was 78.479 nM, which is well aligned with the urea concentration found in human blood and exhibits a good sensitivity of 117.98 mA mM−1 cm−2. Additionally, different regression-based ML models were applied to determine CV parameters to predict urea concentrations experimentally. ML significantly improves the accuracy and reliability of screen-printed biosensors, enabling accurate predictions of urea levels. Finally, the combination of ML and biosensor design emphasizes not only the high sensitivity and accuracy of the sensor but also its potential for complex non-enzymatic urea detection applications. Future advancements in accurate biochemical sensing technologies are made possible by this strong and dependable methodology. Full article
(This article belongs to the Special Issue Advances in Biosensing and Bioanalysis Based on Nanozymes)
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