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Biosensors
Special Issues
Intelligent Microfluidic Biosensing
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Intelligent Microfluidic Biosensing

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Nano- and Micro-Technologies in Biosensors".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 918

Special Issue Editor

Prof. Dr. Xiwei Huang

E-Mail Website
Guest Editor
School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: microfluidics; lab-on-a-chip; CMOS biosensors; BioMEMS; intelligent microfluidic sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of microfluidics and intelligent technologies is opening new frontiers in biosensing. Microfluidic systems offer precise, low-volume, and high-throughput handling of biological samples, enabling portable and cost-effective platforms for real-time analysis. When combined with intelligent components—such as machine learning, advanced signal processing, and IoT connectivity—these systems become powerful tools for rapid, sensitive, and automated biosensing. This Special Issue focuses on the latest advancements in intelligent microfluidic biosensing, highlighting interdisciplinary efforts in device design, sensing strategies, and data-driven analysis. We welcome original research and review articles on topics including lab-on-a-chip platforms, wearable or implantable microfluidic biosensors, AI-assisted detection of biomarkers, and integrated systems for point-of-care diagnostics. Applications may range from clinical testing and infectious disease monitoring to food safety and environmental sensing. Contributions that demonstrate system-level innovation, real-world deployment, or address current challenges in sensitivity, selectivity, and robustness are particularly encouraged. This Special Issue aims to showcase how the integration of intelligence into microfluidic biosensing is driving the development of next-generation diagnostic technologies.

Prof. Dr. Xiwei Huang
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 2200 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

  • microfluidics
  • intelligent bio-sensing
  • lab-on-a-chip
  • devices and systems
  • artificial intelligence
  • machine learning
  • data analytics
  • Internet of Medical Things (IoMT)
  • point-of-care testing

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Published Papers (1 paper)

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Research

25 pages, 3630 KB  
Article
When Droplets Can “Think”: Intelligent Testing in Digital Microfluidic Chips
by Zhijie Luo, Shaoxin Li, Wufa Long, Rui Chen and Jianhua Zheng
Biosensors 2026, 16(1), 3; https://doi.org/10.3390/bios16010003 - 19 Dec 2025
Cited by 1 | Viewed by 558
Abstract
Digital microfluidic biochips (DMFBs) find extensive applications in biochemical experiments, medical diagnostics, and safety-critical domains, with their reliability dependent on efficient online testing technologies. However, traditional random search algorithms suffer from slow convergence and susceptibility to local optima under complex fluidic constraints. This [...] Read more.
Digital microfluidic biochips (DMFBs) find extensive applications in biochemical experiments, medical diagnostics, and safety-critical domains, with their reliability dependent on efficient online testing technologies. However, traditional random search algorithms suffer from slow convergence and susceptibility to local optima under complex fluidic constraints. This paper proposes a hybrid optimization method based on priority strategy and an improved sparrow search algorithm for DMFB online test path planning. At the algorithmic level, the improved sparrow search algorithm incorporates three main components: tent chaotic mapping for population initialization, cosine adaptive weights together with Elite Opposition-based Learning (EOBL) to balance global exploration and local exploitation, and a Gaussian perturbation mechanism for fine-grained refinement of promising solutions. Concurrently, this paper proposes an intelligent rescue strategy that integrates global graph-theoretic pathfinding, local greedy heuristics, and space–time constraint verification to establish a closed-loop decision-making system. The experimental results show that the proposed algorithm is efficient. On the standard 7 × 7–15 × 15 DMFB benchmark chips, the shortest offline test path length obtained by the algorithm is equal to the length of the Euler path, indicating that, for these regular layouts, the shortest test path has reached the known optimal value. In both offline and online testing, the shortest paths found by the proposed method are better than or equal to those of existing mainstream algorithms. In particular, for the 15 × 15 chip under online testing, the proposed method reduces the path length from 543 and 471 to 446 compared with the IPSO and IACA algorithms, respectively, and reduces the standard deviation by 53.14% and 39.4% compared with IGWO in offline and online testing. Full article
(This article belongs to the Special Issue Intelligent Microfluidic Biosensing)
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